Just one more episode: A study on self-
awareness of binge-watching behavior, and how the interaction between job stress and self-
control predicts binge-watching behavior
Sara Croux (13419404) Master’s Thesis
Graduate School of Communication
Master’s programme Communication Science Chei Billedo
7357 words July 14th, 2022
There are many types of bingeing behavior, for instance, food bingeing and alcohol bingeing. This study specifically investigates the concept of binge-watching series on streaming services such as Netflix and Videoland. As binge-watching often generates positive gratifications, it may easily lead to addiction. However, most people do not seem to be aware of their own binge-watching behavior and how they are sometimes leaning towards a form of addiction. To investigate whether the sample is aware of their behavior, this study aims to see if there is a relationship between binge-watching behaviors in terms of duration and frequency and the level of binge-watching self-awareness. People have many reasons for binge-watching of which one is coping with stress. To provide new insights into the already existing field of research on academic stress in relation to binge-watching, this paper also focuses on how job stress impacts binge-watching behavior in terms of frequency and duration of binge-watching sessions. As has been discussed in previous research, people with low levels of self-control find it harder to retain themselves from frequent and long binge-watching sessions. Therefore, it is expected that a high level of job stress predicts more frequent and longer binge-watching sessions and that this impact is even stronger for low self-control individuals. The sample consisted of people who worked full-time, meaning 32 hours or more. They filled in a questionnaire that addressed questions about the research topics. The results show that a higher binge-watching frequency and length are related to more self-awareness. To date, only a few studies have investigated the self-awareness among binge-watchers and found that self- awareness is generally low among people. With the results of this study, it seems that people start to become more aware of their binge-watching behavior. However, there seems to be no impact of the interaction between job stress and self-control on binge-watching behaviors. We suggest that although individuals with academic stress and low levels of self-control might
binge-watch more and longer, this does not automatically hold for full-time workers experiencing job stress. Nonetheless, more research should be conducted on this specific matter.
Bingeing is often defined as the rapid consumption of a huge amount of something in a short time frame. It isgenerally associated with food and alcohol, possibly leading to obesity and alcoholism (Devasagayam, 2014). The beforementioned types of bingeing behaviors and their addictions are oftentimes found socially unacceptable. Binge-viewing on streaming services, however, seems to be less of a social issue (Devasagayam, 2014).
With the evolution of smartphones, laptops, and tablets, various ways of viewing series came into existence. Large differences existbetween binge-watching on streaming services and watching regular TV. The latter must be watched at home and shows are scheduled at fixed time slots, whereas streaming services can be accessed from anywhere and at any time (de Feijter et al., 2016). Additionally, streaming services do not have commercial breaks, nor do people have to wait a whole week before the next episode is aired, as is the case with TV series (Devasagayam, 2014). Streaming services often release all episodes of a series at once (Tefertiller & Maxwell, 2018). These features resulted in the growing popularity of services such as Netflix, Amazon Prime, and Hulu (Nanda & Banerjee, 2020). The control here is in the hands of the consumers. For instance, they can watch and also pause the show they are watching whenever they want to. Therefore, binge-watching on these platforms can be defined as
‘watching multiple episodes of a series in one sitting’ (de Feijter et al., 2016; Exelmans & Van den Bulck, 2017; Pittman & Sheehan, 2015; Shim & Kim, 2018; Shim et al., 2018; Sung et al., 2018; Tukachinsky & Eyal, 2018; Viens & Farrar, 2018; Walton-Pattison et al., 2018).
People have various reasons for binge-watching. For instance, they want to entertain themselves or they want to kill time (Nanda & Banerjee, 2020). In addition, they might want to
escape from reality or their daily routines (e.g., school or work). Especially when these routines are stressful and people are searching for ways to relieve themselves from stress, binge- watching is often a result perceived as rather positive (Nanda & Banerjee, 2020).
Previous studies have shown that college students binge-watch frequently due to the stress they experience from academic tasks (Devasagayam, 2014; Merrill & Rubenking, 2019).
However, binge-watching has not been investigated in relation to job stress experienced by employees who work full time, meaning working 32 hours or more per week. Job stress can be defined as “an individual’s reactions to characteristics of the work environment that seem emotionally and physically threatening” (Jamal, 2005, p. 225) or as “a poor fit between the individual’s capabilities and his or her work environment, in which excessive demands are made of the individual or the individual is not fully prepared to handle a particular situation”
(Jamal, 1985, p. 410). Job stress in general is very undesirable, as it can create an aversive reaction in employees. People devote a large part of their energy and time to coping with this stress. Under such circumstances, they are more likely to start their binge-watching session which can last for several hours. In turn, this affects their job performance negatively (Jamal, 2007). Binge-watching behavior can be so intense that it may potentially lead to addiction (Schweidel & Moe, 2016). However, people do not assume that they can be addicted to binge- watching series and oftentimes do not realize that their viewing sessions are extremely long and frequent (Devasagayam, 2014). For this reason, this study focuses on whether people are self- aware of their binge-watching behavior, meaning the frequency and duration of the binge- watching sessions, and whether they are more aware of extreme binge-watching behavior when their viewing sessions are frequent and long.
It requires a certain level of control to retain oneself from binge-watching frequently and for long periods of time. This is called self-control and can be defined as ‘the capability of the self to shift a dominant response tendency and regulate one’s behavior, emotions, and
thoughts’ (de Ridder et al., 2011). When binge-watching, viewers oftentimes do not know what the optimal viewing duration is for binge-watching the show (de Feijter et al., 2016). Self- control plays a significant role here and can determine whether someone is actually able to stop him- or herself from watching. Therefore, this study will also examine whether the relationship between job stress and binge-watching behavior is different for people with low levels of self- control and people with high levels of self-control. This leads to the second focus of the study:
Is the way in which job stress predicts binge-watching behavior moderated by self-control?
