Let’s watch one more…
Investigating emotional enhancement and coping or escapism as predictors of binge-watching behavior.
Anouk den Doelder | 13962108 Master Thesis, Entertainment Communication Track Supervisor | Drs. Ewa Międzobrodzka Dept. of Communication Science | University of Amsterdam July 1st, 2022 Word count:7213
Binge-watching is a relatively new term in societal and academic context. Binge-watching is understood as watching more than one episode of the same TV series in one sitting. Recently various motivations for binge-watching, including emotional enhancement and coping or escapism, were studied in international samples. However, until now, they were not studied in a Dutch population. Therefore, this research aimed to investigate relationships between these two motivations for binge-watching and binge-watching behavior of Dutch adults and whether these relationships were moderated by loneliness. Binge-watching behavior was calculated by multiplying the number of episodes watched in one sitting with the number of week days watched (averaged from a month). Participants (N = 275) filled in an online survey in which they were asked about their binge-watching behavior and motivations for binge- watching and level of their loneliness. Results showed that there was no significant
relationship between emotional enhancement as a motivation and binge-watching behavior.
However, a significant relationship between coping or escapism as motivation and binge- watching behavior was found. Participants who scored higher for coping or escapism as motivation also scored higher on binge-watching behavior. Loneliness did not moderate any of the tested relationships. Exploratory analysis showed that gender could act as a moderator of the relationship between motivation for coping or escapism and binge-watching behavior, namely that the relationship was stronger for females than for males. Therefore, future studies could take other moderations such as gender and age into consideration. Besides this, future research could use a different research design, such as a diary study or a study with donated data.
Key words: Binge-watching, loneliness, emotional enhancement, coping, escapism
Abstract ... 1
Introduction ... 3
Theoretical framework ... 6
Binge watching ... 6
Emotional enhancement ... 7
Coping or escapism ... 8
Loneliness ... 10
Method ... 12
Sample ... 12
Design ... 13
Procedure ... 14
Measures ... 14
Analysis plan ... 16
Results ... 17
Preliminary analyses ... 17
Hypotheses testing ... 20
Exploratory analyses ... 22
Discussion ... 23
Summary of Results ... 23
Theoretical and Societal Implications ... 25
Limitations and Directions for Future Research ... 26
Conclusion ... 27
References ... 29
Appendix A ... 35
Appendix B ... 39
Appendix C ... 41
Appendix D ... 43
Appendix E ... 45
With the increase in subscriptions for on-demand streaming services, the phenomenon of binge-watching has become a popular interest in both society and research (Flayelle et al., 2020). In the second quarter of 2021, the popular streaming service Netflix had 209 million subscriptions worldwide, of which 2.76 million are Dutch accounts (Moody, 2021). Such platforms often offer multiple episodes (several seasons) of one TV series. With such
availability of the entertainment products, a new phenomenon was observed: binge watching, which is watching more than one episode of the same show in one sitting (Schweidel & Moe, 2016; Ahmed, 2017; Merikivi et al., 2017). It takes on average five days to binge-watch a show on Netflix, and 8.4 million accounts worldwide have finished a show within 24 hours of its release (TechJury, 2022), which reflects a global trend of binge watching.
Watching multiple episodes in a row can cause different effects. Flayelle and
colleagues (2019) made a distinction between non-problematic effects, such as higher desire, enjoyment or engagement with the series, and problematic effects, such as problems with mental and physical health (obesity, heart problems). Further, Viewers can experience anxiety or feelings of guilt after binge-watching, and watching more than two episodes in one sitting could become a behavioral addiction (Panda & Pandey, 2017). Importantly, such positive and negative effects may depend on different motivations why people binge-watch.
Recent research has established multiple possible motivations for binge-watching (Flayelle et al., 2019; Panda & Pandey, 2017; Shim & Kim, 2018). Examples of these
motivations are enrichment, mainly to enlarge knowledge or social motivations, to be able to talk about shows with peers (Flayelle et al., 2019). Other motivations include accessibility to content or advertising to promote series (Panda & Pandey, 2017). We will look more closely into two motivations, namely: emotional enhancement – watching to experience an emotional state – and coping or escapism motivations, which is described as watching to avoid real life
4 problems and negative affect (Flayelle et al., 2019). These two motivations were studied in previous research (Flayelle et al., 2020; Panda & Pandey, 2017; Shim & Kim, 2018; Sung et al., 2018) which found that they were significant predictors of increased binge-watching. The motivations of emotional enhancement and coping or escapism will be used to shed light on the binge-watching behavior of the Dutch, since this has not been researched before.
Additionally, we will take participants’ loneliness (as a personality trait) into consideration as a possible moderator, as this might influence their motivations for binge- watching. For example, a person who often feels alone might find solace in watching a TV series, to distance themselves from their life for a while. On the other hand, people who are not lonely at all will not binge-watch to escape reality, but rather to just enjoy themselves (Sun & Chang, 2021). Taking all this into account, this research will answer the following research question:
RQ: To what extent do emotional enhancement and coping or escapism motivations predict binge-watching behavior? How are these relationships moderated by loneliness?
A lot of the aforementioned research acknowledges that it is hard to generalize results, since a specific study has been done in one country only. A systematic review of research on binge-watching by Flayelle et al. (2020) covers results from 24 different studies in 17
countries and nine languages. This review highlights the need for consistency of constructs and operationalizations. Because the field is relatively new, there is no consensus on the definition of binge-watching, as well as clearly operationalized motivations and moderators that can help to predict the effects of binge-watching. This research will add to the growing body of research about binge-watching by creating a specific and informative construct.
