# Player Traits and Gratifications of Casual and Hardcore Players of Pokémon
GO, Harry Potter: Wizards Unite, and Ingress

John Dunham  Niantic x RIT Geo Games and Media Research Lab, Rochester
Institute of TechnologyUSA<EMAIL_ADDRESS>,  Konstantinos Papangelis  Niantic
x RIT Geo Games and Media Research Lab, Rochester Institute of TechnologyUSA
<EMAIL_ADDRESS>,  Nicolas LaLone  University of Nebraska OmahaUSA
<EMAIL_ADDRESS>and  Yihong Wang  University of LiverpoolUK
<EMAIL_ADDRESS>
(2018)

###### Abstract.

Location-based games (LBG) impose virtual spaces on top of physical locations.
Studies have explored LBG from various perspectives. However, a comprehensive
study of who these players are, their traits, their gratifications, and the
links between them is conspicuously absent from the literature. In this paper,
we aim to address this lacuna through a series of surveys with 2390 active LBG
players utilizing Tondello’s Player Traits Model and Scale of Game playing
Preferences, and Hamari’s scale of LBG gratifications. Our findings (1)
illustrate an association between player satisfaction and social aspects of
the studied games, (2) explicate how the core-loops of the studied games
impact the expressed gratifications and the affine traits of players, and (3)
indicate a strong distinction between hardcore and casual players based on
both traits and gratifications. Overall our findings shed light into the
players of LBG, their traits, and gratifications they derive from playing
LBGs.

Location-based Games, Player Traits, Gratifications, Pokémon GO, Harry Potter:
Wizards Unite, Ingress

††copyright: acmcopyright††journalyear: 2018††doi:
10.1145/1122445.1122456††journal: JACM††journalvolume: 37††journalnumber:
4††article: 111††publicationmonth: 8††ccs: Human-centered computing Human
computer interaction (HCI)††ccs: Human-centered computing Collaborative and
social computing

##  1\. Introduction

Location-based games (LBG) represent unique subset of video games. In the
present research, we consider LBG a umbrella term for games which impose
virtual space on the physical world. These games incorporate mobile
technology, and as alluded to above incorporate location as a central gameplay
mechanic and are played in the real world. Since the advent of consumer grade
GPS such games have been a part of the video game landscape, however LBG have
risen to prominence with the release of Niantic’s Pokémon GO in 2016. Today
three games dominate the LBG market: Pokémon GO, Harry Potter: Wizards Unite,
and Ingress. In an effort to better understand the players of LBG we examine
these three games through the lens of player typologies and motivations.

While less prominent in LBG, player typologies have been the focus of much
scholarly work in the context of video games. The canonical “Hearts, Clubs,
Diamonds, and Spades” typology, that was meant to typify the players of Multi-
User Dungeons, served as a foundation to build upon with increasingly complex
instrumentation (Bartle, 1996). Models such as the Game Experience
Questionnaire (GEQ) (IJsselsteijn et al., 2008), the Intensity, Sociability
Games (InSoGa) heuristic model (Kallio et al., 2011), and the player
satisfaction model called BrainHex (Nacke et al., 2014) have provided
developers with instruments to assess their players and design experiences.

Within many of these typologies, there is a binary or continuum of a
colloquial category of hardcore and casual players that these instruments
further typify. Traditionally, hardcore players are considered a grouping of
players who favor more committed, higher intensity play. In contrast, lower
intensity, less committed players are considered casual players. Whether
presented as a binary (Bateman et al., 2011) or continuum (Kallio et al.,
2011), most typologies acknowledge the existence of this colloquial
distinction. Typologies have been becoming increasingly sophisticated,
leveraging more modern psychometric models with the intent of providing more
accurate predictive modeling. Recent work by Tondello has contextualized
player typologies as a collection of traits (Tondello, 2019).

Great strides have been made in general video gaming for player typologies,
LBG research has, for the most, part eschewed this lens. The majority of
research into LBG has been on their direct impacts on the players’ lives. For
example, many studies have investigated the link between Pokémon GO and
physical health, with most finding positive links (Althoff et al., 2016;
Kaczmarek et al., 2017; Kogan et al., 2017). In addition to the physical
health of players, some studies have attempted to understand other potential
health benefits and drawbacks of Pokémon GO (Watanabe et al., 2017; Lindqvist
et al., 2018; Wagner-Greene et al., 2017). The exploration of player
attributes and motivations is comparably lesser. Khalis and Mikami (Khalis and
Mikami, 2018) have conducted exploratory work in assessing player traits of
LBG players. While promising, the exploratory work in LBG player traits does
not adequately advance trait research for traditional games.

Pokémon GO player motivations have recently been explored by Hamari (Hamari et
al., 2019), albeit recontextualized as gratifications. Gratifications
represent the player’s response to the dynamics of the games they play. For
example, the instrument would assess a player who enjoys social activities in
a game as being gratified by socialization when surveyed for that game. In his
work, Hamari (Hamari et al., 2019) used gratifications in the context of LBG
and their impact on in-app purchase intentions. As most LBG employ a free to
play revenue model (Liu et al., 2014), such applications of gratifications are
vital to commercial interests.

Our work builds and extends this line of work and synthesizes player traits,
gratifications, and the hardcore/casual binary to better, more holistically
understand the players of the studied LBG. In detail, we explore the
following: (1) The casual and hardcore players of Pokémon GO, Harry Potter:
Wizards Unite, and Ingress, (2) the traits of casual and hardcore players of
Pokémon GO, Harry Potter: Wizards Unite, and Ingress, and the (3)
gratifications of casual and hardcore players of Pokémon GO, Harry Potter:
Wizards Unite, and Ingress.

The paper is structured as follows. First, we describe the studied game’s core
loops to contextualize the work. Next, we reviewed the current literature and
state of the art on player typologies and LBG. We then describe the
methodologies and data processing techniques employed in this study. This is
followed by the results and by an in-depth discussion of said results.
Finally, we present the limitations of this work and avenues of future
research.

##  2\. Differences Between Pokémon GO, Harry Potter: Wizards Unite, and
Ingress based on their Core Gameplay Loops

Prior to discussing the literature, it is important to contextualize the games
we studied using the concept of core gameplay loops (Tondello et al., 2017). A
core gameplay loop represents the cycle of interaction a player has with a
game while playing it (Sicart, 2015). In LBGs, the core gameplay loops
generally reflect the relationship between a player’s account, the physical
space the player is in, amassing objects, and the concept of progression. Each
game’s core gameplay loop will be detailed below.

In Ingress, the core gameplay loop can be distilled into four game dynamics:
Explore, Collect Resources, Capture Portals, and Create Fields. Players begin
the game with no resources and instructions to explore their surroundings.
Exploration maps the virtual space of the game onto the physical reality
occupied by the players, forcing the player to navigate in physical space for
digital objectives. In the process of exploration, the player encounters
resources which must be collected through the game’s interface. These
resources are then used to capture portals for the player’s in-game team. When
players on a team control portals, they may create fields between the portals
to score points for their team. Ingress has a reasonably basic core loop and
minimal polish (when compared to the other two games in this study); however,
its dynamics appear designed to enforce team socialization and cooperation
(Hatfield, 2014).

Figure 1. The Core Loop of Ingress

The successor to Ingress, Pokémon GO has an more complex core gameplay loop
(Fan, 2016). As with Ingress, the game loop begins with the explore dynamic.
Once again, exploration maps the virtual onto the physical, expecting the
player to interact with the game through physical motion in the world. While
exploring, the player encounters Pokémon, the player then captures Pokémon in
these encounters. Players then gather resources from capturing additional
Pokémon or visiting static locations in the world. Players level up their
avatar and Pokémon, which then enables them to challenge gyms and raids. The
dynamics which comprise Pokémon GO’s core gameplay loop are more entwined than
Ingress; however, the game focuses far more on the individual’s adventures in
the virtual Pokémon world and less on social interactions and cooperation.

Figure 2. The Core Loop of Pokémon GO (Fan, 2016)

While much more focused on the narrative related to the Harry Potter
franchise, the Harry Potter: Wizards Unite’s core gameplay loop most closely
resembles Pokémon GO (Kiiski, 2019). The explore dynamic, similarly to Ingress
and Pokémon GO, acts as the entry point for the player into the game. A player
is expected to gather “foundables”, a dynamic resembling encountering/catching
Pokémon in Pokémon GO. This dynamic directly fuels the complete foundable
registry dynamic wherein players are encouraged to collect full sets of the
foundable collectibles through a reward system. To get better at collecting
foundables players must then gather currency and level up professions. Once
players reach a sufficiently high level, they can play in co-op battles using
the acquired skills. These co-op battles are more or less traditional dungeon
crawls in which players may assemble a party with different skills to handle a
diverse set of challenges earning foundables with more speed (Kiiski, 2019).
This core loop is also entrenched in a more integrated story, however, which
may serve to focus the player more on their individual journey’s than the
players of this game’s contemporaries.

Figure 3. The Core Loop of Harry Potter: Wizards Unite (Kiiski, 2019)

In describing these games, it becomes fairly evident that Ingress, Pokémon GO,
and Harry Potter: Wizards Unite have slightly different core loops. Despite
those differences, the core loops are broadly similar and dynamic. The
gameplay revolves around an exploration mechanic realized through actually
walking to physical locations and interacting briefly in-situ with gameplay
elements.

##  3\. Background

Extant research on games provides a framework for studying player traits and
gratifications, and their relationship to the concept of casual and hardcore
players in LBG. In this section, we outline a general history of the ways that
players have been typographed, what the justification was for that particular
typography, and its empirical consequence in both non-location-based and LBG.
The often dichotomous representation of hardcore versus casual players is a
continual presence in the study of players. Generally, hardcore has been
defined as a grouping of more committed players with a higher intensity of
play, and casual is typically defined a player grouping with low commitment to
gaming. How and why this dichotomy appears, disappears, and is made into a
continuum will also be explored here.

Most histories of player typographies begin with Bartle’s typography for
players of Multi-User Dungeons (MUDs) (Bartle, 1996, 2005). In this
theoretical model, two axes were defined, with the one axis representing
social interactions in the game and the other interactions with the game’s
world. On the game world axis, Achievers tend to seek rewards and points,
while Explorers attempt to discover the secrets of the game world. On the
social axis, Killers have a tendency to enjoy competitive gameplay, while
Socializers attempt to form relationships and socialize with other players.
Bartle (Bartle, 1996, 2005) would later modify this model by adding explicit
and implicit motivations as a third axis. While useful for thinking about game
design, this theoretical model was not empirically verified and no method for
measuring the user’s dimensions was designed by Bartle (Yee, 2005).

In response to the lack of empirical grounding in Bartle’s model, Yee
described an empirically-grounded model of player motivations (Yee, 2006).
Yee’s player motivations differ from Bartle’s typology teleologically. Where
Bartle attempts to classify player personalities, Yee presents a possible
explanation for why players play (Yee, 2005). In this model there are three
main components for describing player motivation: Achievement (Advancement,
Mechanics, Competition), Social (Socializing, Relationship, Teamwork), and
Immersion (Discovery, Role-Playing, Customization, Escapism). Players measured
using this model are given component scores based on their answers to the
model’s instrument. Each subcomponent was graded independently and offered a
mapping of a player’s motivations (Yee, 2005).

Yee (Yee and Ducheneaut, 2015; Yee et al., 2012) would validate this model
using data from Massively Multiplayer Online Role-playing games, specifically
World of Warcraft (N=2,716) and Everquest 2 (N=7,129) (Williams et al., 2009).
Later, this model would be expanded to six clusters with twelve dimensions:
Action (Destruction, Excitement), Social (Competition, Community), Mastery
(Challenge, Strategy), Achievement (Completion, Power), Immersion (Fantasy,
Story), and Creativity (Design, Discovery) (Yee and Ducheneaut, 2015) . This
iteration provides a more granular picture of the player’s motivations
allowing designers to comprehend the dynamics of play preferred by players of
their game. Yee overall did not specifically focus on the hardcore/casual
binary; instead, these terms were distributed among the dimensions of Action,
Mastery and Immersion. These terms are analogs for a continuum of hardcore and
casual, with higher scores indicating more hardcore players.

Contextually this was verified for MMOs and online games only but offered
potential to understand player behavior in single-player or local multiplayer
games. One such attempt at typographing players in other games was the Games
Experience Questionnaire or GEQ. The GEQ was developed as “a self-report
measure that aims to comprehensively and reliably characterize the
multifaceted experience of playing digital games” for the fun of gaming (FUGA)
project (IJsselsteijn et al., 2008). At its core, the GEQ instrument is a
modular questionnaire with multiple sub-components that allows researchers
different types of fidelity of the player experience. These player experiences
were categorized as post-game, social presence, and in-game. The instrument is
to be used immediately after players completed gameplay sessions, with the
intention of most closely capturing intention while playing the game. The GEQ
also has a lighter module for data gathering during gameplay sessions. As is
the case with Yee’s model, the GEQ doesn’t explicitly have a mechanism for
gathering or observing the hardcore/casual dimension directly.

This instrument was deployed in multiple studies to analyze player experience.
The types of research this instrument analyzed range from a study of the
impact of Wiimotes on affective gameplay interactions (N=37) (Nacke, 2010) to
an investigation on human visual attention (Vanden Abeele et al., 2015). While
this research was well received by the academy, some felt that more
instrumentation validation was needed. To wit, Law et al. carried out a
literature review and validation study (N=633) of the GEQ (Law et al., 2018).
The authors found inconsistent reporting of psychometric properties. The
validation study failed to find evidence supporting the original GEQ study. As
a result, the validity of research leveraging this, fairly popular, tool was
essentially invalidated. The development of different approaches was needed.

In contrast to the GEQ, Kallio presented a gamer mentalities model that
heavily relies on the intensity of and sociability of the player’s gaming
styles: committed gamers (frequent and/or long sessions), casual gamers
(occasional and/or short play sessions), and gaming companions (play with
their social group for accompaniment). These mentalities assume the
hardcore/casual spectrum as a three position state: hardcore, casual and
temporary player (a null set).

Kallio rejected this modelling of gamer mentalities during the statistical
analysis of the initial survey and so re-tuned their instrumentation (N=4,000)
(Kallio et al., 2011). The original hypothetical typology was found to be
improperly descriptive due to the diverse nature of the surveyed gamer groups
and overlap of suggested mentalities. It was found that sociability and
intensity could take many forms so in place of the hypothetical model, a
gaming heuristic was adopted in the form of the three-component Intensity,
Sociability, Games (InSoGa) model (Kallio et al., 2011). The InSoGa model was
developed through three qualitative studies that included 73 short interviews,
33 in-depth interviews, and 2 focus groups focused on digital gaming practices
and experiences.

What Kaillo found was that the intensity of gaming is processed as a
composition of three points: gaming session length, gaming regularity, and
concentration (Kallio et al., 2011). This is not a strictly linear
relationship and intensity exists as a continuum of the three points. Next,
the sociability of gaming is likewise a continuum combining the elements of
physical space, virtual space, and metagaming. This particular dimension is
relevant to the focus of this work, as LBG frequently appropriate physical
space to meet playful ends (Hamari et al., 2019). Finally, games consist of
three indicators: individual games or devices, game genres, and accessibility.
Kallio proposes that while players have the agency to choose the games they
play, the games themselves have an inextricable impact on the mentality of the
players. This sentiment is mirrored by Holm (Holm, 2019), albeit with a more
granular focus on game mechanics (Kallio et al., 2011).

