Available via license: CC BY 4.0
Content may be subject to copyright.
1
The Effects of Digitalization on Human Energy and Fatigue:
A Review
Jana Korunovska 1 and Sarah Spiekermann
Vienna University of Economics and Business, Austria
ABSTRACT
Information and communication technologies (ICTs) are generally assumed to save time and
energy, yet user fatigue due to ICT use is assumed to be on the rise. The question about the effects
of ICT use on human energy has sparked increased research interest in recent years, however, the
course is complicated by the fact that the conceptualization of human energy is extremely diverse.
The aim of this paper is therefore twofold. First, we provide a conceptual framework and
classification of subjective energy concepts and reflect on the theoretical embedding of technology
within the theories on subjective energy. Second, we review the leading empirical literature on the
relationship between ICT use and eight different subjective energy concepts prominent in different
disciplines. We also include the new phenomena of social networking sites (SNS) exhaustion and
SNS fatigue. With this, we aim to consolidate the existing research, illuminate the gaps, and
provide a conceptual baseline for future research on the relationship between ICT use and
subjective energy of ICT users. We show that ICTs use has predominantly negative effect on users’
energy, especially in organizational contexts, and show the main patterns and mechanisms through
which technology drains as well as energizes users.
Keywords: Human energy, exhaustion, vigor, fatigue, vitality, depletion, ICTs, ICT use
1. INTRODUCTION
As we approach the third decade of the 21st century, our lives appear more and more like
the wildest sci-fi dreams of the previous millennium: we own pocket-sized devices and wearables
that are connected not only to our friends, family and colleagues, but also to our refrigerators,
radiators and home cameras. At our fingertips or voice commands we have access to a library of
infinite human knowledge and even at the remotest locations we can accomplish most of our tasks.
The new information and communication technologies (ICTs) that help us achieve this were
invented to make humanity more efficient and productive, save us time and energy, and improve
our quality of life and well-being.
Yet at the same time, lack of time and energy is the number one complaint of modern
society, and digital technology is often seen as a cause. The number of employees who suffer from
fatigue and emotional exhaustion seems to be rising and often tips over 25% of the general working
1 Corresponding author: jana.korunovska[at]wu.ac.at
2
population, depending on country and assessment method (Aumayr-Pintar, Cerf, & Parent-Thirion,
2018; Bültmann, Kant, Kasl, Beurskens, & van den Brandt, 2002; Shanafelt et al., 2015; Shanafelt
et al., 2019). The costs of burned-out employees is estimated to be up to 190 billion dollars per
year in health-care spending in the US alone, and additional 5 billion US dollars in turnover and
productivity loss among physicians only (Garton, 2017; Han et al., 2019; Waldman, Kelly, Aurora,
& Smith, 2004).
Digitalization of the work place has been cited as a common reason for this decrease in
employees’ energy levels, and terms such as “techno-stress”, “tech-invasion” and “digital fatigue”
have entered the vocabulary (Ayyagari, Grover, & Purvis, 2011; Ragu-Nathan, Tarafdar, Ragu-
Nathan, & Tu, 2008; Shin & Shin, 2016). Additional technology-related phenomena such as
Internet addiction and addiction to Social Networking Sites (SNS) have also been associated to
fatigue, which extends the problem beyond work environments to the general population,
especially the younger generations (Bener et al., 2018; Lin, Tsai, Chen, & Koo, 2013). So the
question arises, are ICTs saving or are they costing us energy?
Considering the trends, it is not surprising that research on this topic has started to
proliferate in the last couple of years. As of 2019, 83% of the articles published on the topic in the
leading Human Computer Interaction (HCI) and Information Systems (IS) journals date no earlier
than 2014 and 69% as recently as 2016. However, if one attempts to review the results, it quickly
becomes evident that human energy, especially the subjective experience thereof, is an extremely
elusive and complicated construct. Different disciplines use different terms, different definitions
and different theories to describe and explain subjective energy. For example, organizational
scholars mostly refer to employees’ feelings of “emotional exhaustion” and “vigor” (Bakker,
Schaufeli, Leiter, & Taris, 2008; Cropanzano, Rupp, & Byrne, 2003). Personality and social
psychologists on the other hand focus on “depletion” and “vitality” (Deci & Ryan, 2011; Muraven,
Tice, & Baumeister, 1998). All these terms come with their own measurement instruments,
sometimes multiple per term. Other terms such as mental or psychic energy, fatigue, tiredness,
activity, (positive) arousal, inertia, etc., are frequently used to describe the subjective experience
of having energy as well. Little consensus seems to exist on terminology and measurement,
sometimes even within a single discipline like psychophysiology, cognitive and affective sciences,
etc. When HCI and IS scholars now enter the field and borrow from all these disciplines, they risk
using the terms arbitrarily and interchangeably.
The broadness of terms and conceptualizations makes it difficult (if not impossible) to
systematically study the effects of IT use on human energy. Empirical studies are not comparable
if they all use different definitions and measure different impacts in different ways. Thus, there is
an urgent need for consolidation of the research, but even before that, for a systematic structure of
all the different terms, their theories, and the way that they have been measured and used.
In this paper, we therefore provide a conceptual framework for the subjective energy
constructs first, before we then attempt to structure the landscape of the existing research. Namely,
in Section 2, we first investigate how individual energy-related terms are defined and how they are
operationalized, i.e., measured. Here, we also delve into the prominent theories about the specific
energy-concepts and reflect how technology can be fitted in these theories. In sum, Section 2
provides a detailed conceptual framework about subjective energy and technology and classifies
the different energy-related terms. Only after this differentiated understanding of all relevant
subjective energy constructs, and the method of our systematic literature review (Section 3), we
3
present the empirical findings of our literature review. Here we explore how each energy concept
presented in Section 2 is influenced by IT use. In Section 5, we conclude by summarizing the
general patterns across specific energy-related terns and by discerning the main gaps in research
on the effects of digitalization on subjective energy.
The paper has several contributions to the field. First, we provide clarity and classification
of the subjective energy constructs, which are otherwise scattered and sometimes ill-conceived.
This section should help HCI and IS researchers interested in human energy to get an overview of
the constructs and theories and choose the one (or several) construct(s) that are most suitable and
relevant for their studies. Additionally, we help illuminate the state of the art on the relationship
between digital technology and subjective energy. This is a first step to understanding what aspects
of technology use are causing fatigue or are energizing the users. Together with the provided
directions for future research, the paper provides a roadmap toward designing technology that truly
nourishes human energy.
2. THE DIFFERENT FACES OF SUBJECTIVE ENERGY AND FATIGUE:
CONCEPTUAL FRAMEWORK AND CLASSIFICATION OF THE TERMS
When scholars study subjective energy or subjective fatigue, the scope of the analyzed
construct varies: Some study short-term, momentary experiences of energy or tiredness, while
others look into longer lasting states thereof (duration). Some focus very narrowly on the
experience of subjective energy and fatigue, while others combine it in broader concepts that entail
attitudes or motivation (range). Again others emphasize the cause of energy loss or gain, for
example work-stress as a cause of emotional exhaustion, while others postulate no specific cause
for the experience of subjective energy. Last but not least, scholars embrace different
conceptualizations of subjective energy: Some base their work on a bipolar (univariate) view,
which sees subjective energy and subjective fatigue as two opposite ends on one energy dimension
that ranges from tired to energetic. On the contrary, others embrace the more timely bivariate
(unipolar) view, which postulates that subjective energy and subjective fatigue are separate feelings
or mental states that are based on different energy dimensions and processes (Bakker & Demerouti,
2007; Shirom, 2011). This view thus recognizes that, somewhat contraintuitively, mixed feelings
of energy and fatigue are possible. According to the bipolar view, if someone is feeling energetic
and vigorous, they by definition cannot feel tired and fatigued at the same time. However, recent
evidence, including neurological studies, have shown that subjective energy and subjective fatigue
are based on two different brain networks, have different antecedents and behavioral consequences
and can therefore be experienced at the same time (Mäkikangas et al., 2014).
In the following, we present all the relevant terms related to subjective energy, their
underlying theories as well as their operationalization. Within the sections, we reflect on the place
ICTs can have within these theories. Finally, we classify the concepts based on their duration,
range, cause and conceptualization. We start, in Section 3.1., with emotional exhaustion and vigor,
which come from the organizational sciences and are exclusively concerned with employees’
energy. In Section 3.2., we present depletion and vitality, two prominent terms in personality and
social psychology. In section 3.3., we discuss subjective energy and fatigue from the viewpoint of
affect theories. The terms from these two sections apply to the general population. In Section 3.4.,
we conclude by introducing the new concepts of SNS exhaustion and SNS fatigue which is
restricted to SNS users. An overview of the classification is presented in Table 1.
4
Table 1. Summary and classification of subjective energy constructs
Construct
Terms used for:
Main definition Main measures Duration Range/Scope Specific cause Field/Context
Subjective
energy
Subjective
fatigue
Emotional
Exhaustion
Rarely,
when
reverse
scored
Yes Feeling exhausted
and drained by
work
Emotional
exhaustion
subscale of the
MBI
Long lasting,
chronic
Narrow to broad: work
strain is also part of
the experience
Work,
work demands
Organizational
science
Occupational
psychology
____________
Applied to
employees
Vigor Yes No
Feeling high levels
of energy and
mental resilience
while working
Vigor subscale of
the UWES Long lasting
Broad: resilience and
willingness to invest
energy are also part of
the experience
Work resources
Depletion No Yes
Temporary
reduction in the
available energy
for self-control
Dual-task
paradigm.
Performance
measure on self-
depleting tasks
Temporary,
fluctuates
daily
Broad: inability and
unwillingness to invest
energy are also part of
the experience
Prior voluntary and
self-controlling
activities
Personality
psychology
Social psychology
____________
Applied to the
general population
Vitality Yes No
Experience of
possessing energy
and aliveness that
comes from the
self
Subjective Vitality
Scale Long lasting
Broad: optimism,
feeling alive, awake
and alert are also part
of the experience
Satisfaction of basic
psychological needs,
especially the need for
self-determination
(autonomy)
Vitality Yes Yes
Feeling energetic,
vigorous, vital, full
of pep
Vitality-fatigue
subscale of SF-36
1 month Narrow, only feeling
of energy or fatigue No specific cause Psychophysiology
Cognitive and
Affective science
____________
Applied to the
general population
Vigor, energy Yes No
Vigor-energy
subscale of POMS
1 week Narrow, only feeling
of energy No specific cause
General a
activation,
energy Yes
No
General activation
subscale of
AD ACL
Momentary
feeling Narrow, only feeling
of energy No specific cause
Continues on next page
5
Construct
Term used for:
Main definition Main measures Duration Range/Scope Specific cause Field/Context
Subjective
energy
Subjective
fatigue
Fatigue
Yes Yes
Feeling tired, worn
out
Vitality-fatigue
subscale of SF-36
1 month
Narrow, only feeling
of energy and fatigue
No specific cause
Psychophysiology
Cognitive and
Affective science
____________
Applied to the
general population
No Yes Feeling fatigued,
exhausted Fatigue subscale
of POMS 1 week Narrow, only feeling
of fatigue, exhaustion No specific cause
Deactivation No Yes Feeling tired,
sleepy
Deactivation
subscale of
AD ACL
Momentary
feeling Narrow, only feeling
of tiredness, sleepiness No specific cause
SNS
Exhaustion No Yes Feeling exhausted
and drained by
SNS
SNS exhaustion
(adopted from the
MBI)
Long lasting Narrow to broad:
strain is also part of
the experience SNS use
Human Computer
Interaction Science
Information
Systems
____________
Applied to SNS
users
SNS Fatigue No Yes
Feeling exhausted
and drained by
SNS.
Feeling bored
disinterested and
indifferent
Adopted Mental
Fatigue scales.
Various self-
constructed scales
Long lasting
Broad: indifference,
boredom and
disinterest are also
part of the experience
SNS use
6
2.1. Work-related Emotional Exhaustion and Vigor
The most researched concepts related to subjective energy in the HCI and IS communities
are work-related emotional exhaustion (n=17) and work-related vigor (n=9). The two concepts are
primarily used in the fields of organizational sciences as well as education and are the focus of
almost half (49%) of the studies we identified. There is a great agreement in these two disciplines
about the definition of the two constructs, including their underlying theories. Consequently, the
studies presented hereafter use the definitions, measures and theories relatively consistently.
Emotional exhaustion
Emotional exhaustion is defined as a feeling of chronic fatigue and being drained of
emotional energy by one’s work (Maslach & Jackson, 1981; Maslach, Schaufeli, & Leiter, 2001;
Moore, 2000; Schaufeli, Leiter, Maslach, & Jackson, 1996). It is considered to be the most
important and energetic aspect of work-related, chronic ill-being (Maslach, Schaufeli, & Leiter,
2001). Together with cynicism and inefficacy, it is a core symptom of job burnout. It is typically
measured with a subscale from the Maslach Burnout Inventories (MBI), which contain items such
as “I feel emotionally drained from my work” or “I feel fatigued when I get up in the morning and
have to face another day on the job”. Five different versions of the MBI tailor the measurements to
different target populations, including workers in human services, medical personnel, educators,
the general worker’s population or students.
Emotional exhaustion is a key construct for the two most prominent work related stress
theories: the Job Demands-Resource Model (J-DR) and the Person-Environment (P-E) fit model
(Ayyagari et al., 2011; Bakker & Demerouti, 2007; Demerouti, Bakker, Nachreiner, & Schaufeli,
2001; Edwards, 1991; French, Rodgers, & Cobb, 1974; Maslach et al., 2001). Both theories
consider emotional exhaustion to be a consequence of work stressors. According to the JD-R,
prolonged work demands cause exhaustion when the work resources are low (Bakker & Demerouti,
2007). Work demands are those “aspects of a job that require sustained physical or mental effort
and are therefore associated with physiological or psychological costs” (Demerouti et al., 2001,
p.501). Workload and time pressure are common job demand examples. Job resources, on the other
hand, are those aspects of a job that reduce the job demands, stimulate personal growth, or facilitate
achieving work goals (Demerouti et al., 2001). Social support, autonomy and feedback have been
established as prime examples of job resources.
