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Neural correlates of flow, boredom, and anxiety in gaming: An
Neural correlates of flow, boredom, and anxiety in gaming: An
electroencephalogram study
electroencephalogram study
Tejaswini Yelamanchili
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NEURAL CORRELATES OF FLOW, BOREDOM, AND ANXIETY IN GAMING: AN
ELECTROENCEPHALOGRAM STUDY
by
TEJASWINI YELAMANCHILI
A THESIS
Presented to the Faculty of the Graduate School of the
MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY
In Partial Fulfillment of the Requirements for the Degree
MASTER OF SCIENCE IN INFORMATION SCIENCE & TECHNOLOGY
2018
Approved by
Dr. Fiona Fui-Hoon Nah, Advisor
Dr. Keng Siau
Dr. Richard Hall
iii
ABSTRACT
Games are engaging and captivating from a human-computer interaction (HCI)
perspective as they can facilitate a highly immersive experience. This research examines
the neural correlates of flow, boredom, and anxiety during video gaming. A within-
subject experimental study (N = 44) was carried out with the use of
electroencephalogram (EEG) to assess the brain activity associated with three states of
user experience – flow, boredom, and anxiety – in a controlled gaming environment. A
video game, Tetris, was used to induce flow, boredom, and anxiety. A 64 channel EEG
headset was used to track changes in activation patterns in the frontal, temporal, parietal,
and occipital lobes of the players’ brains during the experiment. EEG signals were pre-
processed and Fast Fourier Transformation values were extracted and analyzed. The
results suggest that the EEG potential in the left frontal lobe is lower in the flow state
than in the resting and boredom states. The occipital alpha is lower in the flow state than
in the resting state. Similarly, the EEG theta in the left parietal lobe is lower during the
flow state than the resting state. However, the EEG theta in the frontal-temporal region of
the brain is higher in the flow state than in the anxiety state. The flow state is associated
with low cognitive load, presence of attention levels, and loss of self-consciousness when
compared to resting and boredom states.
Keywords: Electroencephalogram, Fast Fourier Transformation, Flow, Frontal, Parietal,
Occipital, Frontal-Temporal, Human-Computer Interaction, Mid-Beta, Theta, Alpha,
Neural Correlates
iv
ACKNOWLEDGMENTS
I would like to take this opportunity to thank my thesis advisor, Dr. Fiona Fui-
Hoon Nah, from the bottom of my heart for her constant guidance and continuous support
throughout the process of this research. She has been a true inspiration to me – a
conversation with her always reminded me of the fact that there is a lot to learn and
achieve in my life and has forever motivated me to strive harder for excellence. Her
energy and enthusiasm have had a positive influence on my personality and have made
me a better person both professionally and personally. Working with Dr. Nah has always
been a pleasure; I have the freedom to express my thoughts and ideas; also, the
constructive discussions and the systematic approach have brought the best out of me. I
was fortunate enough to get all those wonderful opportunities, which include presenting
my research at the 2017 MWAIS conference, attending the prestigious 2017 CHI
conference, and having my work presented at the 2017 HCI international conference. I
have no doubt in my mind in saying that Dr. Nah has been the major reason for my
success at Missouri S&T and I will miss her and Missouri S&T for sure after my
graduation.
I would also like to thank my thesis committee members, Dr. Keng Siau and Dr.
Richard Hall, for taking time off their hectic schedule to attend my thesis defense.
I acknowledge my fellow research student, Chandana Mallapragada, for assisting
me in conducting the experiment. LITE lab manager, Samuel Smith, has been very
supportive as well; the training program he conducted bettered my understanding of
nuances of the LITE lab experiments.