The current research will also shed light on the binge-watching behavior of the Dutch population. De Feijter et al. (2016) performed an in-situ monitoring survey among Dutch young adults. Results showed that the amount of time watched correlated with participants’
5 amount of free time and emotional wellbeing. Besides this study, very little has been
researched regarding binge-watching behavior of the Dutch. Therefore, the current study will focus on this population, with emotional enhancement and coping or escapism as motivations in mind. These motivations occur often in research concerning binge-watching, albeit
operationalized differently. From a theoretical viewpoint, it is interesting to get a clearer image of emotional enhancement and coping or escapism as motivations. Because of the different operationalizations, we can not group these results in the same category. By proving that results of various studies are similar, we can define these motivations more concretely.
In addition to the theoretical relevance, it is also interesting to see what binge-
watching can mean in a Dutch societal context. During the COVID-19 pandemic, people have been inside their houses more than ever, which could motivate them to binge-watch more often. A recent study showed that people used binge-watching to escape reality and regulate their emotions (Sigre-Leirós et al., 2022). A study that compared binge-watching data between 2015 and 2020 also showed an increase in binge-watching, with a peak during the COVID-19 lockdowns (Rubenking & Bracken, 2021).
A South-African survey by Padmanabhanunni and Pretorius (2021), registered during the COVID-19 pandemic, showed that young adults felt more isolated, socially disconnected and lonelier during the pandemic than before. Results of an American survey (Rosenberg et al., 2020) showed a similar finding, namely that feelings of loneliness, isolation and
depression increased during the pandemic. This relationship was found significant for women aged 20-29. Given that binge-watching occurs mostly among young adults (Starosta &
Izydorczyk, 2020), it is important to research this age group.
A systematic review by Starosta and Izydorczyk (2020) shows that binge-watching can cause negative effects, such as lack of control, sleeping problems or ignoring duties.
Taking this into account, it makes sense that binge-watching is often seen as a negative
6 phenomenon. If we can map out what exactly motivates people to binge-watch and how often they binge-watch, we might be able to either take away concerns regarding the amount of binge-watching or find ways to decrease the amount of binge-watching to a healthy level.
Theoretical framework Binge watching
Binge-watching can be defined in different ways. Flayelle et al. (2020) defined it as watching multiple episodes of TV series in a single sitting, while research by Netflix and Harris Interactive (2013) defined binge-watching as watching from two to (on average) six episodes of the same show in one sitting. However, binge-watching is not always solely defined in terms of number of episodes. Research is often defined through assessing the number of episodes watched and the time-span of watching. Panda and Pandey (2017) defined binge-watching as watching a minimum of two or three episodes of the same series, or at least one hour of the same TV series in one sitting, thus also including a number of hours. Some research defines binge-watching only through a certain number of hours. Horvath et al. (2017) defined binge-watching as viewing of 3 or more hours of programming within a single sitting.
Most of the research defined binge-watching based on two aspects: a quantity of episodes and a time-span of watching.
Besides studying the number of episodes watched, it is also important to consider frequency and duration of binge-watching. For example, watching three episodes of thirty minutes is different than watching two episodes of 45 minutes. However, this research will not focus on these different durations. For the current study, binge-watching is understood as watching more than one episode of the same show in one sitting (Schweidel & Moe, 2016;
Ahmed, 2017; Merikivi et al., 2017), regardless of the length of the episodes.
7 Emotional enhancement
Emotional enhancement is one of the two motivations of binge watching (predictors) in this study. Most of the earlier research on motivations for binge-watching addressed emotional enhancement in a similar way. However, some researchers also considered the entertaining value, emphasizing either the fun and relaxation of binge-watching (Panda and Pandey, 2017) or being a fan of a certain show (Shim & Kim, 2018). In the current project, the definition by Flayelle et al. (2019) is applied, which understands emotional enhancement as a motivation to experience an emotional state. Such motivation is associated with positive feelings about the show people binge-watch, or an attachment to characters they have
developed and parasocial relationships that have been formed (Anghelcev et al., 2020).
According to the uses and gratifications theory (Steiner & Xu, 2018) people use certain types of media to satisfy their personal needs and expectations. Based on that, it could be argued that people who binge-watch for entertainment seek an emotional enhancement in the shows that they watch. Viewers can tailor the content they watch specifically to their needs. Steiner and Xu (2018) used the uses and gratifications theory to explain people’s motivations for binge-watching. They showed that people binge-watch mostly for catching up, relaxation and sense of completion. This matches the emotional enhancement motivation, since it focuses on binge-watching to experience a positive emotional state
An online study among (predominantly young) adults by Shim and Kim (2018) found that people who binge-watched experienced a higher level of enjoyment. A survey study by Sung et al. (2018) proved that entertainment motivation is a significant predictor of binge- watching. Participants got a binge-watching score that consisted of the amount of hours watched weekly multiplied with the episodes watched weekly. The participants were divided into light binge-watchers (with a binge-watching score of lower than nine) and heavy binge- watchers (with a binge-watching score of nine or higher). The found effect was present for
8 light binge-watchers and heavy binge-watchers. Lastly, Flayelle et al. (2020) found a positive correlation between emotional enhancement and binge-watching behavior in their cross- cultural survey study. Therefore, to study the relationship between emotional enhancement motivation and binge-watching behavior, the following hypothesis is proposed:
H1: Emotional enhancement motivation predicts more frequent binge-watching behavior.
Coping or escapism
The second predictor of binge-watching in this research is coping or escapism. Skinner and Zimmer-Grembeck (2016) define coping as “regulation under stress” (p. 12). Starosta et al. (2019) used escapism as a motivation in their study. They interpreted it as escaping from everyday problems. Coping is used to describe the level of performance while dealing with negative feelings, while escapism is used to describe removing oneself from a certain negative situation. Both concepts, however, focus on dealing with negative feelings. To stay in line with the definition of emotional enhancement, the definition for coping or escapism as a motivation used in this study is created by Flayelle et al. (2019): a motive to watching TV series to avoid thinking about real life problems and negative affect.
