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Approaching Collaborative Flow in Collaborative
Gaming, a Survey Study
Jaakko Ohrankämmen, Paula Alavesa, Leena Arhippainen
University of Oulu
Oulu, Finland
jaakko.ohrankammen, paula.alavesa, leena.arhippainen@oulu.fi
Abstract—Flow is an aspect of player experience that has
been often targeted in game studies. Still, similar to presence
it can be ambiguous and difficult to quantify. In this study, we
approach the concept of flow in cooperative gaming and frame
the target of this study as ”collaborative flow”. Team-play and
Flow relevant aspects of player experience, Flow, Co-presence,
Sensory Immersion, Imaginative Immersion, Behavioral involve-
ment, Social Presence, and Engagement, were mapped in an
online survey with 75 participants. We noticed small significant
differences between semi-professional gamers and hobbyists in
some flow related aspects of player experience. In addition,
weekly gaming time influenced the results. In this study we used
existing validated questionnaires and can only state that better
metrics and definitions for this aspect of player experience are
needed for future research.
I. INTRODUCTION
Flow is considered a positive deep state of consciousness
experienced when people engage in tasks [1], [2]. It is also
possible in many situations for people to have joined sensation
of flow, described as group flow [3] or team cognition [4].
Collaboration and cooperation are a key in team esports.
While esports encompasses many genres, and both individual
and team performances, it is considered a competitive sports,
where performance is a key. The interest of research on the
topic and knowledge on the psychological factors in esports
has increased over the past decades [5], [6]. Esports research
has evolved from trying to explain the phenomena to audiences
to describing the complex mechanics that make esports what
it is. Many fields study esports from different perspectives [6].
Some approaches are heavily influenced by methods used in
traditional sports. While the recreational gaming has shifted
to professional domain, there is still a connection. The same
video games that are played in esports are still played by
gamers of all levels [5]–[7]. Walker’s studied sense of enjoy-
ment comparing social and solo flow in a controlled setting
while conducting tasks [8]. In his study Walker observed that
people enjoy social flow more. It is also well known that
people tend to enjoy social aspects of gameplay and find them
highly motivating [9], [10].
Much of attention in esports research is on improving
performance from the perspective of psychology. Attention
is placed to aspects of cognitive performance [11]. In this
study we used a psychometric questionnaires used in game
user research. The aspects chosen for for mapping were:
Co-presence [12], Flow, Sensory and imaginative immersion,
Behavioral involvement, social presence [13], [14], Cognitive
engagement (Conscious Attention & Absorption) and Behav-
ioral engagement (Social connectedness & Interaction) [15],
[16]. The survey was answered by 75 participants. The goal of
our study is to explore how solo, collaborative and avid gamers
perceive flow and flow related aspects of player experience. We
hope our findings have relevance for esports and possibly any
human endeavours that require joined action that is mediated
by digital media.
II. RELATED WORK
A. Flow
Flow is defined by Csikszentmihalyi [1], [2] as a deep
state of consciousness people feel, while focusing on tasks.
According to Csikszentmihalyi’s definition of Flow experience
it is an optimal experience, where a user is completely
focused on own task and forgot all surroundings. It has been
unequivocally described as a positive rewarding experience.
The original description of flow was based on observation
creative processes but since then the sense of flow has been
observed in a wide variety of different contexts including
digital gaming and esports [17]–[19].
B. Collaborative flow
While flow can be categorized as a habitual state of deep
concentration, the people experiencing it are not asleep. They
are operating, playing or interacting, which allows us to
consider outside influences to this experience, even social
aspects. In this study we call this experience collaborative
flow, a state of mind where emergent coordinated action [20],
[21] enhances the flow experience. Takalo et al. [19] describe
an optimal state of flow as emergent of an ”ideal situation,
where skills and challenges are high and in balance”. We
propose adding joined action to the ”skills and challenges”
to find optimal collaborative flow. Freeman and Wohn [22]
and Lipovaya et al. [4] note that in some situations the
team’s performance is so high that players start to accurately
predict actions of their fellow players. This results in on
extremely fine-tuned teamwork called team cognition by [22].
Kaye [3] calls this collaborative flow ”group flow” due to
the how differently flow is perceived by solo and cooperative
gamers. Solo players experienced stronger flow probed using
the psychometric questionnaire Flow State Scale-Short Form
[23], [24].