The components were then converted into three sets of nine total gaming
mentalities. Social mentalities, including Gaming with Children, Gaming with
Mates, and Gaming for Company, outlined a collection of mentalities which
“highlight the importance of accessibility, games and game devices” (Bateman
et al., 2011). These mentalities represented a less regular aspect of gaming,
only occurring when players had the opportunity to socialize. This
socialization exists outside of the casual/hardcore dichotomy and acts as more
of a modifier to more hardcore and casual states. Casual mentalities in this
model, Killing Time, Filling Gaps, and Relaxing, indicated mentalities
unconcerned with commitment to gaming. In the InSoGa model a casual mentality
is considered the opposite of committed. A casual player in this model tends
to play games that they are familiar with, as a context switch, and/or to fill
periods of time in their day. Gaming in the casual mentality is done when
convenient. The hardcore representation in InSoGa model is represented by
committed mentalities, Gaming for Fun, Immersive Play, and Gaming for
Entertainment. In contrast with the casual mentalities, committed mentalities
players find the act of playing itself as fun, lose themselves in the game,
and/or game for gaming’s sake.

These mentalities are intended to be interpreted as heuristic representations
of player motivations. Kallio found this lens to provide higher quality
information about player motivation than strictly bounded typographies (Kallio
et al., 2011). The more nuanced approach to typographies does appear to allow
for a more holistic understanding of players. Due to the lack of
generalizability, there exists no simple method to measure this model and as a
result, this work is best used as a framing device.

In contrast to Kaillo’s qualitative measures of player mentalities, Bateman,
Lowenhaupt, and Nacke (Bateman et al., 2011) adapted the Myers-Briggs Type
Indicator (MBTI) (McCrae and Costa Jr, 1989) in an effort to create an easily
measured survey of player types. This instrument was called the Demographic
Game Design mode. In this research, the hardcore and casual player
classifications were assessed. Players were asked to self identify as hardcore
or casual players. As Bateman, Lowenhaupt, and Nacke (Bateman et al., 2011)
evaluated the data, this iteration of the model allowed them to identify four
play styles: Conqueror, Manager, Wander, and Participant. These styles
contrasted heavily with the existing Bartle types.

Accordingly, this resulted in Bateman and Nacke (Bateman et al., 2011)
asserting that hardcore and casual represented not a playstyle, but a trait
dimension of the player’s personality. Bateman, Lowenhaupt, and Nacke (Bateman
et al., 2011) continued to posit that the concept of hardcore players may be
better conveyed as gamer hobbyists, players who play a range, and diversity of
many games. In their view, a gamer hobbyist is a player who has a higher
degree of game literacy, or a player’s ability to comprehend game mechanics,
rules, and patterns in digital games (Buckingham and Burn, 2007) and generally
has a capacity for more imaginative play. The promising results of DGD1
(Bateman and Boon, 2005) led to further testing.

The DGD1 design (Bateman and Boon, 2005) was iterated on incorporating the
Temperament Theory described by Berens (Berens, 2000). Temperament theory
suggests that there are four skill sets: Catalyst, Stabilizer, Strategic and
Tactical, with each relating directly to a MBTI type result. The second
Demographic Game Design Model, DGD2, re-purposed these skill sets into four
hypothetical player archetypes: Logistical, Diplomatic, Strategic, and
Tactical (Bateman et al., 2011). When applied to the DGD2 survey (N=1,040) a
bug prevented assessment of the Diplomatic skillset, but analysis found
loading game literacy with Tactical skills, and Strategic with Logistical
skills. The survey also identified that self selection for hardcore and casual
may have issues when the binary is viewed through a game literacy lens, as
respondents around 90% of respondents claimed they had top marks in game
literacy, despite only 50.1% identifying as hardcore (Bateman et al., 2011).

The DGD2 was immediately succeeded by the more systemic BrainHex (Nacke et
al., 2014). This model was created from a collection of neurobiological papers
and player surveys relating to the neurobiological basis of emotion. Hardcore
versus casual players were not tracked by this study in lieu of more concrete
neurobiological concepts. The collection of player surveys suggested there are
seven player types: Seeker (curiosity motivated), Survivor (fear motivated),
Daredevil (excitement motivated), Mastermind (strategy motivated), Conqueror
(challenge motivated), Socializer (social interaction motivated), and Achiever
(goal motivated). Each of the seven player types were effectively models of
motivation for players which could be used to foster deeper understanding of a
game’s player base. In the introductory study, a survey was conducted
(N=50,000) to establish relationships between personality types and the above
mentioned player types (Nacke et al., 2014).

This typology moved more towards a motivation-based model, in line with Yee’s
(Yee and Ducheneaut, 2015) findings, moving away from the more rigid type
model of Bartle (Bartle, 2005). BrainHex has since been used in research on
tailoring persuasive health games to gamer types (Orji et al., 2013), however,
independent (Busch et al., 2016; Fortes Tondello et al., 2018) studies have
found issues with its psychometric properties: the Seeker, Survivor, and
Daredevil types required revision, while the Conqueror type may not have been
distinct enough. Additionally, the MBTI is being replaced by trait theories in
psychology literature (McCrae and Costa Jr, 1989). However, the authors note
that BrainHex was a transitory model meant to foster more robust typologies or
trait models (Bateman et al., 2011).

One model that grew out of the work on Brainhex was provided by Park (Park et
al., 2011) who proposed a link between personality traits and motivations to
play online games. Park used the Big-Five (Park et al., 2011) personality
domains (extraversion, agreeableness, conscientiousness, emotional stability
and openness to experience) to evaluate the link between personality traits
and motivations. Park performed an exploratory survey (N=524) measuring player
demographics, motivations, genre preferences and the Big-Five personality
domains using the Ten Item Personality Index, TIPI, instrument (Gosling et
al., 2003). The TIPI is a collection of fifty questions, ten per domain, to
generate an individual’s Big-Five personality scores. The instrumentation did
not include affordances for the hardcore/casual binary.

Instead, Park discovered five motivations through factor analysis:
Relationships, Adventure, Escapism, Relaxation, and Achievement (Park et al.,
2011). The central finding of the study was that agreeableness and
extroversion acted as strong motivation predictors for playing online games;
however, traits did not seem to be reliable predictors for player’s behavior.

The Big-Five personality domain and BrainHex were later combined by Tondello
(Tondello et al., 2016) as the gamification user types Hexad scale. The Hexad
model’s user types are the intrinsic and extrinsic motivations defined in Self
Determination Theory (SDT) (Ryan and Deci, 2000). As the name suggests the
model features six user types each with their own motivations:

  * •

Philanthropists are motivated by purpose (altruism, no expectation of reward).

  * •

Socializers are motivated by relatedness (interact with others, social
connections).

  * •

Free Spirits are motivated by autonomy (freedom to express themselves).

  * •

Achievers are motivated by competence (seek to complete tasks, challenges).

  * •

Players are motivated by extrinsic rewards (work to earn rewards in a system).

  * •

Disruptors are motivated by triggering change (derived from empirical
observation).

In the introduction of this model (Tondello et al., 2016), Tondello carried
out a survey (N=133) students at the University of Waterloo, with 40 agreeing
to take a follow-up survey (Tondello et al., 2019). The survey consisted of
the following:

  * •

Demographic information: (age, gender, etc.)

  * •

Hexad User Types: Thirty 7-point Likert scale questions in which participants
were asked to assess how they aligned with statements (e.g. “I like to
provoke” and “I like being part of a team” (Tondello, 2019))

  * •

Game element preferences: Thirty-two 7-point Likert scale questions asking
participants about their affinity for specific game design elements (e.g.
Easter Eggs, Quests, Customizing). These elements were pre-classified as being
representative of certain types in the hexad scale.

  * •

Personality: BIDR-6 (Winkler et al., 2006) and BFI-10 (Rammstedt and John,
2007) personality surveys were employed with a 7-point Likert scale.

The initial study was an effort to determine the efficacy of the Hexad model
for modeling user game preferences, assess the scale’s correlation with
participants’ personality traits (Tondello et al., 2016), and verify the
stability of the Hexad survey through a retest (Tondello et al., 2019).
Tondello found the Hexad user types had positive correlations on all user
types, except Philanthropist, with their expected design preferences (Tondello
et al., 2019). Additionally, the scale showed significant correlations with
the Big-Five personality traits they had been theoretically designed. It was
determined the instrument could be reduced to 24 entries with no loss in
accuracy. Generally, the individual hexad types had the desired reliability in
test and retest, however, the Player scale was determined to need additional
work in future studies to improve reliability.

The resulting 24 question instrument was validated and could theoretically be
applied in the field with reasonable accuracy (Tondello et al., 2019).
However, it still needed refinement to address concerns stated above. Due to
the overlap found with the Big-Five personality model (Park et al., 2011),
this type of model was further refined into a trait-based model by Tondello in
further work. This trait model was proposed in an effort to “address the
building blocks of actionable game design” (Tondello et al., 2019).

In pursuit of this goal Tondello conducted an exploratory survey of 350
participants, recruited through social media and mailing lists. Participants
were incentivized to participate with the opportunity to win one of two $50
international gift cards. Of the 350 participants, 332 responses were accepted
after data cleaning and 157 agreed to take a follow up survey to calculate
test-retest reliability. Of the 157 only 70 participants completed the retest
(Tondello et al., 2019).

This survey consisted of 50 items measured on a 7-point Likert scale, ten for
each of the five proposed trait types:

  * •

Aesthetic orientation: high score correlates with players enjoying aesthetic
experiences in games. Such experiences include world exploration or
appreciation of the graphics, sound, and/or art style.

  * •

Narrative orientation: high score corresponds to player enjoyment of complex
narratives and stories within games.

  * •

Goal orientation: players who score high in this trait enjoy completing goals
in the game and tend to like 100% completing games.

  * •

Social orientation: high scorers typically enjoy playing with other players.

  * •

Challenge (Action) orientation: high scoring players typically prefer games
more difficult games and hard challenges. This trait more or less represents
the hardcore/casual continuum in this model.

Initial factor analysis reduced the 50 items to 25, 5 for each of the traits.
In the retest the reduced 25 item survey was deployed, and analysis showed
stability of testing results despite the reduced number of items. Overall the
findings of these surveys indicate a fairly reliable model of representing
player traits. While distinct player traits are suspected to be tied to
personality traits, it appears that personality is not the sole determinant of
play preferences. Unlike prior typologies, as this trait model is a collection
of five separate scores it’s less restrictive and allows for the full range of
player preferences to be captured.

Models of player typologies and motivations are ubiquitous in the study of
games. However, this ubiquity is centered on more traditional video games. The
instruments provide researchers and designers a means to assess the desires
and needs of player bases. Games such as Ingress, Pokémon GO, and Harry
Potter: Wizards Unite have are somewhat different than traditional video
games. When contextualized to LBG, these player typologies can help
researchers and designers understand the players of these types of games.

Kallio’s work (Kallio et al., 2011), as mentioned above, has clear
applications in the LBG space. The rendering of the sociability indicator is
easily adapted, with the physical space component represented by the act of
players interacting with the world through the lens of a virtual space
presented by the game itself. Further work has begun to take place in
researching LBG, particularly in the context Pokémon GO, and the impact of
personality traits on player outcomes and behavior. Additionally, the existing
modeling of hardcore/casual in the InSoGa provides researchers instrumentation
to measure the hardcore/casual divide in the LBG space as well.

Pokémon GO has been a routine target of inquiry since its release in July of
2016. Preliminary research conducted near the release of the game by Althoff
et.al. (Althoff et al., 2016) indicated Pokémon GO contributed significantly
to increases in player physical activity. Exploratory research conducted by
Kaczmarek et al. (Kaczmarek et al., 2017) in the form of an online survey
(N=444) supported this finding. Players self reported that they had a greater
incentive to spend more time outdoors and with greater amounts of physical
activity. Kogan et al. likewise found that players were more likely to
exercise with family and pets in an exploratory study (N=269) (Kogan et al.,
2017). The benefits of LBG such as Pokémon GO extend to mental health as well;
a survey (N=2530) of Japanese workers by Watanabe et al. (Watanabe et al.,
2017) found that players of the game had a significant improvement in their
psychological distress over non-players.

LBG are not beyond critique, however, as several studies have outlined the
inherent risks. Lindqvist et al. (Lindqvist et al., 2018) studied the effects
of Pokémon GO on eight families by collecting their experiences through focus
groups. While generally positive, supporting the studies that indicated a
positive correlation of increased physical activity with game play, Lindqvist
et al. (Lindqvist et al., 2018) did outline potential safety concerns. In the
study participants reported mild injuries which had occurred while playing the
game (e.g. falling into a ditch), and parents expressed concern that the game
would result in players not taking proper heed of their surroundings. A larger
exploratory study (N=662) by Wagner-Green et al., supported this sentiment
(Wagner-Greene et al., 2017). Risky behaviors such as driving, biking and
walking while focusing on the game were uncovered, and a third of players had
reportedly lost sleep to play the game.

Existing studies of LBG appear to support the conclusion that the benefits of
such games (at least Pokémon GO) are varied, albeit with some risk. Models of
player traits in traditional gaming modalities focused on adjusting player
outcomes and behaviors. It stands to reason that a deeper understanding of
player traits, particularly in the context of LBG, might enable designers to
encourage the more positive benefits of LBG. At the very least such work could
attempt to discourage the aforementioned risky behavior. More in-depth work on
player traits and motivations in LBG is currently taking form.

In a step towards such research Khalis and Mikami (Khalis and Mikami, 2018)
explored player personality traits in the context of how they impacted player
behaviors in Pokémon GO. The exploratory study (N=101) used a questionnaire
coupled with game data. It was found that higher social competence,
conscientiousness, agreeableness, and lower social anxiety predicted the
frequency in which players interacted with the game (Khalis and Mikami, 2018).
While not a strict survey of traits outlined in this background, these nascent
findings outline the further study of trait theory on player behaviors in LBG.

In pursuit of improving understanding player motivation, Hamari (Hamari et
al., 2019) conducted a study observing player gratifications and inhibitors.
In the study, Hamari (Hamari et al., 2019) observed these gratifications and
inhibitors and their impact on the player’s intention to reuse (ITR) and in-
app purchase intentions (IPI). Methodologically, Hamari (Hamari et al., 2019)
employed the use of a player survey (N=1190) and modeled player gratifications
across several dimensions assessed with use and gratifications. Challenge
represents the player’s capabilities being tested against the game itself,
Competition meanwhile represents a player’s capabilities being tested against
other players. Both gratifications target similar dynamics, however, the key
delimiter is their degree of socialization. When compared to Tondello’s
(Tondello et al., 2016) traits the sum total of these motivations represent
the action trait.

