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Measuring flow in gamification: Dispositional Flow Scale-2

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This paper measures flow in the context of gamification and investigates the psychometric properties of the Dispositional Flow Scale-2 (DFS-2). We employ data gathered from users of an exercise gamification service (N=200). The results show that the original DFS-2 factorial structure does result in a similar model fit as the original work. However, we also present a factorial respecification that satisfies more recent model fit thresholds. Beyond validating the original DFS-2 instrument in the context of gamification, the psychometric analysis and the respecifications suggest that the components of flow divide into highly correlated conditions of flow (which were also found to be more salient in the context of gamification: autotelic experience, balance of skill and challenge, control, clear goals, and feedback) and into possible outcomes (merging action-awareness, concentration, loss of sense of time, and loss of self-consciousness) from achieving flow.
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Measuring flow in gamification: Dispositional Flow Scale-2
Juho Hamari
a,b,
, Jonna Koivisto
a,1
a
Game Research Lab, School of Information Sciences, FIN-33014 University of Tampere, Finland
b
Department of Information and Service Economy, Aalto University School of Business, P.O. Box 21220, 00076 Aalto, Finland
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Article history:
Keywords:
Flow
DFS-2
Gamification
Games for health
Exergames
Persuasive technology
abstract
This paper measures flow in the context of gamification and investigates the psychometric properties of
the Dispositional Flow Scale-2 (DFS-2). We employ data gathered from users of an exercise gamification
service (N= 200). The results show that the original DFS-2 factorial structure does result in a similar
model fit as the original work. However, we also present a factorial respecification that satisfies more
recent model fit thresholds. Beyond validating the original DFS-2 instrument in the context of gamifica-
tion, the psychometric analysis and the respecifications suggest that the components of flow divide into
highly correlated conditions of flow (which were also found to be more salient in the context of
gamification: autotelic experience, balance of skill and challenge, control, clear goals, and feedback) and
into possible outcomes (merging action-awareness, concentration,loss of sense of time, and loss of self-
consciousness) from achieving flow.
Ó2014 Elsevier Ltd. All rights reserved.
1. Introduction
The flow state has been widely used to describe an optimal expe-
rience characterized as a state of being fully focused and engaged in
an activity (Csíkszentmihályi, 1975, 1990). Csíkszentmihályi (1990)
initially defined flow as an experience, which is likely to occur
when the demands of the task and the abilities of the performer
are balanced. In other words, the individual performs at the height
of their skills, and the task is optimally challenging (Nakamura &
Csíkszentmihályi, 2002). Since then, the theoretical constitutes
and measurement instruments for flow have become more refined.
Current factorial structures for flow measurement have converged
to contain nine dimensions (Csíkszentmihályi, 1990; Fu, Su, & Yu,
2009; Jackson & Eklund, 2002), although the constructs might
slightly differ qualitatively between models. Flow is regarded as
an especially pertinent experience for challenging activities where
individual skill and concentration are important, such as in sports
(Csíkszentmihályi, 1990; Jackson & Eklund, 2002) and in games
(Hsu & Lu, 2004). The Dispositional Flow Scale-2 (Jackson &
Eklund, 2002) is currently one of the most used measurement
instruments for flow. Thus far, the psychometric properties of the
Dispositional Flow Scale-2 (DFS-2) have been investigated in both
the context of exercise (Jackson & Eklund, 2002; Kimiecik &
Jackson, 2002) as well as in video gaming (Procci, Singer, Levy, &
Bowers, 2012; Wang, Liu, & Khoo, 2009). The psychometric proper-
ties of DFS-2 have been found to be acceptable in both contexts;
albeit more varied in the context of video gaming.
Recently, the contexts of exercise and video gaming have been
increasingly converging through introduction of game design into
a variety of exercise and health related activities. In general terms,
this development is referred to as ‘‘gamification’’ (Deterding,
Dixon, Khaled, & Nacke, 2011; Hamari, 2013; Hamari, Huotari, &
Tolvanen, 2014; Hamari & Lehdonvirta, 2010; Huotari & Hamari,
2012; McGonigal, 2011), which refers to computers/technology
being used to affect people’s motivations and behavior through
game-like systems. Due to gamification and the general prolifera-
tion of video games in contemporary society, people are believed
to be increasingly engaging in activities that are more likely to
induce the flow state. Therefore, through gamification of common
activities, being in a flow state could be increasingly relevant with
respect to everyday experiences. Especially, activities requiring
perseverance and commitment from the individual, such as exer-
cise, ecological consumption and education have widely been the
targets of using computer technologies in changing human behav-
ior (e.g. gamification and persuasive technologies – Hamari,
Koivisto & Sarsa, 2014; Hamari, Koivisto & Pakkanen, 2014). Com-
puter-supported gamified services such as Nike+, Zombies, Run!,
Fitocracy, and Runkeeper all aim at structuring, supporting and
motivating the exercise activities (see Hamari & Koivisto, 2013;
Koivisto & Hamari, 2014) through the provision of optimally
http://dx.doi.org/10.1016/j.chb.2014.07.048
0747-5632/Ó2014 Elsevier Ltd. All rights reserved.
Corresponding author at: Game Research Lab, School of Information Sciences,
FIN-33014 University of Tampere, Finland. Tel.: +358 50 318 6861.
E-mail addresses: juho.hamari@uta.fi (J. Hamari), jonna.koivisto@uta.fi
(J. Koivisto).
1
Tel.: +358 45 126 5525.
Computers in Human Behavior 40 (2014) 133–143
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
difficult challenges and feedback. Therefore, gamified exercise is an
interesting avenue for further flow research in addition to the sep-
arate contexts of exercise and gaming which both have been dom-
inant veins of flow research. However, currently there are no
studies investigating flow in the aforementioned computer-sup-
ported gamified exercise and examining the applicability of cur-
rent measurement instruments in this setting. Furthermore, for
the purposes of study of flow in gamification, validation of mea-
surements instruments for furthering this vein of study is required.
This paper seeks to contribute to the vein of literature examin-
ing the psychometric properties of flow measurement instruments
(Jackson & Eklund, 2002; Procci et al., 2012; Wang et al., 2009). In
this paper particularly, we investigate the psychometric properties
of the Dispositional Flow Scale 2 (henceforth DFS-2) in the context
of the computer-supported gamification. We test model fit indices,
validity and reliability of the original DFS-2 model by Jackson and
Eklund (2002) and the respecification made by Procci et al. (2012).
Furthermore, we present our respecification of the scale in the
studied context. Findings of the study regarding the validity of
the DFS-2 model in gamification of exercise are discussed.