v
TABLE OF CONTENTS
Page
ABSTRACT
……………………………………………………………………………………………………….. iii
ACKNOWLEDGMENTS ……………………………………………………………………………………. iv
LIST OF ILLUSTRATIONS
………………………………………………………………………………. viii
LIST OF TABLES
……………………………………………………………………………………………….. x
SECTION
1. INTRODUCTION
………………………………………………………………………………………… 1
2. LITERATURE REVIEW
………………………………………………………………………………. 5
2.1. FLOW STATE AS OPTIMAL EXPERIENCE …………………………………………. 5
2.2. DEFINITION AND CORRELATES OF BOREDOM STATE
………………….. 15
2.3. STATE OF ANXIETY AND COGNITIVE PERFORMANCE …………………. 18
2.4. BRAIN COMPUTER INTERFACE – EEG FOR NEURO-IS
……………………. 20
2.5. EEG FOR HCI AND GAMES ………………………………………………………………. 23
2.6. NEURAL CORRELATES OF USER EXPERIENCE STATES ………………… 26
2.6.1. Neural Correlates of Flow-Related User Experience ……………………….. 26
2.6.2. Neural Correlates of Boredom-Related User Experience …………………. 31
2.6.3. Neural Correlates of Anxiety-Related User Experience …………………… 33
3. THEORETICAL FOUNDATION AND HYPOTHESIS DEVELOPMENT
………. 38
3.1. THEORETICAL FOUNDATION
………………………………………………………….. 38
3.1.1. Frontal Lobe ………………………………………………………………………………. 40
3.1.2. Parietal Lobe
………………………………………………………………………………. 41
3.1.3. Temporal Lobe …………………………………………………………………………… 42
vi
3.2. HYPOTHESIS DEVELOPMENT………………………………………………………….. 43
3.2.1. Flow vs Resting: EEG Theta over Frontal Lobe ……………………………… 43
3.2.2. Flow vs Resting: EEG Theta over Parietal Lobe …………………………….. 44
3.2.3. Flow vs Boredom: EEG Theta over Frontal Lobe …………………………… 45
3.2.4. Flow vs Anxiety: EEG Theta over Frontal-Temporal Network
…………. 46
3.2.5. Flow vs Resting: EEG Alpha over Frontal Lobe …………………………….. 46
3.2.6. Flow vs Boredom: EEG Alpha over Frontal Lobe
…………………………… 47
3.2.7. Flow vs Resting: EEG Mid-Beta over Frontal Lobe
………………………… 48
3.2.8. Flow vs Boredom: EEG Mid-Beta over Frontal Lobe ……………………… 49
3.2.9. Flow vs Resting: EEG Alpha over Occipital Lobe ………………………….. 50
4. RESEARCH METHODOLOGY ………………………………………………………………….. 53
4.1. EXPERIMENTAL DESIGN …………………………………………………………………. 53
4.2. RESEARCH PROCEDURE
………………………………………………………………….. 54
4.3. MEASUREMENT ……………………………………………………………………………….. 56
4.4. PILOT TESTS …………………………………………………………………………………….. 58
5. DATA ANALYSIS AND RESULTS ……………………………………………………………. 63
5.1. DATA PROCESSING STEPS ………………………………………………………………. 64
5.2. DATA ANALYSIS STEPS …………………………………………………………………… 65
5.3. RESULTS …………………………………………………………………………………………… 74
6. DISCUSSIONS OF RESULTS
…………………………………………………………………….. 80
7. LIMITATIONS AND FUTURE RESEARCH ……………………………………………….. 84
8. THEORETICAL AND PRACTICAL IMPLICATIONS …………………………………. 86
8.1. THEORETICAL IMPLICATIONS
………………………………………………………… 86
vii
8.2. PRACTICAL IMPLICATIONS …………………………………………………………….. 87
9. CONCLUSION ………………………………………………………………………………………….. 88
APPENDIX
……………………………………………………………………………………………………….. 89
BIBLIOGRAPHY
………………………………………………………………………………………………. 90
VITA ……………………………………………………………………………………………………………… 112
viii
LIST OF ILLUSTRATIONS
Figure
Page
1.1. Experience Fluctuation Model Related to Challenges and Abilities ……………………… 3
2.1. The Challenge/Skill Balance Model …………………………………………………………………. 8
3.1. Effort vs. Demands in Effective Action in the Flow State …………………………………. 39
3.2. Functions of Four Lobes of the Human Brain ………………………………………………….. 42
4.1. Tetris Game at Different Levels to Evoke User Experience States ……………………… 56
4.2. Retrospective Process Tracing to Extract a Best 30-Second Segment …………………. 57
4.3. A Subject Wearing 64-Channel EEG Device During the Experiment …………………. 58
4.4. EEG Headset 10/20 Positioning System
………………………………………………………….. 60