There are several motivators that lead to binge-watching. Previous research has often focused on motives that are explained by the Uses and Gratifications Theory (Katz et al., 1974).
These authors suggest that individuals actively select media to meet certain needs. These needs may include social involvement, information seeking, and entertainment (Katz et al., 1973), where the latter two are usually related to television watching (Rubin, 1981). Another motivation that reinforces the intention to binge-watch is the tackling of negative emotional feelings. Tefertiller and Maxwell (2018) explored the relationship between mental health concerns such as anxiety and the tendency to binge-watch and found that an individual’s anxiety is related to the intention to binge-watch. Mood management theory supports this and proposes that viewers use entertainment platforms to undertake negative moods and provoke positive moods (Zillmann, 1988, 2000). An example of such a negative mood is stress that is experienced (Halfmann & Reinecke, 2021). Feelings of stress can cause individuals to select binge-watching as an entertainment platform to escape and distance themselves from this stress (Nanda & Banerjee, 2020). For instance, if a person is tired from stress, they are more likely to choose binge-watching as a way of coping (Halfmann & Reinecke, 2021). This is because
binge-watching, in particular the binge-watching of series, is considered a cognitively undemanding activity as we repeatedly follow the same storyline and the same characters (Perks, 2018). Therefore, binge-viewing is known to cost very little effort. Additionally, it diminishes the negative emotional state that comes with stress (Halfmann & Reinecke, 2021).
Thus, binge-watching seems to be a helpful coping strategy for stress. However, in previous work, researchers have primarily focused on stress as a general concept (Halfmann & Reinecke, 2021; Nanda & Banerjee, 2020) or academic stress among students (Merrill & Rubenking, 2019). The latter focused on undergraduates and shows that students tend to binge-watch more frequently due to procrastination. This study builds on this academic work because for adults, job stress may similarly lead to procrastination which causes them to binge-watch more frequently.
This study aims to highlight job stress in particular. Job stressors may include high workload, high working hours, high expectations from management, and interpersonal conflicts (Behr & Glazer, 2001; Nixon et al., 2011). Investigating job stress is important because extreme levels can, for instance, cause low job performance. Besides that, job stress has not been investigated in relation to binge-watching. Also, this study does not focus on the gratifications obtained during and after binge-watching as many previous studies did, but rather on how the motivator job stress affects binge-watching behavior in terms of frequency and duration of the sessions.
The issue with severe binge-watching behavior is that people oftentimes do not recognize their behaviors. They do not realize that they are watching frequently and for long periods of time and that severe binge-watching can actually be an addiction (Devasagayam, 2014).
Many researchers have focused on how the need for binge-watching affects the gratifications during and post-binge-watching or film-watching and show that it can enhance positive moods and may serve as a coping mechanism for stress (Nanda & Banerjee, 2020;
Oliver & Bartsch, 2010; Oliver et al., 2012; Pittman & Sheehan, 2015). Even though these studies focus on the benefits of binge-watching behavior, it may also cause procrastination of important tasks, goal conflicts, and feelings of guilt (Granow et al., 2018). In both cases, binge- watching may lead to addiction. Schweidel and Moe (2016) saw that 25 percent of Hulu users watched 10+ hours of a series consecutively. The main reason behind this was constantly wanting to know what happens next, also called ‘the need for a sense of completion’. This is explained by Steiner and Xu (2018), who suggest that binge-watchers do not want to stop in the middle of a show, but rather complete it in one viewing session. Moreover, individuals become dependent on streaming services to feel whole throughout their daily lives. As such, they continually crave to watch the next episode (Devasagayam, 2014).
Devasagayam (2014) shows that the individuals do not think they can be addicted to Netflix, even though they displayed severe addictive behavior, such as continuously thinking about their show during the day. The optimal viewing time is a struggle for many people (de Feijter et al., 2016). Generally, society seems to have few problems with binge-watching, whereas food and drinking addictions are mostly socially unacceptable. This is because they are not yet familiar with the binge-watching type of addiction, whereas food and drinking addictions are well-known. Probably, the visible side effects that food- and alcohol-bingeing
fosters, but binge-viewing lacks, cause this effect (Devasagayam, 2014). People’s self- awareness of severe binge-watching behavior still seems to be low, but it is relevant to investigate it as it can easily lean towards addiction, and only two studies have shed light on this phenomenon (de Feijter et al., 2016; Devasagayam, 2014). Therefore, this study will investigate how aware people are of their own binge-watching behavior and whether individuals who binge-watch frequently and for long periods of time are also more aware of their intense bingeing behavior in terms of frequency and length of viewing sessions. This results in the following hypotheses (see Figure 1):
H1a: There is a positive relationship between binge-watching frequency and binge- watching self-awareness, meaning that a higher frequency of binge-watching sessions is related to higher awareness of one’s binge-watching behavior, and vice versa.
H1b: There is a positive relationship between binge-watching length in hours and binge- watching self-awareness, meaning that longer duration in terms of hours watched during binge-watching sessions is related to higher awareness of one’s binge-watching behavior, and vice versa.
H1c: There is a positive relationship between binge-watching length in episodes and binge-watching self-awareness, meaning that longer duration in terms of episodes watched within binge-watching sessions is related to higher awareness of one’s binge- watching behavior, and vice versa.