2. Background
2.1. Flow
In his seminal work, Csíkszentmihályi (1990) studied athletes,
artists and professionals in various fields in order to understand
the flow experience and its antecedents. Based on his findings, he
defined the following conditions for flow: (1) Perceived challenges
of the activity match and stretch the capabilities of the individual,
thus producing an experience of being fully engaged in the task
and acting on the height of his/her skills (Csíkszentmihályi, 1990;
Nakamura & Csíkszentmihályi, 2002). (2) The goals of the activity
are explicit and reachable, and one receives instant feedback for
his/her progress on the activity (Nakamura & Csíkszentmihályi,
2002). To further describe the state of flow, Csíkszentmihályi
(1990) defined the nine dimensions of flow which are common
to the experience: (1) a balance between the challenge of the task
and skills of the individual, (2) a merging of action and awareness,
i.e. one performs the activity almost ‘‘automatically’’, (3) clear per-
ceived goals, (4) unambiguous feedback, (5) focusing on the task at
hand, (6) a sense of control of the activity, (7) a loss of self-con-
sciousness or a reduced awareness of self, (8) time transformation,
i.e. sense of time becomes distorted, and (9) an autotelic, intrinsi-
cally rewarding experience, implying that the activity in itself is a
reason for performing it instead of any external objectives
(Csíkszentmihályi, 1990; Nakamura & Csíkszentmihályi, 2002).
These nine dimensions have served as a basis for several measure-
ment instruments (e.g. Fu et al., 2009; Jackson & Eklund, 2002) for
capturing the flow state and the conditions for experiencing it.
In contexts related to human behavior and computers, flow has
most notably been studied in human–computer interaction
(Webster, Trevino, & Ryan, 1994), video games (Hsu & Lu, 2004;
Procci et al., 2012; Wang et al., 2009; Weibel, Wissmath,
Habegger, Steiner, & Groner, 2008), instant messaging (Lu, Zhou,
& Wang, 2009), mobile technologies (Chen, 2006), web sites in gen-
eral (Skadberg & Kimmel, 2004), and game-based learning
(Admiraal, Huizenga, Akkerman, & Dam, 2011; Fu et al., 2009).
2.2. DFS-2
Some of the most widely used measurement instruments for
flow have been developed by Jackson and colleagues (Jackson &
Eklund, 2002; Jackson & Marsh, 1996; Jackson, Martin, & Eklund,
2008). The Flow State Scale (Jackson & Marsh, 1996) was designed
to examine the flow experience in a given situation, while the Dis-
positional Flow Scale (DFS) (Jackson et al., 2008) assesses the ten-
dency of experiencing flow (Jackson & Eklund, 2002). Both of the
models build on the nine dimensions of flow by Csíkszentmihályi
(1990).Jackson and Eklund (2002) further refined the models
and replaced some of the initial items of the DFS. The resulting ver-
sion was titled DFS-2.
Since then, the DFS-2 has been widely applied especially to the
study of various physical activities, education, arts and digital gam-
ing. In the context of physical activity, the scale has been employed
for study of flow in, for example, recreational outdoor activities
(Whitmore & Borrie, 2005), physical education (González-Cutre,
Sicilia, Moreno, & Fernández-Balboa, 2009), among elite athletes
(Hodge, Lonsdale, & Jackson, 2009), and various sports (Crust &
Swann, 2013; Gucciardi, Gordon, & Dimmock, 2009; Jackson &
Eklund, 2002; Jackson et al., 2008; Koehn, Morris, & Watt, 2013;
Nicholls, Polman, & Holt, 2005). In other settings, the DFS-2 has
been applied for study of flow, for example, among musicians
and music students (Fritz & Avsec, 2007; Sinnamon, Moran &
O’Connell, 2012) and in studying (Cermakova, Moneta, & Spada,
2010).
Most pertinently to the scope of this paper, the model fit of the
DFS-2 has been investigated in the contexts of online games (Wang
et al., 2009) and video gaming (Procci et al., 2012). In their study,
Wang et al. attempted to validate the adequacy of the instrument
in internet gaming. However, the DFS-2 did not provide an appro-
priate model fit (Wang et al., 2009). Procci et al. (2012) sought to
validate the scale in a video gaming setting. The hierarchical model
did not yield sufficient model fit in their study either. Therefore,
Procci et al. (2012) attempted to respecify the model, but the res-
pecification also failed to meet the set standards for acceptable
goodness-of-fit. See Table 1 for the fit indices of the studies by
Wang et al. (2009) and Procci et al. (2012).
It should be noted, however, that both Wang et al. (2009) and
Procci et al. (2012) had set the thresholds for Comparative Fit Index
(henceforth CFI) and Non-Normed Fit Index (henceforth NNFI) at
0.95, which is considered an excellent level for the index (Hu &
Bentler, 1999). The thresholds are higher than the one used by
Jackson and Eklund (2002) in their validation of the instrument
(see Table 1).
Currently, the research in the context of computer-supported
gamification is scarce. Both, the context of games and exercise have
been regarded as one of the most likely contexts for people to
experience flow. Therefore, we believe that also the context of
gamified exercise affords an interesting area for studying it. Fur-
thermore, flow is often discussed to be an important psychological
target state pursued by gamification efforts (Huotari & Hamari,
2012; McGonigal, 2011). However, as with all psychometric mea-
surement, the validity of the instrument is of crucial importance
(Jackson & Eklund, 2002; Procci et al., 2012) in capturing the
researched phenomenon. Thus, as the first step in conducting
research on flow in gamification, the psychometric properties of
the DFS-2 in a gamified setting will be examined.
3. The empirical study
3.1. Data
The data was gathered via an online questionnaire from the
users of a service called Fitocracy that gamifies exercise. At its core,
Fitocracy enables their users to track exercise. The system does not
contain automatic tracking. Instead, the users enter the details of
their activities into the service. The service contains a database of
exercises ranging from, for example, various physical outdoor
and recreational activities to gym activities and sports. Depending
134 J. Hamari, J. Koivisto/ Computers in Human Behavior 40 (2014) 133–143
on the type of the activity, the user may enter the length, the inten-
sity, the distance or the weights used in each exercise. As a gami-
fying feature, the service calculates a point value which is based on
an amount of points allocated to the given exercise and further
adjusted according to the details of the user’s activity. For example,
lifting heavier weights gives more points than lifting lighter
weights, or swimming for 30 min gives more points than swim-
ming for 20 min. Thus, the users gain points for each exercise they
track in the service.
Furthermore, users gain level-ups and unlock achievements (on
game achievements, see Hamari & Eranti, 2011) based on the
points gained. Users can also perform quests by tracking an exer-
cise that corresponds to a given set of conditions, or challenge
other users into duels. In addition to the system giving feedback
to the users on their progress, the community members may pro-
vide feedback to one another and communicate with each other.
With features such as group-forming, profile-building, and content
sharing the service holds similarities with many social networking
Table 1
Goodness-of-fit indices of the DFS-2 validation studies.