Escapism has been seen as a motivation for entertainment use since the fifties
(Klimmt, 2008). A study by Katz and Foulkes (1962) showed that escapism was a motivation for individuals and society at large to use various forms of entertainment media. Overall, previous research on this motivation indicated that a binge-watching viewer is transported into another world and/or forgets their current situation, problems and thoughts (Flayelle et al., 2020; Panda & Pandey, 2017). Panda and Pandey (2017) defined stress relief and escape reality as motivations as well. The authors state that viewers want to keep watching to prolong their positive state of escapism and not return to their stressful life.
9 The cultivation theory instigates the long-term effects of watching TV on viewers’
perceptions, beliefs, attitudes, and values (Gerbner & Gross, 1976). Even though binge- watching was not a known concept when the cultivation theory was instigated, Gerbner and Gross (1976) did acknowledge different types of media users, among which the so-called medium and heavy users. These heavy users watched at least three hours of TV per day, which seems similar to the phenomenon we now call binge-watching. Because the cultivation theory suggests that people who watch more TV also see the world as a darker place, this could explain the relationship between binge-watching for coping or escapism and binge- watching behavior. The cultivation theory might explain the relationship between binge- watching for coping or escapism and binge-watching behavior.
Stenseng et al. (2021) developed a two-dimensional model of escapism for leisure activity (online gaming and streaming). The two dimensions, self-expansion (the motivation to increase one’s self-efficacy), and self-suppression (stopping oneself from feeling or doing something) both originate from escapism. According to the model, streaming online TV series and films can stimulate avoidance coping and create escapism and in particular, binge-
watching (Stenseng et al., 2021).
Flayelle et al. (2020) researched binge-watching behavior of 12616 participants in nine countries. Results show that coping or escapism positively predicted binge-watching
behavior. In a study with sixty American university students, Panda and Pandey (2017) performed interviews and focus groups on binge-watching behavior. Transforming the input into a questionnaire, they found an effect similar to previous research. Results among 229 students showed that participants used escaping from reality as a motivation for binge- watching. Lastly, in a Polish survey study by Starosta et al. (2019), it was found that participants who binge-watch a lot often use escapism as a motivation to binge-watch. To
10 study the relationship between coping or escapism as a motivation and binge-watching
behavior, the following hypothesis is defined:
H2: Coping or escapism motivation predicts more frequent binge-watching.
The last construct in this study is loneliness. Ascher and Paquette (2003) defined loneliness as ‘‘the cognitive awareness of a deficiency in one’s social and personal
relationships, and ensuring affective reactions of sadness, emptiness, or longing’’ (p. 75). We consider this to be a general definition for loneliness as a trait. However, in the context of the current study the definition of loneliness by Ditcheva et al. (2018) will be applied: “the persisting, subjective perception of the discrepancy between one’s actual and desired social relationships and is associated with increased negative emotions” (p. 181). This definition is applicable to the concept of binge-watching. The current study considers loneliness a trait rather than a current emotional state, due to the stability in time that defining loneliness as a trait entails.
Based on the Differential Susceptibility to Media Effects Model (DSMM; Valkenburg and Peter, 2013) loneliness as a trait can be proposed as a moderator of the relationship between emotional enhancement and coping or escapism as motivations, and binge-watching behavior. The DSMM explains how different individual factors may predict users’ media use.
In the current study, the dispositional factors such as personality traits are relevant. Traits can predict or moderate media use effects. In this case, loneliness acts as the predictor for media use, even though loneliness is the moderating variable of the current study. We use the predictor role of Proposition 3, because the moderation is about the media use, rather than the media effect. Further in the DSMM, users may respond to the media product in an emotional, cognitive or excitative way, which ultimately creates a media effect, namely that the user’s binge-watching behavior increases.
11 On the one hand, it is likely that people who binge-watch as a coping mechanism do this because they are lonely. On the other hand, people that binge-watch for emotional enhancement might not be lonely, because they will watch with friends or their partner. Until now, studies on the relationship between loneliness and binge-watching brought mixed
findings. While a survey study by Tefertiller and Maxwell (2019) did not find any relationship between loneliness and binge-watching, Tukachinsky and Eyal (2018) found that people who felt lonelier were more inclined to binge-watch more often. Finally, research by Sun and Chang (2021) proved that people who scored higher on a problematic binge-watching scale (based on e.g., watching more than planned or being told to quit watching as much by others) also were lonelier.
It is difficult to establish why exactly these results are contradicting. While all three studies created a survey, the samples differ. Two of the studies recruited adults of all ages (Sun & Chang, 2021; Tefertiller & Maxwell, 2019) while the study by Tukachinsky and Eyal (2018) recruited college students only. However, the study by Sun and Chang (2021) also consisted of mostly young adults. Two of the studies had predominantly female participants (Sun & Chang, 2021; Tukachinsky & Eyal, 2018), while the study by Tefertiller and Maxwell (2019) was divided equally in terms of gender. Even though previous research shows mixed results, we assume that loneliness will have a moderating effect on the relationship between both predictors and binge-watching behavior. This is due to the nature of the results: the studies that did have a significant result had similar findings. Therefore, the following two hypotheses are proposed:
H3: The relationship between emotional enhancement motivation and binge-watching behavior is negatively moderated by loneliness. The relationship between emotional enhancement motivation and binge-watching behavior is stronger for people who are less lonely.
12 H4: The relationship between coping or escapism motivation and binge-watching behavior is positively moderated by loneliness. The relationship between coping or escapism motivation and binge-watching behavior is stronger for people who experience higher level of loneliness.