The fact that people might display severe binge-watching behavior and that they do not recognize it can be linked to self-control. Previous studies that focused on binge behavior, such as binge drinking (Wechsler et al., 2002), binge eating (Faber et al., 1995; Heatherton &
Baumeister, 1991), and binge shopping (Faber et al., 1995) illustrated that this behavior is often the result of a lack of self-control. Tangney et al. (2004) also propose that people with high self- control are less likely to perform bingeing behavior such as heavy eating or drinking. The self- regulatory strength model, which suggests that self-control becomes a strength when people defy temptations and engage in desirable behavior, provides an explanation for Tangney et al.’s (2004) proposition: consumers with high self-control demand gratification less frequently and immediate than their low self-control counterparts, regardless of what the need is (Baumeister
& Heatherton, 1996; Baumeister et al., 2007). Through this great gratification demand, caused by low self-control, bingeing behavior develops.
Generally, people are either classified as low self-control or high self-control individuals. There is a clear contrast between the two. Low self-control people often do not recognize that giving in to temptations comes with long-term costs. People with high self- control, on the other hand, do recognize these costs and less often concede to temptations (de Ridder et al., 2011). This concept is supported by the hot-cool system approach of the self- regulation theory (Loewenstein, 1996; Metcalfe & Mischel, 1999; Mischel, Shoda, &
Rodriguez, 1989). This theory explains two different approaches regarding decision-making: a cool-pragmatic principle, where choices are thought out and goal-directed, and a hot-feeling principle, where people do things because it feels good. The cool approach is related to high- self control and lack of impulsive decision-making, whereas the hot approach is linked to low self-control and the tendency to perform impulsive actions. Thus, self-control theories refer to behavioral decisions in which individuals choose short-term, instant outcomes over long-term, delayed interests because they do not identify possible negative outcomes.
With bingeing series, a similar pattern exists. Individuals displaying low self-control cannot refrain from certain tendencies and fail to control their behavior. Therefore, these individuals often lead towards binge behavior and show comparable behaviors when it comes
to binge-watching series (de Ridder et al., 2011). Self-control in relationship to binge-watching has been the focus of previous research (Merrill & Rubenking, 2019; Rubenking & Bracken, 2018), which show that low self-control leads to more intense binge-watching behaviors.
Nonetheless, self-control has not been investigated for people who experience job stress, and in what way self-control levels moderate the relationship between job stress and binge-watching behavior. Specifically, people with high levels of job stress are expected to binge-watch more, and this effect will be stronger for people who possess low levels of self-control and make rapid, impulsive decisions. They will more quickly give in to the temptation of binge-watching due to experienced job stress than individuals with high self-control. Here, self-control will be treated as a trait rather than a temporary state. Self-control is a trait that individuals either possess or not, but the trait is inherent and always present, irrespective of experienced job stress and binge- watching behavior. The following hypotheses will be investigated1 (presented in Figure 2):
H2a: Higher levels of job stress predict a higher frequency of binge-watching sessions, and this effect will be stronger for people with low levels of self-control.
H2b: Higher levels of job stress predict a longer duration in terms of hours spent on binge-watching sessions, and this effect will be stronger for people with low levels of self-control.
H2c: Higher levels of job stress predict a longer duration in terms of episodes watched during binge-watching sessions, and this effect will be stronger for people with low levels of self-control.
1 The moderation model has three specific hypotheses with two main variables and one interaction variable, but for the interest of conciseness it will be stated as one hypothesis.
Conceptual Model with Binge-Watching Frequency, Length (Hours), Length (Episodes), and Binge-Watching Self-Awareness as Correlational Variables
Conceptual Models with Binge-Watching Frequency, Length (Hours), and Length (Episodes) as Dependent Variables
The data for this research was collected via an individual cross-sectional online survey for two reasons. Firstly, with a survey, a large amount of data can be gathered in a relatively short timeframe. Secondly, surveys allow researchers to get information about people’s behaviors, attitudes, and knowledge. Experiments, for example, are often aimed at measuring respondents’ reactions to something in manipulated conditions, rather than investigating their behaviors.
One of the inclusion criteria for participation in the study was that the participants should be working 32 hours or more (full-time). The number of hours did not refer specifically to the hours stated in their job agreement, but rather to the actual number of hours that the respondent worked in one week. This criterion was chosen to eliminate the possibility of the number of
working hours influencing the results. It is possible that a person who works eight hours a week will have less stress than someone who works 36 hours (Nixon et al., 2011). Therefore, 32 hours per week was selected as the minimum number. Further, all respondents had to be 16 years or older. Many people younger than 16 are not yet working full time, as education is compulsory until the age of 16 (I am Expat, n.d.; Government of the Netherlands, n.d.).
To check for face validity and questionnaire errors, the survey was pre-tested among three people in the researcher’s network. They were sent the recruitment text (see Appendix A) and told to specifically pay attention to the flow of the survey and whether everything was understandable. Some examples of their suggestions were to change one item for binge- watching self-awareness and to highlight some words in several questions to make it clearer what the question referred to.
After pre-testing and modifying the survey, the final version was distributed from May 16 until June 3 through non-probability convenience and snowball sampling. This means that the sample was mostly collected within the researcher’s network (convenience sampling), which passed it on to their own networks (snowball sampling). The recruitment text containing the link to the survey was posted on LinkedIn and Facebook and shared by other people.
Additionally, the recruitment message was shared via WhatsApp, where it was forwarded by the recipients to their networks as well.
When participants opened the link, they were briefly introduced to the research topic, after which they were told it would take them approximately 10 minutes to fill in the survey (see the factsheet in Appendix B). Lastly, they were informed about participation rights and guidelines (see Appendix C). When the respondents confirmed that they were older than 16 years and agreed to participate in the study, they were directed to the survey questions (see Appendix D). The presented order of the questions was similar for every participant. After having finished the survey, they were thanked for their time and notified that their answers had
been recorded. Filling in the questionnaire took the respondents on average a little under 10 minutes (M = 585.34 seconds, SD = 723.19). Three respondents were not included in the analysis of the survey duration as they continued the survey after several days and therefore took a long time to complete it.