Technology can be both a demand and a resource or it can indirectly increase or decrease
the demands and the resources (Sardeshmukh, Sharma, & Golden, 2012). For example, too many
e-mails can increase the information load or frequent updates can require permanent learning effort
(Ayyagari et al., 2011). However, technology can also decrease the work demands or increase the
resources, for example when it enables an employee to finish a task while at home or during
commuting (Chen & Karahanna, 2018). Technology is therefore hypothesized as both: a cause of
as well as a buffer against emotional exhaustion.
Unlike the J-DR, the P-E Fit Model does not weigh causes and buffers of emotional
exhaustion, but rather emphasizes the fit between environmental demands (stressors) on one side
and the needs or expectations of the person on the other (Ayyagari et al., 2011; Edwards, 1991;
Maslach et al., 2001). The P-E Fit Model postulates that it is a misfit between a person’s individual
7
needs and their environment that causes emotional exhaustion. In this respect, the same number of
incoming e-mails can be perceived as e-mail overload that cause emotional exhaustion to some
employees, but not to others (Reinke & Chamorro-Premuzic, 2014).
Emotional exhaustion is a long lasting, chronic feeling of low energy (duration). People
are unable to recover and re-energize in their free time, thus permanently experiencing exhaustion.
The construct is relatively narrow (range), but it does embrace related experiences like stress,
because the MBI scales include the item “Working all day is a strain for me”. Emotional exhaustion
has a specific cause in the work of the employee. The demands, job characteristics, or the act of
working itself are the direct causes of the feeling of exhaustion and hence the cause is part of the
experience (e.g. “I feel emotionally drained from my work”). Originally, emotional exhaustion was
conceptualized as low vigor, i.e., as the low end on one bipolar energy scale, however the authors
now accept that vigor is a separate energy dimension (Maslach, Jackson, & Leiter, 1996; Maslach,
Jackson, Leiter, Schaufeli, & Schwab, 1986; Maslach et al., 2001).
Vigor at Work and School
Vigor is defined as “high levels of energy and mental resilience while working, willingness
to invest effort in work, and persistence in the face of difficulties” (Schaufeli, Salanova, González-
Romá, & Bakker, 2002, p. 74). It is the ability not to be easily fatigued at work (Llorens, Schaufeli,
Bakker, & Salanova, 2007). Similarly to emotional exhaustion, vigor is defined as a core and
energetic dimension of work-related well-being (Bakker et al., 2008). Work-related vigor is a broad
concept (range), which embraces a motivational side mirrored in the mental resilience of
employees and their sustained willingness to invest effort. Together with dedication and absorption,
it is one core aspect of the broader mental state of work engagement. The most used instrument to
measure work-related vigor is the vigor subscale from the Utrecht Work Engagement Scale
(UWES) (Schaufeli, Bakker, & Salanova, 2006). Example items are “At my work, I feel bursting
with energy”, “At my job, I am very resilient, mentally”, and “When I get up in the morning, I feel
like going to work”. Unsurprisingly, the focus on work-related vigor” is part of the ‘positive
psychology movement’ (Seligman & Csikszentmihalyi, 2014).
Vigor is a relatively long-lasting feeling of high energy levels (duration). Vigorous people
feel energized during the workday and are re-energized by the thought of work in the morning.
However, vigor is not specifically caused by work but it is rather the felt energy at work and while
working. According to the JD-R model, it is the job resources that lead to work-related vigor
especially when the demands are also high, resembling a state of “flow” which requires optimal
challenge (M. Csikszentmihalyi, 1991; Mihaly Csikszentmihalyi, Abuhamdeh, & Nakamura,
2014). This motivational process that leads to high energy while working is conceptualized as a
separate to the energy draining one that causes emotional exhaustion (Bakker et al., 2008;
Mäkikangas et al., 2014; Shirom, 2011).
Any technology that can serve as a job resource can potentially energize and invigorate, for
instance technology that supports autonomy and personal growth through learning or supportive
technology that facilitates achieving work goals (Llorens et al., 2007; Sardeshmukh et al., 2012;
van Zoonen & Rice, 2017).
8
2.2. Depletion and Vitality: Energy as a Fuel for Self-regulation and a Result
of Self-determination
The next most prominent terms related to subjective energy are depletion (n=6) and vitality
(n=4). Together they represent 19% of the considered studies. Both terms come from personality
and social psychology and are theories about self-control or self-regulation. Depletion is rooted in
the Strength Model of Self-Control (Baumeister, Vohs, & Tice, 2007). Vitality in contrasts stems
from Self-determination Theory, which expands the Strength Model of Self-Control and shows
why and when self-regulation is not depleting (Deci & Ryan, 2008; Ryan & Deci, 2000). Both
theories are well established and so a rather consistent use of their definitions and methods is
evident across the considered studies that focused on depletion and vitality.
Depletion
Depletion, also termed ego-depletion, is a temporary lack of capacity for volitional action
and self-control caused by previous exertions of self-control (Baumeister et al., 2007; Muraven &
Baumeister, 2000; Muraven et al., 1998). Depletion demonstrates itself in feelings of low energy
and unwillingness to engage in further self-control. It is often accompanied with self-regulatory
failures ranging from inability to regulate emotion, impulses, cravings and temptations to inability
to control attention (Baumeister et al., 2007; Hagger, Wood, Stiff, & Chatzisarantis, 2010). Thus,
depletion is broad in range in that it is not only a feeling of low energy, but also encompasses lack
of motivation mirrored in the unwillingness to engage in many different tasks that require effort
and self-control. Unlike emotional exhaustion or work-related vigor, it is not a lasting state but it
is a relatively shorter reaction (duration).
Typically, depletion is measured with performance on tasks that are known to require self-
control (Baumeister et al., 2007; Hagger et al., 2010). Typical examples are persistence on
unsolvable anagrams, snack choice when presented with healthy vs. unhealthy options or
performance on attention and cognitive-control tasks such as the Stroop task, which is a famous
color-naming task that requires inhibition of the automatic response to read the text rather than
name the color. Prior to the depleting task, participants are required to do an initial task that varies
in its degree of required self-regulation. Drop in performance on the second task is then interpreted
as evidence for energy depletion in the first one.
According to the Strength Model of Self-Control, people possess “psychic energy” in order
to self-regulate. This resource is limited and can be drained by any deliberate control of cognition,
emotion or behavior. Once depleted, people are no longer able or willing to exert self-control until
rested or restored (for instance by sugar intake). Energy reservoirs are considered to be domain-
independent (Baumeister et al., 2007), which means that prolonged self-regulation in any form will
have hindering effects on any subsequent self-control tasks, even if these are unrelated. For
example, prolonged cognitive effort will drain the energy for subsequent emotional impulse-control
or decision making. Resisting unhealthy temptations will drain the energy for subsequent focused
attention. The depleted energy can be restored quite easily however, by rest or engaging in activities
that do not require self-regulation (Hagger et al., 2010). Self-control can also be trained so that
frequent self-control in any domain will lead to slower depleting effects overall (Muraven &
Baumeister, 2000). The cause for depletion is not as specific as job demands, however it is still
specific enough to exclude tasks that do not require any effort.
9
Seen from this lens, any technology that requires self-restraint can deplete its users of their
energy. For instance trying to abstain from mobile phone or social network use can be depleting
(Du, van Koningsbruggen, & Kerkhof, 2018). On the contrary, if technology is created to decrease
mental effort, reduce the need for self-regulation or even train self-control it can also nourish this
valuable energy resource (Cranwell et al., 2014; Engin & Vetschera, 2017).
Vitality
The term “vitality” stems from Self-Determination Theory and is defined as a feeling of
aliveness and vigor, a state of experiencing calm energy (Ryan & Deci, 2008). It is a eudemonic
well-being related to self-realization and growth. This is in contrast to hedonic well-being, which
is happiness that arises from immediate pleasures (Ryan & Deci, 2001; Ryan, Huta, & Deci, 2008).
Vitality is felt as coming from within the self and is not related to any immediate external cause. It
is often measured with Ryan and Frederick’s seven-item subjective vitality scale (SVS), with items
like “I feel alive and vital”, " “I look forward to each new day” or "I don't feel very energetic”
(Ryan & Frederick, 1997).
Self-Determination Theory (SDT) describes vitality as the optimal end of a favorable
interaction between people and their environment. The theory postulates three basic human
psychological needs: autonomy, efficacy and relatedness (Deci & Ryan, 2000, 2011; Ryan & Deci,
2000). When these needs are thwarted, the person cannot grow and achieve vitality. In contrast,
when these needs are continuously satisfied, people are energized and experience vitality. Thus,
autonomous (self-determined) activities, as well as activities that develop competences and
belonging can all enhance vitality even if they imply invested effort. This is in direct contrast to
the Strength Model of Self-Control, which postulates that self-regulating activities deplete people
of energy. This is because autonomous activities and activities that promote competence and
belongingness are intrinsically motivating and energizing. In sum, SDT is a theory that postulates
the process of creating vitality as a function of (not always conscious) need satisfaction throughout
life.
In our classification, vitality is a long lasting state of energy and “aliveness” (duration) that
is felt at the present moment and that gives optimism to the future ("I look forward to each new
day"). Thus, vitality’s range is broad in that it encompasses optimism, but also aliveness,
awakeness and alertness. According to SDT, technologies that support the autonomy, competence
and belongingness of their users can increase their vitality (James, Wallace, & Deane, 2019), those
that thwart those needs deplete it (Akın, 2012; Jang, Bucy, & Cho, 2018; Satici & Uysal, 2015).
2.3. Subjective Energy and Fatigue as Positive and Negative Affect
At the core, the experience of energy or fatigue is an affective state, i.e. an affect. In
psychology, an affective state is the subjective experience of a feeling. Affective states are
commonly divided into emotions and moods. Emotions are usually shorter, more intense affective
states with a known object, for example being angry with someone (the object of anger). Moods
on the other hand are generalized affective states that are typically longer, fluctuating, and less
intense. They are usually not related to specific objects, for example feeling blue (O'Connor, 2006;
Shirom, 2011; Thayer, 1990). Subjective energy and fatigue are more often described as moods
because feeling energetic or tired does not necessarily need a specific object and is experienced for
longer time frames than typical emotions. From the concepts that we described so far, vigor can be
10
described as broader mental state that encompasses the energetic mood whereas emotional
exhaustion is the chronic experience of the fatigue mood (O'Connor, 2006).
Theoretically, the nature and structure of affective states have been disputed since the birth
of psychology. Especially contested are the number and the labeling of the core dimensions of
affect, i.e., what constitutes the fundamental underlying characteristic of affective experience
(Kuppens, Tuerlinckx, Russell, & Barrett, 2013). In the different theories of affect, the energy and
fatigue have different prominence, from being a core underlying dimension to being just part of a
broader positive or negative affect. They also appear under very different names, such as activation,
arousal 2, vigor, fatigue, energy, tiredness, inertia, vitality, depletion, etc. In spite of these
differences, the measures used to assess the feelings of energy and fatigue are remarkably similar.
If we combine all the studies that have used simple affect scales to measure subjective
energy or fatigue they make a little more than one fifth (22%, n=12) of all the considered studies,
with half of them considering both positive and negative affect. In the following, we present the
most common measures of subjective energy and fatigue and the theories of affect that have
included them. Subjective energy as positive affect
Subjective Fatigue as Positive Affect
Vigor, energy, activity, activation, arousal and vitality are amongst the many terms used to
describe the experience of subjective energy as affective state. Subjective energy as affect can
therefore be defined as the subjective feeling of being vigorous, energetic, active, and vital.
Subjective energy as affect is thus very narrow in range with a focus only on the affective
experience of energy and excluding other mental states or motivation, and without any focus on a
specific cause of the experience. The most common way to measure it is with adjective scales that
describe the exact feelings studied, such as to feel vigorous, energetic, active, wakeful, alert, lively,
full of pep, etc. The three most widely used instruments across disciplines to measure subjective
energy are the vitality-fatigue subscale of the MOS 36- Item Short-Form Health Survey (SF-36;
Ware & Sherbourne, 1992); the vigor-energy subscale of the Profile of Mood States (POMS;
McNair, Lorr, & Droppleman, 1971; Shacham, 1983) and the general activation subscale of the
Activation Deactivation Adjective Checklist (AD ACL; Thayer, 1986). The vitality-fatigue scale
of the SF-36 is a bipolar scale that measures subjective energy and subjective fatigue
simultaneously (bipolar conceptualization), whereas the POMS and AD ACL are bivariate scales
that have additional, separate fatigue scales (bivariate conceptualization). The adjectives among all
scales overlap greatly; in fact, the POMS and the AD ACL are almost identical. The only notable
differences between the scales are their time frames and their answering formats: Whereas the SF-
36 inquires how one felt in the past month and the POMS in the past week, the AD ACL inquires
how one feels right now. Depending on measure, the duration of the subjective energy can
consequently range from momentary feeling (if assessed with the AD ACL) to a month-long feeling
(if assessed with the SF-36).
There is no clear place for technology within the affect theories, since the theories
themselves do not postulate the specific causes for affect (other than some physiological or
neurological correlates). However, technologies can induce mood either by design (Herrero,
Garcia-Palacios, Castilla, Molinari, & Botella, 2014; Serrano, Baños, & Botella, 2016) or as a
2 Arousal and activation have mostly the same meaning within psychophysiology, but modern psychologists prefer
the term activation. Neuropsychological literature, however, sometimes distinguishes between the two.
11
byproduct and thus the cited measurement instruments have been utilized for the study of the
relationship between technology and subjective energy (Botella, Baños, Etchemendy, García-
Palacios, & Alcañiz, 2016; Du et al., 2018; Huang, Wong, Yang, Chiu, & Teng, 2017; Kelley &
Gruber, 2010; J. E. Lee, Xiang, & Gao, 2017).
Subjective Fatigue as Negative Affect
Subjective fatigue as affect can be defined as the subjective feeling of being fatigued, tired,
worn-out, exhausted and weary. It is often seen as a symptom of ill-being. The variety of terms
used to describe fatigue is much smaller than that for energy with a clear preference for the term
“fatigue”. Conceptually, subjective fatigue is the negative opposite of subjective energy, just as
narrow in its conceptual range, measured with precise adjectives and rather short-termed in
duration especially when compared to emotional exhaustion. Subjective fatigue is often studied
separately from subjective energy and has its own subscales within the measurement instruments
mentioned above. Most often it is measured with the vitality-fatigue scale of the SF-36 (e.g. items
“tired”, “worn-out”) and the fatigue-inertia scale of the POMS (e.g. items “fatigued”, “exhausted”).