5.1. Calculating Sample Size Using G*Power Statistical Power Analysis
………………….. 63
5.2. Changing Sampling Rate – Downsampling………………………………………………………. 65
5.3. Optimizing Channel Selection ……………………………………………………………………….. 66
5.4. Raw Data Inspection: Inspection Mode
…………………………………………………………… 67
5.5. Raw Data Inspection: Maximum Voltage Criteria_1 ………………………………………… 68
5.6. Raw Data Inspection: Minimum Voltage Criteria_2 …………………………………………. 68
5.7. EEG Signal After Raw Data Inspection …………………………………………………………. 68
5.8. Ocular Correction ICA: Mode Selection …………………………………………………………. 70
5.9. Ocular Correction ICA: Reference Channel Selection ………………………………………. 70
5.10. Ocular Correction ICA: Identifying and Accepting Eye-Blinks
………………………. 70
5.11. Applying Infinite Impulse Response Filters
………………………………………………….. 71
ix
5.12. EEG Signal Segmentation: Manual Division ………………………………………………… 72
5.13. EEG Signal Segmentation: Time Frames of User’s States ……………………………… 73
5.14. Exporting FFT Values for Theta Spectral Band ……………………………………………… 74
x
LIST OF TABLES
Table
Page
2.1. Components of the Flow State
…………………………………………………………………………. 6
2.2. Components of Flow in the HCI Context ………………………………………………………….. 9
2.3. Summary of Research on Neural Correlates of the Flow State …………………………… 27
2.4. Research on Neural Correlates of the Boredom State ……………………………………….. 32
2.5. Research on Neural Correlates of the Anxiety State …………………………………………. 34
3.1. Functions of the Lobes of the Human Brain
…………………………………………………….. 40
3.2. Study Hypotheses…………………………………………………………………………………………. 52
5.1. Results of Paired t-tests for Neural Correlates
………………………………………………….. 75
5.2. Secondary Analysis Results of Paired t-test for Neural Correlates ……………………… 77
1. INTRODUCTION
Computer games, being interactive in nature, have been actively adopted and
enjoyed by people irrespective of their age group and background. From the Human-
Computer Interaction (HCI) perspective, games are captivating and engaging as they
form an ideal ground for interactivity and communication (Hartmann & Klimmt, 2006).
There has been a growing interest in HCI to understand user states of experience and the
design of applications with dynamic user experience (UX) as its core (McCarthy &
Wright, 2004; Jacko, 2012). Video games, with their ability to draw people in, can
generate different user experiences while gaming. In a gaming context, cognitive and
emotional states generated by dynamic gaming conditions can lead players to be happy,
cognitively efficient, intrinsically motivated, fully focused, and in control of the total
gaming environment (Cowley et al., 2008; Moneta & Csikszentmihalyi, 1999).
Games with incremental difficulty levels and an immersive nature provide the
opportunity to decide, take actions, and influence the gameplay. According to the flow
and emotion theories, if the skills of a person meet the challenge of a task, then strong
involvement in a task can be observed (Cowie et al., 2001). The challenge level in the
gameplay is related to sensorimotor abilities and cognitive challenges (Ermi & Mäyrä,
2005). Csikszentmihalyi (1975, 1990, 1997) identified three main states of user
experience based on challenge and skill: boredom, flow, and anxiety. Boredom is a state
when the challenge is much lower than skill. A state of flow or optimal experience
emerges when the difficulty of a task at hand and the skills of a player are balanced
(Jackson, 1992; Millan et al., 2004; Moneta & Csikszentmihalyi, 1999; Okada, 1993).
2
Anxiety occurs when the challenge is much greater than the skill (Moneta &
Csikszentmihalyi, 1999).