The original sample contained 134 participants. However, one person did not give consent to participate and was removed. Additionally, 35 people had to be deleted from the final dataset due to largely unfinished surveys. Five of the participants did not answer either the last question or the last two questions but were included in the rest of the analyses. Therefore, we analyzed data from a total number of 98 respondents.
Demographics. Of the total sample of 98 respondents, 62 (63.3%) were female and 36
(36.7%) were male. They were aged between 22 and 62 years old (M = 37.29, SD = 13.07).
Most of the participants had completed a university master’s degree (n = 46, 46.9%), followed by a university of applied sciences degree (n = 30, 30.6%), then a secondary vocational education degree (n = 11, 11.2%), a university bachelor’s degree (n = 6, 6.1%), followed by a high school degree (n = 4, 4.1%), and lastly a postgraduate degree (n = 1, 1.0%). None of the respondents had elementary school as their highest level of education. The largest number of respondents had the Dutch nationality (n = 96, 98%), one was German (1.0%) and one was Filipino (1.0%).
Correlates. Participants were also asked to categorize their job in terms of job industry
(see Figure 3). Some worked in health care (n = 24, 24.5%), others in finance (n = 17, 17.3%), and also some in government and public administration (n = 11, 11.2%). Other frequently chosen job industries were consultancy (n = 8, 8.2%), marketing and communications (n = 7, 7.1%), and education (n = 5, 5.1%). Remaining job industries with few respondents performing
them were media, sales, real estate, retail, human resource, data science/ICT, food, engineering, beauty, law, day-care, and youth care (depicted in the category ‘Other’ in Figure 3). The actual working hours ranged from 32, which was the required minimum to participate in this survey, to 70 hours per week (M = 40.66, SD = 7.49). Most respondents worked 40 hours per week (n
= 25, 25.5%), followed by 36 hours (n = 16, 16.3%), and then 32 hours (n = 15, 15.3%). A large number spent most of their free time watching movies and series (n = 76, 77.6%), hanging out with family and friends (n = 75, 76.5%), scrolling through their phones (n = 55, 56.1%), going for a walk (n = 55, 56.1%), working out (n = 44, 44.9%), reading (n = 38, 38.8%), cooking (n
= 34, 34.7%), and listening to music (n = 33, 33.7%). Other activities included playing an instrument, learning a new hobby, meditating, photography, going out, watching and reading news, gardening, and gaming, but were not chosen often (shown in Figure 4). They were also asked which platforms they used to binge-watch series (see Figure 5). Netflix was the most common platform (n = 94, 95.9%), followed by Videoland (n = 56, 57.1%), followed by Amazon Prime (n = 29, 29.6%), Disney+ (n = 26, 26.5%), and HBO Max (n = 23, 23.5%).
Other binge-watching platforms were NPO Plus, Viaplay, Ziggo Movies & Series, Kijk, NL Ziet, Apple TV+, Hayu, Discovery+, F1 TV, YouTube, Pathé Thuis, and Google Films (illustrated in the category ‘Other’ in Figure 5). Three respondents indicated that they never used any platforms to binge-watch. In terms of type of series they watch most of the time, 52 (53.1%) said comedy, 45 (45.9%) answered documentary, 42 (42.9%) said action & adventure, 39 (39.8%) said romantic series, 35 (35.7%) indicated drama, 34 people (34.7%) watched thrillers, 33 (33.7%) said crime fiction, 32 (32.7%) frequently watched reality TV, and 23 (23.5%) indicated sci-fi & fantasy. Types of series that were watched less often were cooking shows, horror, and anime. One respondent did not watch series at all (summarized in Figure 6).
Job Industry Categories (N = 98)
Activities Carried Out by Respondents in Their Free Time (N = 98)
0 5 10 15 20 25 30
Number of respondents
0 10 20 30 40 50 60 70 80
Watching movies and
Hanging out with family and
Going for a walk
Reading Cooking Listening to music
Other (playing an instrument, learning a
Number of respondents
Binge-Watching Platforms Used by Respondents (N = 98)
Types of Series Watched by Respondents (N = 98)
0 10 20 30 40 50 60 70 80 90 100
Netflix Videoland Amazon Prime
Disney+ HBO Max Other (NPO Plus, Viaplay,
Number of respondents
0 10 20 30 40 50 60
Number of respondents
Binge-Watching Self-Awareness. The dependent variable ‘binge-watching’ was
measured first in the survey as it came directly after the covariate questions about binge- watching. The variable binge-watching measured several elements of binge-watching behavior.
Firstly, binge-watching self-awareness was assessed utilizing the 5-item scale established by Granow et al. (2018). This scale was operationalized to measure the self-awareness of frequent and consecutive binge-watching of series and contained items such as ‘Marathon viewing, where I watch multiple episodes of a series in succession, is typical for me’ and ‘I binge-watch a lot’. The item ‘When I watch series, I prefer to watch a single episode at a time (not in succession)’ was recoded to align with the wording of the other items. Further, ‘in a single sitting’ was added to the third item after the pre-test sample highlighted that it was unclear whether it meant watching a series consecutively or not. Thus, this item became ‘Typically, I watch series straight from first to last episode in a single sitting’. The items were preceded by the phrase ‘Using the scale provided, please indicate how much each of the following statements reflects your binge-watching behavior’. The answer options ranged from 1 = strongly disagree, to 5 = strongly agree, where greater scores indicated a higher intensity of self-awareness of binge-watching behavior.
