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Binge-Watching: What Do we Know So Far? A First Systematic Review of the Evidence

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Purpose of Review Along with the expansion of on-demand viewing technology, the practice of binge-watching (i.e., watching multiple episodes of TV series back-to-back) has recently gained increasing research interest, given its potential harmfulness and presumed addictive characteristics. The present article provides the first systematic review of the evidence regarding this increasingly widespread behavior. Recent Findings The results of this systematic review (including 24 studies and 17,545 participants) show that binge-watching remains an ill-defined construct as no consensus exists on its operationalization and measurement. Although such methodological disparities across studies hinder the comparability of results, the preliminary findings gathered here mainly point to the heterogeneous nature of binge-watching which covers at least two distinct realities, i.e., high but non-harmful engagement and problematic involvement in TV series watching. Summary In these early stages of research, there is a major need for more consistency and harmonization of constructs and their operationalizations to move forward in the understanding of binge-watching. Just as important, future research should maintain the distinction between high and problematic involvement in binge-watching to avoid overpathologizing this common behavior.
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TECHNOLOGY ADDICTION (J BILLIEUX, SECTION EDITOR)
Binge-Watching: What Do we Know So Far? A First Systematic
Review of the Evidence
Maèva Flayelle
1
&Pierre Maurage
2
&Kim Ridell Di Lorenzo
1
&Claus Vögele
3
&Sally M. Gainsbury
4
&Joël Billieux
1,5
#Springer Nature Switzerland AG 2020
Abstract
Purpose of Review Along with the expansion of on-demand viewing technology, the practice of binge-watching (i.e., watching
multiple episodes of TV series back-to-back) has recently gained increasing research interest, given its potential harmfulness and
presumed addictive characteristics. The present article provides the first systematic review of the evidence regarding this
increasingly widespread behavior.
Recent Findings The results of this systematic review (including 24 studies and 17,545 participants) show that binge-watching
remains an ill-defined construct as no consensus exists on its operationalization and measurement. Although such methodological
disparities across studies hinder the comparability of results, the preliminary findings gathered here mainly point to the hetero-
geneous nature of binge-watching which covers at least two distinct realities, i.e., high but non-harmful engagement and
problematic involvement in TV series watching.
Summary In these early stages of research, there is a major need for more consistency and harmonization of constructs and their
operationalizations to move forward in the understanding of binge-watching. Just as important, future research should maintain
the distinction between high and problematic involvement in binge-watching to avoid overpathologizing this common behavior.
Keywords Binge-watching .TV series .Systematic review .Operationalization .Assessment .Correlates
Introduction
Video streaming platforms (e.g., Netflix, Hulu, Amazon
Prime) have been expanding at a fast pace in the past few
years. Combining ease of use (affordability and wide accessi-
bility through just about any internet-connected device) and
prolific content libraries available on-demand at onesconve-
nience, these services are now part of millions of TV series
viewersdaily routines [13]. Central to the changes afforded
by these technologies is the move away from the traditional
week-by-week release of episodes with entire seasons of TV
series now being made available at once. As a prime indicator
of the cultural shift in watching, binge-watching (i.e.,
watching multiple episodes of a TV series back-to-back) has
rapidly become the new normative mode of viewing TV
shows, especially among young adults [1,4].
Nevertheless, in this unparalleled era where viewers are
free to watch literally as many TV series episodes as wanted,
and where problematic online behaviors are taken seriously, a
new sector of research recently emerged, building on the
This article is part of the Topical Collection on Technology Addiction
*Maèva Flayelle
maeva.flayelle@gmail.com
*Joël Billieux
Joel.Billieux@unil.ch
1
Addictive and Compulsive Behaviours Lab, Institute for Health and
Behaviour, University of Luxembourg,
Esch-sur-Alzette, Luxembourg
2
Louvain Experimental Psychopathology Research Group (LEP),
Psychological Sciences Research Institute, Université catholique de
Louvain, Louvain-la-Neuve, Belgium
3
Department of Behavioural and Cognitive Sciences, Institute for
Health and Behaviour, University of Luxembourg,
Esch-sur-Alzette, Luxembourg
4
School of Psychology, Brain and Mind Centre, University of Sydney,
Sydney, Australia
5
Institute of Psychology, University of Lausanne,
Lausanne, Switzerland
Current Addiction Reports
https://doi.org/10.1007/s40429-020-00299-8
notion that prolonged involvement in binge-watching leads to
problematic patterns of TV series viewing and deleterious
consequences. Among the initial evidence of impairments as-
sociated with excessive binge-watching are insomnia and
chronic fatigue [5], a sedentary and unhealthy lifestyle [6],
negligence of other activities [7,8], and reduction of social
relationships [7,9]. While the compelling nature of TV series
may be considered as posing a genuine challenge to viewers
self-control abilities, there is a widespread asssumption in the
literature that binge-watching has addictive qualities [6,
1013] although a specific framework of understanding still
needs to be elaborated.
In a structured effort to progress in this direction, the pres-
ent article aims at providing the first systematic review of
existing data on binge-watching.
Methods
In accordance with PRISMA (Preferred Reporting Items for
Systematic Reviews and Meta-Analyses) guidelines [14], we
carried out a systematic literature review. We identified relevant
studies by consulting two academic databases (Scopus and
PsycINFO)andGoogle Scholar, using the following algorithm:
[Binge-watchingOR Binge-viewingOR Marathon view-
ingOR Marathon watchingOR Media marathoningOR
Increased viewingOR Excessive viewingOR
Problematic viewingAND TV seriesOR TV shows
OR TV dramas]. Articles were retained for consideration de-
pending on whether they were: (1) published in a peer-reviewed
journal from 1st of January 2013 to 11th of September 2019
(this time window covering the period from the inaugural year
1
when the term binge-watchingentered the popular vocabu-
lary to our search date); (2) published in English; (3) dealing
with the practice of binge-watching episodes of TV series (i.e.,
involving a measurement of this specific behavior or, at least, of
the extent of engagement in TV series watching); and (4) rely-
ing on quantitative data (theoretical articles, qualitative studies
and single case reports were excluded).
The initial search yielded 892 results (11 in Scopus,176in
PsycINFO,705inGoogle Scholar) that were processed ac-
cording to the multi-step procedure depicted in Fig. 1. A first
removal of duplicates led to the retention of 789 records. All
of them were subsequently screened from their title/abstract.
As a result, 19 articles were found to match the current search
criteria (see Fig. 1) and were therefore subjected to a full-text
reading for appraising their overall relevance to our topic. This
step led to the further deletion of 1 article reporting the results
of a study designed for marketing research. Finally, the refer-
ence lists of the 18 retained articles were considered for the
purpose of identifying other potentially relevant studies,
which resulted in the inclusion of 6 additional articles follow-
ing full-text review. Consequently, 24 papers were included in
the current systematic literature review.
For all retained articles, the following data were systemat-
ically extracted: (1) the identification of the study (names of
the authors, year of publication, country); (2) the characteris-
tics of the sample (sample size, age, gender ratio); (3) the
assessment of binge-watching behavior (operationalization,
measurement, reported prevalence); (4) the design of the study
(methodology type, set of variables measured); and (5) the
identified correlates of binge-watching (divided across the
following categories: socio-demographics, motivations, per-
sonality traits, positive/negative outcomes, and mental health).
Additionally, an assessment of each studys methodological
quality was conducted by using the Appraisal tool for
Cross-Sectional Studies(AXIS) [15], the selection of which
was guided by the fact that most included studies were obser-
vational and cross-sectional in design. This 20-item scale, de-
veloped on the basis of an international Delphi procedure,
evaluates the appropriateness of study design, reporting qual-
ity and risk of bias in cross-sectional studies across disciplines.
Nevertheless, as this tool does not involve any quality assess-
ment score, we used the shortened version from Sacolo,
Chimbari, and Kalinda [16], consisting of 10 yes/no ques-
tions, resulting in a total score to give a quality rating from 1
to 4 (low), 57(moderate)to810 (high). The details of this
assessment per item/question and the total quality score for
each study are presented in Table 1.