2.4. Social Media Exhaustion and Fatigue
The last energy related concepts that we introduce are “SNS exhaustion” and “SNS
fatigue”. They are new energy related phenomena introduced by HCI and IS researchers due to the
establishment of SNS as main portals to the Internet in recent years. We consider SNS exhaustion
and fatigue specifically because they represent 21% of the identified studies (n=11). Next to SNS
exhaustion and fatigue, constructs such as cellphone messenger fatigue, privacy fatigue, general
digital fatigue as well as user fatigue have also been introduced. However, these aspects of
technology fatigue are still not very well established, which is why we do not discuss them here.
Social Media Exhaustion
SNS exhaustion is defined as an “aversive, potentially harmful, and unconscious
psychological reaction” caused by SNS-use (Maier, Laumer, Eckhardt, & Weitzel, 2015) or
excessive SNS use (Cao, Masood, Luqman, & Ali, 2018). It is mostly a user reaction caused by
perceived social overload (Cao & Sun, 2018; Maier et al., 2015). SNS exhaustion demonstrates
itself in feelings of tiredness from SNS use (Luqman, Cao, Ali, Masood, & Yu, 2017). It is often
measured with a 4-item scale created by Maier and colleagues (Maier et al., 2015). The scale is an
adaptation of the Maslach Burnout Inventory (MBI) where “work” and “my job” are replaced by
“SNS activities” or “Facebook”, but the item “I feel fatigued when I get up in the morning and
have to face another day on the job” was omitted from the new scale. Example items of the SNS
exhaustion scale are “I feel drained from activities that require me to use SNS” or “I feel tired from
my SNS activities”. The instruments sometimes specify the concrete social network (e.g. “I feel
tired from my Facebook activities”) or the device (“I feel tired from my mobile SNS activities”).
SNS exhaustion is based on the work by Ayyagari et al. (2011) and is explained with the
PE-fit model as well as job burnout theory. Ayyagari et al. (2011) investigated how new
technologies and their features can cause “technostress” and were the first to explicitly specify
ICTs as cause of emotional exhaustion with work. Even though they stayed within the borders of
job burnout theory, this work inspired other HCI and IS researchers to investigate the role of ICTs
12
beyond work-related exhaustion. The basic premise is that some technology features, such as being
always reachable and non-anonymous, create stress and lead to emotional exhaustion.
Based on its operationalization, we would argue that social media exhaustion is a longer
lasting state (duration). However, the chronic aspect evident in work-related emotional exhaustion
is not apparent in SNS exhaustion. Neither the definition nor the instruments (because of the
omitted item) imply that one cannot recover from SNS exhaustion or that the permanence of these
feelings of tiredness persist throughout the day and reoccur in the morning. The range of the term
is narrower similarly to emotional exhaustion. Finally, SNS exhaustion has a very specific cause
that is the prolonged or excessive use of SNS. Unlike job demands and work stressors that can vary
greatly, social media exhaustion has only this one proposed stressor. Thus, SNS exhaustion seems
more specific and less chronic than work-related exhaustion.
Social Media Fatigue
SNS fatigue has been defined as the subjective feeling of tiredness caused by SNS stress,
overwhelming amounts of information, and too many SNS friends (Bright, Kleiser, & Grau, 2015;
Lee, Son, & Kim, 2016). It is also seen as a tendency for SNS users to “back away” from social
media (Bright et al., 2015). In this sense it is similar to SNS exhaustion. However, unlike SNS
exhaustion, it is also characterized by feelings of boredom, indifference, and lower interest, as well
as inability to relax and focus (Zhang, Zhao, Lu, & Yang, 2016). SNS fatigue has been measured
with adopted subjective fatigue items such as (“After a session of using SNSs, I feel
really…“fatigued”, “tired”, “worn out”). However, other self-constructed items include the most
diverse statements and feelings like “After a session of using SNSs, I feel really bored”, “I find it
difficult to relax after continually using SNSs” or “After using SNSs, it takes effort to concentrate
in my spare time”. In this sense SNS fatigue is a very broad (range) negative emotional reaction to
SNS use. It is also has a shorter duration than SNS exhaustion, mostly describing the immediate
feeling post SNS use. Because of the specific focus on negative affect, there is no place for a
univariate vs. bivariate distinction within the concepts of both SNS exhaustion and SNS fatigue.
Being energized by SNS or having energy because of SNS use is not discussed as a potential
process.
3. METHOD
For the purpose of our study we performed a systematic literature research on the
relationship between digital technology and subjective energy in the top-ranked journals in the
fields of Human-Computer Interaction (including Human Factors and Ergonomics) and
Information Systems (including Information System Management and Management Information
Systems), as well as the leading journals in Applied Psychology, Organizational Behavior and
Consumer Research. In the first step of the analysis we identified the 30 leading journals in these
fields (top 25 journals in the HCI and IS domain; top 5 in the external domains) using the Scimago
Journal and Country Rank database which ranks the journals based on the SJR2 indicator
(Guerrero-Bote & Moya-Anegón, 2012).
In the second step we searched these journals using the search terms “subjective energy”,
“mental energy”, “psychological energy”, “vitality”, “vigor”, “vigour”, “positive arousal”,
“fatigue”, “exhaustion” OR “depletion”. For the journals outside the HCI and IS domains we
additionally restricted the search (with the AND operator) to include “media”, “phone”, “laptop”,
13
“computer”, “social network”, “digital”, “ICT*”, OR “information technology”. We used the Web
of science, Ebsco, ProQuest, and Science Direct databases to perform the search within the
journals.
After excluding for duplicates, the search resulted in a total of 506 studies which were then
scanned for relevance. The inclusion criteria required:
1. The study is an original empirical research
2. At least one measure of subjective energy is included in the study
3. At least an indirect relationship between the energy measure(s)and technology use is
assumed and tested
Figure 1. Overview of the selection process for the review
Thus, the review excluded theoretical papers and papers that measured physiological proxies of
subjective energy. The screening resulted in 53 studies that are discussed in this paper. The
overview of the selection process is presented in Figure 1. All reviewed studies are presented and
summarized in Taböe 2 of the Appendix.
4. RESULTS FROM THE LITERATURE REVIEW: THE EFFECTS OF
TECHNOLOGY USE ON SUBJECTIVE ENERGY AND FATIGUE
In the following, we report the main empirical findings from our literature review on the
relationship between ICT use and the different energy constructs. All reviewed studies, their
methods, samples and main findings are presented in Table 2 in the Appendix. In the narrative
review that follows, we follow the order of Section 2 and present the results for each term
separately, focusing on the main patterns that arise from the literature.
Entries retrieved through
database and journal
searches
N = 9.413
Entries excluded because
they were duplicates
N = 8907
Entries remaining after
excluding for duplicates
N = 506
Entries excluded based on
exclusion criteria
N = 453
Articles included
in review
N = 53
14
Even though a meta-analysis of the results was out of scope of this review (not least because
of the variation in definitions and assessment of subjective energy among the studies), we also
present the effect sizes of these relationships, where possible. We derived the effect sizes either
from the standardized beta coefficients from regression and structural equation models, the partial
eta squares from analysis of variance, and Cohen d values from t-tests. Where possible, we
calculated the effects sizes from reported mean differences and standard deviations. Where no
effect sizes were reported and no derivation was possible or applicable, we annotated it with not
reported/not applicable (NR/NA; Table 2, Appendix). In the narrative review that follows, a slight
increase/decrease refers to a small effect size in the relationship (e.g. ß < .3; Cohen d < .2), a large,
substantial increase/decrease refers to a large effect size (e.g. ß >.5; Cohen d < .8).
4.1. The Effects of Technology Use on Work-related Emotional Exhaustion
and Vigor
Studies on the effects of technology use on emotional exhaustion bear an overwhelming
support that technology use is related to employees’ exhaustion. All seventeen studies that
examined this relationship found an association, at least among some groups of employees.
The first and main cause for emotional exhaustion is the ubiquity of ICT reach and the
interruptions that come with it. Technology makes the employees available for interruptions
anywhere and at any time during office hours (Ayyagari et al., 2011), but also at home and after
work hours (Gaudioso et al., 2017; Piszczek, 2017; Ragsdale & Hoover, 2016; Xie et al., 2018).
As a result, the employees seem to have no private sphere left that allows them to unwind or recover
from the workday. Chen and Karahanna (2018) show for example that these ubiquitous
interruptions leave the employees emotionally exhausted and drained not only by their work, but
also by the demands of their personal life.
The interruptions are not only strain per se, but also cause personal and work-life conflicts,
which are one of the strongest predictors of emotional exhaustion. Whereas the main, direct effect
of work-related ICT use after work hours on emotional exhaustion is usually non-significant
(Piszczek, 2017) or small (ß =.20 to ß =.27; Ragsdale & Hoover, 2016; Xie et al., 2018), the effect
it has indirectly through work-life conflict is substantial (all effects over ß =.44). This phenomenon
has therefore also been termed “tech-invasion” (Gaudioso et al., 2017). The results can be
interpreted as an organizational policy problem rather than a technology problem because different
organizations have different expectations about how available their employees should be in their
free time (Piszczek, 2017). The fact remains however, that due to its ubiquity, ICTs blur the work-
life boundary and demand a lot of conscious and sometimes equally tiring effort from employees
to control this boundary.
A second cause for emotional exhaustion caused by ICT ubiquity and frequent interruptions
is the creation of information or interruption overload (Ayyagari et al., 2011). This effect of
overload on emotional exhaustion is on the border between small and medium (ß =.26 to ß =.33)
and is usually caused by what attention-scholars call “task-switching” (Ayyagari et al., 2011; Chen
& Karahanna, 2018). Task-switching due to interruptions is especially aggravating after work hours
where the constant engaging and disengaging from work can account for as much as 23% of the
variance in emotional exhaustion (Chen & Karahanna, 2018).
15
Work-related technology use after work hours is not the only cause of emotional
exhaustion. Using the internet at work for private purposes, i.e., cyberloafing, has also been related
to emotional exhaustion (ß =.28 to ß =.47; Aghaz & Sheikh, 2016). Rhee and Kim (2016) have
shown that even taking work breaks on a cellphone causes emotional exhaustion in comparison to
conventional breaks. Specifically, messaging and surfing on a phone during the breaks cause
employees to feel more fatigued directly after the break and more emotionally exhausted at the end
of the workday when compared to employees who have lunch, talk to colleagues, or take a walk
during their breaks.
The introduction of new organizational systems and the learning and adaptation this
requires has also been found to wear down (Bala & Bhagwatwar, 2018; Gaudioso et al., 2017). The
technological pace of change, which creates overload and role ambiguity at work, is an additional,
although weaker factor (Ayyagari et al., 2011). Employees are often forced by technology to work
faster, which drains their energy (Ayyagari et al., 2011; Gaudioso et al., 2017). This is especially
true for employees whose personal values are not compatible with the introduction of a particular
information systems (Hennington et al., 2011) or for employees low on self-efficacy (Salanova et
al., 2000). The results imply that mandatory introduction of technology as well as inadequate tech-
training can contribute to employees’ emotional exhaustion.
The negative effect of technology use on employees’ subjective energy at work and at home
is however dependent on the individual. For example, Reinke and Chamorro-Premuzic (2014) have
shown that the relationship between perceived e-mail overload and emotional exhaustion has more
to do with the employee’s personality than the actual number of received e-mails. Some employees
have better coping strategies with stress caused by technology (Gaudioso et al., 2017), others prefer
to integrate their work into their personal life (Xie, Ma, Zhou, & Tang, 2018). Such employees
suffer no emotional exhaustion consequences from work-related ICT use after hours. Similarly,
employees high on “cellphone attachment”, i.e., those who are voluntarily non-stop on their phones
and who respond immediately to notifications demonstrate surprisingly little emotional exhaustion
after work-related cellphone use during non-work hours (Ragsdale & Hoover, 2016). This seems
to contradict the findings by van Zoonen and Rice (2017) who have shown that it is low-
responsiveness that buffers against emotional exhaustion. Judging by the correlation paths and
mean values in all of the considered studies, we can conclude that ICTs are much more strongly
related to maladaptive than adaptive coping strategies and that on average the employees want to
separate their work and personal lives. They furthermore want to feel autonomous and control when
to respond to messages, i.e., feel in control of the work-life boundary (Xie et al., 2018). This means
that it is only a very small population of employees who prefers and can cope with the “techno-
overload” and “techno-invasion” caused by ICT ubiquity.
The only domain where the majority of studies do not show overwhelming support for a
negative effect of technology use on employees is the domain of telework. Namely, when
employees are allowed to work remotely they can show slightly reduced (n.s. to ß = .28) emotional
exhaustion (Sardeshmukh et al., 2012; Windeler et al., 2017). Telework can reduce exhaustion
because it reduces the interpersonal interactions and role conflicts that are common in the office,
as well as the time pressure. Telework also slightly increases employees autonomy, which is one
of the strongest buffers (ß = -.34 to ß = -.44) against emotional exhaustion. Being able to get “task
closure”, i.e., finish some tasks from home gives some employees a positive feeling and can also
slightly decrease exhaustion (Chen & Karahanna, 2018). However exhaustion caused by emotional
involvement with clients is not erased by telework (Ishii & Markman, 2016), and can sometimes
16
even be amplified because of it, potentially because telework also decreases feedback and social
support from colleagues and it increases the role ambiguity (Sardeshmukh et al., 2012).
Finally, not all technologies are equal in the extent to which they cause emotional
exhaustion. For example, answering e-mails after office hours is not as detrimental as using the
phone and messaging (Chen & Karahanna, 2018), while SNS use has an overall negative effect
(van Zoonen & Rice, 2017). For students, different types of internet use also have different effects:
whereas SNS use, blogging and social gaming slightly increase exhaustion, action gaming slightly
decreases it (Hietajärvi et al., 2019).
The studies that focused on the effects of technology use on the positive energy dimension
at work and at school, i.e., work- and school-related vigor, do not fully support such an
overwhelmingly negative picture as the studies that focused on emotional exhaustion. Six out of
nine studies found a negative association but just as many found that there are types of employees
and contexts that can induce a positive association between technology use and vigor.
First, it is interesting that some of the processes that were shown to exhaust employees were
also found to invigorate. For example, using cellphones during work breaks can increase work
vigor in the same way that conventional breaks do (Rhee & Kim, 2016). Specifically, using a
cellphone increases short-term energy affect after the break and work vigor at the end of the
workday. This might explain why employees use cellphones during work breaks, even though these
types of breaks are also regularly associated with fatigue and exhaustion. Note again here that
exhaustion and vigor are bivariate constructs that can co-exist.