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Fig. 1. The games survey participants reported playing. Miscellaneous (MISC) multiplayer and MISC singleplayer are collections of answers that got only
single reply
C. Evaluating flow with psychometric questionnaires
Aspects of player experience are overlapping and intercon-
nected. Poels et al. [25] characterize, as one subcomponent
of player experience, flow divided into concentration and
absorption. Sweetser and Wyeth [26] describe more broadly
game flow, an experience that is comprised of concentration,
challenge, skills, control, clear goals, feedback, immersion,
and social interaction. In other words, they sum up game
flow as a collection of many positive rewarding aspects of
player experience. In this study we chose to map Co-presence
[12], Flow, Sensory and imaginative immersion, Behavioral
involvement, social presence [13], [14], Cognitive engagement
(Conscious Attention & Absorption) and Behavioral engage-
ment (Social connectedness & Interaction) [15], [16]. Our goal
was to explore the aspects of player experience that influence
or might result into emergence of collaborative flow with avid
gamers.
III. METHOD AND MATERIALS
This paper presents findings from an online survey that
was distributed via Discord and University of Oulu, Finland,
mailing lists. 75 participants responded to the survey during
April 5th- May 14th 2021.
A. Participants
The survey respondents were aged between 14-56 (M=24.2,
SD=5.5). The gender distribution of the respondents was
uneven, with only 8 female respondent, 66 male and one
undisclosed. The gender enquiry was open to other than binary
answers. For dividing the players to three groups based on their
weekly hours played, we used the categorization by Hussain
and Griffiths [27] that divides gamers based on the hours
played into three groups casual (0-15 h/week), regular (15-
30 h/week) and excessive (>30 h/week) gamers. The survey
participants were not subjected to the names of the categories
as we though it might influence how they answer. Eight
participants reported being semi-professional gamers while the
rest reported the gaming to be a hobby. The participants had
gaming experience between 1-31 years (M=15.2, SD=5.9).
The highest variance amongst players was in the games they
reported playing (Fig. 1).
B. Survey structure
The survey for player experience consisted of 52 statements
derived of three individual questionnaires. English versions of
the questionnaires were used. Short co-presence questionnaire
by [12], game experience questionnaire (GEQ) [13], [14] and
a more recent questionnaire the consumer videogame engage-
ment scale (CVES) by Abbasi et al. [15], [16]. Categories
perceived relevant to the concept of collaborative flow were
chosen. The full list of questionnaire items is as follows:
•Co-presence [12]
•Flow [13], [14]
•Sensory and Imaginative Immersion [13], [14]
•Social Presence: Behavioral Involvement [13], [14]
•Social Presence: Psychological Involvement empathy
[13], [14]
•Social Presence: Psychological Involvement negative
feelings [13], [14]
•Cognitive Engagement: Conscious Attention [15], [16]
•Cognitive Engagement: Absorption [15], [16]
•Behavioral Engagement: Social Connectedness [15], [16]
•Behavioral Engagement: Interaction [15], [16]
The item ”Behavioral Engagement: Interaction” specifically
refers to social interaction according to the original source
[15], [16], despite the formal name of the questionnaire item.
The survey also informed the respondents that the collected
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TABLE I. STATISTICALLY SIGNIFICANT RESULTS FROM ONE WAY ANOVA COMPARING SOLO
AND TEAM PLAYERS
Group N Mean Std.
Deviation
95% Confidence
Interval for Mean p-value
Lower
Bound
Upper
Bound
Co-presence Alone 72 4,32 2,135 3,82 4.82 <0.001
Team 215 5,33 1,390 5,14 5.52
Social Presence: Behavioral
Involvement (GEQ) Alone 126 5,13 1,743 4,82 5.43 0.001
Team 324 5,65 1,335 5,50 5.79
Social Presence: Psychological
Involvement Empathy (GEQ) Alone 126 3,99 1,887 3,66 4.32 <0.001
Team 324 5,32 1,449 5,16 5.48
Social Presence: Psychological
Involvement Negative Feelings (GEQ) Alone 105 3,56 1,941 3,19 3.94 0.005
Team 270 4,20 1,954 3,96 4,43
Behavioural Engagement:
Social Connection (CVES) Alone 63 5,63 1,506 5,26 6,01 0.009
Team 162 6,17 1,291 5,97 6,37
Behavioural Engagement:
Interaction (CVES) Alone 105 4,56 1,966 4,18 4,94 0.004
Team 270 5,13 1,623 4,94 5,32
material would be used for research purposes. All material was
collected anonymously.