Key Characteristics of the Studies
A summary of the information extracted from each of the 24
included articles is presented in Table 2. The reviewed studies
primarily focused on the following: (1) the investigation of
factors (e.g., personality traits, psychopathology) related to
binge-watching (58% of the studies); (2) the identification of
binge-watching motivations (25%); (3) the development and
validation of related measurement instruments (17%); (4) the
characterization of binge-watching frequency (13%) and its
definition (8%); and (5) the experimental testing of its impact
on audience engagement (8%). The flourishing of binge-
watching research over recent years is reflected by the growing
number of scholarly articles, with the first one published in
2015 [17],2in2016[18,19], 7 in 2017 [2026], 9 in 2018
[2735], and already 5 released until 11th of September 2019
(i.e., date on which the literature search was performed)
[3640]. In most instances, these studies were carried out in
the USA (n= 12), while the remaining ones took place in
Belgium (n= 2), Hungary (n= 2), South Korea (n=2),
1
Google Trends (https://www.google.com/trends/) clearly shows that binge-
watchingstarted to become a search term of interest in February 2013,
coinciding with the first time when Netflix released simultaneously all 13
episodes of the first season of House of Cards.
Curr Addict Rep
Australia (n=1),China (n= 1), Germany (n=1),Poland(n=
1), the United Arab Emirates (n=1), andtheUK(n=1).With
the exception of two studies involving experimental designs
[22,36], all are online cross-sectional survey-based studies. A
total of 17,545 participants took part in the 24 reviewed studies
with an average female representation of 69.3% (n= 12,162)
and a mean age of 26.4 years (SD = 5.60; range 1882), calcu-
lated on the basis of papers reporting this information (n= 19)
[1722,24••,2529,3335,3740].AsshowninTable1,the
quality scoring of these studies ranges from moderateto
highvalues, with 63% of them [18,19,2126,28,29,
3135,3740] assessed as highin methodological quality.
Operationalization of Binge-Watching
We identified considerable variability in the operational defi-
nitions proposed for binge-watching, with some articles even
specifying two different options [17,23,29,30,33,39], thus
bringing the total number of distinct possibilities to 19 across
the 28 definitions listed in the studies directly operationalizing
binge-watching (22/24). These operationalizations almost sys-
tematically consist of the following sequence of sub-
components: (1) a quantity based-index, (2) the characteriza-
tion of the content, and (3) a time pattern. With respect to the
first feature (i.e., quantity-based index), it appears that binge-
watching is predominantly understood as the amount of epi-
sodes (n=19) and programs(n=1) [17,18,20,21,2325,
2832,34••,3540] or, more rarely, of hours spent viewing
(n=2)[22,23], comprising an underlying notion of multiplic-
ity [18,21,27,30,31,37] or the genuine specification of
quantitative cutoffs, ranging from watching more than 1 epi-
sode (n=3)[17,20,28], to 2 episodes (n=6)[17,23,25,32,
35,40], and 3 episodes (n=7)[24••,29,34••,36,38,39], or
watching for more than 1 h [23]or3h[22]. Rubenking and
Bracken [29] added a further subtlety by adapting their pro-
posed threshold to the typical length of the show (i.e., 30-min
or hour-long episodes), but this constitutes an exception
among current definitions. In turn, last alternatives involved
more broad-based patterns by relying on the viewing of a full
season [30,33,39]oranentireseries[33]. From the second
feature (i.e., characterization of the content), most
operationalizations referred to the viewing of the same series
(n=20)[1721,23,24••,2830,3237,39], while the rest of
them delt with undifferentiated programs (n=5) [22,27,29,
30,38] or did not specify the type of binge-watched content
Fig. 1 Flowchart of studies screening and selection process
Curr Addict Rep
(n=3) [25,31,40]. Finally, with regard to the third feature
(i.e., time pattern), the proposed operationalizations involved
various timeframes, the majority of which referring to the
notion of consecutiveness, i.e., in a single sitting(n=22)
[1732,34••,35,36,37,38,39], whereas the remaining ones
relied on the following distinct temporalities: in a small
amount of time[33], a day[40], in several days[17,
30], and within a week[39]. A graphical overview of these
operational disparities across studies is provided in Fig. 2.
Unsurprisingly, the lack of a validated and common definition
Table 1 Study assessments and total scores using the Appraisal Tool for Cross-Sectional Studies (AXIS), shortened version
Authors (year) Scores for each item Total score Quality rating
12345678910
Pittman & Sheehan [17] YYNYNNYYYN 6 Moderate
Conlin et al. [18] YYNYYYYYNY 8 High
Orosz et al. [19] YYNNNYYYYY 7 Moderate
Ahmed [20] YYNYNNYYNN 5 Moderate
Exelmans and Van den Bulck [21]YYNYYYYYYY 9 High
Horvath et al. [22] YYNYYYYYNY 8 High
Panda and Pandey [23] YYNYYYYYYN 8 High
Riddle et al. [24••] YYNYYYYYYY 9 High
Spruance et al. [25] YYNYYYYYYY 9 High
Tóth-Király et al. [26] YYNNYYYYYY 8 High
Granow et al. [27] YYNYYNYYYN 7 Moderate
Merikivi et al. [28] YYNYYYYYYN 8 High
Rubenking and Bracke n [29] YYNYYNYYYY 8 High
Shim et al. [30] YYNYYNYYYN 7 Moderate
Shim and Kim [31] YYNNYYYYYN 7 Moderate
Sung et al. [32] YYNYYYYYYY 9 High
Tefertiller and Maxwell [33] YYNNYYYYYY 8 High
Tukachinsky and Eyal [34••] YYNYYYYYYY 9 High
Walton-Pattison et al. [35] YYNNNYYYYY 7 Moderate
Erickson et al. [36] YYNYYNNYYN 6 Moderate
Flayelle et al. [37] YYNYYYYYYY 9 High
Merill and Rubenking [38] YYNYYNYYYY 8 High
Pittman and Steiner [39] YYYNNNYYYY 7 Moderate
Starosta et al. [40] YYNYYYYYYY 9 High
Questions related to each item (the main or complementary factors assessed are in italics)
Introduction
(1) Were the aims/objectives of the study clear?
We notably evaluated the clarity of the research question and its relevance in view of the presented literature.
Method
(2) Was the study design appropriate for the stated aim(s)?
(3) Was the sample size justified?
Be it based on previous studiessample sizes or on statistical calculation.
(4) Was the target/reference population clearly defined? (Is it clear who the research was about?)
We centrally checked whether inclusion/exclusion criteria were specified.
(5) Were the risk factor and outcome variables measured correctly using instruments/measurements that had been trialed, piloted or published previously?
(6) Were the methods (including statistical methods) sufficiently described to enable them to be repeated?
We also evaluated the validity and reliability of the measures used.
Results
(7) Were the results presented for all the analyses described in the methods?
We also evaluated the validity of the analyses conducted and results obtained.
Discussion
(8) Were the authorsdiscussions and conclusions justified by the results?
(9)Were the limitations of the study discussed?
Other
(10) Was ethical approval or consent of participants attained?
Nno, Yyes
Curr Addict Rep
of binge-watching is clearly identified by the authors as a
major obstacle to coherence and reproducibility in current
early binge-watching research [17,20,21,29,30,33,3537].