As illustrated in Figure 1.1, a player can be engaged in gaming, and potentially
experience various states of user experiences such as boredom, apathy, absorption, flow,
frustration, and anxiety, based on the challenge of the game and the player’s ability
(Massimini & Massimo, 1988). Sometimes, people find games so deeply captivating that
time flies during gaming and they do not even notice their surroundings (Agarwal &
Karahanna, 2000). During that period of engagement and absorption, most or all of their
attention is on the game. Such a state has been called “in the game” (Jennett et al., 2008)
or “in the zone” (Marr, 2001). Assessing such user states of experience (i.e., those
presented in Figure 1.1) is important because it provides designers, developers, and
usability specialists with opportunities to improve the user experience. Such assessments
have been widely used in the field of HCI, an evolving research field that studies user
experience with a product, system, or an application (Tondello, 2016). More information
on assessing various user states of experience based on the challenge and abilities has
been presented in Figure 1.1 will be discussed in Section 2 which covers the literature
review. The most common and traditional approaches to assess user experience are self-
reported measures (e.g., questionnaires and interviews) that are typically retrospective in
nature and are subjected to biases, such as social desirability and recall biases
(Bhattacherjee, 2012). With advancements in technology, alternative approaches are
available that can assess user experience in real-time. One such approach is
electroencephalogram (EEG). Unlike traditional approaches, EEG can provide
continuous and concurrent assessments of user experience without having to interrupt the
3
user. EEG can be used to capture spontaneous brain activity associated with constructs
related to or in the context of information systems (IS), such as cognitive workload,
emotion, and user states of experience, that can be used to develop neuro-adaptive IS
(Müller et al., 2015). EEG technology is still relatively new and underexplored in the
context of HCI, and more research is needed to relate EEG activities to specific states of
user experience. This research is a step in this direction – it aims to identify EEG
correlates for user experience: flow, boredom, and anxiety.
Figure 1.1. Experience Fluctuation Model Related to Challenges and Abilities, adopted
from Massimini & Massimo (1988)
Given the need to understand the importance of optimal experience in HCI in the
gaming context, and the potential of EEG to provide a better and more reliable way of
assessing user experience, our research question is:
4
Research Question: What are the neural correlates of flow, boredom, and anxiety
in the gaming context?
Although EEG has been used in the medical area for decades, its applications in
HCI are emerging and very promising (Van Erp et al., 2012). EEG can be used to assess
the real-time experience of users and can provide continuous assessments of user states in
HCI. It has the potential to provide more reliable and objective (i.e., less subjective)
assessments than self-reported assessments of user experience (Berka et al., 2007).
The rest of the paper is organized as follows: Section 2 provides a review of the
related literature. Section 3 provides the theoretical foundation for the research
hypotheses. Section 4 describes the research methodology, and Section 5 presents the
data analysis procedure and the results. Section 6 discusses the results. Section 7 provides
the limitations of the research as well as future research directions. Section 8 highlights
the theoretical and practical implications, and Section 9 concludes the paper.
5
2. LITERATURE REVIEW
In this section, the background work related to flow, boredom, and anxiety is
reviewed. Research studies that have utilized a variety of qualitative and quantitative
techniques to capture and understand the above-mentioned user states are reviewed as
well. The importance of Brain-Computer Interface (BCI) and EEG research in NeuroIS
and HCI fields is also discussed. In addition, this chapter discusses and summarizes
previous studies’ findings related to neural and physiological correlates of the above three
user states.
2.1. FLOW STATE AS OPTIMAL EXPERIENCE
Csikszentmihalyi (1990) conceptualizes the state of flow as an optimal
experience. He also theorizes nine components for flow: balance of challenge and skill,
clear goals, immediate feedback, perceived total control, loss of self-consciousness,
focused concentration, time distortion, merging of action and awareness, and autotelic
experience. These nine components of the flow state are summarized in Table 2.1.
Flow, which is the optimal state of experience, is defined as a “holistic sensation
that people feel when they act with total involvement” (Csikszentmihalyi, 1975, p. 36).
Flow occurs when an individual is completely engaged and involved in a task or a
system, giving an immersive experience of being ‘in the zone’ (Fang et al., 2013). A
person in the flow state experiences focused attention, time distortion, intrinsic
motivation, perceived control, merging of action and awareness, and loss of self-
consciousness (Csikszentmihalyi, 1990; Csikszentmihalyi & LeFevre 1989).
6
Table 2.1. Components of the Flow State (Csikszentmihalyi, 1990)
Dimension
Description
Balance of challenge
and skill
A key aspect of the state of flow is that the skill of the
individual and the challenge of the activity need to be in balance
with each other.
Clear goals
The goals/objectives of the task or activity must be clear and
unambiguous.
Immediate feedback
The performance feedback on the task or activity should be
clear, immediate, and unambiguous.
Control
The individual perceives control of his/her actions and the
environment.
Loss of self-
consciousness
Because of the pre-occupied activity, the individual “loses”
oneself and experiences a sense of separation from the world
around him/her.