Enjoyment represents the player’s pleasure derived from playing the game, a
gratification resembling the InSoGa (Kallio et al., 2011) Gaming for Fun
mentality. Trendiness is the social gratification of one’s gameplay being
approved by peers. Socializing is the gratification received by interacting
with other players of the game physically, a direct analog for Kaillo’s
(Kallio et al., 2011) social mentality. A unique attribute in digital games of
LBG, Outdoor Activity, represented player gratification from playing outdoors.
Due to the nature of Pokémon GO as a target of childhood brand nostalgia,
Hamari (Hamari et al., 2019) also measured Nostalgia, or a player’s
gratification from a desire to relive their past. Ease of Use, Privacy
Concerns, In-app Purchase Intentions and Intentions to Reuse were all also
measured. Hamari (Hamari et al., 2019) found enjoyment, outdoor activity, ease
of use, challenge, and nostalgia were all positively associated with ITR.
Outdoor activity, challenge, competition, socializing, nostalgia and ITR were
all associated with IPI. Privacy concerns and trendiness, meanwhile were found
to have no association with ITR or IPI.

The results of this study have implications on the way LBG designers may
design their game. It stands to reason that targeting dimensions that impact
both ITR and IPI would result in the greatest yield for a studio attempting to
drive in-app purchases. For example, targeting ease of use would possibly have
a greater impact than targeting trendiness or privacy concerns. While this
study assessed the impact of gratifications on ITR and IPI, further research
could re-target the effect being studied. Gratifications could be leveraged
more specifically to determine what dimensions contribute positively to mental
health or the strengthening of a community.

Building on the state of the art this study attempts to get a clearer picture
of the players of Pokémon GO, Harry Potter: Wizards Unite, and Ingress. A
particular emphasis has been placed on studying the differences between the
hardcore and casual players of these games. The models constructed by Tondello
(Tondello et al., 2016) and Hamari (Hamari et al., 2019) have been leveraged
as a framework to this end to explore the following about the players of
Pokémon GO, Harry Potter: Wizards Unite, and Ingress:

  1. (1)

The casual and hardcore players.

  2. (2)

The traits of the casual and hardcore players.

  3. (3)

The gratifications casual and hardcore players derive from the games.

To explore these topics, we evaluated the results of a survey instrument
administered to a number of communities devoted to LBG. Our collection of
2,390 survey responses represents the most in-depth evaluation of players of
LBG to date.

##  4\. Data and Methods

###  4.1. Data Gathering and Demographics

We recruited participants through the /r/PokemonGo, /r/HarryPotterWU,
and/r/Ingress subreddits. Participants were required to be active players of
one of either Pokémon GO, Harry Potter: Wizards Unite, or Ingress to
participate. We deployed the questionnaires on mid-July and collected the
responses for 10 days. During the period, there were in total 1253
participants in Pokémon GO survey, 1281 participants in Harry Potter: Wizards
Unite survey and 310 participants in Ingress survey. However, we had to
discard the responses that did not include answers to all the items in the
questionnaires. In addition, responses which were observed to be sloppy (e.g.
answer “agree” to all the items) were eliminated as well to ensure the quality
of the dataset. Therefore, we have 1071, 1057 and 262 valid records remained
in Pokémon GO, Harry Potter: Wizards Unite and Ingress respectively. The
demographic information regarding the gender, age, occupation, education and
countries/regions of our participants are provided in Table LABEL:survey-
demographic-data.

####  4.1.1. Gender

Most of the player in Pokémon GO (74.4%) and Ingress (80.5%) are males.
Notably, there is a gender balance in Harry Potter: Wizards Unite as 51.1% of
the participants are males while 47.1% of the participants are females. The
percentage of non-binary payers in all the 3 games is low ranging from 1.5% to
2.1%.

####  4.1.2. Age

Approximately two thirds of the players in Pokémon GO (65.3%) and Harry
Potter: Wizards Unite (68.4%) are young players between 21 and 35 years old
while elder players show more interests in Ingress as half of the players in
Ingress are 36 years old or above. In addition, Pokémon GO is more popular
among kids and teenagers given around one fifth of the participants (20.4%) in
this game are under 20 years old while only 3.9% and 7.6% of the players in
Harry Potter: Wizards Unite and Ingress belong to the same age group.

####  4.1.3. Occupation

Most of the players in Pokémon GO (60.0%), Harry Potter: Wizards Unite (76.9%)
and Ingress (76.7%) are employed while the second largest group of players in
all the 3 games are students. In comparison to Harry Potter: Wizards Unite and
Ingress in which students account for slightly over 10% of the total sample,
nearly 1/3 of participants in Pokémon GO are students. This is in accordance
with the finding in Age which illustrates that Pokémon GO is more popular
among young people.

####  4.1.4. Education

Over half of the participants in Pokémon GO (50.6%), Harry Potter: Wizards
Unite (64.4%) and Ingress (50.8%) reported that they graduated from University
with bachelor’s degrees or above. Moreover, approximately one fifths of the
participants in all 3 games have college or vocational degrees. This indicates
that most of the players in our survey are well-educated. In addition, it is
unsurprising to find the 33.2% of the participants in Pokémon GO have high
school or lower degrees since many players are found to be students at less
than 20 years old. However, it is interesting to find that similar percentage
of participants (31.7%) in Ingress also have the relatively low education
level with high school or lower degrees given half of the players are 36 years
or elder.

####  4.1.5. Countries/Regions

LBG Players from over 60 countries/regions around the world responded to our
survey. However, in order to keep the data clear and informative, we decide to
report the top 10 countries according the number of respondents. Based on the
statistics, most of the participants in Pokémon GO (66.9%), Harry Potter:
Wizards Unite (66.9%) and Ingress (70.2%) are from English-speaking countries
namely United States, United Kingdom, Canada and Australia. In addition,
European countries such as Germany, Netherlands, Poland and Finland also
contributed a considerable number of participants in our surveys.

Table 1. Demographic Data from our Survey Characteristics | Pokémon GO (N=1071) | HPWU (N=1057) | Ingress (N=262)
---|---|---|---
Gender
Male | 797 (74.4%) | 540 (51.1%) | 211 (80.5%)
Female | 252 (23.5%) | 498 (47.1%) | 47 (17.9%)
Non-binary | 22 (2.1%) | 19 (1.8%) | 4 (1.5%)
Age
¡ 21 years | 219 (20.4%) | 41 (3.9%) | 20 (7.6%)
21-35 years | 699 (65.3%) | 723 (68.4%) | 98 (37.4%)
¿ 35 years | 153 (14.3%) | 293 (27.7%) | 144 (50%)
Occupation
Employed | 642 (60.0%) | 813 (76.9%) | 201 (76.7%)
Unemployed | 95 (8.9%) | 94 (8.9%) | 29 (11.1%)
Student | 334 (31.1 %) | 150 (14.2%) | 32 (12.2%)
Education
University degree or above | 542 (50.6%) | 681 (64.4%) | 133 (50.8%)
College degree | 129 (12.1%) | 173 (16.4%) | 41 (15.6%)
Vocational degree | 44 (4.1%) | 34 (3.2%) | 5 (1.9%)
High school or lower | 356 (33.2%) | 169 (16.0%) | 83 (31.7%)
Countries/Regions
United States | 498 (46.5%) | 534 (50.5%) | 126 (48.1%)
United Kingdom | 123 (11.5%) | 93 (8.8%) | 44 (16.8%)
Germany | 65 (6.1%) | 56 (5.3%) | 17 (6.5%)
Canada | 56 (5.2%) | 60 (5.7%) | 11 (4.2%)
Australia | 40 (3.7%) | 20 (1.9%) | 3 (1.1%)
Netherlands | 24 (2.2%) | 25 (2.4%) | 5 (1.9%)
Poland | 19 (1.8%) | 14 (1.3%) | 1 (0.4%)
Italy | 17 (1.6%) | 12 (1.1%) | 1 (0.4%)
France | 16 (1.5%) | 13 (1.2%) | 1 (0.4%)
Finland | 15 (1.4%) | 17 (1.6%) | 4 (1.5%)
Others | 198 (18.4%) | 210 (19.9%) | 49 (18.7%)

###  4.2. Data Preparation and Cleaning

####  4.2.1. Data Filtering

Before analyzing the validity and reliability of our questionnaires, we
initially eliminated the records with missing data as well as the outliers
detected through our observation and SPSS built-in functions. This is based on
the theories that (1) the missing data and outliers should be removed in order
to ensure the quality of the data, and (2) that missing data and outliers
would have serious impacts on the analysis of EFA (Liu et al., 2012).
Therefore, there are 1071, 1057 and 262 valid records remained in Pokémon GO,
Harry Potter: Wizards Unite and Ingress respectively.

In addition, based on the suggestions from Tabachnick and Fidell (Tabachnick
et al., 2007) we conducted normality test on the sample in Ingress through
checking the skewness and kurtosis and found that there are no items involved
in the questionnaires were beyond the recommended thresholds. The reasons why
we did not conduct normality test on samples in Pokémon GO and Harry Potter:
Wizards Unite were because that previous study suggested that in large dataset
most statistical methods rely on the Central Limit Theorem, which states that
the average of a large number of independent random variables is approximately
normally distributed around the true population mean (Lumley et al., 2002).
Furthermore, in large sample the violation of normality should not cause major
problems and one should not even worry about the normality in a dataset
consisting of over 200 participants (Elliott and Woodward, 2007; Field, 2005).
Therefore, all the items were retained for further analysis of the
questionnaires in this study.

###  4.3. Instrument Reliability and Validity

####  4.3.1. Validity Test

After readying the data, we decided to conduct both exploratory factor
analysis (EFA) and confirmatory factor analysis (CFA) to test the validity of
the questionnaires we used in this study. Although one could test only CFA if
there are solid assumptions about the structure of the factors being tested
(Henson and Roberts, 2006), we found that it may be risky to skip the EFA in
our case. This is because that (1) both the Traits questionnaire and the
Gratification questionnaire we used were newly published with limited
empirical researches grounded on them; (2) data from different samples may
result in different factor solution of the measuring instruments (Gaskin et
al., 2017). Therefore, based on the suggestions from Hair et al. (Hair et al.,
1998), we split the sample in each survey in half and conduct EFA on the first
half of the data to extract the underlying factor structures in the
questionnaires and, then, do the CFA on the other half of the data to confirm
the identified structures are valid.

Exploratory factor analysis (EFA) was conducted using SPSS. First, simple
principal components analysis was conducted. It was recommended by Kaiser
(Kaiser, 1960) that the eigenvalue of the extracted factor be higher than
1.00. Secondly, a principal component analysis with a varimax rotation was
studied to analyze the possible solutions. According to (Field, 2005;
DeCoster, 1998; Hair et al., 1998), the following criteria were set to
determine if the dataset is suitable for EFA and which items should be
retained in the test:

  * •

Kaiser–Meyer-Olkin (KMO) should be above 0.6

  * •

Bartlett’s Test of Sphericity should be significant at $\alpha$ ¡ .05

  * •

The communalities of items should be above 0.3

  * •

Loading of item should be more than 0.32.

  * •

An item should not be permitted to load on two or more factors.

  * •

Retained items and factors should make sense conceptually.

  * •

A factor in the model of principal component analysis should have at least 3
items.

To test the factorial validity of Traits and Gratification sections in our
surveys, confirmatory factor analysis (CFA) was conducted using the structural
equation modelling (SEM) method with the help of the AMOS program. The
following indexes were examined and reported in this study according to the
commonly accepted standards (Bennett and Rundle-Thiele, 2002; Kelloway, 2014;
Hair et al., 1998; Hancock and Mueller, 2013; Yan et al., 2019):

Table 2. These are the typical criterion for good and acceptable statistical results Index | Criteria
---|---
chi square to degree of freedom ratio ($\chi$2/df) | Good: $\chi$2/df <3
Acceptable: $\chi$2/df <5
Standardized Root Mean Square Residual (SRMR) | Good: SRMR<0.08
Acceptable: SRMR <0.1
Root Mean Square Error of Approximation (RMSEA) | Good: RMSEA <0.08
Acceptable: RMSEA<0.1
Tucker Lewis index (TLI) | Good: TLI>0.9
Acceptable: TLI>0.8
Comparative fit index (CFI) | Good: CFI>0.9
Acceptable: CFI>0.8

####  4.3.2. Reliability test

Cronbach’s alpha was used to investigate the reliability of obtained factors.
Although 0.70 is usually considered as an acceptable cut-off point (Nunnally,
1994), it is argued by Hair et al. (Hair et al., 1998) that a Cronbach’s alpha
of more than 0.6 indicates a reasonable internal consistency. Therefore, the
lowest standard of Cronbach’s alpha in this study was set to be at least 0.6.

In order to investigate the traits of casual and hardcore players of Pokémon
GO, Harry Potter: Wizards Unite, and Ingress, we utilized a traits model and
scale of game playing preferences that is proposed by Tondello (Tondello et
al., 2019). This measurement consists of 25 items in 5 dimensions, namely (1)
Social orientation; (2) Action (Challenge) orientation; (3) Immersion
(Narrative) orientation; (4) Aesthetic orientation and (5) Goal orientation.

###  4.4. Reliability and Validity of Traits Section of Survey

####  4.4.1. Pokémon GO

##### Results of EFA

The results KMO and Barlett’s test indicate that the data in Traits section is
suitable for exploratory factor analysis, given a KMO ¿ 0.6 suggests that the
sample is large enough to perform EFA and the significance in Barlett’s test
(Sig. ¡ 0.05) indicates that the correlation between items were sufficiently
large.

Table 3. Results from the KMO and Bartlet’s Tests for our Pokémon GO Survey
KMO and Bartlett’s Test
---
Kaiser-Meyer-Olkin Measure of Sampling Adequacy | .869
Bartlett’s Test of Sphericity | Approx. Chi-Square | 8236.408
df | 300
Sig. | .000

Based on the aforementioned criteria regarding the retention of items (see
section 4.2.1), no items were removed in the exploratory factor analysis of
the Traits section in Pokémon GO survey. The factor loadings and communalities
as well as the extracted factors and their cumulative explanations of the
variance are shown in the following table. The results illustrated that all
items correspond perfectly to the dimensions described in Tondello’s paper.
Therefore, the first factor is “Social orientation”, which states the player’s
preference for playing together with others online or in the same space.
Factor 2 is “Action (Challenge) orientation”, which describes the player’s
preference for challenging and fast-paced gameplay. The third factor is
“Immersion (Narrative) orientation”, which reveals the player’s preference for
complex stories or narratives within the games. Factor 4 is “Goal
orientation”, which is related to player’s preference for gameplay that
involves completing quests or tasks. The last factor is about the player’s
preference for aesthetic experiences such as exploring the game world and
appreciating the sound, graphics and art style in the game. Therefore, it is
named by Tondello as “Aesthetic orientation”. 68.316% of the variance was
explained by the 5 factors in total.