Paper Context CFI/NNFI threshold for
acceptable fit used
in the study
Specification N
v
2
dfCFI NNFI RMSEA CI90 lo –
hi/PCLOSE
Fit?
*
Jackson & Eklund (2002) Physical activity
*
0.90 First-order 574 1427.219 558 0.912 0.901 0.052 0.049, 0.055 Yes
Second-order 574 1606.487 585 0.897 0.889 0.055 0.052, 0.058 Yes
Wang et al. (2009) Online gaming
*
0.95 First-order 1578 1925.49 558 0.936 0.927 0.047 0.045, 0.049 No
Second-order 1578 1522.58 548 0.954 0.947 0.040 0.038, 0.042 Yes
Procci et al. (2012) Video games
*
0.95 Hierarchical
(second-order)
314 1351.995 558 0.906 n/a 0.067 0.063, 0.072 No
Respecified 314 1348.158 539 0.901 n/a 0.069 0.065, 0.074 No
Indices:
v
2
= Chi Square, CFI = Comparative Fit Index, NNFI = Non-Normed Fit Index, RMSEA = Root Mean Square Error of Approximation, PCLOSE = p of close fit.
*
As reported in the original work.
Table 2
Length of experience, age, gender and exercise information of the respondent data.
Frequency Percent Frequency Percent
Gender Length of experience
Female 102 51 Less than 1 month 24 12
Male 98 49 1–3 months 38 19
Age (mean = 29.5, median = 27.5) 3–6 months 29 14.5
Less than 20 9 4.5 6–9 months 26 13
20–24 51 25.5 9–12 months 33 16.5
25–29 54 27 12–15 months 38 19
30–34 41 20.5 15–18 months 7 3.5
35–39 22 11 More than 18 months 5 2.5
40–44 16 8 Exercise sessions per week (mean = 5.3, median = 5.0)
45–49 3 1.5 1–4 83 41.5
50 or more 4 2 5–9 106 53.0
10–14 6 3.0
15 or more 5 2.5
Exercise hours per week (mean = 7.2, median = 6.0)
1–4 51 25.5
5–9 99 49.5
10–14 40 20.0
15 or more 10 5.0
Table 3
The used thresholds for goodness-of-fit indices.
Indicator Excellent Acceptable Source
Model fit measure
CFI >0.95 >0.90 Hair et al. (2010), Hu and Bentler (1999)
NNFI >0.95 >0.90
RMSEA Good < 0.5, passable 0.5–1
Validity & Reliability
*
Square root of the AVEs Square root of the AVE of each construct <all correlations between it and other constructs Fornell and Larcker (1981)
Inter-correlations
*
No inter-correlations are higher than 0.9 Pavlou, Liang, and Xue (2007)
Item loadings Items are most highly loaded with the intended construct Chin (1998)
CR > AVE > MSV > ASV Hair et al. (2010)
AVE >0.5
CR >0.7
Indices: CFI = Comparative Fit Index, NNFI = Non-Normed Fit Index, RMSEA = Root Mean Square Error of Approximation, CR = Composite Reliability, AVE = Average Variance
Extracted, MSV = Maximum Shared Variance, ASV = Average Shared Variance.
*
It should be noted that high inter-correlations between constructs might not in reality pose a problem since all the constructs are regarded as components of flow and
therefore are expected to be highly correlated.
J. Hamari, J. Koivisto / Computers in Human Behavior 40 (2014) 133–143 135
services (Baker & White, 2010; Boyd & Ellison, 2008; Lin & Lu,
2011; Ellison, Steinfeld & Lampe, 2007; Pfeil, Arjan & Zaphiris,
2009).
The data gathering was executed by posting a description of the
study and the survey link to the discussion forum and groups
within the service. Only registered users of the service were able
Challenge /
Skill balance
Concentration
1 10 19 28
5 14 23 32
Merging
action -
awareness
2 11 20 29
Clear goals
3 12 21 30
Feedback
4 13 22 31
Control
6 15 24 33
Loss of self-
conciousness
7 16 25 34
Time
8 17 26 35
Autotelic
experience
9 18 27 36
Deleted association Association Item Deleted itemConstruct Deleted construct (Arrows representin
g
correlations between constructs are not featured in the fi
g
ure)
Fig. 1. Original DFS-2 factorial structure.
Table 4
Step 1a: correlation table.
CR AVE MSV ASV T CSB MAA G F C CTRL LSC A
T 0.914 0.726 0.152 0.081 0.852
CSB 0.841 0.573 0.762 0.476 0.266 0.757
MAA 0.845 0.579 0.434 0.285 0.383 0.659 0.761
G 0.847 0.583 0.805 0.515 0.233 0.873 0.597 0.763
F 0.883 0.654 0.767 0.397 0.290 0.674 0.494 0.876 0.809
C 0.902 0.699 0.677 0.404 0.290 0.750 0.621 0.774 0.676 0.836
CTRL 0.886 0.660 0.805 0.499 0.230 0.860 0.584 0.897 0.802 0.823 0.813
LSC 0.956 0.843 0.170 0.102 0.072 0.412 0.374 0.377 0.168 0.362 0.377 0.918
A 0.884 0.659 0.637 0.389 0.390 0.786 0.481 0.798 0.705 0.577 0.767 0.232 0.812
The bolded figures represent the square roots of the AVE of the corresponding constructs.
Indices: CR = Composite Reliability, AVE = Average Variance Extracted, MSV = Maximum Shared Variance, ASV = Average Shared Variance.
Constructs: T = Time transformation, CSB = Challenge-Skill Balance, MAA = Merging Action & Awareness, G = clear Goals, F = Feedback, C = Concentration, CTRL = Control,
LSC = Loss of Self Consciousness, A = Autotelic experience.
Challenge /
Skill balance
Concentration
1 10 19 28
5 14 23 32
Merging
action -
awareness
2 11 20 29
Clear goals
3 12 21 30
Feedback
4 13 22 31
Control
6 15 24 33
Loss of self-
conciousness
7 16 25 34
Time
8 17 26 35
Autotelic
experience
9 18 27 36
Flow
Deleted association Association Item Deleted itemConstruct Deleted construct (Arrows representing correlations between constructs are not featured in the figure)
Fig. 2. Original DFS-2 s-order factorial structure.
136 J. Hamari, J. Koivisto / Computers in Human Behavior 40 (2014) 133–143
to access the survey. The respondents were entered in a prize draw
for a $50 Amazon gift certificate. Altogether 200 valid responses
were gathered. The sample size satisfies several different criteria
for sufficient sample size. According to Hoelter (1983), samples
sizes of 200 and above are sufficient for structural equation mod-
eling data analyses. Bentler and Chou (1987) propose a minimum
ratio of 5 respondents per 1 construct in the model (for the model
of the present paper the threshold would be 180 respondents).