Furthermore, technology use after work hours can invigorate those employees who
voluntarily engage in work-related activities and are not expected to respond immediately after
receiving notifications (Llorens et al., 2007; van Zoonen & Rice, 2017), or those employees who
want to respond immediately (Ragsdale & Hoover, 2016). The results imply that when employees
are in control of the interruptions and their tasks, that is, when autonomy is given, technology can
increase vigor. This is confirmed with studies on teleworkers, which show that the increased
autonomy is the biggest factor through which telework increases vigor at work (Sardeshmukh et
al., 2012). Yet, vigor can also slightly suffer from the decreased feedback and colleges’ support for
teleworkers (Sardeshmukh et al., 2012), as well as from technical failures for tele-lecturers (Chen,
2017).
The studies on the relationship between technology use and vigor amongst students are
equally mixed. Technology use (including social media and gaming) has been moderately
positively related to vigor at school in some samples (Rashid & Asghar, 2016) but slightly
negatively or neutrally in others (Hietajärvi et al., 2019). Using ICTs to gain and share knowledge
is slightly positively (ß = .18 to ß = .28) related to school vigor at all ages (Hietajärvi et al., 2019)
4.2. The Effects of Technology Use on Depletion and Vitality
The major pattern in the studies that focused on depletion was to focus on specific kinds of
online activities or different types of display presentation. For example, customization of online
products (Kang & Shyam Sundar, 2013), playing difficult video games (Engelhardt et al., 2015),
or learning from Wikipedia articles while texting and watching TV (Kononova et al., 2018) have
all been found to deplete self-control on a subsequent task, although to different degrees. Similarly,
17
reading display presentations that do not match one’s cognitive style (Engin & Vetschera, 2017),
or reading e-commerce result pages with a high number of listed results per page (Ahn et al., 2018)
have been shown to deplete cognitive resources. Online shopping while multitasking on the other
hand, seems to deplete less than reading Wikipedia articles does (Kononova et al., 2018). These
insights can be used to create less depleting experiences, for example by matching the presentation
style of the decision maker in graphic displays or lowering the number of search results per page
in e-commerce search results.
This mitigation of depletion effects through better system design can be complemented by
using technology to strengthen self-control. Cranwell and colleagues (2014) investigated how this
could be done. They tested a smartphone application designed to train self-control using a modified
Stroop-task. After four weeks, participants who trained self-control with the app performed better
than a control group on self-control tests.
When it comes to subjective vitality on the other hand, studies found that it is slightly
negatively associated (ß = -.13) with problematic Internet use in general (Akın, 2012) and Facebook
use specifically (ß = -.24; Satici & Uysal, 2015). Problematic use, also termed “compulsive use”,
“excessive use” or “Internet (Facebook) addiction”, is evident when users spend a lot of time
thinking about the Internet or Facebook, when they use it to feel better, when they feel an urge to
use it more often and when they suffer if they are not using it (Aboujaoude, 2010; Andreassen,
2015; Young, 1996). In all conceptualizations of problematic use there is also an aspect of self-
regulation failure, termed “relapse”, “diminished impulse control” or “cognitive-behavioral
control”. As was shown, the strength model of self-control postulates that self-control costs energy,
whereas self-determination theory shows that vitality is an energy construct deeply related to a
person’s autonomy. When users are addicted, they are failing to cut down Internet or Facebook use
and this personal lack of autonomy prevents them from experiencing vitality. Unfortunately, there
is an increasing number of users who fall into this problematic use category, especially among
adolescents. Some numbers suggest that depending on country, up to 38% of adolescents have a
problematic relationship with the Internet, social media or their mobile phones (Durkee et al., 2012;
Pedrero-Pérez, Rodríguez-Monje, & Ruiz Sánchez de León, 2012; Vigna-Taglianti et al., 2017).
A study that focused on the importance of fit between the user and the specific technology,
found that the data management and social features of fitness apps help increase the vitality of
those users who are intrinsically motivated to do sports, that is, those who enjoy exercising (James
et al., 2019). These features could help vitalize also those who are “amotivated”, that is, those who
lack motivation to exercise. For those who are extrinsically motivated and only exercise to lose
weight, such device features decreased vitality. Thus, the same device options can increase vitality
of some users and drain it from others. These results illuminate the role of motivation in how
technology effects play out on energy. No such moderating role could be found for personality
traits though, specifically self-esteem in context of Facebook use (Jang et al., 2018).
4.3. The Effects of Technology Use on the Affective States of Energy and
Fatigue
Exactly half of the twelve studies that looked into the effects of technology on affect were
interested in “positive technology”. Positive technology is explicitly developed to induce positive
affect such as vigor. Examples of positive technologies are relaxation inducing virtual reality (VR)
technology or active video games. Relaxation inducing VR is built to reduce tense energy or
18
increase calm energy (vigor), whereas active video games (also known as “exer-games” or “fitness
games”) are specifically designed to motivate users to exercise as physical activity is part of the
game. A moderate physical exercise, such as a walk, has long been shown to increase and sustain
the feeling of calm energy and decrease the level of tense energy (Thayer, 1987).
Short-term studies considering VR mood induction procedures have shown that technology
can increase momentary vigor and decrease arousal when designed to relax users with variable
success (small to very large effect sizes; Cohen’s d =.38 to d =.73; Herrero et al., 2014; Serrano et
al., 2016). However, in a long-term simulated spaceship mission for astronauts, Botella and
colleagues (2016) found no monthly changes in vigor or fatigue despite mood-inducing relaxation
VR. The authors noted that the astronauts were in exceptionally good health and spirit and this
might be the reason for the finding. Thus, the reported ability of mood-inducing VR technology to
generate desired moods, especially those that last for a longer period of time, is only in its
beginnings and results so far are mixed. Similarly mixed effects were observed for active video
games that were sometimes found to increase vigor, but sometime even decrease it (Huang et al.,
2017; Lee et al., 2017).
The other half of the studies that looked at the relationship between technology and affect
considered everyday technologies, such as cellphones and SNS. The results of these studies are
predominantly negative, even though some showed a positive effect of at least some technologies
for some groups of people. For example Myrick (2015) has shown that when people are asked to
remember their subjective energy pre and post watching online cat videos, they report slightly
higher feeling of being “energetic” and substantially lower feeling of being “depleted” after
watching the videos. Consequently, people might turn to cat videos when they feel depleted in
order to increase energy.
In a diary study, Quinones and Griffiths (2017) have shown that among users who already
have a problematic relationship with the Internet, controlling Internet use at work and at home is
harder when the work day is energy draining. These users are prone to cyberloaf a little more and
be online at home a little longer when the work demands on the day are high. This increased Internet
use then further decreased their after-use momentary energy, i.e., made them even more tired.
However, the study also showed that out-of-control Internet use can also increase feelings of
relaxation and recovery among users who do not perceive their excessive use as a problem. This is
especially true when excessive users believe they can control their use and when they do not feel
the need to self-regulate. Thus, heavy Internet use at work and at home seems particularly
detrimental for those who fight the compulsion or feel guilty about it (Reinecke, Hartmann, &
Eden, 2014).
The negative consequences of compulsive Internet use for subjective energy and fatigue, as
measured with the SF-36 scale, was further demonstrated by Kelley and Gruber (2010). Using this
month-long vitality-fatigue measure, they have shown that problematic Internet use is negatively
related to subjective energy. Similarly, repeated failure to self-control SNS use has been related to
a slight decrease in feelings of subjective energy and an increase in feelings of tiredness, as
measured with the AD ACL, directly after using the social networks (Du et al., 2018). In sum,
problematic Internet or Facebook use decreases both immediate as well as long-term feelings of
subjective energy. The question is then, where is the line between benign, controlled and
compulsive, out of control Internet use and when and why do people cross it?
19
Finally, in a study about airplane automation and pilot experience, Hancock (2007) has
convinsingly shown that the more automated the airplane, the more fatigued the pilots feel after
flying it (the study found no effect on vigor). This study further suports the hypothesis that human
autonomy in relationship with technology is esential for the feeling of subjective energy, that is,
when autonomy is taken away by technology then dealing with it fatigues in the short run and
exhausts in the long.
4.4. Social Media Features that Cause Social Media Exhaustion and Fatigue
When it comes to the newly introduced phenomena of social media exhaustion and fatigue,
the focus of the examined studies was on those features of SNS that cause users to feel exhausted
and fatigued. The results for exhaustion and fatigue were almost identical, even though as we saw,
SNS fatigue is defined as a bit shorter feeling and is broader in range than SNS exhaustion. The
results were similar across devices and SNS platforms.
In the examined studies, the experience of overload appeared as the most prominent cause
for SNS exhaustion and fatigue. Authors have distinguished between information overload and
social overload but also communication overload and system feature overload (Cao & Sun, 2018;
Gao et al., 2018; Lee et al., 2016; Lo, 2019; Luqman et al., 2017; Maier et al., 2015; Zhang et al.,
2016). Information overload refers to the feeling of having to deal with more information than one
can process, which leaves the user overwhelmed by the amount of information available on the
social networks. Social overload refers to the feeling of having to deal with excessive amount of
friends’ problems or caring about the SN friends too much and too often. Both types of overload
have been repeatedly related to social network exhaustion and fatigue, but social overload tends to
be a stronger predictor (ßinfo = .20 to ßinfo = .26; ßsocial = .24 to ßsocial =.62; Cao & Sun, 2018; Gao
et al., 2018; Lee et al., 2016; Lo, 2019; Luqman et al., 2017; Maier et al., 2015; Zhang et al., 2016).
Communication overload, which refers to receiving too many notifications from SNS, has
also been related to SNS fatigue (Lee et al., 2016) but not to SNS exhaustion (Cao & Sun, 2018).
Finally system feature overload, which refers to the social network platforms having too many
unnecessary and poorly designed (complex) features, also relates to SNS fatigue (ß = .24 to ß =-
.25; Lee et al., 2016; Zhang et al., 2016).
Information overload is associated with SN exhaustion because of the ubiquitous reach,
especially due to smartphones (Gao et al., 2018). This is similar to the interruption overload effect
observed for work-related emotional exhaustion. Information overload is further linked to the
ambiguous nature of information posted on the platforms, i.e., the tendency of SN posts to mean
different things to different users (Lee et al., 2016). Social overload on the other hand is associated
with the number of friends one has on the platforms (Maier et al., 2015). Both types of overload
are exacerbated when the social networks are used excessively, that is, the more often one uses
social media the more likely they are to feel information and social overload (Cao et al., 2018;
Maier et al., 2015; Zheng & Lee, 2016). Again, it is the excessive use that occurs as a core factor
for fatigue.
Similarly to after hour work interruptions, excessive use creates family, work and personal
conflicts and is linked to SNS exhaustion because of this (Zheng & Lee, 2016). Excessive use is
also related to self-control failure and consequently diminished energy and increased tiredness (Du
et al., 2018). However, Cao et al. (2018) have shown that trying to cut back and control the SNS
20
use can lower SNS exhaustion. We can thus speculate that there might be different energy
consequences depending on whether the self-control of SNS use is successful or it results in failure.
Another route through which SNS create exhaustion is through the increased possibility of
social comparison and the increased shame that comes with it (Lim & Yang, 2015). A positive side
of SNS for energy is the social support that users can get on the platform. For example, when SNS
users think that their friends are caring and supportive then SNS exhaustion is less likely (Lo,
2019). However, only the emotionally stable and lonely users can profit from such social support
on the platforms. For those who are emotionally unstable (irrespective of whether they are lonely
or not) as well as those who are not lonely (irrespective of whether they are emotionally stable or
not) social support does not prevent exhaustion. That said, social overload exhausts all users alike
and is a stronger predictor of exhaustion than social support, which makes the social support aspect
an insufficient buffer.
Finally, the findings for SNS fatigue can also be applied to mobile messengers: information
overload created by mobile messengers is related to mobile messenger fatigue, especially if the
users care what other people think of them (Shin & Shin, 2016).
5. DISCUSSION, CONCLUSION AND FUTURE OUTLOOK
The first aim of the present study was to systematically structure and classify the many concepts
used to describe and access subjective energy. With that we have created a conceptual baseline for
future HCI and IS researchers who are interested in the effects of digital technology use on the
subjective energy of its users. We show that researchers need to distinguish between long lasting
feelings of subjective energy (e.g. vitality, emotional exhaustion, work-related vigor, and social
network exhaustion) and momentary or relatively short feelings of energy (e.g. depletion, and the
energy and fatigue moods). They also need to distinguish between narrow (e.g. emotional
exhaustion) and broader concepts (e.g. work-related vigor) of subjective energy as well as concepts
bound to the context (e.g. social network fatigue) vs. context-free subjective energy (e.g. fatigue
affect). Most importantly, researchers need to consider the bivariance between subjective energy
and fatigue and not blend the different energy processes together. A lot of valuable information is
potentially lost when energy and fatigue are aggregated in the same variable, especially in relation
to ICT use.
Second, we show that the current state of the art on the relationship between technology and
subjective energy suggests a predominantly negative picture. Technology causes chronic emotional
exhaustion with work. It can cause momentary fatigue and depletion. In the case of social networks
and messengers, it is additionally related to fatigue with the technology itself. On top of this, there
is a negative impact of technology on the positive dimension of energy as well. Digital technology
can deplete momentary feeling of energy as well as the more permanent states of work-related
vigor and life vitality.
Ubiquity, perceived overload, lack of autonomy, and excessive use of technology are the most
common ways through which technology drains subjective energy, especially if the users see it as
a problem or try to control their use. This is true for most of the conceptualizations of subjective
energy, short, long, broad and specific. The ubiquitous reach of technology forces people into a
constant “presenteeism” (Ayyagari et al., 2011) that many suffer from. This permanent reachability
can create work and interruption overload, for example when employees receive work-related
21
notifications after work hours. Permanent reachability can also create social and communication
overload when people receive too many personal messages or too much is happening on their social
media news feeds. If they are expected to answer immediately (also termed responsiveness) the
notifications interrupt their personal lives thereby creating work-life imbalances and personal-life
conflicts. Excessive use is another reason for personal and work conflicts and together with
responsiveness it deprives people of their autonomy. When constantly interrupted or online, people
no longer manage their own time. Even consciously refraining from going online or responding
immediately demands self-control, especially when people care what others think. All these
processes are related to exhaustion and fatigue and deplete vigor and vitality. Thus, ubiquitous
technology reach drains people of subjective energy at their work as well as their personal lives.