Assessment and Prevalence
of Binge-Watching
Similar to operationalizations of binge-watching, its measure-
ment substantially varies across papers. In the absence of ac-
cepted assessment criteria, most studies simply relied on glob-
al quantity estimates, as usually done in media research [41],
revolving around three sets of indicators: (1) the frequency,
assessed in various terms (i.e., generally speaking, over the
last month, over the last week), of binge-watching (n=9)
[1721,24••,29,32,35,38,39]; (2) the average duration
of one viewing session (n=7) [20,21,25,29,32,35,38];
and the number of episodes usually watched (per viewing
session or per day; n=5)[20,21,32,34••,35]. These criteria
were either assessed alone [24••,39] or in different combina-
tions [20,21,29,32,35,38,40], sometimes complemented
by additional idiosyncratic questions relating to the intention
(i.e., planning ahead) and severity of binge-watching [17], the
number of consecutive days spent watching a show recently
[34••], or by a non-validated measure of narrative transporta-
tion (i.e., deep sense of immersion into the world of a story)
[32]. These indicators (or their combination) have been used
as stand-alone dependent variables [17,21,29,38]orcon-
strued as forming a composite binge-watching score [20,32,
34••,35,40]. For the remaining studies where there was no
use of such criteria, binge-watching was assessed through oth-
er questions relating to: its recent occurrence based on partic-
ipantsself-perceived binge-watching duration [25], the gen-
eral tendency to binge-watching [30,31], and the pace of
watching a particular series [18,33]. Finally, beyond mere
measurement items, four studies used custom made and non-
validated binge-watching measures of intention [23], tenden-
cy [27], behavioral/cognitive involvement [28], or excessive-
ness [40], while three studies used proposed psychometrically
validated measurement instruments [19,26,37]. The concep-
tual underpinnings and robustness of methods behind the cur-
rent assessment of binge-watching are thus manifold, this het-
erogeneity again being disclosed as impeding consistency
among existing studies [21,24••,27,29,38]. Such plurality
of measurement alternatives also poses a major challenge to
replication of results and data comparability, which is present-
ly made difficult due to these discrepancies at theoretical and
methodological levels. The available prevalence data offer a
prime example of this as, based on their respective assessment
criteria, studies (n= 12) report a prevalence rate of binge-
watching ranging from 44.6 to 98%. It should be stressed,
however, that all of these form an average prevalence of
72.14%, thus suggesting that binge-watching is not an atypical
viewing practice, but rather the norm across the current sam-
ples, which corroborates recent market reports [1,4].
Emerging Profiles of Binge-Watchers
The results from the studies included in this systematic review
suggest a number of susceptibility factors for binge-watching,
which provides some preliminary insight into binge-watchers
profile. A first category of binge-watching correlates concerns
their socio-demographic characteristics. A number of studies
showed a positive association between female gender and en-
gagement in binge-watching, either in terms of frequency and
intensity of viewing sessions [17,21,25,38], or of loss of
control over watching [19,40]. Nevertheless, results are in-
consistent across studies as, in addition to thoses who found
no gender effect [17,20,2931,38], Exelmans et al. [21]
report that binge-viewing sessions lasted longer among men.
Similarly, some of the reviewed studies suggest that younger
age is positively associated with overall binge-watching [20,
30,31], its frequency [29], and problematic series watching
[19], while others have not reproduced such correlations [17,
38]. Finally, while single individuals (in terms of partnership
status) were generally more severe binge-watchers [20], edu-
cational level was found to be both positively [25] and nega-
tively [19,30] related to binge-watching. Such discrepancies
again underline that current results are highly dependent on
the binge-watching operationalization (and measurement)
used.
Be this as it may, more areas of commonality among the
reviewed studiesfindings can be identified with respect to
binge-watchersmotivations, this time establishing a clearer
picture. Consistent with the Uses & Gratifications framework
asserting that media use is primarily driven by needs satisfac-
tion [42,43], binge-watchersengagement in TV series view-
ing appears to derive from various outcome expectations with
a clear preponderance of hedonistic motivations (i.e., enter-
tainment, enjoyment) [17,29,31,32,37,40]. The motivation-
al pull of TV series binge-watching seems, therefore, to stem
from a first set of drivers that concern the maximization of
enjoyable attributes of viewing: better engagement with the
content [17], greater fan enthusiam [31], deeper experience of
suspense/anticipation [29], and stronger feeling of getting
swept away in the story (i.e., narrative transportation) [39].
In accordance with such a derived-benefitsview of binge-
watching, more eudaemonic (e.g., personal enrichment, infor-
mation seeking) and reward-based motivations have also been
found to play a role for binge-watchersinvolvement [37,38,
40]. At the same time, however, a second cluster of motiva-
tional correlates emerged across studies to make binge-
watching appear as something rather compensatory: high
levels of binge-watching were associated with the motivations
of passing time [32,40], dealing with loneliness [40], and
Curr Addict Rep
Table 2 Description and main results of the studies included in the systematic review
Authors (year) Country Participants Binge-watching assessment Study design
NAge
(M
age
)
Females
(%)
Operationalization Measure Prevalence
(%)
Method
Pittman and Sheehan [17] USA 262 29 62 Watching 2 or more episodes of the same series in a single
sitting,
or watching 1 or more episodes of the same series for several
consecutive days.
Frequency (Fre)
Intention (Int)
Severity (Sev)
97 Online survey
Conlin, Billings and Averset [18] USA 160 35.2 48.8 Consuming multiple episodes of the same TV show in one
sitting.
Pace of watching NR Online survey
Orosz, Böthe and Tóth-Király
[19]
Hungary 1118 25 71.7 NA PSWS NA Online survey
Ahmed [20] UAE 260 25.8 51.9 Watching more than 1 episode from the same TV content
consecutively in the same session.
Frequency
Duration
Number of episodes
(composite score)
44.6 Online survey
Exelmans and Van den Bulck
[21]
Belgium 423 22.2 61.9 Watching multiple consecutive episodes of the same TV show in
one sitting.
Frequency (Fre)
Duration (Dur)
Number of episodes (Num)
80.6 Online survey
Horvath et al. [22] Australia 51 22.2 57 Viewing of 3 or more hours of programming within a single
sitting.
NR NR Laboratory
experiment
Panda and Pandey [23] USA 229 NR 56 Watching a minimum of 23 episodes of the same series
or
at least 1 h of the same TV series in one sitting.
Intention
(created measure)
NR Online survey
Riddle et al. [24] USA 171 19.9 75 Watching 3+ episodes of the same TV program in one sitting. Frequency intentional BW
(Fre-In)
Frequency unintentional BW
(Fre-Un)
98 Online survey
Spruance et al. [25] USA 500 20.6 57.8 Watching between 2 and 6 episodes in one sitting. Self-perceived duration
Occurrence last week
Occurrence last month
20 (weekly)
72
(monthly)
Online survey
Tóth-Király et al. [26] Hungary 1520 30.1 72.2 NA SWES NA Online survey
Granow, Reinecke and Ziegele
[27]
Germany 499 28.2 67 Intense and consecutive consumption of series in a single sitting. Tendency
(created measure)
NR Online survey
Merikivi et al. [28] China 227 21 77.2 Consuming more than one episode of the same television show
in one sitting.
Behavioral involvement (Beh)
Cognitive involvement (Cog)
NR Online survey
Rubenking and Bracken [29] USA 797 35.5 56.5 Watching 3 to 4 or more 30-min shows
or
3 episodes or more of hour-long television episodes of the same
show in one sitting.
Frequency (Fre)
Duration (Dur)
NR Online survey
Shim et al. [30] South
Korea
714 NR 52.4 Watching multiple episodes of programs in a single sitting
or
an entire season over the course of a few days.
Tendency 64 Online survey
Shim and Kim [31] South
Korea
785 NR 53.1 Watching multiple episodes in a single sitting. Tendency 70 Online survey
Sung, Kang and Lee [32] USA 292 NR 76.4 Frequency 75.8 Online survey
Curr Addict Rep
Tab l e 2 (continued)
Watching 2 or more episodes of the same TV series in one
sitting.
Duration
Number of episodes
Engagement
(composite score)
Tefertiller and Maxwell [33] USA 215 36 46 Consuming a full TV season
or
series in a relatively small amount of time.
Pace of watching 80 Online survey
Tukachinsky and Eyal [34] USA 167 20 81 Watching at least 3 episodes of a program in one sitting. Number of consecutive days
Number of episodes
(composite score)
96.5 Online survey
Walton-Pattison, Dombrowski
and Presseau [35]
UK 86 30 67 Watching more than 2 episodes of the same TV show in one
sitting.
Frequency
Duration
Number of episodes
(composite score)
NR Online survey
Erickson, Dal Cin and Byl [36] USA 77 NR 76 Watching multiple episodes, generally 3 or more, of a television
program in rapid succession.
NR NR Laboratory
experiment
Flayelle et al. [37] Belgium 6556 24.5 77.6 Watching multiple episodes of the same TV series in one session. BWESQ NR Online survey
Merill and Rubenking [38] USA 651 20.5 63.6 Watching 3 or more episodes of television content in one sitting. Frequency (Fre)
Duration (Dur)
89.4 Online survey
Pittman and Steiner [39] USA 781 35.4 44.2 Viewing of 3 or more episodes of a show in a row (or 2 episodes,
if it is a longer show)
or
watching a whole season of a show within a week.