Item | Component | Communality
---|---|---
1 | 2 | 3 | 4 | 5
17 I like to interact with other people in a game. | 0.907 |  |  |  |  | 0.866
18 I like playing with other people. | 0.889 |  |  |  |  | 0.834
16 I like to play online with other players. | 0.887 |  |  |  |  | 0.835
20 I often prefer to play games with others. | 0.872 |  |  |  |  | 0.794
19 I like games that let me play in guilds or teams. | 0.858 |  |  |  |  | 0.78
22 I enjoy highly difficult challenges in games. |  | 0.884 |  |  |  | 0.839
23 I like it when games challenge me. |  | 0.878 |  |  |  | 0.826
21 I like it when goals are hard to achieve in games. |  | 0.852 |  |  |  | 0.791
25 I like it when progression in a game demands skill. |  | 0.83 |  |  |  | 0.727
24 I usually play games at the highest difficulty setting. |  | 0.768 |  |  |  | 0.624
7 Story is important to me when I play games. |  |  | 0.885 |  |  | 0.825
9 I like games that pull me in with their story. |  |  | 0.835 |  |  | 0.76
8 I enjoy complex narratives in a game. |  |  | 0.82 |  |  | 0.755
10 I feel like storytelling does not get in the way of actually playing the game. |  |  | 0.745 |  |  | 0.569
6 I usually don’t skip the story portions or the cutscenes when I am playing. |  |  | 0.724 |  |  | 0.586
13 I like completing games 100%. |  |  |  | 0.865 |  | 0.767
11 I like to complete all the tasks and objectives in a game. |  |  |  | 0.814 |  | 0.672
15 I like finishing quests. |  |  |  | 0.668 |  | 0.507
14 I feel stressed if I do not complete all the tasks in a game. |  |  |  | 0.665 |  | 0.462
12 I usually do care if I do not complete all optional parts of a game. |  |  |  | 0.612 |  | 0.39
3 I like to spend some time exploring the game world. |  |  |  |  | 0.743 | 0.622
5 I often feel in awe with the landscapes or other game imagery. |  |  |  |  | 0.723 | 0.561
2 I like games with detailed worlds or universes to explore. |  |  |  |  | 0.717 | 0.61
1 I like games which make me feel like I am actually in a different place. |  |  |  |  | 0.668 | 0.46
4 I like to customize how my character looks in a game. |  |  |  |  | 0.601 | 0.391
Variance explained cumulatively % | 16.596 | 32.066 | 45.836 | 58.876 | 67.416 |

Table 4. EFA Results of Traits Section in Pokémon GO

##### Results of CFA

According to the aforementioned criteria regarding confirmatory factor
analysis, the following results demonstrated that the factorial validity of
the Traits section in Pokémon GO survey are acceptable. To be specific, the
chi square to df ratio ($\chi$2/df) is less than 5, the Tucker Lewis index
(TLI) and the Comparative Fit Index (CFI) are all higher than 0.90, the Root
Mean Square Error of Approximation (RMSEA) is less than 0.1 and the
Standardized Root Mean Square Residual (SRMR) is less than 0.08

Figure 4. Pokémon GO CFA Traits Model with Results $\chi$2 | df |  $\chi$2/df | SRMR | RMSEA | TLI | CFI
---|---|---|---|---|---|---
715.603 | 265 | 2.700777indicates an acceptable fit | .051377footnotemark: 7 | .05677footnotemark: 7 | .94277footnotemark: 7 | 0.94977footnotemark: 7
Table 5. Pokémon GO CFA Traits Results

##### Results of Reliability

Based on the aforementioned criteria regarding reliability, our results
indicated that the dimensions in the Traits section were reliable as their
Cronbach’s alpha were all over the acceptable threshold (0.6) with range from
0.758 to 0.944.

Dimension | Aesthetic Ori | Immersion Ori | Goal Ori | Social Ori | Action Ori
---|---|---|---|---|---
Cronbach’s $\alpha$ | .758 | .897 | .758 | .944 | .914
Table 6. Pokémon GO Results of Reliability

####  4.4.2. Harry Potter: Wizards Unite

##### Results of the EFA

Similar to Pokémon GO, KMO and Bartlett’s test indicate that the data in
Traits section in Harry Potter: Wizards Unite Survey is suitable for
exploratory factor analysis.

Table 7. Harry Potter KMO and Bartlett’s Test Results KMO and Bartlett’s Test
---
Kaiser-Meyer-Olkin Measure of Sampling Adequacy | .878
Bartlett’s Test of Sphericity | Approx. Chi-Square | 9612.987
df | 300
Sig. | .000

The Rotated Component Matrix table forHarry Potter: Wizards Unite (Table 8)
indicates that no problematic item should be removed based on the
aforementioned criteria and, consequently, all the 25 items proposed in
original Traits questionnaire were remained in our survey. In addition, the
extracted factors as well as the items involved in each factor were perfectly
in line with the findings from Tondello (Tondello, 2019). Therefore, the five
factors are: Aesthetic orientation (Q1-Q5), Immersion orientation (Q6-Q10),
Goal orientation (Q11-Q15), Social orientation (Q16-Q20) and Action
orientation (Q21-Q25). The five factors explained 71.481% of the variance
cumulatively.

Items | Component | Communalities
---|---|---
1 | 2 | 3 | 4 | 5
18 I like playing with other people. | .921 |  |  |  |  | .878
17 I like to interact with other people in a game. | .916 |  |  |  |  | .886
16 I like to play online with other players. | .885 |  |  |  |  | .838
20 I often prefer to play games with others. | .878 |  |  |  |  | .817
19 I like games that let me play in guilds or teams. | .873 |  |  |  |  | .824
7 Story is important to me when I play games. |  | .909 |  |  |  | .884
8 I enjoy complex narratives in a game. |  | .897 |  |  |  | .861
9 I like games that pull me in with their story. |  | .862 |  |  |  | .815
6 I usually dont skip the story portions or the cutscenes when I am playing. |  | .841 |  |  |  | .716
10 I feel like storytelling does not get in the way of actually playing the game. |  | .789 |  |  |  | .650
22 I enjoy highly difficult challenges in games. |  |  | .894 |  |  | .846
21 I like it when goals are hard to achieve in games |  |  | .859 |  |  | .782
23 I like it when games challenge me. |  |  | .851 |  |  | .784
24 I usually play games at the highest difficulty setting |  |  | .813 |  |  | .701
25 I like it when progression in a game demands skill. |  |  | .794 |  |  | .665
13 I like completing games 100%. |  |  |  | .870 |  | .789
11 I like to complete all the tasks and objectives in a game. |  |  |  | .842 |  | .728
14 I feel stressed if I do not complete all the tasks in a game. |  |  |  | .765 |  | .594
15 I like finishing quests. |  |  |  | .734 |  | .590
12 I usually do care if I do not complete all optional parts of a game. |  |  |  | .577 |  | .352
1 I like games which make me feel like I am actually in a different place. |  |  |  |  | .769 | .602
3 I like to spend some time exploring the game world. |  |  |  |  | .763 | .681
2 I like games with detailed worlds or universes to explore. |  |  |  |  | .758 | .675
5 I often feel in awe with the landscapes or other game imagery. |  |  |  |  | .691 | .524
4 I like to customize how my character looks in a game. |  |  |  |  | .524 | .388
Variance explained cumulatively % | 16.917 | 32.612 | 48.275 | 60.368 | 71.481 |

Table 8. EFA Results of Traits Section in Harry Potter: Wizards Unite

##### Results of the CFA

Besides the results of EFA which indicated a good construct validity, we
further confirmed that the factorial validity of Traits in Harry Potter:
Wizards Unite was ideal. This is reflected in Table 5 which indicates that all
the index reach a good fit of the model to the data.

Figure 5. Harry Potter CFA Traits with Results $\chi$2 | df |  $\chi$2/df | SRMR | RMSEA | TLI | CFI
---|---|---|---|---|---|---
711.638 | 265 | 2.685777indicates an acceptable fit | .053777footnotemark: 7 | .05677footnotemark: 7 | .94377footnotemark: 7 | 0.94977footnotemark: 7
Table 9. Harry Potter: Wizards Unite CFA Traits Results

##### Results of Reliability

The Cronbach’s alphas displayed in Table 10 suggest that all of the dimensions
(factors) in Traits section in Harry Potter: Wizards Unite were reliable given
the lowest value was beyond the acceptable threshold (0.6) at 0.735 while 3
out of 5 dimensions had excellent reliabilities with Cronbach’s alphas more
than 0.9.

Dimension | Aesthetic Ori | Immersion Ori | Goal Ori | Social Ori | Action Ori
---|---|---|---|---|---
Cronbach’ $\alpha$ | .758 | .897 | .758 | .944 | .914
Table 10. Harry Potter: Wizards Unite Results of Reliability

####  4.4.3. Ingress

##### Results of the EFA

Based on the KMO and Bartlett’s Test, the Traits of Ingress survey was also
suitable for explorative factor analysis as KMO was larger than 0.6 and
Bartlett’s Test of Sphericity was statistically significant with p value
equals to .000. These results can be seen in Table LABEL:tab-ingress-kmo.

Table 11. Ingress KMO and Bartlett’s Test Results KMO and Bartlett’s Test
---
Kaiser-Meyer-Olkin Measure of Sampling Adequacy | .843
Bartlett’s Test of Sphericity | Approx. Chi-Square | 2605.331
df | 276
Sig. | .000

In the exploratory factor analysis of Traits section in ingress survey, Q5 -
“I often feel in awe with the landscapes or other game imagery” - should be
removed during the test as it was found to load on two factors, which is not
permitted based on the aforementioned criteria regarding item retention (see
section 4.3.1). The results of the EFA presented in Table 12 suggest that the
rest items remain a stable 5-factor construct, in which all the items load on
expected factors as Tondello (Tondello, 2019) suggested. Therefore, for the
sake of consistency in this report, the five factors are labeled as Aesthetic
orientation (Q1-Q4, Q5 removed), Immersion orientation (Q6-Q10), Goal
orientation (Q11-Q15), Social orientation (Q16-Q20) and Action orientation
(Q21-Q25). The five factors explained 75.698% of the variances.

Items | Component | Communalities
---|---|---
1 | 2 | 3 | 4 | 5
18 I like playing with other people. | .941 |  |  |  |  | .912
17 I like to interact with other people in a game. | .935 |  |  |  |  | .900
20 I often prefer to play games with others. | .896 |  |  |  |  | .855
19 I like games that let me play in guilds or teams. | .888 |  |  |  |  | .839
16 I like to play online with other players. | .833 |  |  |  |  | .810
22 I enjoy highly difficult challenges in games. |  | .888 |  |  |  | .864
23 I like it when games challenge me. |  | .859 |  |  |  | .797
21 I like it when goals are hard to achieve in games. |  | .852 |  |  |  | .802
25 I like it when progression in a game demands skill. |  | .830 |  |  |  | .726
24 I usually play games at the highest difficulty setting. |  | .820 |  |  |  | .715
6 I usually dont skip the story portions or the cutscenes when I am playing. |  |  | .880 |  |  | .816
7 Story is important to me when I play games. |  |  | .863 |  |  | .843
8 I enjoy complex narratives in a game. |  |  | .855 |  |  | .832
9 I like games that pull me in with their story. |  |  | .814 |  |  | .863
10 I feel like storytelling does not get in the way of actually playing the game. |  |  | .750 |  |  | .626
13 I like completing games 100%. |  |  |  | .844 |  | .812
11 I like to complete all the tasks and objectives in a game. |  |  |  | .831 |  | .730
14 I feel stressed if I do not complete all the tasks in a game. |  |  |  | .735 |  | .621
15 I like finishing quests. |  |  |  | .680 |  | .612
12 I usually do care if I do not complete all optional parts of a game. |  |  |  | .595 |  | .400
2 I like games with detailed worlds or universes to explore. |  |  |  |  | .856 | .792
3 I like to spend some time exploring the game world. |  |  |  |  | .841 | .769
1 I like games which make me feel like I am actually in a different place. |  |  |  |  | .815 | .767
4 I like to customize how my character looks in a game. |  |  |  |  | .607 | .468
Variance explained cumulatively % | 18.394 | 34.814 | 50.938 | 63.373 | 75.698 |

Table 12. EFA Results of Traits Section in Ingress

##### Results of the CFA

Although the removed Q5 could potentially confound the factorial validity of
the Traits section in Ingress survey, we confirmed that the questionnaire
without Q5 is still valid for further analysis. The following statistics
reveal that $\chi$2/df, SRMR, TLI and CFI are all good and RSMEA was also
within the acceptable threshold. These results can be see in Figure 6 and
Table 13.

Figure 6. Ingress CFA Traits Model with Results $\chi$2 | df |  $\chi$2/df | SRMR | RMSEA | TLI | CFI
---|---|---|---|---|---|---
451.589 | 242 | 1.866777indicates an acceptable fit | .073277footnotemark: 7 | .08277footnotemark: 7 | .89677footnotemark: 7 | 0.90977footnotemark: 7
Table 13. Ingress CFA Traits Results

##### Results of Reliability

The results of Cronbach’s alpha also indicate that all the dimensions
(factors) in the Traits section in Ingress survey were reliable ranging from
0.780 to 0.944. These results can be seen in Table 14

Dimension | Aesthetic Ori | Immersion Ori | Goal Ori | Social Ori | Action Ori
---|---|---|---|---|---
Cronbach’ $\alpha$ | .758 | .897 | .758 | .944 | .914
Table 14. Ingress Results of Reliability

###  4.5. Reliability and Validity of Gratifications Section of Surveys

In order to explore the gratifications that the casual and hardcore players
derive from playing Pokémon GO, Harry Potter: Wizards Unite, and Ingress, we
utilized 33 items in 7 dimensions from Hamari’s scale (Hamari et al., 2019),
namely Challenge, Competition, Enjoyment, Trendiness, Socializing, Outdoor
activity and Nostalgia.

####  4.5.1. Pokémon GO

##### Results of the EFA

The results of KMO and Bartlett’s test (Table 15) indicate that the data in
Gratification section in Pokémon GO is suitable for exploratory factor
analysis. To be specific, the KMO equals to 0.890 suggest that the sample is
large enough to perform EFA. In addition, the significance (p¡0.05) of
Bartlett’s of Sphericity indicated that the correlation between items are
sufficiently large.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | .880
---|---
Bartlett’s Test of Sphericity | Approx. Chi-Square | 8957.427
| df | 325
| Sig. | .000
Table 15. Pokémon GO KMO and Bartlett’s Test Results

According to the criteria we set to determine the retention of items, we
removed 5 out of 33 items in the Gratification section in Pokémon GO survey.
The detailed items as well as the reasons why they were removed are listed in
Table 16:

Items |  Reasons
---|---
Q14 I play Pokémon GO because it is a habit. |  A valid factor should have at least 3 items. Q14 is the items with the higher loading in a two-item factor.
Q25 I play Pokémon GO because it motivates me to go out. |  A valid factor should have at least 3 items. Q25 is the items with the higher loading in a two-item factor.
Q15 I play Pokémon GO because it occupies my free time. |  Low factor loading and low Communalities
Q3 I feel excited when I catch a new Pokémon. |  Loaded on 2 or more factors
Q26 I play Pokémon GO because it motivates me to explore new places. |  Loaded on 2 or more factors
Q6 I like to prove that I am one of the best players. |  Loaded on 2 or more factors
Q10 I play Pokémon GO because it is exciting. |  Loaded on 2 or more factors
Table 16. Questions Removed from our Survey and reasons for their removal

The final result of EFA (as seen in Table 17 suggest that a 6-factor structure
can be extracted from the data in Gratification section in Pokémon GO survey,
although we initially employed items from 7 dimensions in the original
Gratification questionnaire. The difference is that, instead of having
Socializing and Outdoor activity dimensions separately, our data suggest that
the two dimensions should be merged as one. This is reflected in Table 17. The
first extracted factor in our result has 8 items, among which, 6 items (Q19,
Q20, Q21, Q22, Q23, Q24) belong to Socializing dimensions and 2 items (Q27,
Q28) belong to Outdoor Activity dimensions in original Gratification
questionnaire. However, this is not surprising as we deployed our
questionnaire in new samples who may have different play experiences and the
difference in samples would have impact on the structure of factors (Gaskin et
al., 2017). In addition, the participants in our sample may interpret
questions differently in comparison to those in Hamari (Hamari et al., 2019)
because more than half of our participants were native English speakers who
came from US and UK while nearly 70% of the participants in Hamari (Hamari et
al., 2019) were non-native English speakers who were mainly from Finland and
Philippines.