Hair, Black, Babin, and Anderson (2010) suggest the same rule of
thumb for factor analyses. Loehlin (1998) suggests that 100
respondents are required and 200 would be preferable. Anderson
and Gerbing (1984, 1988) recommend a threshold of 150 respon-
dents for models where constructs comprise of three or four indi-
cators. Hair et al. (2010) suggest similar thresholds but suggest
that complex models with few indicators per construct (less than
three) and low communalities might require larger samples. In this
study, all of the constructs comprise from minimum of four or
more items and the communalities are high (referring to a more
explorative SEM). Based on these sources, the sample size should
be sufficient for the analyses carried out herein.
See Table 2 for the demographic information of respondents as
well as details regarding length of experience with the service and
reported amounts of exercise.
3.2. Measurement
As the Dispositional Flow Scale-2 is dispositional rather than a
state scale, it measures the tendency to experience flow in a given
setting rather than flow experience in a single given instance of
activity (Jackson & Eklund, 2002). Here, we were interested in flow
in the computer-supported gamified exercise particularly. There-
fore, instructions for answering were provided in the survey
accordingly so that it was made sure the respondents understood
that all of the statements referred to exercise performed when
using the Fitocracy service (instead of exercise in general).
DFS-2 scale consists of constructs based on the nine dimensions
of flow defined by Csíkszentmihályi (1990). Thus, the constructs
included in DFS-2 are the following: time (transformation) (T), chal-
lenge-skill balance (CSB), merging of action and awareness (MAA),
clear goals (G), feedback (F), concentration (C), control (CTRL), loss
of self-consciousness (LSC), and autotelic experience (A). Each of the
nine constructs contains four items. Together the constructs form
a 36-item scale for measuring the flow experience. The DFS-2 relies
on self-reported data. Therefore, similarly to previous DFS-2 stud-
ies, the items were measured on a Likert scale ranging from strong
disagreement (1 on the Likert scale) to strong agreement (7 on the
Likert scale) with the statement.
3.3. Procedure
After the data collection, the procedure for the analyses con-
sisted of 6 steps each investigating model fit indices as well as
validity and reliability of the given factorial structure (see Table 3
for thresholds): (1) Confirmatory factor analysis (henceforth CFA)
for both original single and second-order DFS-2 factorial structures
by Jackson and Eklund (2002), (2) CFA for the Procci et al. (2012)
Challenge /
Skill balance
Concentration
1 10 19 28
5 14 23 32
Merging
action -
awareness
2 11 20 29
Clear goals
3 12 21 30
Feedback
4 13 22 31
Control
6 15 24 33
Loss of self-
conciousness
7 16 25 34
Time
8 17 26 35
Autotelic
experience
9 18 27 36
Mastery
Deleted association Association Item Deleted itemConstruct Deleted construct (Arrows representing correlations between constructs are not featured in the figure)
Fig. 3. Factorial structure in Procci et al. (2012) respecification.
Table 5
Step 2: correlation table.
CR AVE MSV ASV MAA Mastery F T A C LSC
MAA 0.845 0.579 0.408 0.258 0.761
Mastery 0.932 0.535 0.714 0.447 0.639 0.731
F 0.897 0.636 0.714 0.338 0.491 0.845 0.797
T 0.914 0.726 0.156 0.089 0.382 0.260 0.274 0.852
A 0.881 0.715 0.637 0.313 0.477 0.798 0.704 0.395 0.846
C 0.899 0.750 0.692 0.344 0.620 0.832 0.683 0.286 0.559 0.866
LSC 0.956 0.844 0.161 0.087 0.375 0.401 0.188 0.072 0.227 0.357 0.919
The bolded figures represent the square roots of the AVE of the corresponding constructs.
ndices: CR = Composite Reliability, AVE = Average Variance Extracted, MSV = Maximum Shared Variance, ASV = Average Shared Variance.
Constructs: T = Time transformation, MAA = Merging Action & Awareness, F = Feedback, C = Concentration, LSC = Loss of Self Consciousness, A = Autotelic experience,
Mastery = see Fig. 3.
J. Hamari, J. Koivisto / Computers in Human Behavior 40 (2014) 133–143 137
respecification for video games, (3) Exploratory factor analysis
(henceforth EFA), (4) CFA on the factorial structure suggested by
EFA, (5) finding a factorial structure with adequate fit via omitting
items, (6) finding an adequate factorial structure via partial sec-
ond-ordering. The confirmatory factor analyses were carried out
in AMOS 21. The exploratory factor analyses as well as data screen-
ing were carried out in SPSS 21. We opt for model fit indices used
also in other papers investigating DFS-2.
3.3.1. Step 1a: CFA for single-order DFS-2
The test indicated an adequate fit (
v
2
= 1044.955, CFI = 0,918,
NNFI = 0.907, RMSEA = 0.066, CI90 = 0.060–0.073) if we adopted
the same model fit indices as in the original work. However, when
using current thresholds, the model does not have adequate fit
(Table 3). This first-order model (Fig. 1) also shows rather high
inter-correlation between several constructs (Table 4). Most nota-
bly challenge-skill balance (CSB), clear goals (G), control (CTRL) and
autotelic experience (A) are highly correlated with each other. Fur-
thermore, there seems to be some overlap between clear goals (G)
and feedback (F).
3.3.2. Step 1b: CFA for second-order DFS-2
The test for the second-order model (Fig. 2) indicated fairly sim-
ilar indices as the original test by Jackson and Eklund (2002), albeit
too low to satisfy the recent thresholds (Table 3):
v
2
= 1136.599,
CFI = 0.906, NNFI = 0.899, RMSEA = 0.069, CI90 0.063–0.075.
3.3.3. Step 2: CFA for DFS-2 respecification by Procci et al. (2012)
We then tested the model fit of the Procci et al. (2012) respeci-
fication of DFS-2 (Fig. 3). The test showed the respecification to
provide a poorer fit than the original model:
v
2
= 1242.882,
CFI = 0.878, NNFI = 0.865, RMSEA = 0.081, CI90 0.075–0.087. In
the Procci et al. (2012) respecification, challenge-skill balance
(CSB), clear goals (G), and control (CTRL) have been combined into
a single factor titled ‘mastery’. However, in our data autotelic expe-
rience (A), feedback (F) and concentration (C) were also associated
with these constructs (Table 5) within this factorial structure.
Table 6
Explorative factor analysis.
123456
f01CSB 0.416
f10CSB 0.562 0.406
f19CSB 0.697
f28CSB 0.562 0.434
f03G 0.600
f12G 0.492 0.473
f21G 0.550 0.449
f30G 0.669
f06CTRL 0.474 0.488
f15CTRL 0.427 0.510
f24CTRL 0.526 0.500
f33CTRL 0.546 0.488
f09A 0.629
f18A 0.691
f27A 0.801
f36A 0.778
f05C 0.713
f14C 0.807
f23C 0.711
f32C 0.743
f04F 0.837
f13F 0.834
f22F 0.649
f31F 0.734
f07LSC 0.910
f16LSC 0.922
f25LSC 0.898
f34LSC 0.918
f08T 0.877
f17T 0.887
f26T 0.779
f35T 0.907
f02MAA 0.725
f11MAA 0.713
f20MAA 0.707
f29MAA 0.819
Variance extracted 16.2 14.5 12.1 10.5 9.6 8.7
Bolded: larger than some item in the core construct.