Frequency deliberate
BW
Frequency background BW
Frequency accidental BW
(composite score)
NR Online survey
Starosta, Izydorczyk and
Lizińczyk [40]
Poland 1004 22 85 Watching from 2 episodes a day. QEBWB 50 Online survey
Authors (year) Study design Binge-watching correlates
Variables measured Socio-demographics Motivations Personality traits Positive outcomes Negative
outcomes
Mental health
Pittman and Sheehan
[17]
Demographics
Programs and platforms used
BW behavior
BW-related motivations
(based on previous non-validated
measure)
+ Being a woman
(Sev)
+ Engagement
(Fre,Int,Sev)
+ Hedonism (Int, Sev)
+ Social (Sev)
Conlin, Billings and
Ave rse t [18]
Demographics
BW behavior
Fear of Missing Out scale
Social media use
+Fearof
missing out
Orosz, Böthe and
Tóth-Király
[19]
Demographics
Problematic Series Watching Scale
Amount of free time
Time spent watching
+ Being a woman
+ Being younger
- Education
Ahmed [20]Demographics + Being younger +Depression
Curr Addict Rep
Tab l e 2 (continued)
Viewing habits
BW behavior
Depression
(based on previous non-validated measure)
UCLA Loneliness scale
+ Being single
Exelmans and Van
den Bulck [21]
Demographics
Perceived physical health
Exercice level
Bedtime TV viewing
BW behavior
Pittsburgh Sleep Quality Index
Fatigue Assessment Scale
Bergen Insomnia Scale
Pre-Sleep Arousal Scale
+ Being a woman
(Fre)
+ Being a man (Dur)
. + Poor sleep
quality (Fre)
+ Daytime
fatigue (Fre)
+ Pre-sleep
arousal (Fre)
+ Symptoms of
insomnia
(Fre)
Horvath et al. [22] Weekly group [1 episode per week over 6
consecutive weeks]
Daily group [1 episode per day over 6
consecutive days]
Binge group [6 episodes in a single setting]
Perceived comprehension
(immediately after show
completion/1 week later/140 days later)
Retention
(24 h later/1 week later/140 days later)
- Enjoyment
- Sustained memory
Panda and Pandey
[23]
Demographics
BW behavior
BW-related motivations
(based on previous qualitative investigation
and non-validated measures)
BW-related outcomes
(based on previous qualitative
investigation)
+ Social engagement
+Escape
+ Accessibility
+ Advertising
influence
+Negative
gratifications
Riddle et al. [24]Demographics
BW behavior
(semester weekdays/ semester
weekends/semester breaks)
TV Addiction Scale
(items adapted to BW)
Barratt Impulsivity Scale
Grade Point Average
+ Impulsivity
(Fre-Un)
+ Addiction
symptoms
(Fre-Un)
Spruance et al. [25]Demographics
BW behavior (weekly/monthly)
Physical activity
Diet
BMI
+ Being a woman
(weekly,
monthly)
+ Education
(monthly)
- Healthy eating
(weekly, monthly)
Tóth-Király et al. [26]Demographics
Series Watching Engagement Scale
+ Harmonious passion + Obsessive
passion
Curr Addict Rep
Tab l e 2 (continued)
Problematic Series Watching Scale
Series Watching Passion Scale
Time spent watching
Big Five Inventory-10 Item Scale
-
Conscientious-
ness
+ Neuroticism
Granow, Reinecke
and Ziegele [27]
Demographics
BW behavior
Goal conflicts
State Shame and Guilt Scale (items
adaptedtoBW)
Autonomy (based on previous
non-validated measure)
Recovery Experience Questionnaire
(psychological detachmentand
relaxationsubscales)
Activation-Deactivation Checklist
(energyand tirednesssubscales)
Enjoyment
(based on previous non-validated measure)
+ Perceived autonomy
+ Recovery
+ Enjoyment
+Goal
conflicts
+ Feelings
of guilt
Merikivi et al. [28]Demographics
BW behavior
Usage satisfaction
(based on previous non-validated measure)
+ Usage satisfaction (Beh)
Rubenking and
Bracken [29]
Demographics
BW behavior
Appointment viewing frequency
Emotion Regulation Questionnaire
Brief Self-Control Measure
Self-efficacy
Self-Report Habit Index
(automaticitysubscale)
Suspense/Anticipation motives
+ Being younger + Emotion regulation
(Fre)
+
Suspense/Anticipat-
ion (Fre)
+ Automaticity
(Fre)
Shim et al. [30]Demographics
BW behavior
Media use
Negative attitudes toward BW
Deferment of Gratification Scale
(items adapted to BW)
Need For Cognition Scale
(items adapted to BW)
+ Being younger
- Education
+ Immediate
gratification
+Needfor
cognition
+Negative
feelings
Shim and Kim [31]Demographics
BW behavior
Media use
BW-related motivations (based on
previous qualitative investigation)
Need For Cognition Scale
(items adapted to BW)
+ Being younger + Enjoyment
+Efficiency
+ Fandom
+Needfor
cognition
+ Sensation
seeking
Curr Addict Rep
Tab l e 2 (continued)
Brief Sensation Seeking Scale
(items adapted to BW)
Sung, Kang and Lee
[32]
Demographics
General TV watching behavior
BW behavior
Programs and platforms used
Viewing Motivation Scale
Transportation
(based on previous non-validated measure)
+ Entertainment
+ Passing time
+ Transportation
Tefertiller and
Maxwell [33]
Demographics
BW behavior
Center for Epidemiological Studies Scale
of Depression
Brief State-Trait Anxiety Inventory Scale
Social and Emotional Loneliness Scale for
Adults (socialsubscale)
Self-control (based on previous
non-validated measure)
Emotion/Affect (while-viewing,
after-viewing; based on previous
non-validated measure)
Hedonic enjoyment and appreciation
(based on previous non-validated
measure)
- Meaningful affect
(after-viewing)
- Positive affect
(after-viewing)
-Depression
+ Anxiety
Tukachinsky and Eyal
[34]
Demographics
BW behavior
Attachment style
(based on previous non-validated
measure)
Center for Epidemiological Studies Scale
of Depression
UCLA Loneliness scale
Self-regulation
(based on previous non-validated
measure)
Narrative Transportation Scale
Enjoyment (based on previous
non-validated measure)
Parasocial Interaction Scale
Identification (based on previous
non-validated measure)
-Secure
attachment
- Self-regulation
+ Parasocial relationships
+ Identification
+Depression
Walton-Pattison,
Dombrowski and
Presseau [35]
Demographics
BW behavior
Viewing habits
Intention
Outcome expectations
(physical/affective/social)
+Outcome
expectations
+ Automaticity + Anticipated
regret
+ Goal conflict
Curr Addict Rep
Tab l e 2 (continued)
Self-efficacy
Self-Report Automatic Index
(items adapted to BW)
Anticipated regret
(based on previous non-validated
measure)
Goal conflict
Goal facilitation
Erickson, Dal Cin and
Byl [36]
Binge-condition [3 episodes in quick
succession]
Traditional condition [1 episode per week
for 3 weeks]
Enjoyment
Parasocial Interaction Scale (immediately
after show completion/1 week later)
Narrative Transportation Scale
+ Transportation
+ Parasocial relationships
(after show
completion/1 week later)
Flayelle et al. [37]Demographics
Watching TV Series Motives
Questionnaire
Binge-Watching Engagement and
Symptoms Questionnaire
Positive and Negative Affect Schedule
Compulsive Internet Use Scale
Alcohol Use Disorder Identification Test
Fagerström Test for Nicotine Dependence
+ Emotional
enhancement
(BW engagement)
+ Enrichment
(BW engagement)
+Social
(BW symptoms)
+ Coping/Escapism
(BW symptoms)
+Negative
affect (BW
symptoms)
+ Problematic
Internet use
(BW
symptoms)
Merill and Rubenking
[38]
Demographics
BW behavior
Motivated Strategies for Learning
Questionnaire (metacognitive
self-regulationsubscale)
Brief Self-Control Scale
Enjoyment Audience Response Scale
(items adapted to BW)
Reward watching
Procrastination (based on previous
non-validated measure)
Regret
+ Being a woman
(Dur)
+ Procrastination (Fre)
+ Reward watching
(Fre)
- Self-regulation
(Dur)
+ Enjoyment (Fre) - Regret (Fre)
Pittman and Steiner
[39]
Demographics
BW behavior (higher attentiveness,
lower attentiveness)
Big Five Inventory-10 Item Scale
Narrative completion motive
Narrative Transportation motive
Multitasking
Regret
+Narrative
transportation
- Agreeableness
- Conscientiousness
- Openness
+ Neuroticism
-Regret(HA
BW)
+Regret(LA
BW)
Starosta, Izydorczyk
and Lizińczyk [40]
Demographics
BW behavior
+ Being a woman + Escape
Curr Addict Rep
escaping from everyday worries [23,40], while higher fre-
quency was related to procrastination [38] and emotion reg-
ulation [29] purposes. In a similar vein, Flayelle et al. [37]
found that coping/escapism motivation was specifically linked
to problematic binge-watching, thus supporting the hypothe-
sis that problematic binge-watching involves maladaptive
coping or emotion-regulation strategies [44]. This line of
thinking is in accordance with results showing that problem-
atic involvement in a wide range of recreational activities
(e.g., drug use, video gaming, gambling, cybersex) reflects
as many different attemps to reduce aversive emotional states
[4547]. With regard to binge-watching, it is moreover note-
worthy that female viewers shown more inclination to such
purposes [37,40]. Other motives in seeking gratification relate
to the opportunity to bond with others by means of TV series
[17,23,37], although some studies have not found any asso-
ciation with such social expectations [32,40]. The current
systematic literature review shows, however, that the latter
relied on the same quantitative instrument assessing motiva-
tions for TV viewing in general (i.e., not specifically applying
to binge-watching), which prompts some reservations as to the
possible conclusions.