More importantly, the Outdoor activity dimension in the original questionnaire
includes questions that are closely related to social activities. For example,
Q27 “I play Pokémon GO because I can meet friends outdoors.” and Q28 “I play
Pokémon GO because I can meet strangers outdoors.”. This may have resulted in
the two dimensions being interpreted as one by the participants. Therefore,
after evaluating the EFA results of a 7-factor construct, which showed poor
correspondences between items and factors, we decided to accept the 6-factor
structure in the Gratification section in Pokémon GO survey.

The first factor is labeled as “Socialization” to distinguish from the
“Socializing” dimension in the original Gratification questionnaire.
Socialization describe the player’s gratification-related interactions with
others. Since the items involved in the rest factors are largely in line with
the original questionnaire, we decide to label the remaining factors with the
same names that Hamari (Hamari et al., 2019) used in order to keep the
consistency in naming.

In detail, the second factor is named “Enjoyment”, which describes the
pleasure an individual gains while playing the game. The third factor is
labeled as “Trendiness” to indicate the extent to which an individual
considers playing game for other’s perceptions. The fourth factor describes
player’s yearning to relive or return to a past period so that it is named
“Nostalgia”. Factor 5 is called “Challenge” as it evaluates a sense that an
individual’s capabilities are being stretched and tested. The last factor
describes the desire and need of players to perform better or beat other
players/ computer while playing a game so that it is named “Competition”. The
6 factors explained 69.872% of the variance cumulatively (seen in Table 17.

Items | Component | Communalities
---|---|---
1 | 2 | 3 | 4 | 5 | 6
24 Pokémon GO enables me to be part of a group. | .838 |  |  |  |  |  | .761
27 I play Pokémon GO because I can meet friends outdoors. | .807 |  |  |  |  |  | .716
21 Pokémon GO enables me to make new friends. | .798 |  |  |  |  |  | .728
19 Pokémon GO enables me to maintain friendships. | .766 |  |  |  |  |  | .675
20 Pokémon GO enables me to improve relationships. | .763 |  |  |  |  |  | .650
23 Pokémon GO enables me to participate in relevant discussions. | .753 |  |  |  |  |  | .655
22 I like to play Pokémon GO because my friends play the game. | .707 |  |  |  |  |  | .546
28 I play Pokémon GO because I can meet strangers outdoors. | .683 |  |  |  |  |  | .617
11 I play Pokémon GO because it is entertaining. |  | .888 |  |  |  |  | .851
12 I play Pokémon GO because it is fun. |  | .869 |  |  |  |  | .836
13 I play Pokémon GO because it is a good pastime. |  | .753 |  |  |  |  | .632
10 I play Pokémon GO because it is exciting. |  | .660 |  |  |  |  | .649
18 Pokémon GO enables me to look stylish. |  |  | .922 |  |  |  | .958
16 Pokémon GO enables me to look trendy. |  |  | .912 |  |  |  | .920
17 Pokémon GO enables me to look cool. |  |  | .912 |  |  |  | .945
33 I have been a fan of Pokémon even before the launch of Pokémon GO. |  |  |  | .867 |  |  | .757
31 I used to watch Pokémon cartoons/anime series/movies. |  |  |  | .805 |  |  | .652
30 I used to play Pokémon games on Nintendo Gameboy or other consoles. |  |  |  | .785 |  |  | .622
32 I used to collect Pokémon merchandise (e.g. toys, stickers, trading cards, books etc.). |  |  |  | .773 |  |  | .610
4 I feel excited when I win a battle. |  |  |  |  | .709 |  | .593
5 I enjoy finding new and creative ways to work through Pokémon GO. |  |  |  |  | .673 |  | .585
1 I feel proud when I master an aspect of Pokémon GO. |  |  |  |  | .634 |  | .576
2 It feels rewarding to get to the next level. |  |  |  |  | .610 |  | .538
8 I get upset when I am unable to earn enough points. |  |  |  |  |  | .813 | .718
7 I get upset when others do better than me. |  |  |  |  |  | .810 | .725
9 It is important to me to be one of the skilled persons playing the game. |  |  |  |  |  | .708 | .653
Variance explained cumulatively % | 19.843 | 31.379 | 42.071 | 52.360 | 61.634 | 69.872 |

Table 17. EFA Results of Gratification Section in Pokémon GO

##### Results of the CFA

According to the aforementioned criteria regarding confirmatory factor
analysis, the results (located in Figure 7 and Table 18) demonstrate that the
factorial validity of the Gratification section in Pokémon GO survey was
acceptable. This acceptability is due to the $\chi$2/df value being acceptable
at 3.552, both the Tucker Lewis index (TLI) and the Comparative Fit Index
(CFI) resulting in values over 0.9, and both the Root Mean Square Error of
Approximation (RMSEA) and the Standardized Root Mean Square Residual (SRMR)
resulting in values less than 0.08.

Figure 7. Pokémon GO CFA Gratifications with Results $\chi$2 | df |  $\chi$2/df | SRMR | RMSEA | TLI | CFI
---|---|---|---|---|---|---
1008.888 | 284 | 3.552777indicates an acceptable fit | .060077footnotemark: 7 | .06977footnotemark: 7 | .90377footnotemark: 7 | 0.91577footnotemark: 7
Table 18. Pokémon GO CFA Gratifications Results

##### Results of Reliability

The statistics in Table 19 indicate that the Gratification was a reliable
instrument as the Cronbach’s alpha in all the six dimensions were more than
0.6 ranging from 0.703 to 0.966.

Dimension | Socialization | Enjoyment | Trendiness | Nostalgia | Competition | Challenge
---|---|---|---|---|---|---
Cronbach’s $\alpha$ | .904 | .849 | .966 | .944 | .703 | .815

Table 19. Pokémon GO Gratifications Results of Reliability

####  4.5.2. Harry Potter: Wizards Unite

##### Results of the EFA

The statistics of KMO and Barlett’s test (Table 20) suggest that data in
Gratification section in Harry Potter: Wizards Unite survey is suitable for
exploratory factor analysis as KMO was larger than 0.6 and result of
Bartlett’s test was significant at p less than 0.05.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | .882
---|---
Bartlett’s Test of Sphericity | Approx. Chi-Square | 10007.259
| df | 378
| Sig. | .000
Table 20. Harry Potter: Wizards Unite Go KMO and Bartlett’s Test Results

In order to draw a valid factor structure of Gratification section in Harry
Potter: Wizards Unite survey, 8 out of 33 items were eliminated during the
exploratory factor analysis. Details of the removed items are provided in
Table 21.

Items |  Reasons
---|---
Q26 I play Harry Potter: Wizards Unite because it motivates me to explore new places. |  A valid factor should have at least 3 items. Q26 is the items with the higher loading in a two-item factor.
Q15 I play Harry Potter: Wizards Unite because it occupies my free time. |  A valid factor should have at least 3 items. Q15 is the items with the higher loading in a two-item factor.
Q32 I used to collect Harry Potter merchandise (e.g. toys, stickers, trading cards, etc.). |  A valid factor should have at least 3 items. Q32 is the items with the higher loading in a two-item factor.
Q25 I play Harry Potter: Wizards Unite because it motivates me to go out. |  Low communalities and Low factor loading
Q14 I play Harry Potter: Wizards Unite because it is a habit. |  Low factor loading
Table 21. Questions Removed from the Gratification Survey for Harry Potter
Wizard’s Unite

The final results of EFA, again, suggest that a 6-factor structure in the
Gratification section in Harry Potter: Wizards Unite survey. These results can
be seen in Table 22. This is because that the aforementioned issue in Outdoor
activity remains. Half of the items in this dimension (Q25, Q26) were removed
due to problematic loading. The retained items (Q27, Q28) in Outdoor activity
dimension together with the 6 items in Socializing dimensions in the original
questionnaire (Q19-24) were merged into a new extracted factor that is marked
as factor 1 in the following table. Therefore, similar to the way we name the
factors in Pokémon GO survey, we label the first factor as “Socialization” to
distinguish it from Socializing dimension and Outdoor activity dimension in
the original questionnaire. Since the rest of the factors as well as their
corresponded items were largely in line with the results in Hamari (Hamari et
al., 2019), factor 2 is named as “Challenge”, factor 3 is named as
“Competition”, factor 4 is named as “Enjoyment”, factor 5 is named as
“Trendiness” and factor 6 is named as “Nostalgia” (detailed definitions of the
factors is provided in section 5.2.1). The 6 factors explained 68.136% of the
variance in total.

Items | Component | Communalities
---|---|---
1 | 2 | 3 | 4 | 5 | 6
24 Harry Potter: Wizards Unite enables me to be part of a group. | .803 |  |  |  |  |  | .699
20 Harry Potter: Wizards Unite enables me to improve relationships. | .796 |  |  |  |  |  | .715
27 I play Harry Potter: Wizards Unite because I can meet friends outdoors | .791 |  |  |  |  |  | .681
21 Harry Potter: Wizards Unite enables me to make new friends | .771 |  |  |  |  |  | .655
19 Harry Potter: Wizards Unite enables me to maintain friendships. | .756 |  |  |  |  |  | .671
22 I like to play Harry Potter: Wizards Unite because my friends play the game. | .736 |  |  |  |  |  | .551
28 I play Harry Potter: Wizards Unite because I can meet strangers outdoors | .709 |  |  |  |  |  | .585
23 Harry Potter: Wizards Unite enables me to participate in relevant discussions. | .695 |  |  |  |  |  | .565
4 I feel excited when I win a battle. |  | .802 |  |  |  |  | .724
3 I feel excited when I return foundables to their proper location. |  | .778 |  |  |  |  | .728
2 It feels rewarding to get to the next level. |  | .681 |  |  |  |  | .618
1 I feel proud when I master an aspect of Harry Potter: Wizards Unite |  | .566 |  |  |  |  | .619
5 I enjoy finding new and creative ways to work through Harry Potter: Wizards Unite. |  | .558 |  |  |  |  | .534
9 It is important to me to be one of the skilled persons playing the game. |  |  | .811 |  |  |  | .749
7 I get upset when others do better than me. |  |  | .794 |  |  |  | .698
6 I like to prove that I am one of the best players. |  |  | .783 |  |  |  | .702
8 I get upset when I am unable to earn enough points. |  |  | .617 |  |  |  | .408
11 I play Harry Potter: Wizards Unite because it is entertaining. |  |  |  | .834 |  |  | .822
12 I play Harry Potter: Wizards Unite because it is fun. |  |  |  | .797 |  |  | .823
13 I play Harry Potter: Wizards Unite because it is a good pastime. |  |  |  | .746 |  |  | .641
10 I play Harry Potter: Wizards Unite because it is exciting. |  |  |  | .615 |  |  | .672
17 Harry Potter: Wizards Unite enables me to look cool. |  |  |  |  | .917 |  | .961
18 Harry Potter: Wizards Unite enables me to look stylish. |  |  |  |  | .902 |  | .945
16 Harry Potter: Wizards Unite enables me to look trendy. |  |  |  |  | .894 |  | .915
33 I have been a fan of Harry Potter even before the launch of Harry Potter: Wizards Unite. |  |  |  |  |  | .842 | .725
31 I used to watch Harry Potter movies. |  |  |  |  |  | .813 | .693
29 I used to read Harry Potter books. |  |  |  |  |  | .734 | .565
Variance explained cumulatively % | 18.600 | 29.757 | 40.225 | 50.549 | 60.367 | 68.136 |

Table 22. EFA Results of Gratification Section in Harry Potter: Wizards Unite

##### Results of the CFA

After examining the construct validity, the results of CFA confirm that the
factorial validity of the Gratification section in Harry Potter Wizard United
survey is acceptable. These results can be seen in Figure 8 and Table 23. The
results are acceptable because the Tucker Lewis index (TLI) and the
Comparative Fit Index (CFI) are more than the acceptable threshold (0.8). The
Root Mean Square Error of Approximation (RMSEA) and the Standardized Root Mean
Square Residual (SRMR) are clearly within the recommended bounds (0.1 for
RMSEA and 0.08 for SRMR).

Figure 8. Harry Potter CFA Gratifications with Results $\chi$2 | df |  $\chi$2/df | SRMR | RMSEA | TLI | CFI
---|---|---|---|---|---|---
1294.885 | 335 | 3.865* | 0.0575** | .074** | .877* | .891*
Table 23. Harry Potter: Wizards Unite Gratifications CFA Results

##### Results of Reliability

Based on the statistics in Table 24, it can be safely said that the
Gratification section in Harry Potter: Wizards Unite survey is reliable as the
Cronbach’s alphas in all the dimensions are beyond the minimum acceptable
threshold at 0.6.

Dimension | Socialization | Enjoyment | Trendiness | Nostalgia | Competition | Challenge
---|---|---|---|---|---|---
Cronbach’s $\alpha$ | .904 | .853 | .958 | .944 | .697 | .794

Table 24. Harry Potter: Wizards Unite Gratifications Results of Reliability

####  4.5.3. Ingress

##### Results of the EFA

Similar to all the previous sections, the results of KMO and Bartlett’s test
in Gratification section in Ingress survey indicate that the data was also
suitable for exploratory factor analysis, given KMO was 0.882 ¿ 0.6 and result
of Bartlett’s test are significant at p¡0.05. These data can be seen in Table
LABEL:tab:hp-gratifications-kmos.

Table 25. Ingress Gratifications Bartlett’s Test Results KMO and Bartlett’s
Test
---
Kaiser-Meyer-Olkin Measure of Sampling Adequacy | .853
Bartlett’s Test of Sphericity | Approx. Chi-Square | 2083.636
df | 210
Sig. | .000

Notably, since there was no Ingress-related merchandise, cartoon or console
games on the market prior to the release of Ingress, we excluded the 5 items
in Nostalgia dimensions in this test. Therefore, in total 28 items from 6
dimensions in the original questionnaire were involved in the exploratory
factor analysis conducted on the Gratification section in Ingress survey. The
6 original dimensions are: Challenge, Competition, Enjoyment, Trendiness,
Socializing and Outdoor activity. In order to examine the valid construct in
this section, 6 out of 28 items were removed. The removed items can be seen in
Table 26.

Items |  Reasons
---|---
Q15 I play Ingress because it occupies my free time. |  A valid factor should have at least 3 items. Q15 is the items with the higher loading in a two-item factor.
Q26 I play Ingress because it motivates me to explore new places. |  A valid factor should have at least 3 items. Q26 is the items with the higher loading in a two-item factor.
Q14 I play Ingress because it is a habit. |  Low factor loading
Q28 I play Ingress because I can meet strangers outdoors. |  Low factor loading
Q10 I play Ingress because it is exciting. |  Loaded on 2 or more factors
Q25 I feel excited when I return foundables to their proper location. |  Loaded on 2 or more factors
Q6 I like to prove that I am one of the best players. |  Loaded on 2 or more factors
Table 26. Questions removed from the Ingress Gratifications Survey

Unsurprisingly, the items in Socializing dimension and Outdoor activity are
found to be merged again while the rest extracted factors were similar to the
results in Hamari (Hamari et al., 2019). Please view the results in Table 27.
Therefore, a five-factor structure was extracted from the data in the
Gratification section in Ingress survey. The extracted factors are: 1.)
Socialization; 2.) Challenge; 3.) Trendiness; 4.) Enjoyment and 5.)
Competition. The detailed definition of the 5 factors are provided in section
5.2.1. 71.730% of the variance is explained by the factors cumulatively.

