Go Long or Go Often: Influences on Binge Watching Frequency and Duration among College Students

Abstract

Binge watching, or serial viewing of a single program over an extended period of time, is a relatively new norm in television viewing that is becoming more popular than traditional appointment viewing. Previous research has explored various influences on binge watching; however, the current research is unique in exploring theoretically and empirically grounded predictors of both binge watching frequency and duration of binge watching sessions by means of a survey administered to college undergraduates (N = 651). Data show that binge watching frequency and duration are predicted by two non-overlapping sets of variables. Binge watching frequency was predicted by low self-regulation, greater tendency to use binge watching as both a reward and a form of procrastination, and less regret; while binge watching duration was associated with being female and experiencing greater enjoyment while binging. Self-control did not predict either binge watching frequency or duration, suggesting that alternative theoretical models should be explored. Findings also suggest that scholars should reconceptualize binge watching by including both frequency and duration measures in future studies.

1. Introduction

Binge watching, or serial viewing of a single program over an extended period of time (Rubenking et al. 2018), is a common act among college students. Nearly 90% of millennials (individuals between the ages of 20–33) binge watch (Statista 2016), and the most common age group to engage in binge watching are those that are enrolled in college (Devasagayam 2014). Not only are college students binge watching frequently, but also for long periods of time, which sparks numerous applied and theoretical questions.

Binge watching demands an individual to spend a lot of time watching television content (Petersen 2016). This is due to the requirement of watching multiple episodes of a series in succession. Researchers have been curious about variables that affect one’s binge watching habits. If an individual has a self-regulatory deficit, it might be hard to stay on task and complete an initial goal (Hofmann et al. 2012), especially when an individual has the possibility to be exposed to thousands of hours of enjoyable television content. A lack of self-control might also cause individuals to continue watching, as they may have a hard time ending their binge watching experience (Matrix 2014). These concerns can lead to other problems as well. For example, an individual may use binge watching as a form of procrastination to delay the completion of another task, such as housework or homework (Rubenking et al. 2018). In doing so, individuals may feel regretful of their binge watching experiences (Walton-Pattison et al. 2016), as the time spent binge watching could have been better allocated to more pressing tasks.

Previous research has noted that there are also positive outcomes associated with binge watching. The binge watching experience is an enjoyable one (Matrix 2014), as individuals are exposed to enjoyable television content. Due to the enjoyable nature of the binge watching experience, individuals might also reward themselves with binge watching either when they have made progress on tasks or once they have completed other tasks (Petersen 2016).

All of the above variables can be taken into consideration when determining what is driving an individual’s binge watching experience. While previous research has begun to explore various influences on this new norm in television viewing, the current research is unique in exploring the theoretically and empirically grounded predictors of self-regulation, self-control, enjoyment, reward watching, procrastination, and regret on both binge watching frequency and duration.

1.1. Binge Watching

In the current investigation, binge watching is defined as an act of watching three or more episodes of television content in one sitting. Those that engage in binge watching do so on various platforms, including broadcast TV, a phone, a tablet, a computer/laptop, and even on streaming websites such as Netflix, Hulu, and Amazon Prime. This unique type of viewing is largely based on the availability of streaming TV services, which depends on capabilities to house streaming technology and relatively new ways of distributing television content. These technological advances have changed the way that we are viewing television content and have largely contributed to the decline in traditional television viewing, such as appointment viewing. Appointment viewing refers to the act of an individual setting aside time to watch television content as it airs live (Conlin et al. 2016). This has become an inconvenient way of viewing television for many individuals that are busy with other tasks when the content is airing live. Thus, individuals are engaging in binge watching more than ever because it allows individuals to watch lots of television content when they please. In fact, individuals are starting to value their paid streaming services more than their cable services (Deloitte 2016), and more people are binge watching than appointment viewing (Hallinan and Striphas 2016).

There are various other aspects of binge watching that make it unique compared to other forms of television viewing. First, compared to appointment viewing, the binge watching experience requires individuals to spend a lot of time watching television in one sitting (Pittman and Sheehan 2015). Second, binge watching generally focuses on content that is narrative driven (Di Placido 2017), that is, only certain genres are usually binge watched and the content has a clear and coherent story to tell from the first to the last episode. Lastly, individuals are often immersed in their binge watching experience, so they may have a hard time stopping the consumption. This immersive experience might cause individuals to lose track of time when watching the television content (Petersen 2016).