Concentration on the
task at hand
The individual pays complete attention to the task or activity,
such that all other distractions are blocked out from his/her
awareness.
Transformation of
time
Time no longer seems to pass the way it normally does. The
individual loses track of time and the perception of time is
distorted.
Merging of action
and awareness
The individual is so involved in the activity that his/her actions
become spontaneous or automatic responses.
Autotelic nature
The activity that consumes the individual is intrinsically
rewarding and motivating to him/her.
For one to get into the flow state, not only should there be a balance of challenge
and skill, but it is also necessary to have clear goals as well as immediate and
unambiguous feedback. A deep level of involvement in gameplay can arise when the skill
level of a player matches the challenge level of the game, and the goals and feedback are
clear (Csikszentmihalyi 1990; Lee et al., 2015; Nah et al., 2011). The flow state is
characterized by total control over the task, loss of consciousness of oneself and the
7
physical environment, focused attention and concentration on the task at hand, distortion
or transformation of time, merging actions with awareness (i.e., actions become
automatic and effortless), and autotelic (i.e., intrinsically rewarding) experience. Thus,
balance of challenge and skill, clear goals, and immediate feedback are regarded as
necessary conditions for flow, and the remaining components describe the flow
experience.
According to the flow theory, the relationship between skill and challenge lays the
foundation for the psychological state of flow (Csikszentmihalyi, 1990, Guo et al., 2016;
Nah et al., 2010). Challenge is considered as an opportunity to perform an action, and
skill is the capability to perform that action. When the goals are clear, and the feedback is
immediate and unambiguous, the congruence between challenge and skill can give rise to
the necessary conditions for the flow state. As presented in Figure 2.1, the flow channel
can be explained as a function on a plane with skills and challenges as axes
(Csikszentmihalyi, 1975) and is considered as the challenge/skill balance model. An
increase in the user’s skill can arise from learning, and an increase in the challenge of
performing an activity could be due to novelty or increased difficulty; flow experience
can be attained by maintaining a balance between the skill and the challenge (Cowley et
al., 2008; Csikszentmihalyi, 1975; Goleman, 1995). A person who is in the flow state is
completely immersed and absorbed in the activity to the point where nothing else seems
to matter Csikszentmihalyi, 1975). In other words, ‘‘people [who are in flow] are willing
to perform an activity for their own sake, with little concern for what they get out of it’’
(Csikszentmihalyi, 1990, p. 71).
8
Figure 2.1. The Challenge/Skill Balance Model adopted from Csikszentmihalyi &
Csikszentmihalyi (1992)
Table 2.2 summarizes the key components of flow, and its related construct,
cognitive absorption, in the IS literature (HCI context). Agarwal and Karahanna proposed
the construct, cognitive absorption, based on the concepts of flow, absorption, and
cognitive engagement (Agarwal & Karahanna, 2000). They defined cognitive absorption
as a state of deep involvement with software and conceptualized it using five dimensions:
curiosity, control, temporal dissociation, focused immersion, and heightened enjoyment.
Several other terms, such as immersion and presence, have also been used by researchers
in other disciplines to capture the flow phenomenon (Csikszentmihalyi, 1990; Lombard
& Ditton, 1997, Qin et al., 2009). For example, Qin et al. (2009) described immersion as
an intense experience where a user is involved both mentally and physically in a given
task. They identified seven components for immersion, which are curiosity,
concentration, challenge and skill, control, comprehension, empathy, and familiarity.
Lombard and Ditton (1997) provided six conceptualizations for presence as social
9
richness, realism, transportation, immersion, social actor within a medium, and medium
as a social actor. Presence has also been represented as “the perceptual illusion of non-
mediation” (Lombard & Ditton, 1997, p. 755). Hence, presence (or telepresence) is also
closely related to and an important aspect of the flow construct (Chen, 2006; Lee &
Chen, 2010; Nah et al., 2011; Skadberg & Kimmel, 2004).
Table 2.2 highlights components of the flow and cognitive absorption constructs,
which are terms that have been commonly used by the IS research community. As shown
in Table 2.2, there have been variations across researchers’ conceptualizations and
operationalizations of the flow and cognitive absorption constructs.