A principal axis factor analysis (PAF) was performed for binge-watching self- awareness. Bartlett’s test of sphericity, which tests for the significance of the correlations within the correlation matrix, was significant (χ2 (10) = 163.99, p < .001), indicating that it was acceptable to use the factor analysis on this variable. The Kaiser-Meyer-Olkin measure of sampling adequacy showed that the strength of the relationships among the variables was acceptable (KMO = .78), thus the analysis could proceed. The factor analysis indicated that the five items for binge-watching self-awareness formed a single unidimensional scale: only one component had an eigenvalue above 1 (eigenvalue 2.79) and there was a clear point of inflection
after this component in the scree plot. Together, these factors explained 55.87% of the variance in the original items. After a direct oblimin rotation, all items correlated positively with the first factor. The item ‘Marathon viewing, where I watch multiple episodes of a series in succession, is typical for me’ had the strongest association (factor loading .88). The reliability of the scale was good, α = .79. The variable was computed into a mean scale composed of the five items (M = 2.81, SD = .81). On average, the respondents were moderately self-aware of their binge- watching behavior, scoring 2.81 on a scale from 1 to 5.
Binge-Watching Frequency and Length. Secondly, two questions were asked about
their actual binge-watching behavior in terms of frequency and length of the sessions. Binge- watching frequency was measured through the question ‘Based on the following definition:
“Binge-watching is an act of watching 3 or more episodes of the same show in one sitting”, how often do you binge-watch series?’ (Merrill & Rubenking, 2019). The 9-point answer scale ranged from 1 = never, to 9 = for a large part of every day (Rubenking & Bracken, 2018) (M = 3.08, SD = 1.52). This means that on average, people binge-watch ‘monthly’, based on the definition mentioned above and on the scale from ‘never’ to ‘for a large part of everyday’.
Additionally, participants answered the questions ‘How many hours do you binge-watch per session?’ and ‘How many episodes do you binge-watch per session?’ to measure the length of the binge-watching session. The answers to these questions were presented on a slider bar and ranged from 0 hours to 15 hours (Rubenking & Bracken, 2018) (M = 2.88, SD = 1.26) and from 0 episodes to 15 episodes (M = 3.23, SD = 1.31). Thus, on average, the respondents binge- watch around 3 hours and approximately 3 episodes per session.
Job Stress. The predictor variable job stress was measured using the 13-item scale
developed by Parker and DeCotiis (1983). Example statements in the survey were: ‘My job gets to me more than it should’ and ‘Sometimes when I think about my job I get a tight feeling in my chest’. In some of the items, a single word was changed to make them more comprehensible.
For example, ‘I have felt fidgety or nervous as a result of my job’ became ‘I have felt nervous as a result of my job’. These items followed after the phrase ‘Using the scale provided, please indicate how much each of the following statements reflects how you feel about your current job’. The answer options ranged from 1 = strong disagreement, to 5 = strong agreement, where higher scores illustrated a higher level of job stress.
A principal axis factor analysis (PAF) was conducted for job stress. Bartlett’s test of sphericity was significant (χ2 (78) = 446.63, p < .001), meaning that the factor analysis could be used on this variable. The Kaiser-Meyer-Olkin measure of sampling adequacy showed that the relationship strength among the variables was acceptable (KMO = .77), so the analysis could proceed. The factor analysis indicated that the 13 items for job stress formed a three- dimensional scale: three components had an eigenvalue above 1 (eigenvalue 4.82; eigenvalue 1.57; eigenvalue 1.13). Together, these three factors explained 57.86% of the variance in the original items. However, after a direct oblimin rotation, it appeared that the groupings of the items within all three factors were not sufficiently associated. For instance, the items ‘Too many people at my level in the company get burned out by job demands’ and ‘I feel like I never have a day off’ do not seem to measure the exact same thing in one factor (see Appendix E for all the factor loadings of the items). Previous studies used the job stress scale as a unidimensional scale (Jamal, 1999; Jamal, 2007; Jamal & Baba, 2000) which generated high Cronbach’s alphas.
For comparability, it was decided to keep the scale as it originally was and proceed with one factor. The reliability of the scale was good, α = .85. Finally, the scale was computed into a mean variable comprised of the 13 items (M = 2.35, SD = .59) and used for further analyses.
The sample did not experience a really high level of job stress, on average 2.35 on a scale from 1 to 5.
Self-Control. Lastly, self-control was measured as a moderating variable employing the Brief Self-Control Scale (BSCS) (Tangney et al., 2004). This scale consisted of 13 items such
as ‘I am good at resisting temptation’ and ‘I have a hard time breaking habits’ (reverse coded).
Nine items (items 2, 3, 4, 5, 7, 9, 10, 12, 13) that were negatively worded were recoded so that for these items a high score on the scale would also mean a high level of self-control, as was the case for the other, non-recoded items. The items were preceded by the sentence ‘Using the scale provided, please indicate how much each of the following statements reflects how you typically are’. The answer options were based on a 5-point scale ranging from 1 = not at all, to 5 = very much, where greater scores indicated a higher level of self-control.