C. Results and analysis
The tool for statistical analysis was SPSS Statistics 27 [28].
OneWay ANOVA was conducted for comparisons.
1) Solo and team players: As expected there were statis-
tically significant difference between those who played solo
(21 participants) and in a team (54 participants) differed Co-
presence, Social Presence: Behavioural Involvement, Social
Presence: Psychological Involvement (empathy & negative
feelings), Social Connectedness and Interaction (Table I).
None of the other aspects of player experience produced
statistically significant results.
2) Influence of team size: Since we also enquired the team
size from the participants we separated those who play solo
(21), and teams of 2-3 (17 participants) or >3 (36). Three
values were missing from the team size. In the group of >3
the mean group size was 5.0 with SD=0.8. The most played
game of each participant would have effected the team size.
The most common team size for those who mainly played
Counter Strike was 5. One MISC multiplayer (Fig. 1) gamer
gave the value of 40, guild size perhaps, despite reporting
being a solo player. We had two participants who reported
mainly playing solo, but in addition having a team of two
players. For this comparison, they were kept in the solo-
group based on their main game style. There were not enough
participants to compare reported team sized individually and
gain significant results. Just as with comparison of solo and
team players there were significant results in all social player
experience measuring items in the questionnaire: Co-presence,
Social Presence: Behavioural Involvement, Social Presence:
Psychological Involvement (empathy & negative feelings),
Social Connectedness and Interaction. In addition, Sensory and
Imaginative Immersion (GEQ) yielded statistically significant
results (Table II).
3) Influence of game time: We also observed some in-
fluence on weekly game time on player experience (Table
III). Social Presence: Psychological Involvement Empathy was
higher with participants playing 0-30h weekly, making them
either casual or regular gamers according to [27]. We also no-
ticed quite low p-values (0.060 and 0.061) on two other items:
Sensory and Imaginative Immersion (GEQ) and Cognitive
Engagement: Conscious Attention (CVES). Suggesting higher
immersion with casual and regular gamers, but slightly higher
conscious attention amongst regular and excessive gamers.
4) Observations on avid gamers: We had only eight semi-
professional gamers in our sample. They were all regular or
excessive players. Comparing this small number of players to
the others yielded statistically significant results (Table IV) in
Social Presence Behavioural Involvement (GEQ), Cognitive
Engagement: Conscious Attention (CVES) and Behavioural
Engagement: Interaction (CVES). All of these were higher
amongst the semi-professional gamers.
Based on our study setup and survey findings different
aspects of player experience are influenced by four factors:
Solo or team play, Team size, Game time and Player dedication
(Fig. 2). Solo play has different dynamics than team play
hence it has been separated from Team size. Similarly, Player
dedication and Game time are connected however our results
show that more professional players and hobbyists experience
different aspects of collaborative flow very differently, therefor
these influences need to be presented and studied separately.
IV. DISCUSSION
In this study we began approaching collaborative flow
amongst gamers. However, our findings show that while social
aspects of gameplay can be measured using the co-presence
questionnaire by [12], GEQ [13], [14] and CVES [15], [16],
we need better metrics to approach flow related aspects of
solo and team gaming. In addition, metrics or other methods
are needed for observing collaborative flow. Our findings
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TABLE II. STATISTICALLY SIGNIFICANT RESULTS FROM ONE WAY ANOVA COMPARING SOLO AND TEAM PLAYERS
WITH TEAM SIZE 1-3 AND ABOVE 3
Group N Mean Std.