Figure 1. Conceptual model
In total, 590 people filled in the survey. However, after data preprocessing (removing incomplete responses, responses from the same person and responses without submitted consent), 449 responses remained. Responses from the same person were brought back to one by only including the most recent response. Since the survey was part of a larger research, only a subsample of all participants who were Dutch was used for the current study: N = 277 (61.7% of the total sample). Based on the effect sizes of similar previous research by Panda and Pandey (2017), we expected an effect size of f = 0.2. Based on this effect size, the desired sample size was at least 250 participants.
Since the current study focused on Dutch adults, the inclusion criteria to the current study were: Dutch nationality and age 18 years or older. There was no upper age limit.
13 Another requirement for participation was that the respondents had to use a streaming on- demand service, such as Netflix, HBO MAX, Apple TV, etc. Participants were recruited from April 25th 2022 to May 11th. The sampling method that was used was convenience sampling, due to the time frame in which the responses had to be collected. Participants were recruited through social media (41.8%), since that was the quickest way of assembling responses.
Participants were also recruited through the participants’ pool for University of Amsterdam Communication Science students (58.2%). Upon completing the survey, these participants could click a link through which they would receive 0.5 research credits.
A total of N = 275 consenting participants filled in the full survey correctly. Of these participants, 78.9% was female and 21.1% was male. Participants’ ages ranged from 18 to 74 (M = 25.78; SD = 10.29). Most participants finished or currently followed a University
Bachelor (41.8%); 52 participants finished or currently followed a University Master (18.9%);
50 participants finished or currently followed HBO (Higher Vocational Education) (18.2%);
36 participants finished or currently followed High School (13.1%), 18 participants finished or currently followed MBO (post-secondary vocational education) (6.5%) and 2 participants finished or were currently doing a PhD (0.7%). The other 0.8% was made up of one missing value (0.4%) and one participant whose current or highest followed education level was a Post Bachelor (0.4%).
The research was in a correlational design, in the form of an online survey (see Appendix A). A correlational design was applied because relationships between continuous variables were tested. Further, this research aimed to test relationships, and not causal effects.
Emotional enhancement and coping or escapism were used as the independent variables (predictors). The dependent variable was binge-watching behavior and the moderator was loneliness.
Prior to taking the survey, the participants filled in an informed consent form, which explained how their data was be handled and that they could retract their participation at any time. First, the participants filled in their demographical information (gender, age, education level), followed by questions about participants’ loneliness. Next, participants answered questions about their TV series watching behavior: how many days they watched per week and how many episodes they watched per session. Then, they were asked to think of up to three TV shows that they had watched two or more episodes in a row of, in the past month.
After this, participants were asked to pick the show out of the three possible answers that they watched most. With this show in mind, participants answered questions that measured their binge-watching behavior: the frequency of days watched per week, multiplied with the number of episodes watched per session. This was followed by two sets of questions
measuring participants’ motivations for binge-watching. Since the current study was part of a bigger project, other variables were measured in the survey, which are not related to the focus of this research. Finally, participants were debriefed about the goal of the research and were asked for consent to submit their data. Participants recruited through the participants’ pool could click a link to receive their research credits. The whole procedure took about 12 minutes.
Independent variables: Motives for Binge-watching TV Series
The Watching TV Series Motives Questionnaire (WTSMQ) by Flayelle et al. (2019) was applied to measure two motivations of interest:
Emotional Enhancement. The WTSMQ included 5 items on emotional enhancement motivations. An example of an item was: “I watch series to get attached to characters and feel joy watching them in each episode.” or “I watch TV series because I know I'll have a
15 good time if I get carried away by the story.” The items were answered on a 4-point Likert scale, where 1= “not at all” and 4= “to a great extent”. The higher the mean, the higher the score for binge-watching for emotional enhancement. For the current study, all scores of all five emotional enhancement questions were combined into a variable that calculated the average score for emotional enhancement. The scale has a sufficient reliability of Cronbach’s α = .78.
Coping or Escapism. The WTSMQ included 8 items on coping or escapism motivations. An example of an item was: “I watch series to overcome loneliness.” or “I watch TV series to escape a number of responsibilities.” The items were answered on a 4- point Likert scale, where 1= “not at all” and 4= “to a great extent”. The higher the mean, the higher the score for binge-watching for coping or escapism. For the current study, all scores of all eight coping or escapism questions were combined into a variable that calculated the average score for coping or escapism. The scale has a good reliability of Cronbach’s α = .83.
Dependent variable: Binge-watching behavior
In the current study, binge-watching was understood as watching more than one episode of the same show back-to-back (Schweidel & Moe, 2016). Participants were asked to name up to three shows that they had seen in the last month. It is important that they had seen at least two episodes of these shows back-to-back, at least once that month. With these shows in mind, they answered the questions about their binge-watching behavior.
Flayelle et al. (2019) measured binge watching through four different variables that are assessed separately. These variables were (1) frequency of watching, (2) watching time per working day, (3) watching time per day off, and (4) quantity of episodes seen in one session.
Because we asked participants to average their binge-watching behavior of the last month in one week, we combined some of these variables to create a scale. Binge-watching behavior was measured by multiplying the variables of frequency of watching (tv series) and quantity
16 of episodes seen. Because participants only answered the questions with shows they binge- watched in the last month in mind, the questions excluded shows of which participants watched only one episode in one sitting (not binge-watched).
Frequency of watching (TV series) was measured by asking the question: “Averaged from the last month, how many days a week do you watch more than one episode of these series?” There were seven possible answers, with once a week to seven times a week as possible answers. Quantity of episodes seen was a numerical variable that answered the question: “On average, how many episodes would you watch per session?”. Participants could fill in their own answer, however, numbers lower than two and higher than fifteen were not allowed.