Previous research has shown that binge watchers are motivated to do so by a number of gratifications sought or goals, including engagement, relaxation, passing time, hedonism, and social motives (Pittman and Sheehan 2015), as well as habit-driven motives (Conlin et al. 2016; Pena 2015), and escapism (Pena 2015). Age is a common individual difference that has been associated with binge watching (Devasagayam 2014), as previous research has demonstrated that younger individuals engage in binge watching more frequently than older individuals (Shannon-Missal 2013). More specifically, college students and other younger adults binge watch more frequently than older adults (Devasagayam 2014). This is likely due to younger individuals having more free time to dedicate towards binge watching compared to older individuals that are committed to working 40 h a week.

1.2. Trait Individual Differences

Previous research on binge watching is grounded in multiple theories. In this study, we are interested in the theoretical perspectives of self-regulation and self-control. We refer to these two theoretical perspectives as trait individual differences, that is, these are variables that remain relatively stable for an individual.

Self-Regulation and Self-Control

By establishing goals, individuals can regulate their own actions to ensure that sufficient progress is made towards the completion of a goal. Hofmann et al. (2012) refer to this goal-oriented behavior as self-regulation. Previous research has shown that an individual’s media usage, including watching TV, is affected by their self-regulatory decisions (Reinecke and Hofmann 2016). That is, those with lower levels of self-regulation are more likely to engage with secondary tasks or media devices. This is due to these secondary media devices being in conflict with the original goal. By engaging with these secondary devices, the completion of the original goal is delayed.

Although very little research focuses specifically on self-regulation and binge watching, previous research has found support for binge watching affecting an initial goal. Rubenking et al. (2018) found that individuals often engage in binge watching to put off other tasks. Petersen (2016) found similar results, as participants reported avoiding academic tasks by binge watching. Further Sung et al. (2015) found support for a positive relationship between self-regulation deficiencies and greater binge watching.

Self-control, which is a dimension of self-regulation, is a commonly researched individual difference that is often associated with binge watching behaviors. Self-control focuses on an individual’s ability to resist temptation of secondary tasks to ensure the completion of a primary task (Metcalfe and Mischel 1999). Individuals with lower levels of self-control will have a harder time resisting the temptation of other tasks compared to those with higher levels of self-control (Baumeister et al. 2008). In fact, individuals with lower levels of self-control were found to engage with various media more frequently than individuals with higher levels of self-control (Reinecke and Hofmann 2016). Since binge watching requires a large amount of an individual’s time, researchers are particularly interested in one’s self-control ability during this immersive experience. Individuals who engage in frequent binge watching are often viewed as having a deficit in their self-control ability (Matrix 2014; Melichar 2014).

This immersive experience can warrant individuals to not only lose control of the frequency of their binges, but also the duration of their binges. Petersen (2016) interviewed college students that regularly binge watch television and reported being immersed in television content and often losing track of time when binge watching. The participants reported becoming so easily immersed in the television content when binge watching because of the automatic options to continue watching that are present on streaming platforms such as Netflix and Hulu. These streaming platforms have made it easier to watch another episode as opposed to stopping the binge watching experience. Researchers have also proposed that a lack of self-control can lead to more immersive television experiences (Matrix 2014; Petersen 2016), which may influence how long an individual engages in binge watching. This is due to individuals becoming immersed in the binge watching experience, losing track of time, and finding it hard to stop watching. Therefore, the following hypotheses are proposed:

Hypothesis 1 (H1).

Individuals with lower levels of self-regulation more likely to binge watch more frequently and for longer periods of time compared to individuals with higher levels of self-regulation.

Hypothesis 2 (H2).

Individuals with lower levels of self-control are more likely to binge watch more frequently and for longer periods of time compared to individuals with higher levels of self-control.

1.3. Situational Individual Differences

In addition to the trait individual differences that may influence binge watching behavior, several situational variables have appeared in emerging research on the topic. We refer to these variables as situational, as they depend on the specific experience, which is binge watching in this study. The situational individual difference variables of interest in this study that have a relationship with binge watching include enjoyment/entertainment (Matrix 2014); binge watching as a reward; or reward watching (Petersen 2016), procrastination (Rubenking et al. 2018), and regret (Walton-Pattison et al. 2016).