A principal axis factor analysis (PAF) was run for self-control. Bartlett’s test of sphericity was significant (χ2 (78) = 285.84, p < .001), so it was sufficient to use the factor analysis on this variable. The Kaiser-Meyer-Olkin measure of sampling adequacy showed that the strength of the relationships among the variables was acceptable (KMO = .75), thus the analysis could continue. The factor analysis indicated that the 13 items for self-control formed a five-dimensional scale: five components had an eigenvalue above 1 (eigenvalue 3.80;
eigenvalue 1.53; eigenvalue 1.23; eigenvalue 1.12; eigenvalue 1.02). Together, these five factors explained 66.92% of the variance in the original items. Nonetheless, a direct oblimin rotation showed that the groupings of the items within the five factors were not logically associated. Examples are the items ‘I often act without thinking through all the alternatives’
and ‘I am lazy’ (see Appendix E for all the factor loadings of the items). These are both components of self-control but do not seem to be associated well enough to be taken as separate measures for self-control. Previous researchers employed the BSCS as a unidimensional scale with high Cronbach’s alphas (Malouf et al., 2014; Merrill & Rubenking, 2019; Pechorro et al., 2021; Rubenking & Bracken, 2018; Tangney et al., 2004). Therefore, it was decided to keep the original scale and continue with one factor here as well. The reliability of the scale was good, α = .79. Therefore, the scale was applied here to measure self-control. Using the 13 items,
a mean variable was created that was applied in the further analyses (M = 3.25, SD = .55).
People in this study had a fairly high level of self-control, namely 3.25 on a scale from 1 to 5.
The correlations of the covariables with the main variables were investigated using Pearson’s r. Tables 1 to 3 provide summaries of the results.
Correlation Between Working Hours and Job Stress
There was a significant but weak, positive correlation between the number of working hours and job stress, r = .22, p = .033. This shows that the more hours someone works in a week, the more job stress they experience, and vice versa.
Correlation Between Age and Binge-Watching Behaviors
There was a significantly weak and negative correlation between age and binge- watching frequency, r = -.27, p = .007, meaning that the older one gets, the less frequent they binge-watch, and vice versa. Additionally, the correlation between age and binge-watching length in hours was found to be significant, but weak and negative, r = -.24, p = .018. This shows that the higher the age, the fewer hours they spend binge-watching, and vice versa. Also, we saw a significant, but weak and negative correlation between age and binge-watching length in episodes, r = -.29, p = .003. Older people’s duration of the binge-watching session is shorter than relatively younger people regarding the number of episodes watched.
Correlations Between Binge-Watching Self-Awareness and Binge-Watching Behaviors There was a significant, moderate to strong positive correlation between the frequency of binge-watching sessions and binge-watching self-awareness, r = .52, p < .001. This indicates
that the more frequent one’s binge-watching sessions were, the more aware one was of his own binge-watching behavior, and vice versa. Therefore, hypothesis 1a is supported: ‘There is a positive relationship between binge-watching frequency and binge-watching self-awareness, meaning that a higher frequency of binge-watching sessions is related to higher awareness of one’s binge-watching behavior, and vice versa’. Moreover, there was a significantly positive moderate correlation between the length of the binge-watching session in terms of hours and binge-watching self-awareness, r = .47, p < .001. The more hours one spent binge-watching series, the more aware he was of his binge-watching behavior, and vice versa. Thus, hypothesis 1b is also confirmed: ‘There is a positive relationship between binge-watching length in hours and binge-watching self-awareness, meaning that longer duration in terms of hours watched during binge-watching sessions is related to higher awareness of one’s binge-watching behavior, and vice versa’. It was also shown that there was a significantly strong positive correlation between the duration of binge-watching sessions in terms of episodes watched in succession and binge-watching self-awareness, r = .57, p < .001. We can see that the more episodes one watched during a binge-watching session, the more aware they were of their own binge-watching behavior, and vice versa. This means that hypothesis 1c is also supported:
‘There is a positive relationship between binge-watching length in episodes and binge-watching self-awareness, meaning that longer duration in terms of episodes watched within binge- watching sessions is related to higher awareness of one’s binge-watching behavior, and vice versa’.
Correlations Between Binge-Watching Frequency and Binge-Watching Length
There was a significant, but weak, positive correlation between the frequency of binge- watching sessions and the length of the session in terms of hours, r = .22, p = .03. This means that the more frequent one’s binge-watching sessions are, the more hours they spend on one
binge-watching session, and vice versa. Also, there was a significantly positive moderate correlation between binge-watching frequency and the duration of the session regarding the episodes watched, r = .42, p < .001. This indicates that the more frequent one’s binge-watching sessions are, the more episodes they watch within one session, and vice versa.
Correlation Between Binge-Watching Length Hours and Binge-Watching Length Episodes
There was a significantly strong and positive correlation between binge-watching length in hours and binge-watching length in terms of episodes, r = .76, p < .001, meaning that the more hours they spend binge-watching, the more episodes they watch, and vice versa.
Correlation Between Working Hours and Job Stress
Variable n M SD 1 2
1. Working hours
98 40.66 7.49 —
2. Job stress 94 2.35 .59 .22* —
Note. ** Correlation is significant at the .01 level (2-tailed).
* Correlation is significant at the .05 level (2-tailed).
Correlations Between Age and Binge-Watching Behaviors
Variable n M SD 1
1. Age 98 37.29 13.07 —
2. BW frequency 98 3.08 1.52 -.27**
3. BW length hours 98 2.88 1.26 -.24*
4. BW length episodes
98 3.23 1.31 -.29**
Note. ** Correlation is significant at the .01 level (2-tailed).
* Correlation is significant at the .05 level (2-tailed).
Correlations Between Variables of Binge-Watching
Variable n M SD 1 2 3 4
1. BW self- awareness
98 2.81 .81 —
2. BW frequency
98 3.08 1.52 .52** —
3. BW length hours
98 2.88 1.26 .47** .22* —
4. BW length episodes
98 3.23 1.31 .57** .42** .76** —
Note. ** Correlation is significant at the .01 level (2-tailed).
* Correlation is significant at the .05 level (2-tailed).