Finally, giving credence to theories of media exposure stat-
ing that userspersonality is a strong predictor of the intensity
of media consumption [48,49], specific associations between
individual differences in personality traits and propensity to
binge-watch also emanated. While viewers who get drawn
into binge-watching were found to be characterized by inse-
cure attachment [34••], low agreeableness, conscientiousness,
and openness [26,39], they presented, in contrast, high levels
of both neuroticism [26,39], need for cognition and sensation
seeking [30,31]. But above all, the reviewed literature reveals
the impulsive personality of binge-watchers. Riddle et al.
[24••], for example, found that high impulsivity was related
to increased levels of unintentional binge-watching (i.e., oc-
curring unexpectedly), which echoes other findings demon-
strating the relationship between self-regulation deficits and
binge-watching [34••,38]. Such evidence is in line with sub-
stantial media research showing that both impulsivity and self-
regulation failure constitute significant predictors of increased
(and even excessive/problematic) media use [5056]. In close
connection with the foregoing, the included studies also sug-
gest that heavy binge-watchers reported a higher predilection
toward immediate gratification [30], and that the frequency of
binge-viewing sessions was related to automaticity [29,35].
Binge-Watching Outcomes
In conjunction with the motivational profile of binge-watchers
described above, it comes as no surprise that binge-watching
is especially gratifying in the light of the review of its associ-
ated outcomes, according to which this behavior seems
Tab l e 2 (continued)
Viewing habits
Viewing Motivation Scale
Questionnaire of Excessive
Binge-Watching Behaviors
+ Dealing with
loneliness
+ Information
+ Spending free time
+ Entertainment
Note. + indicates a positive relationship whereas - indicates a negative relationship
NA not applicable, NR not reported, BWESQ binge-watching engagement and symptoms questionnaire, PSWS problematic series watching scale, QEBWB questionnaire of excessive binge-watching
behaviors, SWES series watching engagement scale
Curr Addict Rep
mainly supported by the deepening of viewersexperience
(and therefore engagement) during viewing. Coherent with
the widely held notion that increased engagement enhances
media effects (i.e., impact of media consumption on ones
beliefs, emotions, or behaviors) [57], binge-watching was re-
lated to higher levels of enjoyment [27,38], narrative trans-
portation [32], and identification with featured characters
[34••], with whom binge-watchers were also found to develop
stronger parasocial relationships [34••]. These conclusions are
further corroborated by Erickson and colleaguesexperimen-
tal findings showing that, of two groups of viewers being
asked to watch a TV show under different schedules (tradi-
tional episodic versus binge modes of viewing), the ones in
the binge-condition experienced higher narrative transporta-
tion while forming stronger and lasting parasocial relation-
ships with the seriesprotagonists [36]. Finally, binge-
watching was positively associated with several indicators of
well-being via perceived autonomy [27], as well as with usage
satisfaction [28] and harmonious passion [26]. This set of
results, however, contrasts with studies that failed to identify
a link between binge-watching and narrative transportation
[34••] or positive gratifications such as hedonic enjoyment
[20,34••]. Moreover, the other experimental study currently
available found that individuals who were (experimentally)
required to watch TV series episodes back-to-back not only
reported significant less enjoyment than those following a
dailyorweeklypaceofwatching[22], but also less enduring
content memory [22], which, in itself, is a likely indicator of
program engagement.
In parallel to this, a second line of evidence shows a
rather uniform picture of outcomes associated with binge-
watching, this time in a more negative light. Binge-
watching frequency was associated with reduced sleep
quality, daytime fatigue and insomnia, with cognitive
pre-sleep arousal mediating those relationships [21], while
a healthy diet was negatively correlated with overall
binge-watching [25]. Another self-report study found that
binge-watchers tend to experience a decrease of meaning-
ful and positive affect right after viewing, which led the
authors to suggest a post-binge-viewing show hole,i.e.,
a feeling of emptiness following show completion [33].
Binge-watching is also associated with obsessive passion
[26] and with goal conflicts and emotional distress (i.e.,
guilt, regret) [23,27,30,35], through the effect of which
such viewing practice was, besides, negatively related to
well-being [27]. Shim et al. [30] notably showed that,
among viewers characterized by a higher preference for
instant gratification, post-binge-watching feelings of re-
gret and guilt constitute positive predictors of subsequent
binge-viewing sessions. The same observation was made
by Panda and Pandey [23] who further commented that
viewers may alleviate such negative emotional states pre-
cisely by continuing to binge-watch TV series, thus
paving the way for a vicious circle that both research
teams consider as addictive in nature. Only one study
stands in stark contrast to the above claims by identifying
regret as a negative predictor of binge-watching frequency
[38], while other findings shed some light on the matter
by evidencing the moderating role of the level of atten-
tiveness paid to a show in whether motivations for binge-
watching predict decreased or increased later regret [39].
All these preliminary findings are very revealing about
how a nuanced understanding is necessary when ap-
proaching binge-watching. The two-sided picture
resulting from its reviewed correlates thus gives further
credit to the fact that media use may imply both positive
and negative media effects on userswell-being [58],
which are generally moderated by self-control abilities
exerted in those contexts [51].
Mental Health Correlates of Binge-Watching
The current systematic review emphasizes that heavy binge-
watchers experience psychopathological symptoms such as
anxiety (including fear of missing out) [18,33], depression
[20,34••]the effect of which is mediated by self-
regulation deficits [34••]addiction-like symptoms [24••],
and problematic Internet use [37], although results are some-
times mixed (e.g., Tefertiller et al. [33] found that depression
was associated with a decreased likelihood of binge-
watching). Consistent with this, the positive relationship be-
tween negative affect and problematic binge-watching [37]
continues to argue in favor of the notion of binge-watching
as an emotion-focused coping strategy. These associations
convey the idea that there are problematic comorbid versions
of binge-watching to be considered, for which preliminary
assumptions can be made in terms of underlying mechanisms.
In this respect, the Interaction of Person-Affect-Cognition-
Execution (I-PACE) model [59,60] provides a sound frame-
work within which the general results of this systematic re-
view can be interpreted. The I-PACE model describes the
processes involved in the development and maintenance of
the problematic use of online applications of any type (e.g.,
online gambling and gaming, cybersex, social networking,
online shopping) by considering both predisposing variables
representing core characteristics of the person (P), affective
and cognitive responses to external or internal stimuli (AC),
and executive functions, inhibitory control, and the decision to
use certain applications/sites (E). According to such a concep-
tual basis, it may be proposed that the impulsive personality of
binge-watchers acts as a predisposing factor which, in combi-
nation with misplaced coping mechanisms, interacts with de-
pressive mood to likely potentiate the risk of developing prob-
lematic binge-watching behavior.