1.3.1. Enjoyment and Reward Watching

Generally, television content is enjoyable, and individuals are exposed to hours of enjoyable television content when binge watching (Matrix 2014). While there are many reasons why individuals engage in binge watching, Rubenking et al. (2018) found that one of the motives to engage in binge watching is for excitement/enjoyment purposes. Feeney (2014), Petersen (2016), and Pittman and Sheehan (2015) also found support for individuals enjoying their binge watching experiences, as these individuals find binge watching to be engaging, exciting, and interesting. Recently, researchers have focused on binge watching being an enjoyable act and how it might affect future binge watching intentions (Pittman and Sheehan 2015; Shim and Kim 2018). Pittman and Sheehan (2015) found that engagement and hedonistic motives predicted future binge watching intentions. Shim and Kim (2018) also found that greater enjoyment predicts binge watching behavior. That is, the more the individual enjoys the binge watching experience, the more they will continue to engage in binge watching.

Since binge watching is often viewed as an enjoyable experience, individuals often engage in binge watching as a type of reward (Feeney 2014; Jenner 2016; Pittman and Sheehan 2015). This phenomenon is commonly referred to as reward watching (Petersen 2016). Binge watching can function as a reward in two ways. First, binge watching as a primary task can be intrinsically rewarding, as an individual might feel accomplished when they finish an entire series of a television show. This is a form of self-enjoyment (Feeney 2014), and individuals often gain personal satisfaction when finishing a TV series quickly because they are up-to-date with the narrative. Second, binge watching can also be extrinsically rewarding, as it can be used as a reward for completing a primary task (Pittman and Sheehan 2015). For example, after completing an academic task or housework, one may reward themselves by binge watching either a few episodes or an entire series. However, very little research has investigated how college students use binge watching as a reward. Petersen (2016) found preliminary support for students who reward watch after completing substantial academic tasks. Based on the aforementioned literature, the following hypotheses are proposed:

Hypothesis 3 (H3).

Greater enjoyment of binge watching will predict more frequent and longer binge watching sessions.

Hypothesis 4 (H4).

Greater tendency to reward oneself with binge watching will predict more frequent and longer binge watching sessions.

1.3.2. Procrastination and Regret

Research also suggests a more nuanced picture than binge watching as purely hedonistically positive. Petersen (2016) and Rubenking et al. (2018) found that college students often report binge watching as a form of procrastination. In fact, it is quite common for individuals to use different forms of media as a way to procrastinate (Reinecke and Hofmann 2016).

Putting off other tasks, and particularly engaging in longer binge watching sessions, may also engender regret. Regret occurs when individuals realize that a different outcome could have occurred had they initially acted more efficiently (Zeelenberg 1999). Thus, individuals might regret their binge watching experiences when they realize that the time spent binge watching could have been allocated towards other tasks. Previous research has focused on the regret that an individual may feel from engaging with various secondary media experiences, as this reduces the time that could be spent on a primary task. College students, in particular, were found to spend a lot of time on secondary media tasks, such as watching TV and engaging with others on social networking sites, as opposed to completing primary tasks (Panek 2014). Instead of spending time on leisure activities, these students could have completed other pressing tasks, such as their academic coursework, in a more timely manner. When coming to this realization, students might regret the decision of engaging in leisure activities. Thus, students might feel a sense of regret when they reflect on their prior binge watching experiences. Walton-Pattison et al. (2016) found support for regret negatively predicting binge watching. Based on preliminary findings about binge watching and these variables, the following hypotheses are proposed:

Hypothesis 5 (H5).

Greater procrastination motives predict more frequent and longer binge watching sessions.

Hypothesis 6 (H6).

Less regret will predict more frequent and longer binge watching sessions.

2. Results

Data Analysis

The hypotheses posited the influence of self-regulation, self-control, enjoyment, reward watching, procrastination, and regret on binge watching frequency and duration. The hypotheses were tested via two hierarchal regressions. Age and sex were controlled for in the first block of each regression. The trait individual differences of self-regulation and self-control were added in the second block. We separated these two variables from the others in block three, as these variables are relatively unchanging variables of a person. In the third and final block, the situational variables of enjoyment, reward watching, procrastination, and regret were added.