To test the moderation model, three outcome variables were included using Hayes’s Process, model 1 (Hayes, 2022).
Binge-Watching Frequency. To test whether binge-watching frequency could be
predicted by job stress and whether this relationship was moderated by self-control, a moderation analysis using Hayes’s Process was conducted (see Table 4). The model was shown to be significant, F (3, 89) = 3.87, p = .012. The predictors job stress and self-control, and the interaction between job stress and self-control as a moderating variable predicted 11.6% of the variance in binge-watching frequency (R2 = .116). Job stress did not predict binge-watching frequency, b = .09, SE = .27, t = .34, p = .735, 95% CI [-.45; .64]. Self-control, however, did predict frequency of binge-watching, b = -.91, SE = .29, t = -3.12, p = .002, 95% CI [-1.49; - .33]. For each additional point on the 5-point scale of self-control, the frequency of binge- watching decreased by .91, the other predictors held constant. The interaction of self-control and job stress did not predict binge-watching frequency, b = -.04, SE = .50, t = -.08, p = .939, 95% CI [-1.03; .96]. Therefore, hypothesis 2a ‘Higher levels of job stress predict a higher frequency of binge-watching sessions, and this effect will be stronger for people with low levels of self-control’ is not confirmed.
Binge-Watching Length Hours. To test whether level of job stress predicted binge-
watching duration in hours and whether there was an interaction of self-control and job stress, another moderation analysis using Hayes’s Process was conducted (shown in Table 5).
However, the model was not significant, F (3, 89) = .87, p = .458. Job stress, self-control, and the interaction of self-control and job stress only predicted 2.9% of the variance in binge- watching length (hours) (R2 = .029). Neither of the variables predicted binge-watching length in terms of hours: job stress, b = .20, SE = .24, t = .83, p = .407, 95% CI [-.28; .68]; self-control, b = -.16, SE = .26, t = -.63, p = .530, 95% CI [-.67; .35]; interaction of self-control and job stress b = -.43, SE = .44, t = -.98, p = .329, 95% CI [-1.30; .44]. This means that hypothesis 2b is also not supported: ‘Higher levels of job stress predict a longer duration in terms of hours spent on
binge-watching sessions, and this effect will be stronger for people with low levels of self- control’.
Binge-Watching Length Episodes. The last effect that was tested in a moderation
analysis using Hayes’s Process was the prediction of binge-watching length in episodes by job stress and the interaction of job stress and self-control (see Table 6). Again, this model was insignificant, F (3, 89) = .77, p = .512. Job stress, self-control, and the interaction between job stress and self-control only explained 2.5% of the variance in binge-watching length (episodes) (R2 = .025). Job stress, b = .08, SE = .25, t = .34, p = .733, 95% CI [-.41; .58], self-control, b = -.33, SE = .26, t = -1.26, p = .211, 95% CI [-.86; .19], and the interaction between job stress and self-control, b = -.15, SE = .45, t = -.32, p = .748, 95% CI [-1.05; .76] all did not predict binge- watching length in terms of the episodes watched. Hypothesis 2c, thus, cannot not be confirmed:
‘Higher levels of job stress predict a longer duration in terms of episodes watched during binge- watching sessions, and this effect will be stronger for people with low levels of self-control’.
Moderation Analysis for Job Stress, Self-Control, and the Interaction of Job Stress and Self- Control as Predictors of Binge-Watching Frequency (N = 93)
Variable b SE t p 95% CI
Job stress .09 .27 .34 .735 -.45 .64
Self-control -.91 .29 -3.12 .002* -1.49 -.33
Interaction job stress and self-control
-.04 .50 -.08 .939 -1.03 .96
Note. * Regression is significant at the .01 level (2-tailed).
Moderation Analysis for Job Stress, Self-Control, and the Interaction of Job Stress and Self- Control as Predictors of Binge-Watching Length (Hours) (N = 93)
Variable b SE t p 95% CI
Job stress .20 .24 .83 .407 -.28 .68
Self-control -.16 .26 -.63 .530 -.67 .35
Interaction job stress and self-control
-.43 .44 -.98 .329 -1.30 .44
Moderation Analysis for Job Stress, Self-Control, and the Interaction of Job Stress and Self- Control as Predictors of Predictors of Binge-Watching Length (Episodes) (N = 93)
Variable b SE t p 95% CI
Job stress .08 .25 .34 .733 -.41 .58
Self-control -.33 .26 -1.26 .211 -.86 .19
Interaction job stress and self-control
-.15 .45 -.32 .748 -1.05 .76
This research looked at whether the number of working hours was related to experienced job stress among the sample. Although people who worked more hours per week experienced more job stress, or people who had severe stress worked many hours, the relationship was very weak. This is partly in line with previous studies, which suggest that a high number of working hours contributes to job stress (Behr & Glazer, 2001; Nixon et al., 2011). Nonetheless, the sample did not experience a high level of job stress. This might be explained by the fact that the people in this sample worked approximately 40 hours per week, which is considered a normal full-time job. Future research could consider comparing people with regular job hours and people with extended job hours, as well as people working in traditionally categorized as high-stressed industries and how their binge-watching behaviors are formed due to this stress.
This study also aimed to investigate binge-watching behaviors in terms of frequency and duration and how these are related to binge-watching self-awareness. It was found that the
more frequent a person binge-watches, the higher their self-awareness about their binge- watching behavior is, and vice versa. This was similar for binge-watching duration. So, the more hours one spends binge-watching, and the more episodes this person watches during a binge-watching session, the higher their level of binge-watching self-awareness. The current research provides new insights into the binge-watching self-awareness field because previous studies conducted in previous years showed that people are not aware of their own behavior (De Feijter et al., 2016; Devasagayam, 2014). The findings of this current study provide support to the assumption that people’s disbelief about their own level of binge-watching is congruent to their binge-watching behaviors. These findings contribute significantly to the research in this area of study. Note though that this current research still relied on self-reports of binge-watching behaviors. Future research should continue to investigate this using more objective measures of binge-watching behaviors to validate the current findings. This line of research contributes to knowledge on binge-watching addiction.