Curr Addict Rep
Conclusions
As the digitization of TV series puts viewers in control within
an unprecedented all you can watchculture, binge-watching
has become a widespread behavior that has attracted increas-
ing researchinterest over the last 4 years. By summarizing and
discussing available quantitative data derived from these ini-
tial studies, the present overview of the current evidence
shows a coherent and nuanced picture where preliminary pat-
terns can be described. Navigating between gratification and
compensation, binge-watching appears not to represent a sin-
gle and uniform behavior but constitutes a complex phenom-
enon which shows at least two manifestations: (1) a highly
rewarding and pleasurable experience that may drive deliber-
ate and harmonious significant viewing involvement per-
formed in response to various needs and desires; and (2) an
excessive/problematic behavior not only associated with neg-
ative outcomes, but also with a range of risk factors associated
with dysfunctional use of technologies (e.g., age, underlying
coping motives, impulsivity, automaticity) and diverse mental
health conditions. Echoing a recent recommendation made for
video-gaming [61], high but healthy engagement in TV series
watching should be distinguished from problematic binge-
watching to avoid pathologizing this highly popular activity.
Additionally, in order to promote healthy patterns of engage-
ment among TV series viewers, future research should inform
policy and practices in the development and implementation
of strategies to minimize harms associated with problematic
use of such emerging technologies. For example, education on
potential risks to ones health and well-being (especially
among youths), provision of clear user guidelines on appro-
priate and inappropriate use of streaming platforms, as well as
the introduction of in-app tools to aid self-regulation in binge-
watching should be proposed [62].
Nevertheless, the current systematic review also demon-
strates recurring discrepancies in studiesfindings that need
to be put into perspective with the particular operationalization
of binge-watching and its related assessment. As highlighted
in this paper, binge-watching remains an ill-defined construct
without consensus regarding its (operational) definition across
studies, which use a whole host of assessment methods that
continue to impair comparability of data and results.
Therefore, this systematic review places a strong emphasis
on the current need to structure research efforts devoted to
binge-watching to overcome fragmentation and to promote
the soundness of this fast-developing research area. To this
end, particular avenues for future research are evident includ-
ing, among others, the development of a common evidence-
based definition of binge-watching (e.g., by determining ex-
pert consensus through a Delphi technique), and the expan-
sion of the factors investigated (in connection with both un-
problematic and problematic related involvement) with reli-
ance on standardized binge-watching self-report measures that
have proven to be reliable for use across research teams. Only
Fig. 2 Operationalization of binge-watching used in the studies (22/24) included in the systematic review. Each operational definition is decomposed
into its key elements that are color-coded. The x-axis refers to the quantitative cutoffs used where applicable
Curr Addict Rep
then will research on binge-watching be able to generate find-
ings likely to best deepen our understanding of this prominent
behavioral phenomenon in todays technological landscape.
Funding Information This research did not receive any specific grant
from funding agencies in the public, commercial, or not-for-profit sectors.
Pierre Maurage (Senior Research Associate) is funded by the Belgian
Fund for Scientific Research (FRS-FNRS, Belgium).
Compliance with Ethical Standards
Conflict of Interest The authors declare that they have no conflict of
interest. This article has been edited by Editor-in-Chief Marc Potenza
instead of Joël Billieux, as Joël Billieux is the Section Editor of the
Technological Addictionstopical collection.
Human and Animal Rights and Informed Consent This article does not
contain any studies with human or animal subjects performed by any of
the authors.
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... Many factors exacerbate compulsive binge-watching. The predominant causes include the need for immediate gratification of hedonistic demands through increased positive stimulation [13]. Social motivation, forming relationships with people, is also important. ...
... The characteristics mentioned above generate stronger feelings of frustration, stress and other negative emotions, more often resulting in a desire to escape from the problems of everyday life by losing oneself in fictional content. Also important is the impulsivity of the viewer, which is a major predictor of behavioural addiction, through systematic reinforcement of positive affect and stimulation or escape from anxiety or sadness [4,13]. All the motivations mentioned above have been shown to be heightened in people with anxietydepressive disorders. ...
... Istnieje wiele czynników nasilających kompulsywne oglądanie seriali. Wśród dominujących przyczyn wymienia się potrzebę natychmiastowego zaspokojenia hedonistycznych potrzeb, poprzez wzrost pozytywnej stymulacji [13]. Istotna jest także motywacja społeczna, tworzenie relacji z ludźmi. ...
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Introduction: With the increase in popularity of VOD (Video on Demand) platforms, there has been an increase in binge-watching and associated processes, which may influence the development of ‘mean world syndrome’. The aim of this study is to analyse current knowledge of the above phenomena and their interrelationships. Material and methods: A narrative review of the available literature was conducted by searching PubMed and Google Scholar databases using the following keywords: binge-watching, mean world syndrome, fear of missing out (FOMO), speed-watching from 2000 to 2021 Results: The most important motivations for the development of binge-watching are social aspects, fear of missing out (FOMO), hedonistic needs and escape from reality. The process is exacerbated by depressive-anxiety disorders, loneliness, pathological overeating and neglect of responsibilities. To save time, viewers often practice speed-watching. A positive correlation has been shown between the severity of binge-watching and mean-world syndrome in viewers who watch series such as: House of Cards, The Unbreakable Kimmy Schmidt, Marco Polo, Bloodline and Daredevil, as well as the frequency of watching horror films and viewers’ belief that they are more likely to die. In contrast, no relationship was shown with fear for safety in one’s home. People, who watch reality shows with a competitive scenario, perceive the world to be more hypocritical and manipulated. Conclusions: Compulsive viewing of violent programmes co-occurs with the phenomena of FOMO and speed-watching and can exacerbate the perception of the world as a dangerous place. In the era of the COVID-19 pandemic, both phenomena have increased, affecting the functioning of society.
... The continuous development and improvement of on-demand streaming platforms providing unlimited access to a wide array of content (e.g., Netflix, Hulu or Amazon Prime) has promoted a new pattern of TV series consumption called binge-watching (i.e., watching multiple TV series episodes in one session; Flayelle et al., 2020a;Starosta & Izydorczyk, 2020). Between 2015 and 2020, binge-watching (together with serial viewing; i.e., watching series over multiple days, weeks or months) strongly increased at the expense of traditional appointment viewing (i.e., watching an episode each week, when aired) (Rubenking & Bracken, 2021) and ultimately became the new normative way to watch TV series (Business Wire, 2019; Statista, 2020). ...
... In parallel with this expansion, a growing body of research has explored the phenomenology and correlates (e.g., psychological, personality or psychiatric factors) of binge-watching, generating an emerging area of scientific inquiry and promoting debates regarding how to define, assess and conceptualize this new media-related behaviour (Flayelle et al., 2020a;Starosta & Izydorczyk, 2020). Some scholars have notably conceptualized excessive binge-watching as a potential addictive behaviour (e.g., Forte et al., 2021;Orosz et al., 2016;Tóth-Király et al., 2017), as it not only shares phenomenological characteristics with substance use disorders at the symptomatic level (e.g., tolerance; Orosz et al., 2016), but it is also associated with a set of physical (e.g., obesity due to its associated sedentary lifestyle; Spruance et al., 2017) and psychological (e.g., emotional distress; Granow et al., 2018;Shim et al., 2018) outcomes. ...
... Second, the high proportion of female participants (74.60% to 84.70%) might have affected the current results, as women are known to be more prone than men are to negative affect and to engage more than men do in ruminative coping (e.g., Nolen-Hoeksema et al., 1999). Third, although our group criteria were based on previous experimental work (Flayelle et al., 2020b), there remains a need to develop empirically validated criteria for bingewatching operationalization (see Flayelle et al., 2020a andIzydorczyk, 2020). Fourth, the current triadic approach of rumination is based on preliminary evidence (Philippot et al., 2021), which, although based on the previous dualistic approach of rumination (Watkins, 2008), needs further research to ascertain its validity. ...
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... With this evolution in the way TV series are consumed, there has been growing international interest in research on the antecedents and consequences of binge-watching behaviors, as witnessed by the first systematic literature reviews conducted on the topic (Flayelle et al., 2020a;Starosta and Izydorczyk, 2020). These reviews come to the same conclusion in that binge-watching reflects a two-sided phenomenon. ...