However, our analysis also shows that technology can have an energizing effect in all
conceptualizations of subjective energy. The main path through which technology energizes its
users is by increasing their autonomy. Autonomy is relevant for work-related vigor, but also for
vitality of intrinsically motivated users. Technology increases user autonomy when it is designed
with absolute user-control, but also more generally, when it allows for task control, that is, when it
allows the user to decide when and where to do the tasks. These findings are in agreement with
self-determination theory, which postulates that satisfying the need for autonomy, i.e., being self-
determined, enables eudemonic well-being, i.e., vitality.
The second energizing aspect of technology use is its hedonic side. The new ICTs offer
countless entertaining and relaxing content, as well as socializing opportunities. Pleasant online
activities, such as watching cat videos, have been found to energize users at least in the short run,
and thus people use the Internet when they feel low on energy. It is no wonder then that this
behavior is prevalent and increasing during work hours (cyberloafing), on the work breaks, in
schools, as well as in people’s free time.
Still, the most important findings of this review is that the positive impact of technology on the
energy dimension is often accompanied by a negative impact on the fatigue dimension. All but one
study that looked at both a positive and a negative energy construct, found a negative impact of
technology and the strength of the negative impact was usually stronger (Hancock, 2007; Rhee &
Kim, 2016; Sardeshmukh et al., 2012; van Zoonen & Rice, 2017). This means that potentially, the
processes that energize can also deplete energy either at the same time or in the long run. For
example, when people use new digital technology to relax and re-energize, they might be leaving
themselves vulnerable to exhaustion both at work and at home. Trying to increase the hedonic well-
being in the short term might therefore decrease the eudemonic well-being in the long run. What
is more, continuous technology use for hedonic or mood-modifying reasons can form a compulsion
and turn into excessive and problematic use of these technologies, especially social media.
Excessive use, as we have repeatedly seen, drains energy and might create a vicious circle where
in a state of low energy people turn to the “re-energizing” technologies to relax which further drains
them of energy and initiates and exacerbates a “loss-cycle”. Then, both excessive Internet use as
well as social network fatigue create the need to self-regulate usage, which further drains energy
needed for self-control.
5.1. Limitations and Future Outlook
As we considered only studies that made it into the leading HCI and IS journals (n=53), we
cannot exclude that there are some studies or conference papers that we have missed and that would
22
have added additional insights to our review. As it stands though, the current literature points to a
predominantly negative effect of ICTs on human energy, but also gaps in research that need to be
closed in order to increase the confidence of our findings. First, the majority of the studies were
correlational, and only one tested for a reversed causation, which leaves open the important
question of causality in the relationship between ICT use and human energy: Do people use certain
technologies because they are tired, are they tired because they (over)use certain technologies or is
there a cycle of reversed causations? Specifically, if employees cyberloaf because they feel
emotionally exhausted, do they exacerbate their exhaustion because they cyberloaf? Similarly, does
excessive Internet use deplete users of vitality or are the less vital more likely to become excessive
Internet users and enter an energy “loss cycle”? Different authors argued for different causation
paths, which means that both ways are plausible, and we suggest that a reverse causation is
probable. Future research should try to clarify these questions.
Second, the vast majority of the studies measured energy on only one dimension (n = 37,
70%), or combined the energy and fatigue scales (n = 4, 8%). Since energy and fatigue are shown
to be bivariate, a lot of essential information is lost when researchers combine the scales or only
look at one dimension. For example, Rhee and Kim (2016) showed that the correlation between
the momentary feeling of energy and the momentary feeling of fatigue was smaller for the
employees who took cellphone breaks (r = -.30, p < .001) than for employees who took
conventional breaks (r = -.48; p < .001). What is even more interesting is that work-related vigor
and emotional exhaustion were not even related for the employees who took smartphone breaks (r
= 07, n.s.), whereas for the employees who took conventional breaks the correlation was moderate,
as expected (r = -.40, p < .001). In other words, only for those who used conventional breaks the
intuitive was true: feeling more vigorous meant feeling less exhausted and vice versa. This result
suggests that the parallel invigorating and draining processes might be technology specific.
Combining the scales or focusing on only one energy dimension would have missed those insights.
In our sample only 12 studies (22%) used both positive and negative energy measures and almost
all of them found different effects on the different measures. Future studies should always use
bivariate scales, especially when the variance in energy is to be predicted by technology use. This
opens new questions such as can there be social media vigor next to social media fatigue? Studies
of user experience on Facebook that used physiological correlates of arousal and valence have
shown that while on Facebook, users do exhibit optimal physiological experience akin to flow
(Cipresso et al., 2015; Mauri, Cipresso, Balgera, Villamira, & Riva, 2011). Hence, it would be
interesting to see what the relationship between the energizing and fatiguing aspects of social
network use is.
Third, authors either focused on the short-term or long-term experience of energy, even
when they did look at both energy and fatigue as separate, bivariate constructs. Only Rhee and Kim
(2016) used multiple energy instruments for momentary feelings as well as for the durable
experience of exhaustion and vigor. Thus, we do not know what the relationship between short-
term energy boosts and longer-term experience of energy and fatigue is. If short hedonic energy
boosts from certain technologies do result in a durable emotional exhaustion as our review
suggests, it is important to uncover the processes through which this happens. Do short hedonic
boosts turn into excessive use? Do they prevent users from satisfying their growth need by
distracting from long-term self-actualizing goals? Or is it that even though energizing, certain
technologies simply create overload and users cannot get a chance to properly rest and recuperate
in the same way as they would in less stimulating environments (Berman, Jonides, & Kaplan, 2008;
Berto, 2005; Herzog, Maguire, & Nebel, 2003). Another possibility is that the effect of certain
23
technologies on the positive energy dimension is faster than on the fatigue dimension and thus
users learn that the technology is energizing while missing its longer-term fatiguing effect. This is
analogous to people’s mostly unconscious mood modifying tendency to use fast energizing sugar
snacks when tired even though in the long run sugar snacks cause fatigue. While the opposite is
true for a mild physical exercise, fewer people realize the connection. Thus, sugar snacks are the
preferred mood (energy) regulating strategy (Thayer, 1987).
This opens the question about the role awareness plays in the experience of the moods of
energy and fatigue, especially since habitual use is per definition low on awareness. Awareness of
a problem increases the need for self-regulation and can thus itself be depleting. The question is,
would the excessive but non-compulsive users lose the ability to re-energize online if they were
made aware that their excessive use is a problem (Quinones & Griffiths, 2017)? And is awareness
related to fatigue only because aware users are likely to try to self-regulate? For example Reinecke
and colleagues (2014) have shown that when people feel drained and unable to self-control they
are more likely to feel guilty when they fail to stay off entertainment media (games and TV). The
guilt is related to feeling of fatigue post watching or playing. Future research can clarify the
relationship between awareness, self-control and self-control failure both for short term as well as
for long term experiences of mental energy and fatigue.
Finally, our analysis shows that not all technologies have equal effect. Even though very
few studies directly looked at the differences between different media and mental energy, we can
conclude that there is a general trend that links social media and (smart)phones to fatigue and
emotional exhaustion both in private as well as in organizational context (Gaudioso et al., 2017;
Hietajärvi et al., 2019; van Zoonen & Rice, 2017). However, since the majority of studies only
referred to technology in general, such as “the Internet”, future research would profit greatly from
distinguishing between different types of technology, different types of devices, different types of
media as well as different types of media content. Based on what we have shown, we can
hypothesize that seductive technology that is designed to “hook” and that is based on a business
model dependent on excessive use (Eyal, 2014; Zuboff, 2019) will be more depleting than neutral
or positive technology at least in the long run. Future research can try to categorize the different
technologies and may even give a human-centered seal of approval to the ones with a strong
positive relationship with vitality and eudemonic well-being.
5.2. Conclusion
Our review shows that different researchers use different conceptualizations (terms) of
subjective energy, and that these diverse conceptualizations differ in duration, range and specificity
of cause. Depending on term, the relationship between ICTs and subjective energy shows some
specific patterns, but also some overarching ones. The review also shows that some (negative)
patterns are specific to certain technology such as social networking sites.
In general, ICTs drain energy across different energy concepts when they decrease users’
autonomy, increase overload and cause excessive use. On the other hand, ICTs can have the ability
to energize their users when they increase autonomy and when they fit to their users’ needs,
personalities and values. At the moment, the general pattern of ICT use on energy is more negative
than positive, especially on the longer-term energy concepts. However, our review shows that this
topic is under researched and that if the bivariate conceptualizations is more readily embraced, we
might see a more nuanced picture. Future research should try to uncover a more holistic view, so
24
that we can create technology that truly nourishes the valued resource of human energy, i.e.,
improves the well-being of the technology users.
REFERENCES
Aboujaoude, E. (2010). Problematic Internet use: an overview. World Psychiatry, 9(2), 85-90.
Aghaz, A., & Sheikh, A. (2016). Cyberloafing and job burnout: An investigation in the
knowledge-intensive sector. Computers in Human Behavior, 62, 51-60. doi:
https://doi.org/10.1016/j.chb.2016.03.069
Ahn, J.-H., Bae, Y.-S., Ju, J., & Oh, W. (2018). Attention Adjustment, Renewal, and Equilibrium
Seeking in Online Search: An Eye-Tracking Approach. Journal of Management
Information Systems, 35(4), 1218-1250. doi: 10.1080/07421222.2018.1523595
Akın, A. (2012). The Relationships Between Internet Addiction, Subjective Vitality, and
Subjective Happiness. CyberPsychology, Behavior & Social Networking, 15(8), 404-410.
doi: 10.1089/cyber.2011.0609
Andreassen, C. S. (2015). Online social network site addiction: A comprehensive review. Current
Addiction Reports, 2(2), 175-184.
Aumayr-Pintar, C., Cerf, C., & Parent-Thirion, A. (2018). Burnout in the Workplace: A Review
of the Data and Policy Responses in the EU.
Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: technological antecedents and
implications. MIS Quarterly, 35(4), 831-858.
Bakker, A. B., & Demerouti, E. (2007). The job demands-resources model: State of the art.
Journal of managerial psychology, 22(3), 309-328.
Bakker, A. B., Schaufeli, W. B., Leiter, M. P., & Taris, T. W. (2008). Work engagement: An
emerging concept in occupational health psychology. Work & Stress, 22(3), 187-200.
Bala, H., & Bhagwatwar, A. (2018). Employee dispositions to job and organization as
antecedents and consequences of information systems use. Information Systems Journal,
28(4), 650-683.
Baumeister, R. F., Vohs, K. D., & Tice, D. M. (2007). The strength model of self-control.
Current directions in psychological science, 16(6), 351-355.
Bener, A., Yildirim, E., Torun, P., Çatan, F., Bolat, E., Alıç, S., . . . Griffiths, M. D. (2018).
Internet addiction, fatigue, and sleep problems among adolescent students: a large-scale
study. International Journal of Mental Health and Addiction, 1-11.
Berman, M. G., Jonides, J., & Kaplan, S. (2008). The cognitive benefits of interacting with
nature. Psychological science, 19(12), 1207-1212.
Berto, R. (2005). Exposure to restorative environments helps restore attentional capacity. Journal
of environmental psychology, 25(3), 249-259.
25
Botella, C., Baños, R. M., Etchemendy, E., García-Palacios, A., & Alcañiz, M. (2016).
Psychological countermeasures in manned space missions:“EARTH” system for the
Mars-500 project. Computers in Human Behavior, 55, 898-908.
Bright, L. F., Kleiser, S. B., & Grau, S. L. (2015). Too much Facebook? An exploratory
examination of social media fatigue. Computers in Human Behavior, 44, 148-155. doi:
https://doi.org/10.1016/j.chb.2014.11.048
Britt, T. W. (1999). Engaging the self in the field: Testing the triangle model of responsibility.
Personality and Social Psychology Bulletin, 25(6), 698-708.
Bültmann, U., Kant, I., Kasl, S. V., Beurskens, A. J., & van den Brandt, P. A. (2002). Fatigue and
psychological distress in the working population: psychometrics, prevalence, and
correlates. Journal of psychosomatic research, 52(6), 445-452.
Cao, X., Masood, A., Luqman, A., & Ali, A. (2018). Excessive use of mobile social networking
sites and poor academic performance: Antecedents and consequences from stressor-strain-
outcome perspective. Computers in Human Behavior, 85, 163-174.
Cao, X., & Sun, J. (2018). Exploring the effect of overload on the discontinuous intention of
social media users: An SOR perspective. Computers in Human Behavior, 81, 10-18.
Chen, A., & Karahanna, E. (2018). Life Interrupted: The Effects of Technology-Mediated Work
Interruptions on Work and Nonwork Outcomes. MIS Quarterly, 42(4), 1023-1042.
Chen, I.-S. (2017). Work engagement and its antecedents and consequences: A case of lecturers
teaching synchronous distance education courses. Computers in Human Behavior, 72,
655-663.
Cipresso, P., Serino, S., Gaggioli, A., Albani, G., Mauro, A., & Riva, G. (2015). Psychometric
modeling of the pervasive use of Facebook through psychophysiological measures: Stress
or optimal experience? Computers in Human Behavior, 49, 576-587.
Cranwell, J., Benford, S., Houghton, R. J., Golembewksi, M., Fischer, J. E., & Hagger, M. S.
(2014). Increasing Self-Regulatory Energy Using an Internet-Based Training Application
Delivered by Smartphone Technology. CyberPsychology, Behavior & Social Networking,
17(3), 181-186. doi: 10.1089/cyber.2013.0105
Cropanzano, R., Rupp, D. E., & Byrne, Z. S. (2003). The relationship of emotional exhaustion to
work attitudes, job performance, and organizational citizenship behaviors. Journal of
Applied Psychology, 88(1), 160.
Csikszentmihalyi, M. (1991). Flow: The Psychology of Optimal Experience. New York: Harper
Collins.
Csikszentmihalyi, M., Abuhamdeh, S., & Nakamura, J. (2014). Flow Flow and the foundations of
positive psychology (pp. 227-238): Springer.
26
Deci, E. L., & Ryan, R. M. (2000). The" what" and" why" of goal pursuits: Human needs and the
self-determination of behavior. Psychological inquiry, 11(4), 227-268.
Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human
motivation, development, and health. Canadian psychology/Psychologie canadienne,
49(3), 182.
Deci, E. L., & Ryan, R. M. (2011). Self-determination theory. Handbook of theories of social
psychology, 1(2011), 416-433.
Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-
resources model of burnout. Journal of Applied Psychology, 86(3), 499.
Du, J., van Koningsbruggen, G. M., & Kerkhof, P. (2018). A brief measure of social media self-
control failure. Computers in Human Behavior, 84, 68-75. doi:
https://doi.org/10.1016/j.chb.2018.02.002
Durkee, T., Kaess, M., Carli, V., Parzer, P., Wasserman, C., Floderus, B., . . . Bobes, J. (2012).
Prevalence of pathological internet use among adolescents in Europe: demographic and
social factors. Addiction, 107(12), 2210-2222.
Edwards, J. R. (1991). Person-job fit: A conceptual integration, literature review, and
methodological critique: John Wiley & Sons.
Engelhardt, C. R., Hilgard, J., & Bartholow, B. D. (2015). Acute exposure to difficult (but not
violent) video games dysregulates cognitive control. Computers in Human Behavior, 45,
85-92. doi: https://doi.org/10.1016/j.chb.2014.11.089
Engin, A., & Vetschera, R. (2017). Information representation in decision making: The impact of
cognitive style and depletion effects. Decision Support Systems, 103, 94-103.
Eyal, N. (2014). Hooked: How to build habit-forming products: Penguin UK.
French, J., Rodgers, W., & Cobb, S. (1974). Adjustment as person-environment fit In Coping and
Adaptation. Basic Book, New York.
Gao, W., Liu, Z., Guo, Q., & Li, X. (2018). The dark side of ubiquitous connectivity in
smartphone-based SNS: An integrated model from information perspective. Computers in
Human Behavior, 84, 185-193. doi: https://doi.org/10.1016/j.chb.2018.02.023
Garton, E. (2017). Employee burnout is a problem with the company, not the person. Harvard
Business Review.
Gaudioso, F., Turel, O., & Galimberti, C. (2017). The mediating roles of strain facets and coping
strategies in translating techno-stressors into adverse job outcomes. Computers in Human
Behavior, 69, 189-196. doi: https://doi.org/10.1016/j.chb.2016.12.041
Guerrero-Bote, V. P., & Moya-Anegón, F. (2012). A further step forward in measuring journals’
scientific prestige: The SJR2 indicator. Journal of informetrics, 6(4), 674-688.
27
Hagger, M. S., Wood, C., Stiff, C., & Chatzisarantis, N. L. (2010). Ego depletion and the strength
model of self-control: a meta-analysis. Psychological bulletin, 136(4), 495.
Han, S., Shanafelt, T. D., Sinsky, C. A., Awad, K. M., Dyrbye, L. N., Fiscus, L. C., . . . Goh, J.
(2019). Estimating the attributable cost of physician burnout in the United States. Annals
of internal medicine, 170(11), 784-790.
Hancock, P. A. (2007). On the process of automation transition in multitask human–machine
systems. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and
Humans, 37(4), 586-598.
Hennington, A., Janz, B., & Poston, R. (2011). I’m just burned out: Understanding information
system compatibility with personal values and role-based stress in a nursing context.
Computers in Human Behavior, 27(3), 1238-1248.
Herrero, R., Garcia-Palacios, A., Castilla, D., Molinari, G., & Botella, C. (2014). Virtual reality
for the induction of positive emotions in the treatment of fibromyalgia: a pilot study over
acceptability, satisfaction, and the effect of virtual reality on mood. Cyberpsychology,
Behavior, and Social Networking, 17(6), 379-384.
Herzog, T. R., Maguire, P., & Nebel, M. B. (2003). Assessing the restorative components of
environments. Journal of environmental psychology, 23(2), 159-170.
Hietajärvi, L., Salmela-Aro, K., Tuominen, H., Hakkarainen, K., & Lonka, K. (2019). Beyond
screen time: Multidimensionality of socio-digital participation and relations to academic
well-being in three educational phases. Computers in Human Behavior, 93, 13-24. doi:
https://doi.org/10.1016/j.chb.2018.11.049
Huang, H.-C., Wong, M.-K., Yang, Y.-H., Chiu, H.-Y., & Teng, C.-I. (2017). Impact of Playing
Exergames on Mood States: A Randomized Controlled Trial. CyberPsychology, Behavior
& Social Networking, 20(4), 246-250. doi: 10.1089/cyber.2016.0322
Ishii, K., & Markman, K. M. (2016). Online customer service and emotional labor: An
exploratory study. Computers in Human Behavior, 62, 658-665.
James, T. L., Wallace, L., & Deane, J. K. (2019). Using organistic integration theory to explore
the association between users’ exercise motivations and fitness technology feature set use.
MIS Quarterly, 43(1).
Jang, W. E., Bucy, E. P., & Cho, J. (2018). Self-esteem moderates the influence of self-
presentation style on Facebook users’ sense of subjective well-being. Computers in
Human Behavior, 85, 190-199.
Kang, H., & Shyam Sundar, S. (2013). Depleted egos and affirmed selves: The two faces of
customization. Computers in Human Behavior, 29(6), 2273-2280. doi:
https://doi.org/10.1016/j.chb.2013.05.018
Kelley, K. J., & Gruber, E. M. (2010). Psychometric properties of the problematic internet use
questionnaire. Computers in Human Behavior, 26(6), 1838-1845.
28
Kononova, A., McAlister, A., & Oh, H. J. (2018). Screen overload: Pleasant multitasking with
screen devices leads to the choice of healthful over less healthful snacks when compared
with unpleasant multitasking. Computers in Human Behavior, 80, 1-11. doi:
https://doi.org/10.1016/j.chb.2017.10.042
Kuppens, P., Tuerlinckx, F., Russell, J. A., & Barrett, L. F. (2013). The relation between valence
and arousal in subjective experience. Psychological bulletin, 139(4), 917.
Lee, A. R., Son, S.-M., & Kim, K. K. (2016). Information and communication technology
overload and social networking service fatigue: A stress perspective. Computers in
Human Behavior, 55, 51-61. doi: https://doi.org/10.1016/j.chb.2015.08.011
Lee, J. E., Xiang, P., & Gao, Z. (2017). Acute effect of active video games on older children's
mood change. Computers in Human Behavior, 70, 97-103.
Lim, M., & Yang, Y. (2015). Effects of users’ envy and shame on social comparison that occurs
on social network services. Computers in Human Behavior, 51, 300-311.
Lin, S. C., Tsai, K. W., Chen, M. W., & Koo, M. (2013). Association between fatigue and
Internet addiction in female hospital nurses. Journal of advanced nursing, 69(2), 374-383.
Llorens, S., Schaufeli, W., Bakker, A., & Salanova, M. (2007). Does a positive gain spiral of
resources, efficacy beliefs and engagement exist? Computers in Human Behavior, 23(1),
825-841.
Lo, J. (2019). Exploring the buffer effect of receiving social support on lonely and emotionally
unstable social networking users. Computers in Human Behavior, 90, 103-116.
Luqman, A., Cao, X., Ali, A., Masood, A., & Yu, L. (2017). Empirical investigation of Facebook
discontinues usage intentions based on SOR paradigm. Computers in Human Behavior,
70, 544-555.
Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2015). Giving too much social support: social
overload on social networking sites. European Journal of Information Systems, 24(5),
447-464. doi: 10.1057/ejis.2014.3
Mäkikangas, A., Kinnunen, S., Rantanen, J., Mauno, S., Tolvanen, A., & Bakker, A. B. (2014).
Association between vigor and exhaustion during the workweek: A person-centered
approach to daily assessments. Anxiety, Stress, & Coping, 27(5), 555-575.
Maslach, C., Jackson, S. E., & Leiter, M. P. (1996). MBI: Maslach burnout inventory: CPP,
Incorporated Sunnyvale (CA).
Maslach, C., Jackson, S. E., Leiter, M. P., Schaufeli, W. B., & Schwab, R. L. (1986). Maslach
burnout inventory (Vol. 21): Consulting psychologists press Palo Alto, CA.
Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual review of psychology,
52(1), 397-422.
29
Mauri, M., Cipresso, P., Balgera, A., Villamira, M., & Riva, G. (2011). Why is Facebook so
successful? Psychophysiological measures describe a core flow state while using
Facebook. Cyberpsychology, Behavior, and Social Networking, 14(12), 723-731.
McNair, D., Lorr, M., & Droppleman, L. (1971). Profile of mood states (POMS). San Diego:
Educational and Industrial Testing Service.
Muraven, M., & Baumeister, R. F. (2000). Self-regulation and depletion of limited resources:
Does self-control resemble a muscle? Psychological bulletin, 126(2), 247.
Muraven, M., Tice, D. M., & Baumeister, R. F. (1998). Self-control as a limited resource:
regulatory depletion patterns. Journal of personality and social psychology, 74(3), 774.
Myrick, J. G. (2015). Emotion regulation, procrastination, and watching cat videos online: Who
watches Internet cats, why, and to what effect? Computers in Human Behavior, 52, 168-
176.
O'Connor, P. J. (2006). Mental energy: Assessing the mood dimension. Nutrition reviews,
64(suppl_3), S7-S9.
Pedrero-Pérez, E., Rodríguez-Monje, M., & Ruiz Sánchez de León, J. (2012). Mobile phone
abuse or addiction. A review of the literature. Adicciones, 24, 139-152.
Piszczek, M. M. (2017). Boundary control and controlled boundaries: Organizational
expectations for technology use at the work–family interface. Journal of Organizational
Behavior, 38(4), 592-611.
Quinones, C., & Griffiths, M. D. (2017). The impact of daily emotional demands, job resources
and emotional effort on intensive internet use during and after work. Computers in
Human Behavior, 76, 561-575. doi: https://doi.org/10.1016/j.chb.2017.07.020
Ragsdale, J. M., & Hoover, C. S. (2016). Cell phones during nonwork time: A source of job
demands and resources. Computers in Human Behavior, 57, 54-60.
Ragu-Nathan, T., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of
technostress for end users in organizations: Conceptual development and empirical
validation. Information Systems Research, 19(4), 417-433.
Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement
and academic performance: Examining the interrelations. Computers in Human Behavior,
63, 604-612.
Reinecke, L., Hartmann, T., & Eden, A. (2014). The guilty couch potato: The role of ego
depletion in reducing recovery through media use. Journal of Communication, 64(4), 569-
589.
Reinke, K., & Chamorro-Premuzic, T. (2014). When email use gets out of control: Understanding
the relationship between personality and email overload and their impact on burnout and
work engagement. Computers in Human Behavior, 36, 502-509.
30
Rhee, H., & Kim, S. (2016). Effects of breaks on regaining vitality at work: An empirical
comparison of ‘conventional’ and ‘smart phone’ breaks. Computers in Human Behavior,
57, 160-167. doi: https://doi.org/10.1016/j.chb.2015.11.056
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic
motivation, social development, and well-being. American psychologist, 55(1), 68.
Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on
hedonic and eudaimonic well-being. Annual review of psychology, 52(1), 141-166.
Ryan, R. M., & Deci, E. L. (2008). From ego depletion to vitality: Theory and findings
concerning the facilitation of energy available to the self. Social and Personality
Psychology Compass, 2(2), 702-717.
Ryan, R. M., & Frederick, C. (1997). On energy, personality, and health: Subjective vitality as a
dynamic reflection of well‐being. Journal of personality, 65(3), 529-565.
Ryan, R. M., Huta, V., & Deci, E. L. (2008). Living well: A self-determination theory
perspective on eudaimonia. Journal of happiness studies, 9(1), 139-170.
Salanova, M., Grau, R. M., Cifre, E., & Llorens, S. (2000). Computer training, frequency of
usage and burnout: the moderating role of computer self-efficacy. Computers in Human
Behavior, 16(6), 575-590.
Sardeshmukh, S. R., Sharma, D., & Golden, T. D. (2012). Impact of telework on exhaustion and
job engagement: a job demands and job resources model. New Technology, Work and
Employment, 27(3), 193-207. doi: doi:10.1111/j.1468-005X.2012.00284.x
Satici, S. A., & Uysal, R. (2015). Well-being and problematic Facebook use. Computers in
Human Behavior, 49, 185-190.
Schaufeli, W., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of
engagement and burnout: A two sample confirmatory factor analytic approach. Journal of
happiness studies, 3(1), 71-92.
Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement
with a short questionnaire: A cross-national study. Educational and psychological
measurement, 66(4), 701-716.
Seligman, M. E., & Csikszentmihalyi, M. (2014). Positive psychology: An introduction Flow and
the foundations of positive psychology (pp. 279-298): Springer.
Serrano, B., Baños, R. M., & Botella, C. (2016). Virtual reality and stimulation of touch and
smell for inducing relaxation: A randomized controlled trial. Computers in Human
Behavior, 55, 1-8.
Shacham, S. (1983). A shortened version of the Profile of Mood States. Journal of personality
assessment, 47(3), 305-306.
31
Shanafelt, T. D., Hasan, O., Dyrbye, L. N., Sinsky, C., Satele, D., Sloan, J., & West, C. P. (2015).
Changes in burnout and satisfaction with work-life balance in physicians and the general
US working population between 2011 and 2014. Paper presented at the Mayo Clinic
Proceedings.
Shanafelt, T. D., West, C. P., Sinsky, C., Trockel, M., Tutty, M., Satele, D. V., . . . Dyrbye, L. N.
(2019). Changes in burnout and satisfaction with work-life integration in physicians and
the general US working population between 2011 and 2017. Paper presented at the Mayo
Clinic Proceedings.
Shin, J., & Shin, M. (2016). To Be Connected or Not To Be Connected? Mobile Messenger
Overload, Fatigue, and Mobile Shunning. CyberPsychology, Behavior & Social
Networking, 19(10), 579-586. doi: 10.1089/cyber.2016.0236
Shirom, A. (2011). Vigor as a positive affect at work: Conceptualizing vigor, its relations with
related constructs, and its antecedents and consequences. Review of General Psychology,
15(1), 50-64.
Steinberger, F., Schroeter, R., & Watling, C. N. (2017). From road distraction to safe driving:
Evaluating the effects of boredom and gamification on driving behaviour, physiological
arousal, and subjective experience. Computers in Human Behavior, 75, 714-726. doi:
https://doi.org/10.1016/j.chb.2017.06.019
Thayer, R. E. (1986). Activation-deactivation adjective check list: Current overview and
structural analysis. Psychological reports, 58(2), 607-614.