Deviation
95% Confidence
Interval for Mean p-value
Lower
Bound
Upper
Bound
Co-presence Solo 72 4.32 2.135 3.82 4.82 <0.001
1-3 68 5.40 1.508 5.03 5.76
>3 143 5.34 1.332 5.12 5.56
Sensory and Imaginative
Immersion (GEQ) Solo 126 4.73 1.773 4.42 5.04 0.049
1-3 102 4.77 1.628 4.45 5.09
>3 216 4.34 1.818 4.09 4.58
Social Presence: Behavioral
Involvement (GEQ) Solo 126 5.13 1.743 4.82 5.43 <0.001
1-3 102 5.42 1.308 5.16 5.68
>3 216 5.79 1.337 5.61 5.97
Social Presence: Psychological
Involvement Empathy (GEQ) Solo 126 3.99 1.887 3.66 4.32 <0.001
1-3 102 5.57 1.278 5.32 5.82
>3 216 5.22 1.518 5.02 5.43
Social Presence: Psychological
Involvement Negative Feelings (GEQ) Solo 105 3.56 1.941 3.19 3.94 0.005
1-3 85 4.49 1.790 4.11 4.88
>3 180 4.06 2.024 3.76 4.35
Behavioural Engagement:
Social Connection (CVES) Solo 63 5.63 1.506 5.26 6.01 0.005
1-3 51 6,47 0.946 6.20 6.74
>3 108 6.06 1.413 5.79 6.33
Behavioural Engagement:
Interaction (CVES) Solo 105 4.56 1.966 4.18 4.94 0.016
1-3 85 5.20 1.557 4.86 5.54
>3 180 5.11 1.670 4.86 5.35
TABLE III. GAME TIME AND ITS INFLUENCE ON PLAYER
EXPERIENCE
Game
time N Mean Std.
Deviation
95% Confidence
Interval for Mean p-value
Lower
Bound
Upper
Bound
Sensory and Imaginative
Immersion (GEQ) 0-15 180 4.55 1.679 4.30 4.80 0.060
15 -30 180 4.73 1.671 4.48 4.97
>30 90 4.19 2.044 3.76 4.62
Social Presence: Psychological
Involvement Empathy (GEQ) 0-15 180 5.16 1.616 4.92 5.39 0,002
15-30 180 5.02 1.519 4.79 5.24
>30 90 4.39 2.026 3.96 4.81
Gognitive Engagement: Consious
Attention (CVES) 0-15 180 4.76 1.652 4.52 5.00 0.061
15-30 180 5.18 1.641 4.94 5.42
>30 90 5.09 1,987 4.67 5.50
also show that semi-professional and hobbyists have differing
experiences in aspects of player dedication (Fig. 2, Table IV).
This suggests that separating esports player experience and
player experience in general is needed for optimal research
output and better coaching of athlete gamers.
We noticed some interesting differences in groups of players
depending on team size and game time. Casual and regular
gamers rated higher in Social Presence: Psychological Involve-
ment Empathy than gamers who play over 30 hours a week.
Although our sample of semi-professional gamers was small,
when their player experience was compared with the hobbyists
we noticed that they rated higher in two social player experi-
ence items Social Presence Behavioural Involvement (GEQ),
and Behavioural Engagement: Interaction (CVES). In addition,
they rated higher in Cognitive Engagement: Conscious Atten-
tion (CVES). This is encouraging for future studies on player
experience in competitive gaming and differs interestingly
from the larger sample of players comparing the gamers who
play below and over 30 hours a week. With that larger sample
we had a low p-value (p=0.061) on similar difference in
Cognitive Engagement: Conscious Attention (CVES), but the
negative effect on Social Presence: Psychological Involvement
Empathy for those gaming >30 h a week could not be repeated
with the small group of semi-professionals. While the p-value
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TABLE IV. DIFFERENCES BETWEEN HOBBYISTS AND SEMI-PROFESSIONAL
GAMERS
Group N Mean Std.
Deviation
95% Confidence
Interval for Mean p-value
Lower
Bound
Upper
Bound
Social Presence: Behavioral
Involvement (GEQ) Hobby 402 5,43 1,517 5,28 5,58 0,003
Semi-
Professional 48 6,10 0,905 5,84 6,37
Gognitive Engagement:
Conscious Attention (CVES) Hobby 402 4,93 1,705 4,76 5,09 0,016
Semi-
Professional 48 5,56 1,821 5,03 6,09
Behavioral Engagement:
Interaction (CVES) Hobby 335 4,89 1,761 4,70 5,08 0,007
Semi-
Professional 40 5,68 1,403 5,23 6,12
Fig. 2. Four influences on different aspects of player experience
escapes the 95% confidence interval, we chose report it in this
paper due to the implications of the results.