Item codes referring to the original corresponding constructs: T = Time transformation,
CSB = Challenge-Skill Balance, MAA = Merging Action & Awareness, G = clear Goals,
F = Feedback, C = Concentration, CTRL = Control, LSC = Loss of Self Consciousness,
A = Autotelic experience.
Challenge /
Skill balance
Concentration
1 10 19 28
5 14 23 32
Merging
action -
awareness
2 11 20 29
Clear goals
3 12 21 30
Feedback
4 13 22 31
Control
6 15 24 33
Loss of self-
conciousness
7 16 25 34
Time
8 17 26 35
Autotelic
experience
9 18 27 36
Deleted association Association Item Deleted itemConstruct Deleted construct (Arrows representin
g
correlations between constructs are not featured in the fi
g
ure)
Fig. 4. CFA based on EFA.
138 J. Hamari, J. Koivisto / Computers in Human Behavior 40 (2014) 133–143
3.3.4. Step 3: Exploratory Factor Analysis (EFA)
Although the original model met the thresholds of the original
work of Jackson and Eklund (2002), we wanted to proceed further
with the tests. We moved to run an exploratory factor analysis in
order to investigate, which kind of factorial structure the data will
converge in. We used principal component analysis with varimax
rotation for the exploratory factor analysis.
The resulting factorial structure explained 71.7% of the variance
(Table 6). As already suggested by the previous inter-correlation
analyses, and as was also partly confirmed by Procci et al. (2012),
the items of challenge-skill balance (CSB), clear goals (G), and control
(CTRL) seem to mostly load on the same factor. However, in this
data also autotelic experience (A) is strongly associated with these
constructs. All other constructs, however, behave well loading
highly only with their expected factors.
3.3.5. Step 4: Confirmatory Factor Analysis (CFA) for the respecified
model
Based on the EFA, the constructs challenge-skill balance, clear
goals and control did not form their own factors. Instead, all of
the items of these constructs loaded mostly with autotelic experi-
ence, one item with concentration and two items with feedback
(see Table 6). We then ran a CFA based on the results of the EFA
(Fig. 4). The model fit was inadequate:
v
2
= 1208.778, CFI = 0.893,
NNFI = 0.882, RMSEA = 0.075, CI90 0.069–0.081. Overall, it seems
that challenge/skill, clear goals and control form a rather uniform
construct with autotelic experience. However, in CFA concentration
and feedback also seemed to correlate with this dimension to the
extent that it would not pass the discriminant validity thresholds
(Table 7).
Table 7
Step 4: correlation table.
CR AVE MSV ASV T A C F LSC MAA
T 0.907 0.710 0.158 0.077 0.843
A 0.935 0.548 0.731 0.412 0.287 0.740
C 0.900 0.604 0.731 0.382 0.267 0.855 0.777
F 0.906 0.617 0.689 0.325 0.258 0.830 0.738 0.785
LSC 0.959 0.853 0.154 0.098 0.074 0.375 0.392 0.222 0.924
MAA 0.834 0.560 0.416 0.279 0.398 0.645 0.638 0.526 0.373 0.749
The bolded figures represent the square roots of the AVE of the corresponding constructs.
Indices: CR = Composite Reliability, AVE = Average Variance Extracted, MSV = Maximum Shared Variance, ASV = Average Shared Variance.
Constructs: T = Time transformation, MAA = Merging Action & Awareness, F = Feedback, C = Concentration, LSC = Loss of Self Consciousness, A = Autotelic experience.
Challenge /
Skill balance
Concentration
1 10 19 28
5 14 23 32
Merging
action -
awareness
2 11 20 29
Clear goals
3 12 21 30
Feedback
4 13 22 31
Control
6 15 24 33
Loss of self-
conciousness
7 16 25 34
Time
8 17 26 35
Autotelic
experience
9 18 27 36
Deleted association Association Item Deleted itemConstruct Deleted construct (Arrows representin
g
correlations between constructs are not featured in the fi
g
ure)
Fig. 5. Respecification via omitting items.
Table 8
Step 5: correlation table.
CR AVE MSV ASV T A C F LSC MAA
T 0.907 0.711 0.160 0.090 0.843
A 0.895 0.635 0.508 0.263 0.362 0.797
C 0.901 0.697 0.466 0.287 0.283 0.595 0.835
F 0.902 0.649 0.508 0.274 0.270 0.713 0.683 0.806
LSC 0.959 0.854 0.139 0.077 0.074 0.252 0.365 0.206 0.924
MAA 0.833 0.558 0.403 0.248 0.400 0.506 0.635 0.529 0.373 0.747
The bolded figures represent the square roots of the AVE of the corresponding constructs.
Indices: CR = Composite Reliability, AVE = Average Variance Extracted, MSV = Maximum Shared Variance, ASV = Average Shared Variance.
Constructs: T = Time transformation, MAA = Merging Action & Awareness, F = Feedback, C = Concentration, LSC = Loss of Self Consciousness, A = Autotelic experience.
J. Hamari, J. Koivisto / Computers in Human Behavior 40 (2014) 133–143 139
3.3.6. Step 5: Adequately fitting model via omitting items
Following the results of the EFA (Step 3), we omitted all items
that had a poorer loading than the items of the core construct of
each factor. Resulting in a factorial structure depicted in Fig. 5.In
other words, omitting these items effectively omits the three
highly inter-correlating constructs almost entirely with the excep-
tion of items csb01 and g30. This solution represents the cleanest
factorial structure based on the EFA.
With this remaining factorial structure both convergent and
discriminant validities were acceptable (Table 8) and model fit
adequate:
v
2
= 461.259, CFI = 0.955, NNFI = 0.948, RMSEA = 0.067,
CI90 0.048–0.066. Further, the EFA also showed that the remaining
model still converged into the same exact factorial solution
(Table 9).
3.3.7. Step 6: Adequately fitting model without omitting items
In step 5, we could find a factorial structure that had an ade-
quate model fit even when using the tighter thresholds. However,
we wanted to find a factorial structure that would have decent
model fit with the entire instrument.