Thayer, R. E. (1987). Energy, tiredness, and tension effects of a sugar snack versus moderate
exercise. Journal of personality and social psychology, 52(1), 119.
Thayer, R. E. (1990). The biopsychology of mood and arousal: Oxford University Press.
van Zoonen, W., & Rice, R. E. (2017). Paradoxical implications of personal social media use for
work. New Technology, Work and Employment, 32(3), 228-246. doi:
doi:10.1111/ntwe.12098
Vigna-Taglianti, F., Brambilla, R., Priotto, B., Angelino, R., Cuomo, G., & Diecidue, R. (2017).
Problematic internet use among high school students: Prevalence, associated factors and
gender differences. Psychiatry research, 257, 163-171.
Waldman, J. D., Kelly, F., Aurora, S., & Smith, H. L. (2004). The shocking cost of turnover in
health care. Health care management review, 29(1), 2-7.
Ware Jr, J. E., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36):
I. Conceptual framework and item selection. Medical care, 473-483.
Windeler, J. B., Chudoba, K. M., & Sundrup, R. Z. (2017). Getting away from them all:
Managing exhaustion from social interaction with telework. Journal of Organizational
Behavior, 38(7), 977-995. doi: doi:10.1002/job.2176
32
Xie, J., Ma, H., Zhou, Z. E., & Tang, H. (2018). Work-related use of information and
communication technologies after hours (W_ICTs) and emotional exhaustion: A mediated
moderation model. Computers in Human Behavior, 79, 94-104.
Young, K. S. (1996). Psychology of computer use: XL. Addictive use of the Internet: a case that
breaks the stereotype. Psychological reports, 79(3), 899-902.
Zhang, S., Zhao, L., Lu, Y., & Yang, J. (2016). Do you get tired of socializing? An empirical
explanation of discontinuous usage behaviour in social network services. Information &
Management, 53(7), 904-914.
Zheng, X., & Lee, M. K. (2016). Excessive use of mobile social networking sites: Negative
consequences on individuals. Computers in Human Behavior, 65, 65-76.
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new
frontier of power: Profile Books.
- 33 -
APPENDIX. OVERVIEW AND SUMMARY OF THE REVIEWED EMPIRICAL STUDIES
Table 2. Summary of the empirical studies on the relationship between technology use and subjective energy
Study
Subjective
Energy Term
Instrument Methodology; Method Sample Main Findings Effect size
Aghaz and
Sheikh
(2016)
Work-related
emotional
exhaustion
(as part of
job burnout)
MBI
Survey study
Structural equation
modeling
298
employees
in 5 knowledge-
intensive firms
Job burnout is related to both
cyberloafing activities and
cyberloafing behaviors
ß =.28
ß =.47
Ahn,
Bae, Ju,
and Oh
(2018)
(Attentional)
Depletion
Eye tracker
(fixation and
duration)
Experiment 1: Within-
subject experiment;
randomly alternated e-
commerce search results
from various types of
retailers
Experiment 2: identical to
Experiment 1, but changed
length of search results and
placed ads at various
places of the result page
Lorenz curve and Gini
coefficient analysis, mixed
effect regression analysis
97
e-commerce
users
70
e-commerce
users
Consumers' attention span
decreases exponentially, instead
of linearly, from the top to the
bottom of a search result page.
The total number of available
options influences the speed
and pattern of attention
depletion.
Attention can be refreshed and
renewed with ads when they are
positioned in the middle of a
search results listing
Ads are ignored, visual attention
after ads is higher, however it
depletes very rapidly after this
attention renewal
NR/NA
Akın
(2012) Subjective
vitality SVS
Survey study
Hierarchical regression
328
university
students
Internet addiction is negatively
related to vitality (and it further
mediates the link between
ß = – .13
- 34 -
Internet addiction and
happiness)
Ayyagari
et al.
(2011)
Work-related
emotional
exhaustion
(strain)
Adopted
MBI
Survey study
Structural equation
modeling
661
employees
Presenteeism indirectly
increases exhaustion through
work-home conflict, work
overload and role ambiguity
Pace of change indirectly
increases exhaustion through
work overload, role ambiguity
and job insecurity
ß = 52, ß =.17
ß = 61, ß =.26
ß =.61, ß =.27
ß =.14, ß=.26
ß = .23, ß = .27
ß = .14, ß = .10
Bala and
Bhagwatwar
(2018)
Work-related
emotional
exhaustion
Adopted
MBI
Study 1: Longitudinal
study, 3-wave survey
study; Pre-system
implementation, 3 months
and 6 months post-
implementation. System =
functional health and
safety management system
Study 2:
Longitudinal study, 3-
wave survey study; Pre-
system implementation, 3
months and 6 months post-
implementation System =
enterprise system
Structural equation
modeling
257
employees from
a large
manufacturing firm
181
employees from the
same
manufacturing firm
Study 1. Deep structure use
(whether the employee
routinely uses the system
features that were taught in
training) decreased exhaustion.
Cognitive absorption during use
(whether the system use occurs
without distractions, i.e., the
users can immerse in the tasks)
decreases exhaustion
Study 2. Deep structure use
increased exhaustion.
Note. All measures, for both
studies are from the same wave
(Time 3 only)
ß = -.12
ß = -.19
ß = .28
Botella
et al.
(2016)
Vigor
activity
Fatigue
inertia
POMS,
monthly
Longitudinal study; 4
months long spaceship
simulator study for
astronauts with a mood-
inducing VR.
6
astronauts
VR use did not impact month-
long energy
VR use did not impact month-
long fatigue
n.s.
- 35 -
Experiment: 2 monthly
measures (before and after
VR intervention);
Did not report method of
analysis; but did present
raw data
Bright
et al.
(2015)
SNS
fatigue
Self-developed
social media
fatigue scale
Survey study
Regression analysis
747
SNS users
with an active
Facebook account
Social media privacy,
helpfulness, and self-efficacy
increase SNS fatigue
Social media confidence
decreases SNS fatigue
ß =.45, ß =.20,
ß =.22
ß = -.32
Cao
et al.
(2018)
Mobile SNS
exhaustion
(techno-
exhaustion)
Adopted MBI
for mobile
SNS
exhaustion
Survey (paper and pencil)
study
Structural equation
modeling
505
mobile SNS app
users (students)
Excessive SNS use and
cognitive emotional
preoccupation with SNS
increase SNS exhaustion
Cognitive-behavioral control
(awareness and attempt to self-
regulate use) moderates both
relationships, i.e., it weakens
them
ß =.34
ß =.09
ß = -.30
ß = -.15
Cao
and Sun
(2018)
SNS
exhaustion
Adopted MBI
for social
media
exhaustion
Survey study
Structural equation
modeling
258
SNS users
(students)
Information overload and social
overload increase SNS
exhaustion
ß =.26
ß =.37
- 36 -
Chen and
Karahanna
(2018)
Work-related
and non-work
related
emotional
exhaustion
MBI
Survey study
Structural equation
modeling
237
knowledge
workers
Extent of after-hours work-
related interruptions indirectly
increases work exhaustion
through psychological
transitions and interruption
overload
Extent of after-hours work-
related interruptions indirectly
increases nonwork (private life)
exhaustion through interruption
overload
Extent of after-hours work-
related interruptions indirectly
decreases work exhaustion
through task closure
ß =.72; ß =.48
ß =.74; ß = .33
ß =.74; ß = .29
ß =.51; ß = -.18
Chen
(2017)
Work-related
vigor (as part
of work
engagement)
UWES
Longitudinal study: Multi
wave survey study:
Baseline + 10 daily diary
measures on 10
consecutive teaching days;
Multilevel regression
analysis
40
lecturers who teach
synchronous
distance courses
Technical problems with
equipment reduce daily vigor NR/NA
Cranwell
et al.
(2014) Depletion
Stroop test
Handgrip
duration
Experiment. Study 1 Pre-
Post training Stroop test.
Training: 4 weeks self-
control training with an
app
Experiment. Study 2 Pre-
Post training handgrip test.
Training: 4 weeks self-
29
students
33
students
Training with the app improved
self-control (lowered depletion)
both on the Stroop test as well
as the handgrip
ηp2 = 0.20
ηp2 = 0.34
- 37 -
control training with an
app
Analysis of covariance
(ANCOVA)
Du
et al.
(2018)
Energy
Tiredness
(well-being)
AD ACL (they
combined the
scales); Time
frame: “After
using social
media …”
Longitudinal study: 2 wave
survey study (4-weeks
follow up), accessed
Energy at Time 1
Simple correlations
405
SNS users at T1
354
completed
the follow up
Experience of energy/tiredness
after using SNS is related to
(and predicts future) social
media self-control failure
r = – .19 at T1
r = –.15 with T2
Engelhardt,
Hilgard, and
Bartholow
(2015)
Depletion Spatial Stroop
task
Experiment: Manipulated
the difficulty and violence
in a video game (2 x 2
between subject design)
Analysis of variance
(ANOVA), General linear
model (GLM)
205
students
Game difficulty and experience
(and not violent content)
predicts depletion
ß = –.24
(coefficient for
interaction
term)
Engin and
Vetschera
(2017) Depletion
Number of
errors in
decision
making
Experiment: Manipulated
the display presentation
(graph vs tabular) is a
decision support system;
Linear mixed model
regression analysis
227
students
Display type when not matched
with cognitive style of decision
maker leads to depletion NR/NA
Gao, Liu,
Guo, and Li
(2018)
SNS
exhaustion
Adopted MBI
for social
media
exhaustion
Survey study
Structural equation
modeling
528
SNS users
Ubiquitous connectivity causes
SNS exhaustion directly, and
indirectly through information
overload
ß =.18;
ß =.32, ß =.26
- 38 -
Gaudioso,
Turel, and
Galimberti
(2017)
Work-related
emotional
exhaustion MBI
Survey study
Structural equation
modeling
242
employees
in a large
government
organization
Techno-invasion increases
exhaustion trough work-family
conflict and through
maladaptive coping strategies
Adaptive strategies weaken the
relationship but work-family
conflict has stronger
relationship with maladaptive
than adaptive strategies
Techno-overload increases
exhaustion through distress
(adaptive coping strategies
weaken the relationship)
Adaptive strategies weaken the
relationship but distress has
stronger relationship with
maladaptive than adaptive
strategies
ß =.66; ß = .40;
ß = 1.11
ß = -.63;
ß = .16 vs.
ß = .40
ß =.51, ß = .50;
ß = 1.11
ß = –.63;
ß = .21 vs. ß =
.50
Hancock
(2007) Fatigue
Vigor POMS
Experiment: Manipulated
level of automation/pilot
control in a cockpit
simulator; 4 groups
ANOVA
30
experienced
pilots
The pilots experienced
progressive increase in fatigue
as the degree of system control
increased
NR/NA
Hennington,
Janz, and
Poston
(2011)
Work-related
emotional
exhaustion MBI
Survey study
Structural equation
modeling
71
nurses from a large
urban hospital who
have been using the
IS (mandatory
electronic medical
Incompatibility (ß reversed
score for compatibility) of IS
with personal values increases
the emotional exhaustion
through creating role conflict
ß = -..29; ß =.57
- 39 -
record system) for
3 years
Herrero
et al.
(2014)
Vigor
energy
Fatigue
1 item for
intensity of the
emotion vigor;
1 item question
for change of
the mood
fatigue
Experiment: Pre-test Post-
test treatment survey with
2h long mood inducing
VR;
t-tests
40
female patients
with fibromyalgia
VR MIP increased vigor, made
fatigue “somewhat better”(n.s.)
Cohen’s d =.38
Cohen’s d =.07
n.s.
Hietajärvi,
Salmela-Aro,
Tuominen,
Hakkarainen,
and Lonka
(2019)
Study
exhaustion
Study vigor
(as part of
study
engagement)
Adopted MBI
for students
Adopted
UWES for
students
Survey study
Structural equation
modeling
741
elementary school
students from 33
schools;
1371
high school
students from
18 high schools
1232
higher education
students from 3
institutions
Elementary school students:
SNS oriented Internet use and
social gaming increase
exhaustion
Elementary school students:
SNS oriented Internet use
decreases; knowledge oriented
increases engagement
High school students:
SNS, blogging and media
oriented Internet use increase
exhaustion; action gaming
decreases it
High school students:
Knowledge oriented Internet use
and social gaming increase
engagement; action gaming
decreases it.
University students:
SNS oriented Internet use as
well as action and social gaming
decrease engagement,
ß = .17,
ß = .09
ß = -.22
ß = .22
ß = .10,
ß = .13
ß = .11,
ß = -.12
ß = .18
ß = .09
ß = -.18
ß = -.15,
ß = -.25,
ß = -.09,
ß =.21
- 40 -
knowledge oriented use
increases it
Huang
et al.
(2017) Vigor POMS
Experiment: Pre and post-
intervention survey;
control vs. intervention
group; intervention = play
randomly selected exer-
games for 30 consecutive
minutes once a week for 2
weeks;
Repeated measures (RM)
ANOVA
168
intervention
167 control group
participants
(university staff
and students)
Playing exer-games increased
vigor (in comparison to control
group) NR/NA
Ishii and
Markman
(2016)
Work-related
emotional
exhaustion MBI
Survey study
Bivariate regression
analysis
130
IT online customer
help desk employee
(use phone, e-mail
or chat to provide
services9
Remote help providers feel
more emotional presence with
customers when they use phone
than when they use e-mail or
chat. Emotional presence in turn
increases exhaustion.
Frequency of e-mail
communication increases
affective presence which in turn
increases exhaustion
NR/NA
James
et al.
(2019)
Subjective
vitality SVS
Survey study
Structural equation
modeling
880
fitness technology
users (app and/or
device)
Use of social interaction
features of fitness tech
moderates (increases) the
relationship between intrinsic
and integrated regulation and
vitality, as well as non-
regulation, but it decreases it for
NR/NA
- 41 -
external and introjected
regulation
Use of data management
features of fitness tech
moderates (increases) the
relationship between intrinsic
and integrated regulation and
vitality, as well as non-
regulation, but it decreases it for
external and introjected
regulation
Jang
et al.