The Roberts Version of the UCLA (University of California: Los Angeles) Loneliness Scale (RUSL-8), developed by Roberts (1993) was used to measure loneliness in the current study. This scale consisted of 8 items, such as: “I lack companionship” or “I feel left out”.
The possible answers were: 0 = “never” 1 = “rarely”, 2 = “sometimes” and 3 = “often”. The final variable that was used for analysis was based on a sum of all 8 items, thus the loneliness score ranged from 0-24. The current study employed a Dutch translation of the RUSL-8 scale, validated by Goossens et al. (2014) in a sample of Dutch-speaking participants. When the loneliness scores were calculated, the reliability of the RUSL-8 was calculated as well and the Cronbach’s alpha was good with a reliability of .81.
For this research, SPSS version 28.0 was used. Preliminary analysis included an outlier check, a reliability check, correlations, and descriptive statistics.
Because all measures were continuous, a regression-based approach was applied. The
hypotheses were tested using a PROCESS macro model, namely model 1 with one moderator.
17 The first PROCESS analysis focused on the relationship between emotional enhancement motivation and binge-watching behavior with the moderation of loneliness (hypotheses 1 and 3). The second analysis was performed to test the moderation of loneliness on the relationship between coping or escapism motivation and binge-watching behavior (hypotheses 2 and 4).
Results Preliminary analyses
The current study had 590 responses, of which 126 were incomplete and 464 were complete. Of these 464, 457 gave their consent for participating in the study, and also agreed to submitting their data. Some of the responses or user id’s were identical and therefore removed. Of the 449 remaining participants, 277 were Dutch. Since the participants had to have watched at least two episodes of a show back-to-back, a participant who filled in “-“ was excluded from the sample. This answer could not be treated as a missing value, since the binge-watching behavior variable could not be calculated and thus the analyses could not be performed for this response. Finally, one participant had a binge-watching behavior score of 15, indicating that they watched fifteen episodes of a certain show in one sitting, once a week.
Because there were not fifteen episodes available of that certain show to watch in one day, it was decided to remove this outlier. Besides this, this outlier scored higher than three times the standard deviation. With all the invalid and unfinished responses removed, N = 275 (see Table 1 for descriptive statistics).
18 Table 1
Descriptive Statistics for Main Variables Gender Age Emotional
Coping or escapism
M 1.79 25.78 2.63 2.52 8.15 4.57
SD 0.41 10.29 0.65 0.63 6.85 3.29
Minimum 1.00 18.00 1.00 1.00 2.00 0.00
Maximum 2.00 74.00 4.00 4.00 35.00 18.00
According to correlational analysis, several significant relationships were identified (see Table 2). Some of them will be explained. Firstly, the relationship between binge- watching behavior and coping or escapism as a motivation was significant (p = .201), which means that the higher participants scored on coping or escapism as a motivation, the higher their binge-watching score was. Secondly, the relationship between gender and binge-
watching behavior was also significant (p = .168), meaning that binge-watching behavior was higher for females than for males. Thirdly, the correlational analysis showed that the
relationship between age and loneliness was significant (p = -.185). This means that younger people scored higher on the loneliness scale than older people. Lastly, a significant
relationship was identified between loneliness and binge-watching behavior (p = .012), meaning that the higher people scored on the loneliness scale, the higher their binge-watching behavior was.
19 Table 2
Correlations of Main Variables Variable Emotional
Coping or escapism
watching Loneliness Gender Age 1. Emotional
2. Coping or
escapism 0.44 *** —
3. Binge-watching 0.09 0.20 *** —
4. Loneliness 0.08 0.41 *** 0.15 * —
5. Gender 0.02 0.20 *** 0.17 ** 0.06 —
6. Age -0.06 -0.31 *** -0.07 -0.18 ** -0.08 —
* p < .05, ** p < .01, *** p < .001
Reliability analysis and reversed coded items
Before testing the hypotheses, some of the variables needed to be recoded. For the moderator, loneliness, four of the eight questions were reversed coded (questions 1, 3, 4 and 8). While calculating the Cronbach’s α of the scale including reversed questions, question 3 (I do not feel alone) had a low reliability score of .03. The Cronbach’s alpha if item deleted was .81. This could be due to the double negative framing in the Dutch translation and answers, which was noted by several Dutch participants in their feedback about the survey. Due to the specifics of the Dutch translation, we decided to remove this question from the scale, after which the reliability of the loneliness score was .81. The reliability of the scale before removing the item was .74.
Recoding and computing variables
To test the hypotheses, several new variables had to be computed. To calculate the scores for the WTSMQ (emotional enhancement), the individual scores of all five questions were added and divided by five to calculate a mean score and compute a new variable with a
20 mean of 2.63 (SD = .65). To calculate the scores for the WTSMQ (coping or escapism), the individual scores of all eight questions were added and divided by eight to calculate a mean score and compute a new variable with a mean of 2.52 (SD = .63).
To compute the variable for binge-watching behavior, two other variables were
multiplied. The frequency of watching (on average, per week) was multiplied with the number of episodes watched (in one sitting, back-to-back). The frequency of watching ranged from one day a week to seven days a week (M = 2.99, SD = 1.86) and the number of episodes watched ranged from two to six (M = 2.56, SD = .84). Multiplying the two variables calculated a binge-watching behavior score (M = 8.15, SD = 6.85) and the binge-watching behavior variable was computed.
Besides recoding the variables, the scale also had to be adjusted. In the original scale, the score was calculated by adding the scores for all questions. The answers ranged from never to often and were coded from 0 to 3, thus creating a loneliness score between 0 and 24.
However, the answers in the data set were coded from 1 to 4, thus creating a loneliness score between 8 and 32. To avoid getting incorrect scores, the values of the (recoded) individual questions of the RUSL-8 scale were recoded to match the values needed to calculate the loneliness score. The loneliness scores ranged from 0 to 18 (M = 4.57, SD = 3.29).