As noted, CSB, G, F, and A are strongly correlated. Qualitatively,
all these constructs can be considered as pertaining to experience
of mastery, i.e. having clear goals and feedback as an indicator of
challenge-skill balance, which further associated autotelic experi-
ences. These aspects of flow are often regarded as conditions of
flow (Csíkszentmihályi, 1990; Nakamura & Csíkszentmihályi,
2002), whereas, for example, loosing track of time or self-con-
sciousness as well as merging action-awareness can be considered
more as outcomes from reaching flow. Therefore, we modeled a
higher-order construct for the four highly correlated constructs,
and named it mastery (or conditions of flow) similarly to Procci
et al. (2012). This structure (Fig. 6) has an adequate model fit if
Table 9
EFA after omitted items.
123456
f04F 0.871
f13F 0.856
f31F 0.740
f22F 0.697
f30G 0.680
f16LSC 0.927
f34LSC 0.927
f07LSC 0.912
f25LSC 0.907
f27A 0.810
f36A 0.802
f18A 0.720
f09A 0.657
f19CSB 0.638
f35T 0.904
f08T 0.892
f17T 0.885
f26T 0.774
f14C 0.786
f32C 0.784
f23C 0.751
f05C 0.729
f29MAA 0.825
f02MAA 0.755
f11MAA 0.722
f20MAA 0.708
Variance extracted 14.3 14.2 13.1 12.9 11.9 10.9
Item codes referring to the original corresponding constructs: T = Time transformation,
CSB = Challenge-Skill Balance, MAA = Merging Action & Awareness, G = clear Goals,
F = Feedback, C = Concentration, CTRL = Control, LSC = Loss of Self Consciousness,
A = Autotelic experience.
Challenge /
Skill balance
Concentration
1 10 19 28
5 14 23 32
Merging
action -
awareness
2 11 20 29
Clear goals
3 12 21 30
Feedback
4 13 22 31
Control
6 15 24 33
Loss of self-
conciousness
7 16 25 34
Time
8 17 26 35
Autotelic
experience
9 18 27 36
Mastery
Deleted association Association Item Deleted itemConstruct Deleted construct (Arrows representing correlations between constructs are not featured in the figure)
Fig. 6. Respecification via second-order construct.
Table 10
Step 6: correlation table.
CR AVE MSV ASV T MAA C LSC MASTERY
T 0.913 0.726 0.147 0.080 0.852
MAA 0.845 0.579 0.394 0.267 0.383 0.761
C 0.902 0.698 0.666 0.317 0.289 0.621 0.835
LSC 0.956 0.844 0.141 0.103 0.072 0.375 0.362 0.919
MASTERY 0.953 0.804 0.666 0.320 0.289 0.628 0.816 0.368 0.897
The bolded figures represent the square roots of the AVE of the corresponding constructs.
Indices: CR = Composite Reliability, AVE = Average Variance Extracted, MSV = Maximum Shared Variance, ASV = Average Shared Variance.
Constructs: T = Time transformation, MAA = Merging Action & Awareness, C = Concentration, LSC = Loss of Self Consciousness, Mastery = see Fig. 6.
140 J. Hamari, J. Koivisto / Computers in Human Behavior 40 (2014) 133–143
we use the same threshold as Jackson and Eklund (2002) origi-
nally:
v
2
= 1120.066, CFI = 0,907, NNFI = 0.900, RMSEA = 0.069,
CI90 0.063–0.075. Furthermore, the mastery second-order con-
struct had discriminant validity (Table 10).
We further analyzed the means and deviations of the responses
regarding the (original) components of flow in order to see
whether the ratings of the different components would provide
support for the notion of ‘conditions’ and ‘outcomes’ as well as to
generally gauge which flow experiences were most prominent.
The data shows that, indeed, in the gamification context, the expe-
riences most commonly linked to gamification in popular discus-
sions were reported to occur more: autotelic experience (self-
purposefulness/intrinsically motivated experience are often
referred to in the discussions on gamification), having clear goals
and feedback. On the contrary, for example time transformation as
well as merging action and awareness were clearly reported to occur
less (See Table 12 and Fig. 7).
4. Discussion and conclusions
In this paper we investigated the psychometric properties of the
DFS-2 flow measurement instrument in the context of technology-
supported gamified physical exercise (Table 11). The results indi-
cate that the original factorial structure by Jackson and Eklund
(2002) does provide an adequate fit when same lower thresholds
for goodness-of-fit indices are used. However, we wanted to opt
for currently favored higher thresholds (Hu & Bentler, 1999; Pfeil
et al., 2009). We then moved onto testing a respecification of the
model by Procci et al. (2012) of which the factorial structure
resulted in a poorer fit. In order to find a suitable factorial struc-
ture, as the third step, we conducted an explorative factor analysis.
In the fourth step, we conducted a confirmatory factor analysis for
a model that was respecified in accordance to the EFA. This method
did not yield significantly better results, since it seemed that a few
constructs formed combined factors. In the fifth step, we took the
factors created by EFA, but omitted items that loaded below 0.6.
This model reached the higher thresholds of model fit as well as
good convergent and discriminant validity. However, we still
wanted to find a factorial structure that reached model fit and
validity without omitting any items. Therefore, in the sixth step,
we created a second-order construct that contained the constructs
with high inter-correlations (CSB, A, G, F, CTRL) as reflective indica-
tors. This model reached good validity and the lower thresholds for
model fit.
In their DFS-2 model, Jackson and Eklund (2002) did not con-
sider the causality or relationships between the dimensions of
flow. However, theorizations regarding the flow dimensions have
considered the challenge-skill-balance,clear goals,control, and feed-
back as conditions required for reaching flow (Csíkszentmihályi,
1990; Nakamura & Csíkszentmihályi, 2002), whereas loss of self-
consciousness,time,concentration, and merging action-awareness
have been considered outcomes from reaching flow. The final res-
pecification created in this study also suggests that the constructs
divide into two different categories; the conditional aspects and
outcome experiences from reaching the flow state, which are in
line with some of the previous considerations (see also Table 12
and Fig. 7 for differences in means). The final respecification pre-
sented in this paper is thus also theoretically feasible in addition
to presenting good model fit and validity indices.
Furthermore, in our study autotelic experience seemed to corre-
late strongly with other conditions of flow which might pose a
deviation from earlier theorizations. This finding would suggest
that, at least in the context of computer-supported gamified exer-
cise, the autotelic experience, that is, finding the activity intrinsi-
cally motivating, is also a condition for reaching the flow state
rather than being an outcome from reaching flow. In a video gam-
TMAALSCCCSBCTRLFGA
7
6
5
4
3
Constructs: T = Time transformation, CSB = Challenge-Skill Balance, MAA = Merging Action &
Awareness, G = clear Goals, F = Feedback, C = Concentration, CTRL = Control, LSC = Loss of Self
Consciousness, A = Autotelic experience
Fig. 7. Means of the responses on the components of flow.
Table 11
Goodness-of-fit indices for different factorial structures studied in this paper.