(2018)
Subjective
vitality SVS
Experiment 1: Participants
were asked to update their
FB profile by either
writing about their true or
their strategic selves. Post-
experiment survey
Experiment 2: Participants
were asked to share a life
event on FB profile that
either reflected their true
or their strategic selves;
Post-experiment survey
Regression analysis
136
SNS users
146
SNS users
Strategic self-presentation (as
opposed to authentic) in both
experiments had no effect on
vitality
n.s.
Kang and
Shyam Sundar
(2013) Depletion Persistence on
unsolvable
anagram
Experiment: Participants
were asked to customize
an iGoogle webpage. 3
groups: customize for self,
customize for students of
the opposite gender and
54
students
Participants who were
customizing for others were
more depleted than those who
customized for themselves, but
no differences to control group
ηp2 = 0.11
- 42 -
control group; Survey and
depletion task after
customization
ANOVA
Kelley and
Gruber
(2010)
Vitality
fatigue SF 36 Survey study
ANOVA
278
undergraduate
psychology
students from 2
universities
Problematic Internet use is
related to lower vitality/higher
fatigue NR/NA
Kononova,
McAlister,
and Oh
(2018)
Depletion
Energy
Snack choice:
healthy vs.
unhealthy
Arousal,
Valence survey
Experiment: Participants
were asked to multitask; 4
groups: (TV only; TV +
texting; TV + texting +
online reading
(Wikipedia); TV + texting
+ online shopping
(Amazon)), Post
experiment depletion task
and survey
ANCOVA
140
students from 1
large university
Heavy multimedia multitasking,
especially TV + texting + online
reading (Wikipedia) leads to
depletion
Heavy multimedia multitasking,
increased arousal and decreased
valence (especially for TV +
texting + online reading
ηp2 = 0.11
ηp2 = 0.12
ηp2 = 0.33
Lee
et al.
(2016)
SNS
fatigue
Self-developed
SNS fatigue
scale
Survey (online and offline)
study
Structural equation
modeling
250
university
students
Information equivocality
increases SNS fatigue through
information overload
System pace of change
increases SNS fatigue through
system feature overload
System complexity increases
SNS fatigue through system
feature overload
ß =.12, ß =.25
ß =.12, ß =.25
ß =.57, ß =.25
- 43 -
Communication overload
increases SNS fatigue
ß =.23
Lee
et al.
(2017)
Vigor
Fatigue
Adopted
POMS
Experiment: Pre-Post
intervention survey.
Intervention: 30 min
Active video game session
ANOVA, MANOVA
134
elementary school
children
(8-11 years old)
AVG session decreased the
vigor and the fatigue of the
school students, but the results
for fatigue did not reach
significance
ηp2 = -.38
ηp2 = .06
Lim
and Yang
(2015)
SNS
exhaustion
(burnout)
Adopted MBI
for social
media
exhaustion
Survey study
Structural equation
modeling
446
SNS users
Social comparison increases
SNS exhaustion directly, and
through increase of shame
ß =.33
ß =.43, ß =.50
Llorens
et al.
(2007) Vigor UWES
Longitudinal study: 2-
wave study where
participants were solving
group task exclusively via
chat (mIRC), surveys pre
and post task solving
Structural equation
modeling
110
psychology
students
Task resources (time and
method control = perceived
autonomy inherit in chat
technology) increase vigor
through increasing efficacy
beliefs; reverse causation is also
present!
T1:
ß =.44, ß =.61
T1, T2:
ß =.20, ß =.23
Lo
(2019) SNS
exhaustion
Adopted MBI
for social
media
exhaustion
Survey study
Structural equation
modeling
1285
SNS users
(university staff
and students )
Social overload increases SNS
exhaustion among all users
(emotionally stable, emotionally
unstable, lonely and not lonely)
Social support on SNS
decreases exhaustion only
among the emotionally stable
and lonely users
ß =.25,
ß =.30,
ß =.27,
ß =.38
ß = -.14,
ß = -.24
- 44 -
Luqman
et al.
(2017)
SNS
exhaustion
Adopted MBI
for social
media
exhaustion
Survey study
Structural equation
modeling
360
SNS users
(students)
Excessive social use of SNS
increases SNS exhaustion
Excessive hedonic use of SNS
increases SNS exhaustion
Excessive cognitive use
(=information overload) of SNS
increases SNS exhaustion
ß =.17
ß =.14
ß =.27
Maier
et al.
(2015)
SNS
exhaustion
Adopted MBI
for social
media
exhaustion
Survey study
Structural equation
modeling
571
SNS users
Extent of SNS usage; the
number of SNS friends, and the
subjective social support norm
all increase SNS exhaustion
through social overload
ß = .24
ß = .12
ß = .46
ß = .62
Myrick
(2015) Energy,
depletion
1 item
questions about
emotional state
prior and post
watching
videos
Survey study
t-test
6795
Internet users
who watch cat
videos
Watching cat videos increases
energy and decreases depletion Cohen’s d =.91
Cohen’s d =.37
Piszczek
(2017)
Work-related
emotional
exhaustion
MBI
Longitudinal study: 2 wave
survey study (1 month
apart), emotional
exhaustion was assessed at
T2
Regression analysis
163
alumni of a human
resource
management
master’s degree
program
After hours work-related cell-
phone use expectations (from
employer) increase emotional
exhaustion directly, and
indirectly through actual use
and perceived boundary control
(PBC lowers it).
Both relationships are
moderated by work-family
segregation preferences
ß = .34
ß = .87, ß = -.55
ß = .17
ß = -.19
- 45 -
Quinones and
Griffiths
(2017)
Energy
(recovery)
Self-developed
3 item
recovery
survey;
momentary
experience of
energy
Longitudinal study: 3
times-a-day survey for 4
consecutive days
Multilevel mixed model
analysis
84
employees
Compulsive Internet use at work
(on the day) and compulsive
Internet use before bed both
decrease energy (recovery)
before going to bed only for the
more compulsive users
Compulsive Internet use at work
(on the day) decrease energy
(recovery) in the morning after
only for the more compulsive
users
ß = -.31
ß = -.39
ß = -.19
ß = -.52
Ragsdale
and Hoover
(2016)
Work-related
emotional
exhaustion
Work related
vigor (as part
of work
engagement)
MBI,
UWES
Longitudinal study: 2-
wave survey study:
predictors at Time 1,
criteria at Time 2 (after
one week)
Hierarchical regression
213
full time
employees,
cell-phone
users
Work-related cell-phone use
(WRCPU) increases emotional
exhaustion only for those low
on “cell-phone attachment”
(CPA; those who answer
immediately and cannot imagine
their lives without a phone)
WRCPU increases vigor (as part
of work engagement) only for
those high on CPA
ß = -1.38
(WRCPU x
CPA)
ß =1.57
(WRCPU x
CPA)
Rashid
and Asghar
(2016)
Student
vigor
(as part of
engagement)
UWES
Survey study
Path analysis,
Regression analysis
761
female
undergraduate
students
Technology use (in general)
increases student engagement
directly, and through self-
directed learning
ß = .31,
ß = .32, ß =.45
Reinke and
Chamorro-
Premuzic
(2014)
Work-related
emotional
exhaustion
(as part of
burnout)
Unipolar OLBI
scale:
(emotional
exhaustion and
Survey study
Structural equation
modeling
201
employees
Core self-evaluation (how
satisfied people are with
themselves) increases (felt) e-
mail overload which in turn
ß = -.33;
ß =.29
- 46 -
Work related
vigor (as part
of work
engagement)
vigor are
combined
increases “burnout” (decreased
vigor)
The number of received e-mails
is not a significant predictor
Rhee
and Kim
(2016)
Energy
as affect
Fatigue
as affect
Work-related
emotional
exhaustion
Work-related
vigor
AD ACL
MBI
UWES
Survey study
Structural equation
modeling
425
employees
Type of break – smart-phone
breaks(sb) vs. conventional
break (cb) –moderate the
relationship between
psychological detachment and
fatigue
Type of breaks do not moderate
the relationship between
psychological detachment and
energy
Energy as affect is related to
after-work vigor (no
moderation)
Fatigue as affect is related to
emotional exhaustion (no
moderation)
ß = -.19 for sb;
ß = .04 for cb
ß =.53 for sb
ß = .42 for cb
ß =.57 for sb
ß = .66 for cb
ß =.46 for sb
ß = .30 for cb
Salanova,
Grau,
Cifre, and
Llorens
(2000)
Work-related
emotional
exhaustion MBI
Survey study
Hierarchical regression
analysis
140
workers from five
different companies
form the tile sector
and public
administration
Computer training increases
exhaustion for those low on
self-efficacy; it decreases it for
those high on self-efficacy
NR/NA
Sardeshmukh
et al.
(2012)
Work-related
emotional
exhaustion
MBI
Britt’s (1999)
job
Survey study
Structural equation
modeling
417
employees from
Extent of telework decreases
exhaustion trough reducing time
pressure and role conflicts, and
through increasing autonomy
ß = .10; ß = .09;
ß = -16; ß = .14
ß = .12; ß = -.34
- 47 -
Vigor
(as part of
job
engagement)
engagement
scale
a large supply chain
management
company
Extent of telework increases
exhaustion trough increasing
role ambiguity and through
decreasing feedback and social
support
Extent of telework increases
engagement trough increasing
autonomy
Extent of telework decreases
engagement trough decreasing
feedback and social support
ß =.13; ß = .16
ß =-.22; ß =-.11
ß =-.10; ß =-.28
ß = .12; ß = .30
ß = -.22; ß = .23
ß = -.10; ß = .19
Satici and
Uysal
(2015)
Subjective
vitality SVS Survey study
Regression analysis
311
university students
from 2 mid-sized
universities
Problematic Facebook use is
related to diminished vitality ß =-.15, ß =-.24
Serrano
et al.
(2016)
Subjective
arousal,
Valence
Arousal,
Valence survey
(self-
assessment
manikin scale)
Experiment: Pre-test Post-
test treatment survey with
1h long mood inducing
VR; 4 different VR
technologies
ANOVA
136
adult participants
who did not have
anxiety and/or
depression
symptoms
After mood induction, arousal
decreased and affective valence
increased NR/NA
Shin
and Shin
(2016)
Mobile
messenger
fatigue
Adopted the
scale from Lee
et al. (2016)
Survey study
Structural equation
modeling
334
mobile messenger
users
Mobile messenger overload
(commercial and non-
commercial) drives messenger
fatigue. Personality (relational
self) moderates the non-
commercial relationship
ß = .12; ß = .63
ß = .67; ß = .69
- 48 -
Steinberger,
Schroeter,
and Watling
(2017)
Subjective
arousal,
Valence
Arousal,
Valence survey
(self-
assessment
manikin scale)
Experiment: Within-
subject, repeated measure
driving simulator study, 2
counterbalanced
conditions, control and
intervention; intervention
= 16 min driving with a
gamification app (phone
with app mounted on the
car window
U-test
32
young male
drivers
The gamified driving did not
change subjective arousal Cohen’s d = .07
van Zoonen
and Rice
(2017)
Work-related
emotional
exhaustion
Work-related
vigor
MBI
UWES
Study 2: Survey study
Structural equation
modeling
364
employees;
102
from a Telecom
provider;
112
from a consultancy
firm and
150
from a consumer
electronics
company
Using SNS for work purposes
(Twitter, Linked In and
Facebook) increases exhaustion
through increasing work
pressure
Using SNS for work purposes
(Twitter, Linked In and
Facebook) decreases exhaustion
through increasing autonomy
Using SNS for work purposes
(Twitter, Linked In and
Facebook) increases vigor
through increasing autonomy
Responsiveness moderates the
relationship between SNS use
and autonomy
ß = .48, ß = .43
ß = .15; ß = -.25
ß = .15, ß = .37
ß = .08
Windeler,
Chudoba, and
Work-related
emotional
exhaustion
MBI
Study 1: Longitudinal
study: 2-wave survey
study: 1 week prior to-
51
employees from a
IT business unit of
Part time teleworking (PTT)
increased exhaustion through
external interaction
ß = -.11 pre;
ß = .37 post
teleworking
- 49 -
Sundrup
(2017)
telework including
baseline exhaustion and 4
months later (post
telework);
Structural equation
modeling
Study 2: Survey study;
Structural equation
modeling
a financial service
firm randomly
selected to part-
time telework
258
employees
(160 part time
teleworkers)
PTT decreased exhaustion
through external interaction
Job interdependence explained
more of the exhaustion variance
for no part time employees than
for PT teleworkers
External interaction explained
less of the exhaustion variance
for no part time employees than
for PT teleworkers
Interaction quantity explained
more of the exhaustion variance
for no part time employees than
for PT teleworkers
ß = .28 pre;
ß = .03 post
teleworking
ß = .31 for No
PTT, ß = .17 for
PTT
ß = .18 for No
PTT, ß = .38 for
PTT
ß = .19 for No
PTT, ß = .05 for
PTT
Xie, Ma,
Zhou,
and Tang
(2018)
Work-related
emotional
exhaustion
MBI
Study 1: Survey study
Regression analysis
Study 2: Survey study
Regression analysis
447
college
councilors
437
full time
employees
Study 1. Work related ICTs use
after work hours increases
exhaustion even after
controlling for integration
preference.
Integration preference is
negatively related to exhaustion
(even after controlling for ICT
use).
The interaction is also
significant predictor
ß = .24
ß = –.26
ß = –.20
ß = .26
ß = .28
- 50 -
Study 2. Work related ICTs use
after work hours increases
exhaustion even after
controlling for integration
preference.
The interaction is between ICT
use and integration preference is
also a significant predictor
Zhang
et al.
(2016)
SNS
fatigue
Adopted and
self-developed
scale for SNS
fatigue
Survey study
Regression analysis
525
SNS (Qzone)
users
System feature overload,
information overload and social
overload drive SNS fatigue
ß = .24
ß = .20
ß = .37
Zheng
and Lee
(2016)
SNS
exhaustion
(strain)
Adopted MBI
for social
media
exhaustion
Survey study
Regression analysis
550
mobile SNS
users
Excessive SNS use and
cognitive preoccupations
increase SNS exhaustion
through creating technology-
family conflict;
through technology-personal
conflict; and through
technology-work conflict.
Technology-personal conflict is
additionally predicted both by
technology-family and
technology-work conflict)
ß = .60, ß = .08
ß = -.08, ß = .65
ß = .42, ß = .23
ß = .11, ß = .08
ß = .18, ß = .65
ß = .17, ß = .23
ß = .35, ß = .45
- 51 -