The binge watching frequency model accounted for 19.6% of the variance (ΔR² = 0.196, F(4,642) = 34.032, p < 0.001) and the binge watching duration model accounted for 7.7% of the variance (ΔR² = 0.077, F(4,642) = 11.932, p < 0.001). Data are presented in

Table 1. Summary of variables predicting binge watching frequency and duration (N = 651).

	Binge Watching Frequency		Binge Watching Duration
Predictors	ΔR2	β	ΔR2	β
Block 1: Demographics	0.008 *		0.007 *
Age		−0.050		−0.063
Sex		0.090 *		0.078 *
Block 2: Trait Independent Variables (IVs)	0.032 **		0.015 *
Age		−0.039		−0.059
Sex		0.103 **		0.091 *
Self-regulation		−0.106 *		0.047
Self-control		−0.093 *		−0.109 *
Block 3: Situational IVs	0.196 **		0.077 **
Age		0.007		−0.026
Sex		0.052		0.085 *
Self-regulation		−0.087 *		−0.005
Self-control		−0.008		−0.067
Enjoyment		0.052		0.248 **
Reward Watching		0.222 **		−0.007
Procrastination		0.216 **		0.065
Regret		−0.215 **		0.002

Notes: * p < 0.05, ** p < 0.01.

. Correlations of all variables of interest are presented in

Table 2. Summary of correlations between variables (N = 651).

Variable	1	2	3	4	5	6	7	8
1. Binge Watching Frequency	1
2. Binge Watching Duration	0.095 *	1
3. Self-regulation	−0.140 **	0.006	1
4. Self-control	−0.121 **	−0.085 *	0.357 **	1
5. Enjoyment	0.164 **	0.263 **	0.177 **	−0.034	1
6. Reward Watching	0.317 **	0.094 *	−0.096 *	−0.176 **	0.224 **	1
7. Procrastination	0.317 **	0.150 **	−0.154 **	−0.272 **	0.220 **	0.403 **	1
8. Regret	−0.192 **	−0.028	0.028	−0.050	−0.151 **	0.065	0.095 *	1
M	3.64	4.02	4.38	3.21	4.51	2.27	4.46	3.36
SD	1.96	2.58	0.86	0.65	1.21	0.95	1.65	1.56

Notes: * p < 0.05, ** p < 0.01.

.

Age was not a significant predictor of binge watching frequency or duration. However, sex was a significant predictor of binge watching duration (β = 0.085, p = 0.028), with females more likely to binge watch for longer periods of time than males. Sex was a significant predictor of binge watching frequency in block 1 (β = 0.090, p = 0.021, and block 2: β = 0.103, p = 0.008), but it failed to remain a significant predictor in the final block.

Hypothesis 1 states that individuals with lower levels of self-regulation are more likely to binge watch more frequently and for longer periods of time compared to individuals with higher levels of self-regulation. Self-regulation was a significant negative predictor of binge watching frequency (β = −0.087, p = 0.026), but it was not a significant predictor of binge watching duration. Thus, Hypothesis 1 is only partially supported, as self-regulation predicts binge watching frequency. Hypothesis 2 states that individuals with lower levels of self-control are more likely to binge watch more frequently and for longer periods of time compared to individuals with higher levels of self-control. Self-control was a significant predictor of binge watching frequency in block 2 (β = −0.093, p = 0.025), but failed to remain significant in the subsequent block. Similarly, self-control was a significant predictor for binge watching duration in block 2 (β = −0.109, p = 0.010), but failed to remain significant in the subsequent block. Thus, Hypothesis 2 is not supported.

Hypothesis 3 states that greater enjoyment of binge watching is associated with more frequent and longer binge watching sessions. Enjoyment was not a significant predictor of binge watching frequency. However, enjoyment significantly predicted binge watching duration (β = 0.248, p < 0.001); thus, Hypothesis 3 is partially supported, as enjoyment predicts binge watching duration. Hypothesis 4 states that using binge watching as a reward is associated with more frequent and longer binge watching sessions. Reward watching was a significant predictor of binge watching frequency (β = 0.222, p < 0.001). However, reward watching did not significantly predict binge watching duration. Thus, Hypothesis 4 is partially supported, as reward watching predicts binge watching frequency.