Specification N
v
2
dfCFI NNFI RMSEA CI90 lo – hi/PCLOSE
Step 1a: Original first-order model by Jackson and Eklund (2002) 200 1044.955 558 0,918 0.907 0.066 0.060, 0.073
Step 1b: Original second-order model by Jackson and Eklund (2002) 200 1136.599 585 0.906 0.899 0.069 0.063, 0.075
Step 2: Respecification by Procci et al. (2012) 200 1242.882 539 0.878 0.865 0.081 0.075, 0.087
Step 3: EFA n/a n/a n/a n/a n/a n/a n/a
Step 4: Respecification based on EFA 200 1208.778 572 0.893 0.882 0.075 0.069, 0.081
Step 5: Respecification with omitted items 200 461.259 279 0.955 0.948 0.057 0.048, 0.066
Step 6: Respecification without omitted items 200 1120.066 577 0,907 0.900 0.069 0.063, 0.075
Indices:
v
2
= Chi Square, CFI = Comparative Fit Index, NNFI = Non-Normed Fit Index, RMSEA = Root Mean Square Error of Approximation, PCLOSE = p of close fit.
Table 12
Means and standard deviation of the components of flow (in descending order).
A G F CTRL CSB C LSC MAA T
Means 5.810 5.715 5.685 5.495 5.298 5.063 4.831 4.259 3.535
SD 1.018 1.025 1.112 1.106 1.013 1.174 1.631 1.168 1.492
T = Time transformation, CSB = Challenge-Skill Balance, MAA = Merging Action & Awareness, G = clear Goals, F = Feedback, C = Concentration, CTRL= Control, LSC = Loss of Self
Consciousness, A = Autotelic experience.
J. Hamari, J. Koivisto / Computers in Human Behavior 40 (2014) 133–143 141
ing environment, Procci et al. (2012) also found that challenge-skill
balance, clear goals and control have a strong inter-correlation.
However, autotelic experience did not load as strongly with this fac-
tor in their study. This could suggest that in gamified exercise,
autotelic experience is potentially more closely associated with
the optimal challenge-skill experience than in pure gaming. Where
as in games, the autotelic experience might also be caused by other
factors that are missing from the common gamification implemen-
tation. Other appeals of video games (see e.g. Hamari & Tuunanen,
2014; Yee, 2006 on gaming motivations), such as audiovisual
immersion and stimuli that are commonly missing in the gamifica-
tion context but often are an elemental aspect of video games. In
other words, video game players can potentially reach an autotelic
experience even without actual goal-oriented play/mastery which
most gamification implementations seem strive for. The data fur-
ther shows that indeed, in the gamification context, those experi-
ence most commonly linked to gamification in popular
discussions had been rated to occur more (autotelic experience
(self-purposefulness/intrinsically motivated experience are often
referred in the discussion on gamification), having clear goals and
feedback. Whereas for example time transformation as well as merg-
ing action and awareness, which are also commonly connected with
audiovisual immersion, were clearly rated to occur less (See
Table 12 and Fig. 7).
Beyond the contributions related to the psychometric proper-
ties of the DFS-2 scale, this study also suggests that flow should
rather be seen as divided between the collection of conditions for
reaching flow state and the psychological outcomes that follow
from reaching the flow state instead of merely seeing all of the nine
dimensions as reflective indicators of the entirety of flow. Previous
studies might not have been able to make a distinction along these
lines, since, naturally, both the conditions and outcomes are highly
correlated as is to be expected. Therefore, we suggest that further
studies could conduct experiments which would specifically focus
on finding evidence pertaining to causal relationships between the
components of the general flow.
Disclosure statement
No competing financial interests exist.
Acknowledgements
This research has been partially supported by individual study
grants for both authors from the Finnish Cultural Foundation as
well as carried out as part of research projects (40311/12, 40134/
13, 40111/14) funded by the Finnish Funding Agency for Technol-
ogy and Innovation (TEKES).
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... FSS-2 and DFS-2 have been translated and validated in various languages, especially often in the context of sports (Doganis et al., 2000, Fournier et al., 2007Calvo et al., 2008;Kawabata et al., 2008;Gonzalez-Cutre et al., 2009;Gouveia et al., 2012;Crust and Swann, 2013;Riva et al., 2017;Nojavan et al., 2017), but also learning, recreation, well-being (Whitmore and Borrie, 2005;Asakawa, 2010, Rufi et al., 2014, Souza Costa Correia et al., 2020, gameplay and internet games (Wang et al., 2009;Procci et al., 2012;Hamari and Koivisto, 2014), mental diseases (Huang et al., 2019). ...
... One of the areas in which flow has been studied is games and gamification, i.e., the application of game mechanics to nongame areas (Wang et al., 2009;Procci et al., 2012;Hamari and Koivisto, 2014). Games, due to the complexity of activities, are a kind of flow base. ...
... The scales assessed in this study are useful tools for a variety of research purposes. They are also relevant to those studying flow in various aspects of life, including video games, which provide a safe yet compelling and variedly challenging environment for exploration (Wang et al., 2009;Procci et al., 2012;Hamari and Koivisto, 2014;Gao and Lu, 2021;Gutierrez, 2021;Cai et al., 2022;Jogo et al., 2022, etc.). ...
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The aim of this study is to evaluate the psychometric properties of the Polish version of the Dispositional Flow Scale-2 (DFS-2) and Flow State Scale-2 (FSS-2), for use with Polish adults and young adults. Currently, there are no tools that would allow us to study flow among Polish speakers. At the same time, due to the great interest in flow and its potential importance for effectiveness, cooperation, and learning, it is worth ensuring that reliable validated measurement questionnaires are available for people studying the Polish population. Study participants completed 856 questionnaires, of which 496 individuals (with an average age of 36.31 years) participated in the DFS-2 study and 360 individuals (with an average age of 33.46 years) participated in the FSS-2 study. The maximum likelihood estimator (MLR) was selected for the CFA analysis. Model fit was assessed using: χ2, comparative fit index (CFI), Tucker-Lewis index (TLI), and standardized root mean square of residuals (SRMR), and root mean square of approximation error (RMSEA). For both questionnaires, formative first-level models with nine factors and second-level models with nine factors loaded on a higher-order flow factor were compared using the Satorra-Bentler Scaled difference χ2 test. The ω coefficient was used to estimate the reliability of the FFS-2 and DFS-2 models tested in the CFA method. Confirmatory factor analysis of both DFS-2 structural models showed satisfactory model fit. Most of the fit indices for the hierarchical 2nd order FSS-2 model presented satisfactory values, except for SRMR. Both DFS-2 and FSS-2 factors tested in the analysis showed good reliability (ω ≥ 0.7). Our findings confirmed the reliability and validity of the Polish versions of DFS-2 and FSS-2 scales. The scales are reliable when applied to Polish adults and young adults.