Items | Component | Communalities
---|---|---
1 | 2 | 3 | 4 | 5
19 Ingress enables me to maintain friendships. | .866 |  |  |  |  | .780
20 Ingress enables me to improve relationships. | .838 |  |  |  |  | .748
23 Ingress enables me to participate in relevant discussions. | .764 |  |  |  |  | .682
21 Ingress enables me to make new friends. | .748 |  |  |  |  | .738
24 Ingress enables me to be part of a group. | .722 |  |  |  |  | .671
27 I play Ingress because I can meet friends outdoors. | .702 |  |  |  |  | .615
22 I like to play Ingress because my friends play the game. | .677 |  |  |  |  | .525
25 I play Ingress because it motivates me to go out. | .450 |  |  |  |  | .467
2 It feels rewarding to get to the next level. |  | .845 |  |  |  | .774
1 I feel proud when I master an aspect of Ingress. |  | .787 |  |  |  | .730
5 I enjoy finding new and creative ways to work through Ingress. |  | .688 |  |  |  | .650
4 I feel excited when I win a battle. |  | .629 |  |  |  | .543
17 Ingress enables me to look cool. |  |  | .944 |  |  | .951
18 Ingress enables me to look stylish. |  |  | .941 |  |  | .947
16 Ingress enables me to look trendy. |  |  | .851 |  |  | .803
11 I play Ingress because it is entertaining. |  |  |  | .840 |  | .811
13 I play Ingress because it is a good pastime. |  |  |  | .804 |  | .786
12 I play Ingress because it is fun. |  |  |  | .804 |  | .825
8 I get upset when I am unable to earn enough points. |  |  |  |  | .861 | .769
7 I get upset when others do better than me. |  |  |  |  | .835 | .782
9 It is important to me to be one of the skilled persons playing the game. |  |  |  |  | .540 | .467
Variance explained cumulatively % | 22.371 | 36.111 | .49.587 | 62.267 | 71.730 |

Table 27. EFA Results of Gratification Section in Ingress

##### Results of the CFA

The results of confirmatory factor analysis displayed in Figure 9 and Table 28
reveal the chi square to degree of freedom ratio ($\chi$2/df), Standardized
Root Mean Square Residual (SRMR), Root Mean Square Error of Approximation
(RMSEA), Goodness-of-Fit Index (TLI) and Comparative fit index (CFI) were all
acceptable according to the aforementioned criteria (see section 4.3.1).
Therefore, the factorial validity of the Gratification section in Ingress
survey is confirmed.

Figure 9. Ingress CFA Gratifications with Results $\chi$2 | df |  $\chi$2/df | SRMR | RMSEA | TLI | CFI
---|---|---|---|---|---|---
348.209 | 179 | 1.945777indicates an acceptable fit | .087177footnotemark: 7 | .08577footnotemark: 7 | .89477footnotemark: 7 | 0.90977footnotemark: 7
Table 28. Ingress CFA Gratifications Results

##### Results of Reliability

The statistics in Table 29 indicate that the Gratification questionnaire are
reliable as the Cronbach’s alphas in all the dimensions are beyond the
acceptable threshold ($\alpha$¿ 0.6) ranging from 0.724 to 0.962.

Dimension | Socialization | Enjoyment | Trendiness | Competition | Challenge
---|---|---|---|---|---
Cronbach’s $\alpha$ | .922 | .858 | .962 | .724 | .733
Table 29. Ingress Results of Reliability of Gratifications

Overall, the (modified) Traits sections and (modified) Gratification sections
in Pokémon GO, Harry Potter: Wizards Unite and Ingress surveys are all shown
to be valid and reliable. Therefore, we can further investigate the casual
relationships and correlations between variables based on the data acquired
through these instruments.

##  5\. Results

###  5.1. The casual and hardcore players of Pokémon GO, Harry Potter: Wizards
Unite, and Ingress.

Based on the statistics provided in Table 30, it can be found that most of the
participants tend to play the 3 games casually as 67.6% of Pokémon GO players,
72.8% of Harry Potter: Wizards Unite players and 60.0% of the Ingress players
describes themselves as casual players. Although hardcore players generally
account for a relatively small part of the whole population in each game,
Ingress has the most hardcore players among all 3 games, reaching a 40%. In
addition, the demographic information of casual and hardcore players in our
survey is for the most part the same. This means that both casual and hardcore
players in the 3 games are mainly male, employed, well-educated and from
English-speaking countries as well as European countries.

That being said, Pokémon GO and Harry Potter: Wizards Unite players are in
average under 36 years old, while Ingress players are in average over 36 years
old. However, we find that in comparison to casual players, hardcore players
of all the 3 games tend to play more often and play for longer time. In
detail, 84.7% of the Pokémon GO hardcore players reported that they would play
the game many times a day and 53.9% of them would play for more than 30
minutes. In contrast, only over half of the casual players (54.8%) would play
Pokémon GO multiple times every day and most of them (60.2%) would play for
less than half an hour. Regarding Harry Potter: Wizards Unite, although many
casual players (62.7%) also play the game many times a day, more hardcore
players (85.8%) would play at the same level of frequency.

More importantly, over half of the hardcore players (52.8%) would play for
more than half an hour in each game session while only around a quarter
(26.8%) of the casual players would spend the same amount of time. The
differences in game frequency and time between casual and hardcore players are
more obvious in Ingress, in which the percentage of hardcore players who play
multiple times per day (76.2%) is over twice that among casual players
(35.7%). Moreover, over 75% of the Ingress hardcore players would spend more
than 30 minutes on each game session while the rest a quarter of the hardcore
players of Ingress would spend relatively shorter time within half an hour. In
contrast, nearly half of the casual players of Ingress (49.6%) would spend
less than 30 minutes on each game session while the other half would spend
more than half an hour every time they play Ingress.

Characteristics | Pokémon GO Players | HPWU players | Ingress players
---|---|---|---
Casual | Hardcore | Casual | Hardcore | Casual | Hardcore
Statistics |  |  |  |  |  |
Number | 724 | 347 | 769 | 288 | 157 | 105
Percentage | 67.60% | 32.40% | 72.80% | 27.20% | 60.00% | 40.00%
Gender |  |  |  |  |  |
Male | 518 (71.5%) | 279 (80.4%) | 381 (49.5%) | 159 (55.2%) | 130 (82.8%) | 81 (77.1%)
Female | 191 (26.4%) | 61 (17.6%) | 378 (49.2%) | 120 (41.7%) | 25 (15.9%) | 22 (21.0%)
Non-binary | 15 (2.1%) | 7 (2.0%) | 10 (1.3%) | 9 (3.1%) | 2 (1.3%) | 2 (1.9%)
Age |  |  |  |  |  |
$\leq$ 20 years | 162 (22.4%) | 57 (16.4%) | 30 (3.9%) | 11 (3.8%) | 11 (7.0%) | 9 (8.6%)
21-35 years | 462 (63.8%) | 237 (68.3%) | 527 (68.5%) | 196 (68.1) | 68 (43.3%) | 30 (28.6%)
$\geq$ 36 years | 100 (13.8%) | 53 (15.3%) | 212 (27.6%) | 81 (28.1%) | 78 (49.7%) | 66 (62.8%)
Occupation |  |  |  |  |  |
Employed | 429 (59.3%) | 213 (61.4%) | 581 (75.6%) | 232 (80.6%) | 121 (77.1%) | 80 (76.2%)
Unemployed | 62 (8.6%) | 33 (9.5%) | 72 (9.3%) | 22 (7.6%) | 14 (8.9%) | 17 (16.3%)
Student | 233 (32.1%) | 101 (29.1%) | 116 (15.1%) | 34 (11.8%) | 22 (14.0%) | 10 (9.5%)
Education |  |  |  |  |  |
$\geq$University degree | 364 (50.3%) | 178 (51.3%) | 500 (65.0%) | 181 (62.8%) | 88 (56.1%) | 45 (42.9%)
College degree | 88 (12.2%) | 41 (11.8%) | 124 (16.1%) | 49 (17.0%) | 20 (12.7%) | 21 (20.0%)
Vocational degree | 28 (3.9%) | 16 (4.6%) | 22 (2.9%) | 12 (4.2%) | 3 (1.9%) | 2 (1.9%)
High school or lower | 244 (33.6%) | 112 (32.3%) | 123 (16.0%) | 46 (16.0%) | 46 (29.3%) | 37 (35.2%)
Countries |  |  |  |  |  |
United States | 337 (53.5%) | 161 (46.4%) | 391 (50.8%) | 143 (49.7%) | 73 (46.5%) | 53 (50.5%)
United Kingdom | 89 (12.3%) | 34 (9.8%) | 75 (9.8%) | 18 (6.3%) | 25 (15.9%) | 19 (18.1%)
Germany | 36 (5.0%) | 29 (8.4%) | 48 (6.2%) | 8 (2.8%) | 11 (7.0%) | 6 (5.7%)
Canada | 42 (5.8%) | 14 (4.0%) | 35 (4.6%) | 25 (8.7%) | 8 (5.1%) | 3 (2.9%)
Australia | 27 (3.7%) | 13 (3.7%) | 14 (1.8%) | 6 (2.1%) | 2 (1.3%) | 1 (1.0%)
Netherlands | 15 (2.1%) | 9 (2.6%) | 20 (2.6%) | 5 (1.7%) | 5 (3.2%) | 0 (0%)
Poland | 14 (1.9%) | 4 (1.4%) | 12 (1.6%) | 2 (0.7%) | 1 (0.6%) | 0 (0%)
Italy | 7 (1.0%) | 10 (2.9%) | 10 (1.3%) | 2 (0.7%) | 1 (0.6%) | 0 (0%)
France | 11 (1.5%) | 5 (1.4%) | 8 (1.0%) | 5 (1.7%) | 1 (0.6%) | 2 (1.9%)
Finland | 11 (1.5%) | 5 (1.4%) | 14 (1.8%) | 3 (1.0%) | 2 (1.3%) | 2 (1.9%)
Others | 135 (18.6%) | 63 (18.1%) | 142 (18.5%) | 51 (17.8%) | 28 (17.8%) | 19 (18.1%)
Play Frequency |  |  |  |  |  |
Many times a day | 397 (54.8%) | 294 (84.7%) | 482 (62.7%) | 247 (85.8%) | 56 (35.7%) | 80 (76.2%)
Once a day | 218 (30.1%) | 39 (11.2%) | 213 (27.7%) | 40 (13.9%) | 47 (29.9%) | 15 (14.3%)
Few times a week | 89 12.3%) | 13 (3.7%) | 53 (6.9%) | 0 (0.0%) | 39 (24.8%) | 9 (8.5%)
Once a week | 4 (0.6%) | 1 (0.3%) | 6 (0.8%) | 0 (0.0%) | 10 (6.4%) | 1 (1%)
Few times a month | 10 (1.4%) | 0 (0.0%) | 6 (0.8%) | 0 (0.0%) | 4 (2.5%) | 0 (0%)
Rarely | 6 (0.8) | 0 (0.0%) | 9 (1.2%) | 1 (0.3%) | 1 (0.6%) | 0 (0%)
Game session |  |  |  |  |  |
$\leq$15 mins | 211 (29.1%) | 61 (17.6%) | 298 (38.8%) | 19 (17.0%) | 23 (14.6%) | 11 (10.5%)
16-30 mins | 225 (31.1%) | 99 (28.5%) | 265 (34.5%) | 87 (30.2%) | 55 (35.0%) | 15 (14.3%)
31-45 mins | 101 (14.0%) | 41 (11.8%) | 97 (12.6%) | 45 (15.6%) | 22 (14.0%) | 12 (11.4%)
46-60 mins | 78 (10.8%) | 36 (10.4%) | 49 (6.4%) | 45 (15.6%) | 26 (16.7%) | 18 (17.0%)
>60 mins | 109 (15.0%) | 110 (31.7%) | 60 (7.8%) | 62 (21.6%) | 31 (19.7%) | 49 (46.7%)
Table 30. The casual and hardcore players of Pokémon GO, Harry Potter: Wizards
Unite, and Ingress

###  5.2. The traits of casual and hardcore players of Pokémon GO, Harry
Potter: Wizards Unite, and Ingress

The results of this section can be found summarized in Table 31. In order to
better understand the results, there are several items that need to be
mentioned before looking into the detailed data:

  * •

Except for Nostalgia dimension, the items in each dimension are scored as “1 -
Strongly agree, 2 - Agree, 3 - Somewhat agree, 4 – Neither agree nor disagree,
5 – Somewhat disagree, 6 – Disagree, 7 – Strongly disagree”. Therefore, a
lower average score in each dimension represents a greater acknowledgement of
participants to the trait or gratification specified in this dimension while a
higher score indicate that participants are less likely to have a certain
trait or gratification.

  * •

Items in Nostalgia (e.g. “I used to read Harry Potter books.”) are binary, in
which 1 stands for “Yes” and 2 stands for “No”. Therefore, a mean close to 1
indicates that participants tend to have nostalgia in the game while a mean
close to 2 indicates that they do not have this gratification.

  * •

Independent sample T-test is conducted in order to investigate the differences
in certain traits and gratifications between casual and hardcore players. The
confidence interval is set up to 95%. Once the significance in certain
dimension is less than 0.05, we reckon that the difference in this dimension
between casual and hardcore players is statistically significant.

####  5.2.1. The traits and gratifications of casual and hardcore players of
Pokémon GO

Again, please note that the results relevant to this section can be seen in
Table 31.

##### Aesthetic - Traits:

All players of Pokémon GO like the aesthetic experiences while casual players
are more likely to be aesthetic-oriented. This is because that both their mean
values are close to 2 which indicates that they “agree” with the items in this
dimension such as “I like to spend some time exploring the game world.”
However, in comparison to hardcore player, casual players scored significantly
less than hardcore players with p value less than 0.05 at 0.035. This means
they are more likely to enjoy the game world, sounds, graphics and art-style
in Pokémon GO.

##### Immersion - Traits:

All players prefer complex stories and narratives in Pokémon GO without
significant differences between the casual and hardcore players. This is
because that (1) their mean values which are close to 2 indicate that they
generally “agree” with the items in Immersion Orientation dimension that
include items such as “Story is important to me when I play games.”, and (2)
that the p value at 0.057 indicates the differences between these two types of
player are not statistically significant.

##### Goal - Traits:

All players would like to complete requests and tasks in Pokémon GO yet
hardcore players tend to be more goal oriented in comparison to casual
players. This is because that both the average score of hardcore players and
casual players are around 3, which suggests that they, to some extent, agree
with the items in Goal Orientation dimension. One typical example can be “I
usually do care if I do not complete all optional parts of a game.” Moreover,
a p value less than 0.05 at 0.000 suggests that hardcore players who scored
less in this dimension are more likely to finish more tasks with higher
completeness.