People who binge-watched frequently also binge-watched for a long time and watch many episodes in one session. This behavior might be explained by the fact that binge-viewers cannot stop watching in the middle of a series (Steiner & Xu, 2018) and constantly crave to watch the next episode of a show (Devasagayam, 2014). There seem to be few people who binge-watch regularly but keep their binge-watching sessions short, which supports both of the beforementioned studies.
Findings Moderation Analyses
The interaction between job stress and self-control did not predict binge-watching frequency and binge-watching length in terms of hours and episodes watched within one binge- watching session. This means that among this sample, job stress was not a motivation to binge- watch more frequently and for a longer time and that this was also not moderated by the level
of self-control. This is contrary to previous findings that showed that academic stress is related to more frequent binge-watching sessions (Devasagayam, 2014; Merrill & Rubenking, 2019).
This might be explained by the fact that academic stress cannot fully be compared to job stress in relationship to binge-watching. The people experiencing academic stress are usually younger and this study shows that the younger people are, the more they binge-watch. Hence, the relatively older sample in this study already binge-watches less than younger people. Self- control, however, did impact binge-watching frequency, meaning that individuals with high levels of self-control binge-watched less frequently compared to individuals with low self- control levels. This finding supports the outcomes of previous research (de Ridder et al., 2011;
Merrill & Rubenking, 2019; Rubenking & Bracken, 2018; Tangney et al., 2004) which suggests that high self-control individuals find it easier to restraint themselves from frequent and long binge-watching sessions. This finding provides a basis for future research on the relationship between self-control and binge-watching behaviors.
As with all research, some limitations can be addressed for this study too. Firstly, as mentioned above, the measures for binge-watching frequency and length were self-reported.
Future research could take into consideration examining people’s actual binge-watching behavior by checking their screen time on their phones, for example. This way, it can be seen how many hours they spend on streaming apps such as Netflix. It will make the measure more objective and less biased.
Secondly, working hours and job stress as well as age and binge-watching behavior correlated in this study. However, they were not added as covariates in the moderation analyses for small sample purposes. In future studies, it might be interesting to add them to the main analyses as covariates for the main variables.
Lastly, future research should examine the scales for job stress and self-control as they seem to be composed of multiple factors. They were mostly used as unidimensional scales in previous research. However, future studies could consider focusing on one of the factors of job stress and self-control.
With binge-watching becoming a more common phenomenon due to the rising popularity of online streaming services, this study aimed to investigate job stress as a binge- watching motivator. Additionally, self-control was highlighted as a moderator that impacted the relationship between job stress and binge-watching frequency and duration. The act of binge-watching may be the cause of looking for needs that can be fulfilled by binge-watching series. However, constantly ensuring to gratify these needs can lead to severe binge-watching addiction. People oftentimes want to continue watching the show so it can be finished within a short time frame and to make them feel whole throughout the day. The issue that arises from this is that even though individuals binge-watch a lot and even for a couple of hours consecutively, they are not aware of their own binge-watching behaviors. Therefore, this study also aimed to examine whether people who binge-watch a lot and for a long time are aware of this behavior. This was examined by means of an online questionnaire. After pre-testing it, it was distributed among the researcher’s network and passed on to their networks too. The findings of the survey show that people who display severe binge-watching behavior in terms of frequency and length are self-aware of this behavior. However, no significant impact was found of the interaction between job stress and self-control on binge-watching frequency, length in terms of hours, and length in terms of episodes. The results of this study contribute to a better understanding of how binge-watching frequency and duration are related to binge-watching
self-awareness. However, there are still many things that can be researched in the field of job stress, self-control, binge-watching behavior, self-awareness, and how these are intertwined.
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Appendix A Recruitment Text
Hallo! Voor mijn masterscriptie zoek ik (Sara Croux) respondenten om mijn survey in te vullen. Het invullen duurt ongeveer 10 minuutjes.
Aangezien streaming diensten zoals Netflix en Videoland steeds populairder worden, doe ik onderzoek naar binge-watching gewoontes. Verder onderzoek ik de algemene werkervaring bij full-time werknemers. Full-time betekent dat je MINIMAAL 32 UUR per week werkt.
Het gaat hierbij niet per se om het aantal uren dat in je contract aangegeven staat, maar om de uren die je daadwerkelijk maakt in een week. De daadwerkelijke uren zouden mogelijk meer of minder kunnen zijn.
Dus, werk jij minimaal 32 uur per week (en ben je ouder dan 16 jaar)? Dan zou je me enorm helpen door mijn survey in te vullen!
Hello! For my master’s thesis, I (Sara Croux) am looking for respondents to fill in my survey.
Filling in the survey will take approximately 10 minutes.
Considering the fact that streaming services such as Netflix and Videoland are becoming more popular, I am conducting a study about binge-watching habits. Additionally, I am investigating the general job experience of full-time employees, full-time meaning that you work AT LEAST 32 HOURS per week. Here, the number of hours refers not to the hours stated in your job agreement, but to the actual number of hours that you work in one week.
The actual number of hours might differ from what is listed in your job agreement.
So, do you work at least 32 hours per week (and are you older than 16 years)? Then I would greatly appreciate it if you could take some time to fill in my survey!
Thank you in advance.
Kind regards, Sara