To confirm directions of relationships found in correlations and to test the hypotheses, PROCESS macro model 1 (Hayes, 2013) with emotional enhancement and coping or
escapism as predictors, binge-watching behavior as an outcome variable and loneliness as a moderator was used. The confidence interval was 95% and the indirect effects were tested by adding 5000 bootstrapping samples.
First, the model with emotional enhancement as predictor, binge-watching behavior as outcome variable and loneliness as moderator was significant: R2 = .06, F(4,270) = 4.47, p =
21 .002. Next, the model with coping or escapism as predictor, binge-watching behavior as outcome variable and loneliness as moderator was also significant: R2 = .07, F(4,270) = 5.02, p < .001.
The relationship between emotional enhancement and binge-watching behavior (H1) H1 expected that emotional enhancement motivation predicts more frequent binge- watching behavior. The results confirmed no significant relationship between emotional enhancement and binge-watching behavior: b = 1.00, SE = .63, t (271) = 1.57; p = .117; CI [- .25, 2.24]. Therefore, H1 is not supported.
The relationship between coping or escapism and binge-watching behavior (H2) H2 expected that coping or escapism motivation predicts more frequent binge- watching behavior. The results confirmed a significant relationship between coping or escapism and binge-watching behavior: b = 1.89, SE = .71, t (271) = 2.66; p = .008; CI [-.16, .59]. This means that binge-watching behavior is positively predicted by coping or escapism motivation. Therefore, H2 is supported.
The relationship between emotional enhancement and binge-watching behavior moderated by loneliness (H3)
H3 expected that the relationship between emotional enhancement motivation and binge-watching behavior was negatively moderated by loneliness. The results confirmed no relationship between emotional enhancement and binge-watching behavior: b = .27, SE = .19, t (271) = 1.41; p = .159; CI [-.11, .65]. This means that the relationship between binge-
watching for emotional enhancement and binge-watching behavior is not stronger for people who are less lonely and thus H3 is not supported.
The relationship between coping or escapism and binge-watching behavior moderated by loneliness (H4)
22 H4 expected that the relationship between coping or escapism motivation and binge- watching behavior was positively moderated by loneliness. The results did not confirm a relationship between emotional enhancement and binge-watching behavior: b = .22, SE = .19, t (271) = 1.12; p = .263; CI [-.16, .59]. This means that the relationship between binge-
watching for coping or escapism and binge-watching behavior is not stronger for people who are lonelier and thus H4 is not supported.
Figure 2. A model illustrating loneliness as a moderator of the relationship between emotional enhancement and coping or escapism and binge-watching behavior. * p < .05; b =
unstandardized regression coefficient.
The correlational analysis showed a positive correlation between loneliness and binge- watching behavior. Therefore, we tested if loneliness would be a direct predictor of binge- watching. A regression analysis with binge-watching behavior as dependent variable and loneliness as independent variable showed that this was indeed significant: b = .32, SE = .13, t (273) = 2.54; p = .012; CI [.06, .56]. This meant that people who score higher on loneliness also have higher binge-watching behavior.
23 Based on the correlations, we included gender as a covariate because it was significant to binge-watching behavior. It did not change the results for the first model for emotional enhancement (see Appendix B). For the second model with coping or escapism the results were changed by adding gender as a covariate (see Appendix C). The changes made the previously significant main effect insignificant. To understand what role gender could play, we performed a third explanatory analysis (see Appendix D) with gender as a second
moderator, which was significant: b = 3.00, SE = 1.49, t (269) = 2.01; p = .045; CI [.07, 5.93].
It indicated that coping or escapism as a motivation predicted higher binge-watching behavior among females than among males. For emotional enhancement as motivator the results did not change, and gender did not interact as a potential moderator (see Appendix E).
Discussion Summary of Results
A correlational analysis showed various significant correlations between the main variables. Emotional enhancement did not have significant correlations with any other variable (besides coping or escapism). However, coping or escapism was significantly
correlated with all other variables, e.g., binge-watching behavior and gender. Binge-watching behavior was significantly correlated with coping or escapism, gender and loneliness, and loneliness and age were also negatively significantly correlated.
Results did not show a significant relationship between binge-watching for emotional enhancement and binge-watching behavior. This was contradictory to earlier research
(Flayelle et al., 2020; Shim & Kim, 2018; Sung et al., 2018), in which results showed that emotional enhancement was a significant predictor for binge-watching behavior. This means that participants who scored higher on emotional enhancement motivation were not more or less likely to binge-watch more often than participants who scored lower on that same scale.
24 However, results did show that binge-watching for coping or escapism had a
significant relationship with binge-watching behavior. This was in line with previous research (Flayelle et al., 2020; Panda & Pandey, 2017; Starosta et al., 2019; Stenseng et al., 2021).
This means that participants who scored higher on coping or escapism as a motivation were more likely to binge-watch more often than participants who scored lower on that same scale.
Both of these results were not significantly moderated by loneliness. This means that the relationship between binge-watching for emotional enhancement and binge-watching behavior was not stronger or weaker for lonely people. It also means that the relationship between binge-watching for coping or escapism and binge-watching behavior was not stronger or weaker for lonely people. This is partly in line with previous research. Tefertiller and Maxwell (2019) also found no significant relationships for loneliness as a moderator.
Besides this, the current results contradict previous studies (Sun & Chang, 2021; Tukachinsky
& Eyal, 2018), where results showed a positive relationship between loneliness and binge- watching behavior.
The exploratory analysis showed additional insights. Firstly, the correlation between loneliness and binge-watching behavior was reason to explore loneliness as predictor instead of moderator. The relationship was significant, meaning that people who score higher on loneliness also have higher binge-watching behavior.