##### Social - Traits:

All players prefer to play Pokémon GO with others online or in the same places
while hardcore players are more socially oriented. This is also based on the
facts that (1) both casual and hardcore players scored around 3 which
indicates that they somewhat agree that they like to, for example, “play with
other people”, and (2) that hardcore players scored significantly less than
casual players. When taken together, the results suggest that players are more
likely to interact with others during game sessions.

##### Action - Traits:

When Pokémon GO becomes a bit more difficult, all players like the challenges.
Hardcore players tend to enjoy them more compared to casual players. This is
because that the average scores of both casual players (mean = 3.1492) and
hardcore players (mean = 2.7764) indicate that they at least partially agree
with items in Action Orientation dimension, given 3 means “somewhat agree”.
One typical example of these items can be that “I like it when goals are hard
to achieve in games”. Moreover, the p value less than 0.05 reveals that the
difference in the preference of hardcore players for in-game difficulties is
significantly higher than casual players, given that lower mean scores
indicate greater acknowledgements to the dimensions.

##### Socialization - Gratification:

Hardcore players tend to be gratified as they can make new friends outdoors
and maintain relationships with others through Pokémon GO while casual players
are more likely to have a neutral attitude towards these social activities.
The average score of hardcore players (mean = 3.3833) shows that they to some
extent agree that Pokémon GO would enable them to “be part of a group” or
“meet strangers outdoors” while the average score of casual players (mean =
4.2778) suggests that they generally “neither agree nor disagree” with these
items in Socialization dimension. Besides, the p value under 0.05 also
indicates that the differences in socialization-related gratification between
hardcore players and casual players is statistically significant.

##### Enjoyment - Gratification:

All players are gratified by the enjoyment Pokémon GO provides while hardcore
players gain more enjoyment from playing the game. This is because that both
casual players and hardcore players scored averagely around 2, which indicates
that they generally agree with the items in Enjoyment dimension. A typical
item is “I play Pokémon GO because it is fun”. In addition, given hardcore
players (mean = 2.2032) scored less than casual players (mean = 2.4848) and
the p value is less than 0.05, it can be safely said (95% sure due to the
confidence interval) that hardcore players gain significantly more pleasures
in Pokémon GO compared to casual players.

##### Trendiness - Gratification:

Given the mean scores of both casual players (mean = 5.9517) and hardcore
players (mean = 5.7041) are close to 6 which stands for “Disagree” in our
scale, the results suggest that all players generally do not reckon that
Pokémon GO would enable them to “look cool/trendy/stylish”. Despite that
trendiness is not an important gratification, hardcore players are more likely
to be gratified with the popularity of the game as they scored relatively
lower than casual players in this dimension and the p value equals to 0.005
indicates the difference is statistically significant.

##### Competition - Gratification:

All players are gratified by the competition in Pokémon GO yet hardcore
players tend to be more satisfied when they perform better or beat others
during the game. This is because that the hardcore players (mean = 2.3317)
generally agree the items in Competition dimension such as “I like to prove
that I am one of the best players.”, while casual players would partially
agree with these items given their mean is 2.7276 which is more close to “3 –
somewhat agree”. Furthermore, the p value less than 0.05 also indicates that
the perceived gratification from in-game competitions is significantly
different between hardcore players and casual players.

##### Challenge - Gratification:

In-game challenges do not result in gratification among casual players while
hardcore players think that being “tested” is fine and are more gratified
after overcoming the difficulties. This result is first reflected in the data
that casual player averagely scored up to 4.8020 which is close to “5 –
somewhat disagree” in Challenge dimension. This means items such as “I feel
proud when I master an aspect of Pokémon GO” may not be the case among casual
players. In contrast, hardcore players tend to have a neutral attitude towards
in-game challenges given their mean score in this dimension, 4.0038, is close
to “4 – neither agree nor disagree”. This suggests that at least some items or
some situations illustrated in the items correctly describe the gratification
that hardcore players gained from challenges so that the overall score reached
a relatively balanced point. In addition, the significant p value (p¡0.05)
indicates that hardcore players would have significantly more gratifications
from challenges in comparison to causal players.

Interestingly, although hardcore players show their favor to in-game
difficulties consistently, casual players who are found to prefer challenging
games in Traits section now tend to reckon that in-game challenges would not
result in gratifications. This indicates that the observed personal
preferences may not correspondingly lead to expected gameplay experiences.
Therefore, the relationships between traits and gratifications need to be
further studied and game designers should consider both aspects while
developing games.

##### Nostalgia - Gratification:

Both casual players and hardcore players of Pokémon GO are gratified by
nostalgia while there is no significant difference between the two groups.
Given that the items in nostalgia (e.g. “I used to read Pokémon manga/books.”)
are binary questions in which 1 stands for Yes while 2 stands for No, the
average scores of both casual players (mean = 1.2175) and hardcore players
(mean = 1.2341) indicate that all players are likely to get to know Pokémon
before the game and therefore get gratified by a yearning to relive or return
to a past period during game sessions. Besides, p value equals to 0.451
suggests that there is no significant difference in Nostalgia dimension
between casual and hardcore players.

Pokémon GO | Casual (724) | Hardcore (347) | Dif | Sig (p) | t
---|---|---|---|---|---
Mean | SD | Mean | SD
Traits |  |  |  |  |  |  |
Aesthetic Orientation | 2.1019 | 0.83839 | 2.2184 | 0.8601 | -0.11651 | 0.035 | -2.111
Immersion Orientation | 2.3719 | 1.19351 | 2.5291 | 1.30297 | -0.15784 | 0.057 | -1.906
Goal Orientation | 3.0072 | 1.03518 | 2.7493 | 1.0093 | 0.2579 | 0 | 3.847
Social Orientation | 3.1925 | 1.53376 | 2.8571 | 1.42207 | 0.33548 | 0 | 3.521
Action Orientation | 3.1492 | 1.24426 | 2.7764 | 1.20529 | 0.3728 | 0 | 4.635
Gratifications |  |  |  |  |  |  |
Socialization | 4.2778 | 1.47517 | 3.3833 | 1.34148 | 0.89451 | 0 | 9.883
Enjoyment | 2.4848 | 0.98147 | 2.2032 | 0.95214 | 0.28164 | 0 | 4.437
Trendiness | 5.9517 | 1.21866 | 5.7041 | 1.42108 | 0.24753 | 0.005 | 2.79
Competition | 2.7276 | 1.02101 | 2.3317 | 0.89719 | 0.39614 | 0 | 6.461
Challenge | 4.802 | 1.38071 | 4.0038 | 1.4701 | 0.79818 | 0 | 8.668
Nostalgia | 1.2175 | 0.33417 | 1.2341 | 0.34492 | -0.01661 | 0.451 | -0.753
Table 31. Pokémon GO Traits and Gratifications values for Casual and Hardcore
Players

####  5.2.2. The traits and gratifications of casual and hardcore players of
Harry Potter: Wizards Unite

Considering that (1) the results in this section are also analyzed based on
the mean values and p values, and (2) the dimensions as well as the involved
items in Harry Potter: Wizards Unite are similar to those in Pokémon GO
section, we decide to simply describe the results without detailed
explanations for the sake of brevity. The results relevant to this section can
be seen in Table 32.

##### Aesthetic - Traits:

Both casual players (mean = 2.2224) and hardcore players (mean = 2.1278)
prefer aesthetic experiences to explore the game world and appreciate the
game’s graphics, sound, and art style, given 2 stands for “Agree” in the scale
of the questionnaire. No significant difference is detected between the two
groups given p value is more than 0.05 at 0.098.

##### Immersion - Traits:

Both casual players (mean = 2.5246) and hardcore players (mean = 2.1278) like
the complex stories and narratives in Harry Potter United Wizards given 2
stands for “Agree” in the scale of the questionnaire. No significant
difference is detected between the two groups as p value is more than 0.05 at
0.081.

##### Goal - Traits:

Both casual players (mean = 2.7508) and hardcore players (mean = 2.3826) have
the preferences for gameplay that involves completing quests or tasks,
collecting digital objects, or similar experiences. Hardcore players are more
likely to be goal oriented as they scored less in this dimension and the p
value is significant with value less than 0.05.

##### Social - Traits:

Both casual players (mean = 3.5732) and hardcore players (mean = 3.1750)
partially admit that they prefer to play together with others online or in the
same space, given 3 stands for “Somewhat agree” in the scale of the
questionnaire. Hardcore players are more likely to play with others as they
scored less in this section and the p value is less than 0.05.

##### Action - Traits:

Both casual players (mean = 3.3599) and hardcore players (mean = 2.8924) to
some extends agree that they like challenging and fast-paced gameplays, given
3 stands for “Somewhat agree” in the scale of the questionnaire. Hardcore
players are more likely to enjoy the challenges, for example playing with more
difficult setting, as they scored less in this section and the p value is less
than 0.05.

##### Socialization – Gratification:

Casual players (mean = 5.0683) are not gratified by the social activities
(e.g. meet others outdoor, maintain relationships with others) while hardcore
players (mean = 4.4983) tend to have a neutral attitudes towards these
activities, given that 5 stands for “Somewhat disagree” and 4 stands for
“neither agree nor disagree” in the scale of the questionnaire. However,
hardcore players tend to be more satisfied with the interactions with others
through Harry Potter: Wizards Unite compared to casual players as they scored
less in this dimension with a p value less than 0.05 which shows the
significance of the difference.

##### Enjoyment – Gratification:

Casual players (mean = 2.8443) tend to partially agree that they enjoyment is
a gratification they derive from Harry Potter: Wizards Unite as their mean
score is more close to “3 – somewhat agree” while hardcore players would
reckon enjoyment as one of their gratification to a greater extend as their
mean score is more close to “2 - Agree” and the p value less than 0.05 also
indicate that this difference is significant.

##### Trendiness – Gratification:

Both casual players (mean = 6.0251) and hardcore players (mean = 5.9514) would
not be gratified by the trendiness of Harry Potter: Wizards Unite, given a
mean score at 6 indicate that participants “disagree” with the items involved
in certain dimension. No significant difference between the two types of
players is detected given the p value is larger than 0.05 at 0.396.

##### Competition – Gratification:

Casual players (mean = 2.8637) tend to partially agree that they are gratified
by the game-related competitions in Harry Potter: Wizards Unite as their mean
score is more close to “3 – somewhat agree” while hardcore players, whose mean
score is more close to “2 - Agree”, would have more gratifications if they can
perform better than others in the game. Besides, the p value less than 0.05
also indicate that this difference is significant.

##### Challenge – Gratification:

Hardcore players (mean = 3.8194) tend to have a neutral attitude towards the
gratification gained from in-game challenges, given 4 stands for “neither
agree nor disagree” in the scale of the questionnaire. In addition, hardcore
players would also be more satisfied once they conquer the difficulties
(p¡0.05) in comparison to casual players who generally “disagree” (mean =
5.9514) that they can be gratified by the in-game challenges.

##### Nostalgia – Gratification:

Given the mean scores of both casual players (mean = 1.2263) and hardcore
players (mean = 1.1910) are close to 1 which stands for “Yes” in nostalgia-
related binary items, all the players are more likely to be gratified by a
yearning to relive or return to a past period. However, according to the p
value that is less than 0.05, hardcore players of Harry Potter: Wizards Unite
tend to gain significantly more gratification from the feeling of nostalgia in
comparison to their casual counterparts.

Harry Potter: Wizards Unite | Casual (769) | Hardcore (228) | Difference | Sig (p) | t
---|---|---|---|---|---
Mean | SD | Mean | SD
Traits |  |  |  |  |  |  |
Aesthetic Orientation | 2.2224 | 0.83099 | 2.1278 | 0.81488 | 0.09459 | 0.098 | 1.656
Immersion Orientation | 2.5246 | 1.30077 | 2.3681 | 1.29392 | 1.5652 | 0.081 | 1.744
Goal Orientation | 2.7508 | 1.03899 | 2.3826 | 1.10462 | 0.36821 | 0 | 5.041
Social Orientation | 3.5732 | 1.59722 | 3.175 | 1.57522 | 0.39821 | 0 | 3.622
Action Orientation | 3.3599 | 1.24719 | 2.8924 | 1.24836 | 0.46759 | 0 | 5.426
Gratification |  |  |  |  |  |  |
Socialization | 5.0683 | 1.34598 | 4.4983 | 1.47386 | 0.57001 | 0 | 5.729
Enjoyment | 2.8443 | 1.06558 | 2.3498 | 1.07403 | 0.49445 | 0 | 6.702
Trendiness | 6.0251 | 1.18668 | 5.9514 | 1.28119 | 0.07375 | 0.396 | 0.88
Competition | 2.8637 | 1.0466 | 2.1847 | 0.94918 | 0.679 | 0 | 10.063
Challenge | 4.6808 | 1.2903 | 3.8194 | 1.33178 | 0.86131 | 0 | 9.619
Nostalgia | 1.2263 | 0.24607 | 1.191 | 0.22377 | 0.0353 | 0.034 | 2.127
Table 32.  Harry Potter: Wizards Unite Traits and Gratifications values for
Casual and Hardcore Players

####  5.2.3. The traits and gratifications of casual and hardcore players of
Ingress

Based on the data in Table 33, it can be observed both casual players and
hardcore players tend to partially agree with the items in all the dimensions
in Traits section as their mean scores of each dimension are all close to 3
which stands for “Somewhat agree” in the scale of the questionnaire.
Therefore, it can be summarized that both casual players and hardcore players
of Ingress to some extent have preferences for:

  * •

Exploring the in-game landscape, graphics, sound and art style (Aesthetic
Orientation)

  * •

Complex stories and narratives in Ingress (Immersion Orientation)

  * •

Gameplay that involves completing quests or tasks, collecting digital objects,
or similar experiences (Goal Orientation)

  * •

Playing together with others online or in the same space (Social Orientation)

  * •

Challenging and fast-paced gameplays (Action Orientation)

Notably, hardcore players of Ingress tend to be more goal oriented and action
oriented compared to casual players as the p values in these two dimensions
are less than 0.05, which indicate that the differences between the type of
players are significant. Since there is no common pattern in the
gratifications of casual and hardcore players of Ingress, we decide to report
the results separately as we did in the previous sections. In detail:

##### Socialization – Gratification:

Casual players (mean = 3.8654) tend to have a neutral attitude towards
socializing through Ingress (e.g. meet others outdoor, maintain relationships
with others) while hardcore players (mean = 3.0429) is more gratified by these
activities, given 4 stands for “neither agree nor disagree” while 3 stands for
“somewhat agree”. In addition, p value less than 0.05 also suggests that
hardcore players would have significantly more gratifications from interaction
with other players.

##### Enjoyment - Gratification:

Both casual players (mean = 2.5074) and hardcore players (mean = 2.1460) would
at least partially agree that they would gain gratifications from leisure in
Ingress as 3 stands for “somewhat agree” while 2 stands for “agree” in the
scale of the questionnaire. Furthermore, in comparison to casual players,
hardcore players would be more gratified by the enjoyments in Ingress as the p
value at 0.004 indicate the difference between the two groups is significant.