Table 2.2. Components of Flow in the HCI Context
Reference
Flow Components
Research
Method
Research
Setting
Agarwal &
Karahanna
(2000)
Curiosity, control, temporal
dissociation, focused immersion,
heightened enjoyment
Survey
questionnaire
IT usage
Chen (2006)
Telepresence, time distortion,
concentration, loss of self-
consciousness, a clear goal, control,
immediate feedback, merging of
action and awareness, positivity of
affect, enjoyable feelings
Survey
questionnaire
Web
navigation
Chen, Wigand,
& Nilan (1999)
Merging of action and awareness,
concentration, control
Survey
questionnaire
Web
navigation
Chen, Wigand,
& Nilan (2000)
Merging of action and awareness,
concentration, loss of self-
consciousness, time distortion,
control, telepresence, enjoyment,
challenge
Survey
questionnaire
Web
navigation
10
Table 2.2. Components of Flow in the HCI Context (cont.)
Cowley,
Charles, Black,
& Hickey
(2008)
Challenge, immersion, control,
concentration, clear unambiguous
goals, immediate feedback, lose
consciousness of passage of time,
lose sense of identity
Conceptual
and literature
review
Video game
Fang, Zhang,
& Chan (2013)
Balance of challenge and skill,
clear goals and feedback,
concentration, control, immersion
(loss of self-consciousness,
merging of action and awareness,
time transformation), autotelic
experience
Survey
questionnaire
Computer
game
Fu, Su, & Yu
(2009)
Concentration, goal clarity,
feedback, challenge, autonomy,
immersion, social interaction,
knowledge improvement
Survey
questionnaire
E-learning
games
Ghani &
Deshpande
(1994)
Enjoyment, concentration
Survey
questionnaire
Computer
use
Guo & Poole
(2009)
Perceived web complexity, balance
of challenge and skill, goal clarity,
feedback, concentration, control,
merging of action and awareness,
transformation of time,
transcendence of self, autotelic
experience
Experiment
and
questionnaire
Online
shopping
Guo, Xiao,
Van Toorn,
Lai, & Seo
(2016)
Balance of challenge and skill,
clear goals, immediate feedback,
telepresence, concentration, loss of
self-consciousness, control, time
distortion
Survey
questionnaire
Online
learning
Hoffman &
Novak (1998)
Challenge, skill, balance of
challenge and skill, interactivity,
vividness, telepresence, focused
attention, involvement
Conceptual
and literature
review
Web
navigation
11
Table 2.2. Components of Flow in the HCI context (cont.)
Hoffman &
Novak (2009)
Challenge, skill, interactivity,
vividness, telepresence, usage,
involvement, motivation, attention,
ease of use, positive subjective
experience, control, exploratory
behavior, curiosity, discovery,
attractiveness, novelty, playfulness,
personal innovativeness, content
Conceptual
and literature
review
Online
marketing
Jiang &
Benbasat
(2004)
Control, attention focus, cognitive
enjoyment
Experiment
and
questionnaire
Online
shopping
Lee & Chen
(2010)
Concentration, enjoyment, time
distortion, telepresence
Survey
questionnaire
Online
shopping
Li & Browne
(2006)
Focused attention, control,
curiosity, temporal dissociation
Survey
questionnaire
Web
navigation
Nah,
Eschenbrenner,
& DeWester
(2011)
Telepresence, enjoyment
Experiment
and
questionnaire
Virtual
world
Nah,
Eschenbrenner,
Zeng, and,
Telaprolu, &
Sepehr (2014)
Balance of challenge and skill,
clear goals, immediate and
unambiguous feedback,
concentration, sense of control, loss
of self-consciousness, merging of
action and awareness, time
distortion, immersion, telepresence,
exploratory behavior, playfulness,
sense of identity, social interaction,
intrinsic motivation, autotelic
experience, enjoyment, curiosity,
heightened state of ability, feeling
of pressure
Conceptual
and literature
review
Video game
Nel, Van
Niekerk,
Berthon, &
Davies (1999)
Control, attention focus, curiosity,
intrinsic interest
Experiment
and
questionnaire
Web
navigation
12
Table 2.2. Components of Flow in the HCI context (cont.)