Secondly, based on the correlational analysis which showed a positive correlation between gender and binge-watching behavior, gender was added as a covariate and second moderator to the PROCESS analysis. This showed that coping or escapism as a motivation predicted higher binge-watching behavior among females than among males. The other relationships between emotional enhancement as a motivation and binge-watching behavior did not change by adding gender as a second moderator.
25 Theoretical and Societal Implications
With the current study, we wanted to add to the growing body on binge-watching behavior. By establishing if emotional enhancement and coping or escapism were significant predictors of binge-watching behavior, we could create a clear definition of these constructs and use them as frequent predictors of binge-watching behavior. Since not all results were significant, we cannot say for certain that these motivations for binge-watching are
recognizable and definable. Another reason to perform this research was to gather information on the binge-watching behavior of the Dutch population. Even though the sample is not big enough to generalize for all Dutch citizens, we have found results on a group that had not been researched before.
Additionally, the current study aimed to find patterns in binge-watching behavior, to establish differences in various motivations. Especially in regard to the COVID pandemic, these motivations and ultimately the behavior might be different as opposed to before the pandemic. The current study found the relationship between coping or escapism as predictor for binge-watching behavior to be significant. This could be due to the increase in feelings of loneliness during the pandemic (Padmanabhanunni and Pretorius, 2021), causing people to seek an escape from reality through binge-watching.
In addition to loneliness, binge-watching is often associated with other negative effects, such as decreased wellbeing, anxiety and depression (Pittman & Steiner, 2021).
However, research has also proven that binge-watching has a positive effect on wellbeing (Granow et al., 2018). It is important to establish these effects of binge-watching behavior, to create goals to decrease the negative effects and promote the positive effects, so that the wellbeing of people worldwide can be ensured. Therefore, future research can focus on the
26 effect of certain binge-watching motivations on wellbeing, mediated by binge-watching behavior.
Limitations and Directions for Future Research
The study had a few limitations. Firstly, the nature of the study was correlational.
Since we conducted a survey, it is impossible to draw conclusions and speak of possible causal effects. Besides this, due to the correlational nature of the study, it is not guaranteed that these results will be the same for a different sample. For example, in this case loneliness was a moderator, while loneliness could also be a predictor. Exploratory analysis showed that loneliness was a significant predictor of binge-watching behavior. To establish effects
between the main variables, we advise to chance the research design. For example, a longitudinal study with a diary measurement (e.g. a week-long daily measurement) would work to create a more accurate picture of the binge-watching behavior. This could also be beneficial when binge-watching is regarded in a COVID-19 related study. The current study is unable to explain relationships about binge-watching behavior during the pandemic, since it only includes data from one point in time. Future research could include data from before, during and after the pandemic to compare results.
Secondly, the reliability of participants’ self-reported data on binge-watching behavior is debatable. It is possible that participants have filled in incorrect data regarding their binge- watching behavior. This could be solved by using donated data from streaming services. It has proven to be successful in similar studies about gaming, where global game publishers
provided objective game-behavior data (Vuorre et al.,2021; Johannes et al., 2022). .Another aspect of self-reporting is the naming of TV shows. Participants answered questions about their behavior with one specific show in mind. Their feelings towards the show or their motivations for watching may differ when they would have thought of a different show. The variety in shows also influenced the binge-watching behavior scores, because not all shows
27 are of the same duration. For example, watching three episodes of a 20-minute show accounts for the same amount of time one episode of a one-hour show absorbs. To guarantee results where the variation in tv shows, genres and duration of episodes is taken into consideration, future research could be qualitative. By conducting f.e. interviews, researchers can find out patterns between certain genres of shows or lengths of episodes, after which a new
quantitative research could be done.
Thirdly, it is difficult to draw conclusions on gender as a covariate or second
moderator, since the sample was not gender balanced. Of the total sample of 275, 78.9% was female and only 21.1% was male. In future research, the sample should be more equal in terms of gender, so that the differences found between males and females are more reliable.
Lastly, due to this narrow time frame, the sample was not as big as it could have been.
Because participants were recruited through convenience sampling, there was not as much diversity in demographical variables. This entailed the sample not being representative for all of the Dutch population, considering the lack of variety in age and education level. For example, because the survey was distributed via the UvA Participant Pool, lots of the
participants were university bachelor students. For future research, the sample could be bigger and more diverse, creating a sample in which the demographical variables are more evenly distributed.
The current research found a significant relationship between binge-watching for coping or escapism and binge-watching behavior, but no significant relationships were found for emotional enhancement as motivation and loneliness as moderator. However, a
correlational analysis showed that loneliness could be a significant predictor, and gender could be a significant moderator. Future research should focus on different types of research methods to create a more valid result. This can be done by using monitored data or a diary
28 studies. Besides that, research can take loneliness as a predictor and gender as a moderator into consideration. To conclude, a lot of research still needs to be done on binge-watching.
Even though various motivations have already been established, it is important to keep mapping out these motivations and finding differences and similarities within different kinds of TV series.
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35 Appendix A
Survey in English
(Excluding consent form, debriefing and questions irrelevant to the current study) Demographics
1. What is your gender?
• Prefer not to say
2. What is your age? (Please provide your answer in a number, e.g., 30 instead of
3. What is the highest education level you have completed or are currently completing?
• High School
• MBO (Dutch school system)
• HBO (Dutch school system)
• University Bachelor
• University Master
• Other, namely:
4. What is your nationality?
• American (US)
• British (UK)
• Other, namely:
5. Please choose the option that best represents your answer to the following statements:
6. Think of up to three tv series you have watched in the last month and answer the following questions with these series in mind. It is important that you have seen at least two episodes of these shows back-to-back, at least once that month.