##### Trendiness – Gratification:

Similar to Pokémon GO and Harry Potter: Wizards Unite, both casual players
(mean = 5.8854) and hardcore players (mean = 5.8603) are not be gratified by
the popularity of Ingress as their mean values are close to 6, which indicates
that the participants tend to simply disagree with the items in Trendiness
dimension. Also, the p value at 0.875 shows no significant difference between
the two groups.

##### Competition – Gratification:

Both causal players (mean = 2.3439) and hardcore players (mean = 1.8619) would
gain gratifications from the in-game competitions in Ingress, given both their
mean scores are close to “2 – Agree”. Moreover, compared to casual players,
hardcore players would be more gratified if they beat others in the game as
the p value less than 0.05 indicate that the difference in the perception of
winning competitions between casual and hardcore players is statistically
significant.

##### Challenge – Gratification:

Given that 5 stands for “Somewhat disagree” while 4 indicates participant
“neither agree or disagree” with the items, casual players (mean = 4.5138) are
found to partially disagree that in-game challenges results in gratification
while hardcore players (mean = 3.8508) would have a neutral attitudes. In
addition, hardcore player would be more gratified in comparison to casual
players when they conquer the in-game challenges as the p value less than 0.05
suggests that this difference is statistically significant.

Ingress | Casual (157) | Hardcore (105) | Difference | Sig(p) | t
---|---|---|---|---|---
Mean | SD | Mean | SD
Traits |  |  |  |  |  |  |
Aesthetic Orientation | 2.6497 | 1.03266 | 2.5571 | 1.07208 | .09254 | .485 | .700
Immersion Orientation | 3.0357 | 1.54364 | 3.3486 | 1.57701 | -.31290 | .112 | -1.594
Goal Orientation | 3.2191 | 1.18404 | 2.9295 | 1.12146 | .28958 | .049 | 1.981
Social Orientation | 3.1248 | 1.44123 | 2.8229 | 1.44896 | .30198 | .098 | 1.658
Action Orientation | 3.0994 | 1.11600 | 2.7352 | 1.15575 | .36412 | .011 | 2.551
Gratification |  |  |  |  |  |  |
Socialization | 3.8654 | 1.32224 | 3.0429 | 1.20144 | .82259 | .000 | 5.116
Enjoyment | 2.5074 | .95823 | 2.1460 | .99992 | .36140 | .004 | 2.940
Trendiness | 5.8854 | 1.17894 | 5.8603 | 1.36640 | .02503 | .875 | .153
Competition | 2.3439 | .89039 | 1.8619 | .70368 | .48204 | .000 | 4.658
Challenge | 4.5138 | 1.28407 | 3.8508 | 1.32367 | .66301 | .000 | 4.045
Table 33. Ingress Traits and Gratifications values for Casual and Hardcore
Players

###  5.3. Different traits and gratifications of the casual and hardcore
players of Pokémon GO, Harry Potter: Wizards Unite, and Ingress

####  5.3.1. Different traits and gratifications of casual and hardcore
players of Pokémon GO and Harry Potter: Wizards Unite

Since the detailed traits and gratifications of casual and hardcore players of
Pokémon GO and Harry Potter: Wizards Unite have been reported in the previous
sections (see sections 5.2.1 and 5.2.2), we would like to focus on the
significant differences (p value less than 0.05) in traits and gratifications
between casual and hardcore players of Pokémon GO and Harry Potter: Wizards
Unite. The results relevant to this section can be seen in Table 34 and Table
35. In detail:

  * •

Aesthetic – Traits: Casual players of Pokémon GO tend to have more preferences
for exploring the game world, graphics, sound and art style in comparison to
the casual players of Harry Potter: Wizards Unite. However, there is no
significant difference between the hardcore players of both 2 games.

  * •

Immersion – Traits: Casual players of Pokémon GO are more likely to enjoy the
complex stories and narratives in comparison to casual players in Harry
Potter: Wizards Unite, while there is no significant difference between the
hardcore players in both games.

  * •

Goal – Traits: Both the casual players and hardcore players of Pokémon GO tend
to have less preferences for gameplay that involves completing quests or
tasks, collecting digital objects in comparison to the casual players and
hardcore players of Harry Potter: Wizards Unite.

  * •

Social – Traits: Both the casual players and hardcore players of Pokémon GO
are more likely to play with others during the game sessions in comparison to
the casual players and hardcore players of Harry Potter: Wizards Unite.

  * •

Action – Traits: Casual players of Pokémon GO tend to prefer challenging and
fast-paced gameplays compared to the casual players of Harry Potter: Wizards
Unite. However, there is no significant difference between the hardcore
players of both games.

  * •

Socialization – Gratification: Both the casual players and hardcore players of
Pokémon GO are more likely to be gratified by the social activities (e.g. meet
others outdoor, maintain relationships with others) in comparison to the
casual players and hardcore players of Harry Potter: Wizards Unite.

  * •

Enjoyment – Gratification: Casual players of Pokémon GO tend to be more
gratified by the in-game pleasures in comparison to the casual players of
Harry Potter: Wizards Unite. There is no significant difference between the
hardcore players of both games

  * •

Trendiness – Gratification: Hardcore players of Pokémon GO are more likely to
be gratified by the popularity of the game in comparison to hardcore players
of Harry Potter: Wizards Unite. Nonetheless, there is no significant
difference between the casual players of both games

  * •

Competition – Gratification: Compared to the casual players of Harry Potter:
Wizards Unite, the casual players of Pokémon GO tend to be more gratified if
they win competitions. Interestingly, the hardcore players of Pokémon GO tend
to be less gratified after winning in-game competitions compared to the
hardcore players of Harry Potter: Wizards Unite.

Casual Players | Pokémon GO (724) | HPWU (769) | Difference | Sig (p) | t
---|---|---|---|---|---
PoGO-HPWU | Mean | SD | Mean | SD
Traits |  |  |  |  |  |  |
Aesthetic Orientation | 2.1019 | 0.83839 | 2.2224 | 0.83099 | -0.12043 | 0.005 | -2.787
Immersion Orientation | 2.3719 | 1.19351 | 2.5246 | 1.30077 | -0.15331 | 0.018 | -2.375
Goal Orientation | 3.0072 | 1.03518 | 2.7508 | 1.03899 | 0.25634 | 0 | 4.773
Social Orientation | 3.1925 | 1.53376 | 3.5732 | 1.59722 | -0.38067 | 0 | -4.692
Action Orientation | 3.1492 | 1.24426 | 3.3599 | 1.24719 | -0.21078 | 0.001 | -3.267
Gratification |  |  |  |  |  |  |
Socialization | 4.2778 | 1.47517 | 5.0683 | 1.34598 | -0.79047 | 0 | -10.795
Enjoyment | 2.4848 | 0.98147 | 2.8443 | 1.06558 | -0.35947 | 0 | -6.785
Trendiness | 5.9517 | 1.21866 | 6.0251 | 1.18668 | -0.07348 | 0.238 | -1.18
Competition | 2.7276 | 1.02101 | 2.8637 | 1.0466 | -0.13616 | 0.011 | -2.542
Challenge | 4.802 | 1.38071 | 4.6808 | 1.2903 | 0.12127 | 0.08 | 1.754
Nostalgia | 1.2175 | 0.33417 | 1.2263 | 0.24607 | -0.00873 | 0.564 | -0.577
Table 34. Casual Players of Pokémon GO and Harry Potter: Wizards Unite Traits and Gratifications Hardcore Players | Pokémon GO (347) | HPWU (288) | Difference | Sig (p) | t
---|---|---|---|---|---
PoGO-HPWU | Mean | SD | Mean | SD
Traits |  |  |  |  |  |  |
Aesthetic Orientation | 2.2184 | 0.8601 | 2.1278 | 0.81488 | 0.09067 | 0.176 | 1.354
Immersion Orientation | 2.5291 | 1.30297 | 2.3681 | 1.29392 | 0.16105 | 0.12 | 1.555
Goal Orientation | 2.7493 | 1.0093 | 2.3826 | 1.10462 | 0.36664 | 0 | 4.366
Social Orientation | 2.8571 | 1.42207 | 3.175 | 1.57522 | -0.31794 | 0.008 | -2.671
Action Orientation | 2.7764 | 1.20529 | 2.8924 | 1.24836 | -0.11599 | 0.235 | -1.188
Gratification |  |  |  |  |  |  |
Socialization | 3.3833 | 1.34148 | 4.4983 | 1.47386 | -1.11498 | 0 | -9.883
Enjoyment | 2.2032 | 0.95214 | 2.3498 | 1.07403 | -0.14666 | 0.072 | -1.803
Trendiness | 5.7041 | 1.42108 | 5.9514 | 1.28119 | -0.24726 | 0.022 | -2.304
Competition | 2.3317 | 0.89719 | 2.1847 | 0.94918 | 0.14669 | 0.046 | 1.998
Challenge | 4.0038 | 1.4701 | 3.8194 | 1.33178 | 0.1844 | 0.099 | 1.669
Nostalgia | 1.2341 | 0.34492 | 1.191 | 0.22377 | 0.04318 | 0.058 | 1.899
Table 35. Hardore Players of Pokémon GO and Harry Potter: Wizards Unite Traits
and Gratifications

####  5.3.2. Different traits and gratifications of casual and hardcore
players of Pokémon GO and Ingress

The results relevant to this section can be found in Table 36 and Table 37.

  * •

Aesthetic – Traits: Both the casual players and hardcore players of Pokémon GO
are more likely to prefer to explore the game world, graphics, sound and art
style in comparison to the casual and hardcore players of Ingress.

  * •

Immersion – Traits: Both the casual players and hardcore players of Pokémon GO
tend to have more preferences for complex stories and narratives in comparison
to the casual players and hardcore players of Ingress.

  * •

Goal – Traits: Casual players of Pokémon GO are more likely to prefer games
which involves completing quests or tasks, collecting digital objects compared
to the casual players of Ingress. However, there is no significant difference
between the hardcore players of the two games.

  * •

Socialization - Gratification: Both the casual players and hardcore players of
Pokémon GO are less likely to be gratified by the game-related interactions
with others in comparison to the casual players and hardcore players of
Ingress.

  * •

Competition – Gratification: Both the casual players and hardcore players of
Pokémon GO tend to gain less gratification from in-game competitions compared
to the casual players and hardcore players of Ingress.

  * •

Challenge – Gratification: In comparison to casual players of Ingress, the
casual players of Pokémon GO tend to be less gratified when they conquer the
in-game challenges.

Casual Players | Pokémon GO (724) | Ingress (157) | Difference | Sig (p) | t
---|---|---|---|---|---
PoGO-Ingress | Mean | SD | Mean | SD
Traits |  |  |  |  |  |  |
Aesthetic Orientation | 2.1019 | .83839 | 2.6497 | 1.03266 | -.54775 | .000 | -6.217
Immersion Orientation | 2.3719 | 1.19351 | 3.0357 | 1.54364 | -.66440 | .000 | -5.074
Goal Orientation | 3.0072 | 1.03518 | 3.2191 | 1.18404 | -.21193 | .039 | -2.077
Social Orientation | 3.1925 | 1.53376 | 3.1248 | 1.44123 | -.06770 | .613 | .507
Action Orientation | 3.1492 | 1.24426 | 3.0994 | 1.11600 | .04981 | .620 | .496
Gratification |  |  |  |  |  |  |
Socialization | 4.2778 | 1.47517 | 3.8654 | 1.32224 | .41235 | .001 | 3.232
Enjoyment | 2.4848 | .98147 | 2.5074 | .95823 | -.02262 | .793 | -.263
Trendiness | 5.9517 | 1.21866 | 5.8854 | 1.17894 | .06631 | .534 | .622
Competition | 2.7276 | 1.02101 | 2.3439 | .89039 | .38361 | .000 | 4.361
Challenge | 4.8020 | 1.38071 | 4.5138 | 1.28407 | .28823 | .017 | 2.400
Table 36. Casual Players of Pokémon GO and Ingress Traits and Gratifications Hardcore Players | Pokémon GO (347) | Ingress (105) | Difference | Sig (p) | t
---|---|---|---|---|---
PoGO-Ingress | Mean | SD | Mean | SD
Traits |  |  |  |  |  |  |
Aesthetic Orientation | 2.2184 | .86010 | 2.5571 | 1.07208 | -.33870 | .004 | -2.962
Immersion Orientation | 2.5291 | 1.30297 | 3.3486 | 1.57701 | -.81946 | .000 | -4.847
Goal Orientation | 2.7493 | 1.00930 | 2.9295 | 1.12146 | -.18024 | .119 | -1.562
Social Orientation | 2.8571 | 1.42207 | 2.8229 | 1.44896 | .03420 | .830 | .215
Action Orientation | 2.7764 | 1.20529 | 2.7352 | 1.15575 | .04113 | .757 | .309
Gratification |  |  |  |  |  |  |
Socialization | 3.3833 | 1.34148 | 3.0429 | 1.20144 | .34043 | .020 | 2.332
Enjoyment | 2.2032 | .95214 | 2.1460 | .99992 | .05714 | .605 | .519
Trendiness | 5.7041 | 1.42108 | 5.8603 | 1.36640 | -.15619 | .320 | -.955
Competition | 2.3317 | .89719 | 1.8619 | .70368 | .46951 | .000 | 4.922
Challenge | 4.0038 | 1.47010 | 3.8508 | 1.32367 | .15305 | .340 | .956
Table 37. Hardcore Players of Pokémon GO and Ingress Traits and Gratifications

####  5.3.3. Different traits and gratifications of casual and hardcore
players of Harry Potter: Wizards Unite and Ingress

The results relevant to this section can be found in Table 38 and Table 39.

  * •

Aesthetic - Traits: Both the casual players and hardcore players of Harry
Potter: Wizards Unite are more likely to prefer to explore the game world,
graphics, sound and art style in comparison to the casual and hardcore players
of Ingress.

  * •

Immersion - Traits: Both the casual players and hardcore players of Harry
Potter: Wizards Unite tend to have more preferences for complex stories and
narratives in comparison to the casual players and hardcore players of
Ingress.

  * •

Goal - Traits: Both the casual players and hardcore players of Harry Potter:
Wizards Unite are likely to refer games which involves completing quests or
tasks, collecting digital objects compared to the casual players of Ingress.

  * •

Social – Traits: Compared to the casual players of Ingress, the casual players
of Harry Potter: Wizards Unite tend to enjoy more about playing with others
during the game sessions. With regard to hardcore players of these two games,
however, it is the hardcore player of Ingress who are more likely to be social
oriented.

  * •

Action – Traits: In comparison to casual players of Ingress, the casual
players of Harry Potter: Wizards Unite are less likely to prefer challenging
and fast-paced gameplays.

  * •

Socialization – Gratification: Both the casual players and hardcore players of
Harry Potter: Wizards Unite would be less gratified by the game-related social
activities (e.g. meet others outdoor, maintain relationships with others) in
comparison to the casual players and hardcore players of Ingress.

  * •

Enjoyment – Gratification: Casual players of Harry Potter: Wizards Unite would
be less gratified by the pleasures provided in the game in comparison to the
casual players of Ingress. However, there is no significant difference between
hardcore players of these two games.

  * •

Competition – Gratification: Both the casual players and hardcore players of
Harry Potter: Wizards Unite would gain less gratifications from in-game
competitions compared to the casual and hardcore players of Ingress.