A. Future research paths
Considering this is a survey study where we would have
liked to observe gaming style specific statistical differences
in results, the sample of 75 is still quite small. A bigger
sample would have helped comparing team sizes and their
influence on aspects of flow related player experience. We
can easily speculate that there is a correlation with the game
played and aspects of player experience, especially the social
aspects, since the game played directly influences the number
of people in a game team. The degree of cooperation does not
just vary from solo to team-play but according to what game
is being played. This is also perhaps the biggest limitation
of our study. The amount of social interactions required for
successful joined action is possibly also influence on the
communication requirements and whether the players are co-
located or teleconferencing. It would be certainly interesting
to investigate how collaborative flow changes depending on
this degree and level of cooperation. In other words, in the
future we would like to continue our survey to see if a bigger
sample would yield more statistically relevant results in the
flow related aspects of game experience.
Based on our study we state that better metrics are needed
to define collaborative flow and measure it. We would like to
define these metrics for studying collaborative flow in gaming
and esports, to learn more about its value for both casual
gamers and for esports coaching. Better metrics would also
allow better resolution when trying to observe differences
between games and degree of required collaboration. In the
future, we would also suggest looking into other popular
ways of collecting data from gamers with biometrics [29] or
game analytics [30] and using a more mixed method approach
for defining and observing collaborative flow. Our goal is to
define collaborative flow as a key aspect of player experience.
Aub´
e, Rousseau and Brunelle [31] observed in their study
on collaborative flow and the social hierarchies of teams
that the best setup for collaborative flow is in teams with
shared leadership. Looking into the hierarchies teams that play
together frequently might also reveal interesting aspects on
how collaborative flow emerges in teams that play together.
B. Limitations
In addition to the small sample size, our survey suffers
from the same uncertainties as many survey studies. We
encouraged the participants to answer the survey right after a
normal gaming session, but we cannot verify this occurred. In
addition, the participants have been playing in an uncontrolled
environment various different games (Fig. 1).
In this study we have adopted the division used by Hussain
and Griffiths [27] for dividing players into three groups based
on the hours played: casual, regular and excessive gamers.
While we adopted the use of the phrase excessive to those
who play >30 h/week, we do acknowledge that the word
excessive is not suitable to describe the gaming habits of semi-
professional gamers to whom >30 h/week might be normal
weekly working/training hours.
Our sample has an obvious gender imbalance. There is a
preconception that female gamers are casual gamers and male
gamers more hard-core, we know that this is not true with
casual gamers of whom the gender distribution is quite close
to equal [32]. Amongst professional gamers this is to an extend
true as most esports gamers and followers are male [33].
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However, we believe the uneven gender division in this study
is partly derived from our method of recruiting participants.
In the future we would hope to use an extended version of
this survey with a more diverse group of gamers. In addition,
to targeting more professional and semi-professional gamers.
GEQ has received some critique [34], [35], however it is
not unusual to critique a psychometric questionnaire that is so
widely used. For this study it was chosen due to its reliability
in studies to produce distinguishable results, we in addition
complemented it with other questionnaires and did not use
the specific factors mentioned by Johnson, Gardner and Perry
[35].
V. C ONCLUSIONS
Collaborative flow is a joined experience of flow distin-
guishable from flow experience in solo gameplay. We hope
these results help not just avid gamers such as esports players
to achieve optimal collaborative flow, but also hope that these
results have some relevance to those who design computer
mediated cooperative systems. In this study we tried to ap-
proach this aspect of gameplay trough a survey targeting flow
related and social aspects of player experience. According
to our findings unsurprisingly the solo players do fair lower
in social aspects of gameplay that those who play in teams.
We also noticed that those who play more than 30 hours a
week fared lower in sensory and imaginative immersion. We
in addition identified future research paths for collaborative
flow in gameplay and esports.
As usual with psychometric questionnaires the observed dif-
ferences are subtle and we warrant more studies in the future
for exploring the collaborative flow with better metrics and
more distinguishable results between different game genres,
game specific communication requirements for cooperation
and number of players in a team.
ACKNOWLEDGMENT
We would like to thank all who participated in the survey.
The second author has, during this research, received funding
from Business Finland funded project Reboot Finland IoT Fac-
tory 33/31/2018, supported by Academy of Finland 6Genesis
Flagship (318927).
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