• Answer 1
• Answer 2
• Answer 3
From the titles above, please pick the tv show which you have watched most frequently in the past month, and based on it answer the following questions.
7. On average, how many days a week did you watch more than one episode of this TV series in the past month?
• Once a week
• Twice a week
Never Rarely Sometimes Often 1. I feel in tune with
people around me.
2. I lack companionship.
3. I do not feel alone.
4. I feel part of a group of friends.
5. I am no longer close to anyone.
6. I feel left out.
7. I feel isolated from others.
8. I can find
companionship when I want it.
• Thrice a week
• Four days a week
• Five days a week
• Six days a week
8. On average, how many episodes did you watch per one session? Please fill in a number.
WTSMQ – emotional enhancement
9. Please choose the option that best represents your answer to the following statements:
Not at all Very little Somewhat To a great extent I watch TV series to feel strong
emotions like the excitement or the thrill they give me.
I watch TV series because I know I’ll have a good time if I get carried away by the story.
I watch TV series to get attached to characters and feel joy
watching them in each episode.
I watch TV series in the hopes of feeling again the elation I felt watching another TV series previously.
I watch TV series to be captivated and experience extraordinary adventures by proxy.
38 WTSMQ – coping or escapism
10. Please choose the option that best represents your answer to the following statements:
Not at all Very little Somewhat To a great extent I watch TV series to pass the
time and escape from boredom.
I watch TV series to relieve stress, anxiety or negative emotions.
I watch TV series to get away from the daily hassles.
I watch TV series to overcome loneliness.
I watch TV series to escape a number of responsibilities.
I watch TV series in order to feel like I am floating in a secondary state for a while.
I watch TV series to escape the routine.
I watch TV series to escape reality and seek shelter in fictionary worlds.
39 Appendix B
Analysis of Model 1 with Gender as Covariate
Run MATRIX procedure:
***************** PROCESS Procedure for SPSS Version 4.0 *****************
Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2022). www.guilford.com/p/hayes3
Model : 1 Y : bwb X : wt_ee_t W : lonelyRF Covariates:
gender Sample Size: 275
R R-sq MSE F df1 df2 p
,2492 ,0621 44,6446 4,4700 4,0000 270,0000 ,0016 Model
coeff se t p LLCI ULCI
constant 2,8438 3,1487 ,9031 ,3673 -3,3554 9,0430 wt_ee_t -,3027 ,9928 -,3049 ,7607 -2,2574 1,6520 lonelyRF -,4545 ,5212 -,8719 ,3840 -1,4806 ,5717 Int_1 ,2791 ,1913 1,4588 ,1458 -,0976 ,6558 gender 2,6679 ,9895 2,6963 ,0075 ,7198 4,6159 Product terms key:
Int_1 : wt_ee_t x lonelyRF
Test(s) of highest order unconditional interaction(s):
R2-chng F df1 df2 p
X*W ,0074 2,1280 1,0000 270,0000 ,1458 ---
Focal predict: wt_ee_t (X) Mod var: lonelyRF (W)
Conditional effects of the focal predictor at values of the moderator(s):
lonelyRF Effect se t p LLCI ULCI
1,2774 ,0538 ,8162 ,0660 ,9474 -1,5530 1,6607 4,5673 ,9721 ,6270 1,5504 ,1222 -,2624 2,2065 7,8571 1,8903 ,9553 1,9788 ,0489 ,0095 3,7711 Moderator value(s) defining Johnson-Neyman significance region(s):
Value % below % above 8,4446 88,0000 12,0000 6,9001 77,4545 22,5455
Conditional effect of focal predictor at values of the moderator:
lonelyRF Effect se t p LLCI ULCI ,0000 -,3027 ,9928 -,3049 ,7607 -2,2574 1,6520 ,9474 -,0383 ,8586 -,0446 ,9645 -1,7288 1,6522 1,8947 ,2261 ,7447 ,3037 ,7616 -1,2399 1,6922 2,8421 ,4906 ,6614 ,7417 ,4589 -,8116 1,7928 3,7895 ,7550 ,6214 1,2150 ,2254 -,4684 1,9784 4,7368 1,0194 ,6329 1,6108 ,1084 -,2266 2,2654 5,6842 1,2838 ,6933 1,8518 ,0651 -,0811 2,6488 6,6316 1,5483 ,7915 1,9561 ,0515 -,0100 3,1066 6,9001 1,6232 ,8245 1,9688 ,0500 ,0000 3,2464 7,5789 1,8127 ,9154 1,9802 ,0487 ,0104 3,6150 8,4446 2,0543 1,0434 1,9688 ,0500 ,0000 4,1086 8,5263 2,0771 1,0561 1,9669 ,0502 -,0020 4,1563 9,4737 2,3415 1,2076 1,9390 ,0535 -,0359 4,7190 10,4211 2,6060 1,3664 1,9072 ,0576 -,0841 5,2960 11,3684 2,8704 1,5301 1,8759 ,0617 -,1421 5,8829 12,3158 3,1348 1,6975 1,8467 ,0659 -,2072 6,4768 13,2632 3,3992 1,8674 1,8203 ,0698 -,2773 7,0758 14,2105 3,6637 2,0393 1,7965 ,0735 -,3513 7,6786 15,1579 3,9281 2,2127 1,7752 ,0770 -,4283 8,2844 16,1053 4,1925 2,3873 1,7562 ,0802 -,5075 8,8925 17,0526 4,4569 2,5628 1,7391 ,0832 -,5886 9,5025 18,0000 4,7214 2,7390 1,7237 ,0859 -,6712 10,1139
*********************** ANALYSIS NOTES AND ERRORS ***********************
Level of confidence for all confidence intervals in output:
W values in conditional tables are the mean and +/- SD from the mean.
--- END MATRIX ---