... There is, therefore, growing agreement that non-harmful involvement needs to be separated from problematic involvement in binge-watching to prevent over-pathologizing this activity. There is a lack, however, of research identifying the specific features and underlying mechanisms behind these two different watching patterns, and the specific conditions or factors that favor the shift from one to the other (Flayelle et al., 2020a;Flayelle and Lannoy, 2021;Steins-Loeber et al., 2020). The current study was designed to address this shortcoming by investigating the distinct psychological predictors of non-harmful and problematic binge-watching patterns. ...
... While promoting positive emotions per se (through intensified enjoyment while viewing; Granow et al., 2018;Merrill and Rubenking, 2019) and more general positive affective outcomes (e.g., viewers' hedonic and eudaimonic well-being; Halfmann and Reinecke, 2021;Muñiz-Velázquez and Lozano Delmar, 2021), binge-watching completely falls within this framework as it has also been associated with mental health conditions which one would seek to alleviate, such as anxiety and depression (Ahmed, 2017;Tefertiller and Maxwell, 2018). Further supporting this notion is the fact that available evidence demonstrates the co-existence of hedonic motivations (i.e., entertainment, enjoyment) -best-known inducers of engagement in leisure activitiesand emotionfocused avoidance coping motivations as two main motivational factors in binge-watching (Flayelle et al., 2020a), beyond other prime-order expected gratifications such as eudaemonic (e.g., personal enrichment; Merrill and Rubenking, 2019) or socialization (Granow et al., 2018;Panda and Pandey, 2017) derived benefits. Lying at the heart of classic hedonic entertainment conceptualizations, such emotionally motivated or mood-repair related media use is assumed to explain why consumption of entertainment media, initially perceived as enjoyable and fulfilling, might become dysfunctional over time when repeatedly invested as the primary option to escape from negative affective states, thereby fueling a misguided self-reinforcing loop (Reer et al., 2021). ...
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As on-demand streaming technology rapidly expanded, binge-watching (i.e., watching multiple episodes of TV series back-to-back) has become a widespread activity, and substantial research has been conducted to explore its potential harmfulness. There is, however, a need for differentiating non-harmful and problematic binge-watching. This is the first study using a machine learning analytical strategy to further investigate the distinct psychological predictors of these two binge-watching patterns. A total of 4275 TV series viewers completed an online survey assessing sociodemographic variables, binge-watching engagement, and relevant predictor variables (i.e., viewing motivations, impulsivity facets, and affect). In one set of analyses, we modeled intensity of nonharmful involvement in binge-watching as the dependent variable, while in a following set of analyses, we modeled intensity of problematic involvement in binge-watching as the dependent variable. Emotional enhancement motivation, followed by enrichment and social motivations, were the most important variables in modeling non-harmful involvement. Coping/escapism motivation, followed by urgency and lack of perseverance (two impulsivity traits), were found as the most important predictors of problematic involvement. These findings indicate that non-harmful involvement is characterized by positive reinforcement triggered by TV series watching, while problematic involvement is linked to negative reinforcement motives and impulsivity traits.
... We need to understand the phenomenon behind binge-watching which lies in the context of digital era, this new era gave rise to many technologies which resulted in the emergence of new consumer behavior (Flayelle et al., 2019). ...
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Binge-watching is a new concept which saw its rise from 2013 onwards, it became one of the significant ways especially for youth to spend free time. It has become one of the popular ways especially for youth to spend free time. Present study attempts to explore the motivations behind binge-watching among youth and the gratification achieve by that. Uses and Gratification Theory, is used to provide theoretical framework. The current theory explains that people use media to satisfy their desires. Bing watching has many effects, the main argument refers to swift gratification and desires related to entertainment, engagement, and relaxation. Survey method was used with purposive sampling technique. Total 110 respondents filled the survey. Findings illustrates that motivation behind binge-watching is the need of learning. Youth indulge in this behavior to feel connected and to fulfil curiosity, moreover youth find binge-watching a good viewing pattern as it relieves their stress. According to the findings binge-watching have positive effects. Keywords: Binge-Watching, Stress, Curiosity, Traditional media, Netflix.
... In short, this mixed methodology provides an original approach to the binge-watching phenomenon, which is not usual to find in the recent literature reviews (Flayelle et al., 2020;Starosta & Izydorczyk, 2020;Alimoradi et al., 2022). ...
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The new modes of television consumption point to the interest of binge-watching as the object of study. This paper builds an intensity index that classifies users into “irregular”, “regular” or “dedicated”. Methodologically, an inter-method sequence is applied, combining descriptive and multivariate statistical analyses, as well as discussion groups from which cleavages or discursive positions derive. The results of the research indicate that most of the University population are binge-watchers so the terms have changed and a new scale is necessary for identifying the level of engagement with binge-watching behaviour in the current. Almost 30% of the university population under study corresponds to the typical-ideal category of “dedicated” and 33% with the “regular”. The growth rate of the phenomenon is exponential between 2016-2019. The triggering motivations for binge-watching are primarily hedonic; its effects affect our moods especially in “dedicated” users. Two different types of viewing are clearly identified. The first is committed or prioritized viewing (with a high attention level, high dependence and sympathy with regards to the story and characters), and secondary or complimentary viewing. The study concludes that, in a pre-pandemic context of over-audiovisual fiction content (fictoxication), the ability to select and self-assess the media diet acquires the fundamental skill status in the socio-educational framework of the younger ones.
... Research suggests that problematic streaming service use, particularly excessive binge-watching, has negative consequences for users' physical, psychological, and social wellbeing (Rahman & Arif, 2021). Excessive binge-watching threatens users' physical health by causing strain to their eyes and body (Flayelle et al., 2020a), sleep deprivation (Starosta & Izydorczyk, 2020), and increased risk of obesity (Groshek et al., 2018). It is also found to harm users psychologically (Raza et al., 2021;Rubin & Wessely, 2020). ...
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This study is a conceptual replication of Chen et al.'s (2020) study that examines factors influencing the intention to decrease problematic Information Systems (IS) use. In contrast with Chen et al.'s smartphone gaming context, we apply their theoretical model to the context of digital streaming services. Aligned with the original study, we tested the model using a scenario-based survey. Results are largely consistent with the original study, albeit with several exceptions. Our findings support that protection motivation theory (PMT) is useful in explaining decreasing problematic use in situations of threats. Threat is the negative consequences caused by problematic streaming service use. Users experience fear when they believe the negative consequences are likely to occur, and the consequential harm will be serious if they occur. When threatened, users are more motivated to decrease use if they believe decreasing use is effective in mitigating the threat and they have confidence in executing it. However, such motivation is not influenced by costs incurred by decreasing use. Further, we validate that invoking fear can break users' viewing habits, which promotes their intention to decrease use. Yet, such effect is limited. Future research might explore other factors that are effective in breaking users' viewing habits.
... Finally, given the lack of relevant studies, two of the initially targeted problematic online behaviors (i.e., PPU and problematic TV series binge watching) were not included in the present systematic review. As research on problematic TV series binge watching is still in its infancy [76], this gap is understandable. In contrast, the volume of available research on PPU and psychological factors leading to its onset and maintenance is far more important. ...
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Purpose of ReviewIn this systematic review, we examine the contribution of rumination (a maladaptive coping style of emotion regulation) when accounting for the onset and severity of different internet use disorders (IUDs).Recent FindingsWe retained and analyzed 42 studies that explored the association between rumination and six different IUDs: problematic smartphone use, problematic internet use, problematic social networks use, problematic online gaming, problematic online gambling, and online buying-shopping disorder. Overall, the available results suggest that rumination is positively associated with IUDs and that this association is consistent across multiple technology-mediated problematic behaviors. Furthermore, many of the reviewed studies underscore the contribution of rumination when explaining how and for whom, or under what conditions, different variables (e.g., unpleasant emotional states) are related to IUDs.SummaryThis systematic review offers a comprehensive overview of the current state of knowledge regarding the association between rumination and IUDs and identifies new areas that warrant further research.
... It is confirmed in systematic reviews of such definitions (e.g. Flayelle et al. 2020;Merikivi et al. 2020;Pierce-Grove 2017;Starosta, Izydorczyk 2020;Sung et al. 2018). Their authors focus on the problems of developing definitions concerning formal features, such as the watching time of a number of episodes qualifying a watching session as binge-watching. ...