Pace (2004)
Joy of discovery and learning,
reduced awareness of physical
surroundings, time distortion,
merging of action and awareness,
control, mental alertness,
telepresence
Interview
Web
navigation
Saade & Bahli
(2005)
Temporal dissociation, focused
immersion, heightened enjoyment
Survey
questionnaire
Online
learning
Seger & Pottts
(2012)
Concentration, merging of action
and awareness, little/no self-
consciousness, skills meet
challenges, time passes quickly,
intrinsically rewarding, unique
sensations, sense of invincibility,
increased physical strength, time
passes slowly, calm relaxation,
attention
Survey
questionnaire
Video game
Siekpe (2005)
Challenge, concentration, curiosity,
control
Survey
questionnaire
Online
shopping
Skadberg &
Kimmel (2004)
Time distortion, enjoyment,
telepresence
Survey
questionnaire
Web
navigation
Sweetser &
Wyeth (2005)
Concentration, challenge, skill,
control, clear goals, feedback,
immersion, social interaction
Expert review
Computer
game
Trevino &
Webster
(1992)
Control, attention focus, curiosity,
intrinsic interest
Survey
questionnaire
Email,
Voice mail
Wang, Liu, &
Khoo (2009)
Balance of challenge and skill,
merging of action and awareness,
clear goals, immediate feedback,
concentration, control, loss of self-
consciousness, time distortion,
autotelic experience
Survey
questionnaire
Internet
gaming
Webster,
Trevino, &
Ryan (1993)
Control, attention focus, curiosity,
intrinsic interest
Survey
questionnaire
Online
learning and
email
13
Table 2.2. Components of Flow in the HCI context (cont.)
Zaman,
Anandarajan,
& Dai (2010)
Intrinsic enjoyment, concentration
Survey
questionnaire
Instant
messaging
Outside the IS context, Jackson and Marsh (1996) developed a scale, known as
the flow state scale (FSS), that is based on all nine components of flow proposed by
Csikszentmihalyi (1990). This scale has been used to capture the flow experience of
athletes as a state, i.e., for a particular event. Among the nine components of flow, it was
found that control, balance of challenge and skill, concentration, and autotelic nature of
experience contributed more to the flow experience of athletes when compared to
transformation of time and loss of self-consciousness (Jackson & Marsh, 1996). In
contrast to the FSS that assesses flow as a state, a dispositional flow scale (DFS) was
later developed by Jackson et al. (1996) to assess the flow experience of athletes as a
trait, i.e., based on the frequency of flow experiences. Jackson and Eklund (2002) also
developed an improved version of the FSS and DFS (i.e., with regard to measurement of
some of the flow components) and named them the flow state scale-2 (FSS-2) and
dispositional flow state-2 (DFS-2) respectively.
Two published papers in the IS domain have operationalized Csikszentmihalyi’s
(1990) nine components of flow and assessed them in the IS context. Fang et al. (2013)
conceptualized clear goals and feedback as one component and developed an instrument
that took into account all the flow components proposed by Csikszentmihalyi (1990) to
capture users’ flow experience in a computer gaming context. Using responses from 260
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participants to carry out a factor analysis, six components emerged: clear goals and
feedback, focused immersion (i.e., loss of self-consciousness, merging of action and
awareness, transformation of time), balance of challenge and skill, autotelic experience,
concentration, and control. Another study by Wang et al. (2009) utilized the DFS-2 to
capture users’ flow experience in Internet gaming. One thousand five hundred and
seventy-eight participants completed the questionnaire. The results suggest that the DFS-
2 has acceptable reliability estimates and convergent validity. Research that has assessed
and validated the FSS-2 or a scale comprising the nine components proposed by
Csikszentmihalyi (1990) in an IS context is still lacking. Fang et al. (2013) found the
flow construct to load onto six instead of nine components. Given the importance of
triangulating assessments or measurements of the flow construct in IS research, we
examine an alternative measurement approach that uses EEG to capture users’ flow
experience in this research.
Bruya (2010) conceptualized the flow state using a new perspective in the
cognitive science of attention and action, and suggested that the flow state results in
effortless attention. In other words, when a person is in the flow state, he or she maintains
a sustained level of efficiency such that increased task demands can be carried out with
no increase in felt effort because of the high level of focus, control, and automaticity
achieved in the flow state (Bruya, 2010). When one’s attention and action are merged in
the flow state, the action becomes automatic and seemingly effortless. Hence, the flow
state has been associated with effortless attention and action, which are key aspects of
autotelic experience (Bruya, 2010).