... Furthermore, we incorporate the flow theory [57] as an overarching frame because flow is intertwined in all three layers. The learning goals should neither be too boring nor too overwhelming, the interaction too must not be casually boring or anxiety introducing (i.e., strict teacher), as well as the game design elements, where flow is widely applied [58]. In summary, we use flow to engage learners as finding flow is an integral part of all activities of our lives [53]. ...
... Overall, by choosing and appropriate setting, challenging but not too difficult tasks as well as appealing game design elements, we ensure that the learners is kept in a pleasant or enjoyable learning flow [53,58], building a good learning experience. Micromate is implemented as Microsoft Teams plugin, featuring tasks and tutoringrole interaction (see II and III), as well as motivational components, particularly unlockable badges that can be collected through challenges (see I). ...
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Driven by circumstances like the global pandemic many learners and educators realize the importance and value of self-regulated digital learning. To better support self-regulated learning, conversational agents have become more relevant. Conversational agents can act as tutor or as learning mate for learners. Although conversational agents have potential to better support self-regulated learning processes, challenges exist requiring implications about how to make these interactions more engaging and supportive. This study discusses the value of gamified conversational learning chatbots that use game elements to engage learners to guide researchers and practitioners to design conversational agents that effectively motivate learners and provide self-regulated learning at the same time. Therefore, we propose a systematically developed framework for gamifying educational conversational agents and contribute to theory by consolidating several theories about games, digital learning, and conversational agents and support practitioners by providing implications about what to care about when gamifying conversational agents.
... In fact, SFX are not just an integral part of games but also considered a gamifcation element in gamifed systems [53], which are characterized as systems using elements known from games in a non-game context [13]. Gamifed systems usually improve the user experience of an activity, turn unpleasant tasks into fun ones, and help users reach a state of fow [18,61]. They have been shown to be successful in many contexts, including health, education, commerce or crowdsourcing [19,61]. ...
Conference Paper
Sound effects (SFX) complement the visual feedback provided by gamification elements in gamified systems. However, the impact of SFX has not been systematically studied. To bridge this gap, we investigate the effects of SFX-supplementing points (as a gamification element)-on task performance and user experience in a gamified image classification task. We created 18 SFX, studied their impact on perceived valence and arousal (N = 49) and selected four suitable SFX to be used in a between-participants user study (N = 317). Our fndings show that neither task performance, affect, immersion, nor enjoyment were signifcantly affected by the sounds. Only the pressure/tension factor differed significantly, indicating that low valence sounds should be avoided to accompany point rewards. Overall, our results suggest that SFX seem to have less impact than expected in gamified systems. Hence, using SFX in gamification should be a more informed choice and should receive more attention in gamification research.
... Four of the documents are about flow and human-computer interaction experience (Bilgihan et al., 2015;Faiola et al., 2013;Gao & Bai, 2014;Kim & Han, 2014), and two are about flow and game experience (Hamari & Koivisto, 2014;Hamari et al., 2016). ...
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Since Dr. Mihaly Csikszentmihalyi outlined the foundation of the flow theory in 1975, the study of flow has grown into a massive multi-disciplinary field. This study aimed to use bibliometric techniques to analyze the vast amount of literature accumulated in flow research. A total of 2700 peer-reviewed documents published between 1979 and 2021 were retrieved from Scopus. The results revealed three major periods of flow research: the inception period (before 1998), the growth period (1998–2010), and the thriving period (2011–2021), with an exponential rise in the number of publications in recent years. Researchers of various disciplinary backgrounds across the globe have contributed to the field, and flow theory has been frequently connected with the Self-Determination Theory and the Technology Acceptance Model. Eight major themes were identified in the publications, including three theoretical themes (antecedents, adjustment, and motivation) and five application themes (technology, gaming, sports, creativity, and education). Flow in the technology area is the current research hotspot. These findings provide a macroscopic overview of the field and guidance for future studies.
... Gamification does not replace the gamified target. Hamari and Koivisto [22] employed the Dispositional Flow Scale-2 (DFS-2) to determine the significance of gamification to the flow in autotelic personality traits. The results indicated that it was significant to autotelic experience, clear goals, immediate feedback, and control and the challenge-skill balance but less significant to time transformation, merging action-awareness, and loss of self-consciousness. ...
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Augmented reality (AR) technology has received much attention in recent years. Users can view AR content through mobile devices, meaning that AR tee no significant differences between the exhibition-viewing behaviors associated with the game mode and the frchnology can be easily incorporated into applications in various fields. The auxiliary role of AR in exhibitions and its influence and benefits with regard to the behavior of exhibition visitors are worth investigating. To understand whether gamified AR and general AR navigation systems impact visitor behavior, we designed an AR app for an art exhibition. Using GPS and AR technology, the app presents a virtual guide that provides friendly guide services and helps visitors navigate outdoor paths and alleys in the exhibition venue, showing them the best route. The AR navigation system has three primary functions: (1) it allows visitors to scan the exhibit labels to access detailed text and audio introductions, (2) it offers a game mode and a free mode for navigation, and (3) it collects data on the exhibition-viewing behavior associated with each mode and uploads them to the cloud for analysis. The results of the experiment revealed no significant differences between the exhibition-viewing time or distance travelled in the two modes. However, the paths resulting from the game mode were more regular, which means that the participants were more likely to view the exhibition as instructed with the aid of gamified AR. This insight is useful for the control of crowd flow.
... On the other hand, schizophrenic patients may achieve 'mastery' experience in different ways because schizophrenic patients are not necessarily driven by intrinsic motivations or enjoyments to participate in their most important or meaningful daily activities. In Table 3, our Schizophrenic Factors 1 and 8 were very similar to the factor of 'mastery' (or the condition of flow) proposed by Hamari and Koivisto, 16 especially including the items of 'autotelic experience' (AE). And, our Schizophrenic Factor 4 contained the items of 'autotelic experience' (AE), 'challenge skill balance' (CSB), and 'feedback' (F). ...
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In the present study we were interested in the concept of flow – an optimal psychical state which is connected with high achievement and positive experiences. It was supposed that experiencing flow during different musical activities (e. g., rehearsals, solo performance, performance with the orchestra) should be related to subjective well being, common in life. Eighty-four students of the Academy ofMusic (28 male and 56 female) completed the Positive Affect Negative Affect Schedule (PANAS), the Satisfaction with Life Scale (SWLS) and the Dispositional Flow Scale (DFS-2), which measures nine dimensions of flow. Results confirmed that several aspects of flow are positively related to measures of subjective well being. Clear goals, challenge-skill balance, concentration on the task, and autotelicexperience are important predictors of positive affect, explaining 36% of its variance, challenge-skill balance is an important predictor for negative affect, explaining 26% of its variance, and clear goals is an important predictor for satisfaction with life, explaining 8% of its variance. We conclude that experiencing flow is more related to emotional than cognitive aspects of subjective well being, which is not surprising, since flow is an extremely emotional experience.
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