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Binging-and binge-watching in particular-has been receiving growing interest from communication scientists for a couple of years. Thus, after five decades of watching audiovisual content according to TV schedules, a recipient gained more autonomy regarding the content and ways of watching. The author aims to analyse the factors influencing the potential permanence of this specific way of media consumption, with literature review as the main method. These factors include different definitions of the concept, synonyms, applying the binging term to different media formats (binge-watching-binge-listening-binge-reading-binge-gaming), technological and social circumstances. These factors differ in their quality and scale. Will their convergence and synergy permanently change the practices of media consumption? Referring to previous changes in media differentiation, accessibility, and reception, although binging would probably become rather next, but not the dominating form of media practices of leisure time. The cognitive value of the article is to offer a theoretical basis for further quantitative and qualitative research on the reception of different media formats and genres; binging included.
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Video-streaming typically describes watching live or prerecorded videos online. This behavior has significantly increased over the past two years in tandem with the global COVID-19 pandemic. The literature describing this behavior is still in its infancy, therefore, it is not well-characterized and our understanding is thereby limited. Different forms of problematic video-streaming have varying prevalence rates in the literature and each requires further operationalization. Overall, the various presentations of problematic video-streaming have been found to be associated with poor mental and physical health and linked to increased impulsivity, reduced academic and work performance, and lower quality of life. This article explores the current literature surrounding the definition, prevalence, validated assessments, associated factors, motivations, and available treatments for problematic video-streaming.
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Background and aims: Despite there exist many benefits of technological advancements, problematic use of emerging technologies may lead to consumers experiencing harms. Substantial problems and behavioral addictions, such as gambling and gaming disorders, are recognized to be related to Internet-based technologies, including the myriad of new devices and platforms available. This review paper seeks to explore problematic risk-taking behaviors involving emerging technologies (e.g., online gambling and gaming, online sexual behaviors, and oversharing of personal information via social networking sites) that have the potential to lead to problematic outcomes for individuals. Results and discussion: Previous research has focused on policy frameworks for responding to specific issues (e.g., online gambling), but a broader framework is needed to address issues as they emerge, given lags in governments and regulators responding to dynamically evolving technological environments. In this paper, key terms and issues involved are identified and discussed. We propose an initial framework for the relative roles and responsibilities of key stakeholder groups involved in addressing these issues (e.g., industry operators, governments and regulators, community groups, researchers, treatment providers, and individual consumers/end users). Conclusion: Multidisciplinary collaboration can facilitate a comprehensive, unified response from all stakeholders that balances individual civil liberties with societal responsibilities and institutional duty of care.
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Background The study focuses on psychological conditions of the phenomenon of binge-watching. The aim of the article was to characterize the frequency and motivation to perform binge-watching in a group of young adults. Another goal of the research was to present the results of preliminary adaptation works on two scales that were previously non-existent in Polish literature, which may be used to study binge-watching. Participants and procedure The study involved 854 female and 150 male participants aged 19-26 years. The participants of the research were students of Polish universities. The following research methods were applied in the study: Polish adaptation of the Viewing Motivation Scale by Alan M. Rubin and the author’s tool – Questionnaire of Excessive Binge-Watching Behaviors by Jolanta Starosta. Results The conducted analysis revealed a significant association between high frequency of binge-watching and escape motivation (ρ = .55, p < .05) and motivation to deal with loneliness (ρ = .42, p < .05). Furthermore, these two motivations correlate signifi-cantly with such predictors of risk for behavioral addiction as Loss of control and neglect of duties and Emotional reactions. Research has shown that the Polish adaptation of the Viewing Motivation Scale and the Questionnaire of Excessive Binge-Watching Behaviors are tools that meet psychometric standards. Conclusions The study shows that participants have various motivations to binge-watch series. The individuals who binge-watch with the highest frequency had a tendency to have escape motivation and motivation to deal with loneliness. There are some relations with various motivation and frequency of binge-watching with risk factors for behavioral addiction. The methods presented in the study – the Viewing Motivation Scale and the Questionnaire of Excessive Binge-Watching Behaviors – may be useful in learning about the phenomenon of binge-watching.
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Purpose of review. The year 2018 was marked by the official recognition of Gaming Disorder (GD) as a mental condition with its inclusion in the proposed eleventh edition of the International Classification of Diseases (ICD-11). Recently, a group of scholars has repeatedly criticized the notion of GD proposed by the World Health Organization (WHO), arguing that its inclusion in ICD-11 will pathologize highly involved but healthy gamers. It is therefore of crucial importance to clarify the characteristics of high involvement versus pathological involvement in video games, the boundaries between these constructs, and the implementation of screening and diagnostic GD tools that distinguish the two. Recent findings. Increasing evidence supports the view that intense video game playing may involve patterns of gaming that are characterized by high involvement but that are non-pathological. Furthermore, some criteria for addictive and related disorders may reflect peripheral features that are not necessarily indicative of pathology, whereas others may reflect core features that are more likely to adequately identify pathological behavior and so have diagnostic validity. Finally, it is key to assess functional impairment associated with gaming, so that a GD diagnosis has clinical utility. Summary. Available evidence supports the crucial need to distinguish between high and pathological involvement in videogames, in order to avoid over-diagnosis and pathologization of normal behavior. The definition of GD adopted in ICD-11 has clinical utility and diagnostic validity since it explicitly mentions the functional impairment caused by problem gaming and its diagnostic guidelines refer to core addiction features, reflecting pathological involvement.
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Extant results on the binge-watching outcomes have been mixed. This study sought to examine the crucial factor of attentiveness that might help to enhance viewer experience and mitigate post-binge regret, as well as differentiate the motivation of narrative transportation from narrative completion. While narrative transportation involves a viewer getting unconsciously swept away by the story, the motivation of narrative completion is a more self-aware, cognizant effort to progress through the story. A survey (N = 800) determined that the degree to which an individual pays attention to a show may either increase or decrease subsequent regret, depending on the motivation for binge-watching.
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Background: A positive relationship between problematic gaming and escapism motivation to play video games has been well established, suggesting that problematic gaming may result from attempts to deal with negative emotions. However, to date, no study has examined how emotion dysregulation affects both escapism motives and problematic gaming patterns. Methods: Difficulties in emotion regulation, escapism, and problematic involvement with video games were assessed in a sample of 390 World of Warcraft players. A structural equation modeling framework was used to test the hypothesis that escapism mediates the relationship between emotion dysregulation and problematic gaming. Results: Statistical analyses showed that difficulties in emotion regulation predicted both escapism motives and problematic gaming, and that escapism partially mediated this relationship. Conclusion: Our findings support the view that problematic players are likely to escape in online games as a maladaptive coping strategy for dealing with adverse emotional experiences.
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Increasingly, audiences are engaging with media narratives through the practice of binge watching. The effects of binge watching are largely unknown, although early research suggests binge watching may be motivated by a need for escape and could be associated with some qualities of addiction. In this study, we ask whether the practice of binge watching impacts audience engagement with a media narrative. Using an experimental approach, we manipulate the format of exposure to media narratives (binge or nonbinge) and test the effect of this manipulation on audience engagement, specifically parasocial relationships with favorite characters and narrative transportation. Results suggest that binge watching increases the strength of parasocial relationships and the intensity of narrative transportation. Media engagement has been shown to increase media effects, suggesting that binge watching could change not only how audiences engage with narrative media but also the effect it has on them.
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Binge watching, or serial viewing of a single program over an extended period of time, is a relatively new norm in television viewing that is becoming more popular than traditional appointment viewing. Previous research has explored various influences on binge watching; however, the current research is unique in exploring theoretically and empirically grounded predictors of both binge watching frequency and duration of binge watching sessions by means of a survey administered to college undergraduates (N = 651). Data show that binge watching frequency and duration are predicted by two non-overlapping sets of variables. Binge watching frequency was predicted by low self-regulation, greater tendency to use binge watching as both a reward and a form of procrastination, and less regret; while binge watching duration was associated with being female and experiencing greater enjoyment while binging. Self-control did not predict either binge watching frequency or duration, suggesting that alternative theoretical models should be explored. Findings also suggest that scholars should reconceptualize binge watching by including both frequency and duration measures in future studies.
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