Hypothesis 5 states that greater procrastination motives predict more frequent and longer binge watching sessions. Procrastination was a significant predictor of binge watching frequency (β = 0.216, p < 0.001), but it was not a significant predictor of binge watching duration. Thus, Hypothesis 5 is partially supported, as procrastination is a significant predictor of binge watching frequency. Hypothesis 6 states that less regret predict more frequent and longer binge watching sessions. Regret was a significant negative predictor of binge watching frequency (β = −0.215, p < 0.001), but it was not a significant predictor of binge watching duration. Thus, Hypothesis 6 is partially supported, as regret is a significant predictor of binge watching frequency.

3. Discussion

This study examined predictors of both binge watching frequency and binge watching duration. Surprisingly, different non-overlapping variables predicted binge watching frequency and binge watching duration. Several interesting takeaways are presented.

First, binge watching frequency was predicted by less self-regulation, greater tendency to use binge watching as both a reward and a form of procrastination, and less regret. Self-regulation negatively predicted binge watching frequency, which supports previous literature (Sung et al. 2015). This is likely due to binge watching functioning as a conflict to the initial goal an individual hoped to complete. Those with lower levels of self-regulation will have a difficult time staying on task and will engage in binge watching more frequently. Self-regulation did not predict binge watching duration. This might be due to the fact that individuals may plan on binge watching for longer periods of time. Thus, binge watching may not be a primary goal that needs to be completed, but instead a planned activity for leisure. Reward watching and greater procrastination motives positively predicted binge watching frequency. These findings might be explained by the notion of binge watching functioning as an instant gratification because of the hours of enjoyable television content that is readily accessible on streaming websites (Matrix 2014). If an individual frequently uses binge watching as a reward when either making progress on or completing tasks, they might be prevented from long binges, as they have to attend to other tasks. Additionally, Reinecke and Hofmann (2016) found that individuals are likely to use instant gratifications to procrastinate. Thus, individuals might binge watch frequently to avoid other tasks. However, if they binge watch for too long as a form of procrastination, it is likely that the primary task may never be completed. Therefore, individuals do not binge watch for long periods of time to procrastinate. Lastly, regret was a negative predictor of binge watching frequency, which supports previous research (Walton-Pattison et al. 2016). Individuals that feel less regretful of their binge watching experiences are engaging in more frequent binge watching. Since binge watching is an enjoyable experience (Matrix 2014), individuals do not regret the entertainment that they received from the experience. However, regret did not predict binge watching duration because individuals might be cognizant of how much time they are spending binge watching.

Second, binge watching duration was predicted by sex and enjoyment. In this study, females were more likely to binge watch for longer periods of time compared to males. This finding supports previous research that has shown females are more likely to engage in binge watching than males (Light Speed Research 2016). Alternately, males and females may have difference preferences in the content they like to binge watch for long periods of time. Binge watching duration was also predicted by greater enjoyment from the binge watching experience. Individuals are exposed to hours of enjoyable television content when they are binge watching (Matrix 2014). If they are enjoying their binge watching experience, then the individuals will be more likely to continue binge watching. This results in long binge watching sessions. Additionally, if the binge watching does not conflict with other goals that the individual hopes to accomplish, then they will be more likely to continue their binge watching experience. Binge watching frequency was not predicted by enjoyment because it is likely that these binges are much shorter in length. During a shorter period of time, individuals might not be as immersed when binge watching as they would with longer binge watching sessions. Thus, the lack of immersion might affect how the individual enjoys their binge watching experience.

Lastly, self-control did not predict binge watching frequency or binge watching duration. This is a notable finding, as it does not support previous research that has suggested an individual’s levels of self-control would affect their binge watching experiences (Damratoski et al. 2011; Matrix 2014; Melichar 2014; Petersen 2016). This anomaly may be explained by reviewing previous research that has utilized self-control theory. Self-control is often associated with risk-taking behaviors (Gottfredson and Hirschi 2003). That is, individuals with lower levels of self-control are often engaging in risk-taking behaviors that conflict with other goals. Previous research has suggested that binge watching might be a risk-taking behavior because it takes up a large amount of an individual’s time and may conflict with their other goals (Petersen 2016). Additionally, binge watching is generally viewed as a negative act because it is a “binge.”

However, the findings in this study suggest that college students may no longer view binge watching as a risk-taking behavior. In fact, there are other binge-related behaviors that college students might view as risk-taking (e.g., drinking and gambling). Instead, it is likely that binge watching functions as a sensation-seeking behavior. Individuals that are binge watching are watching interesting and exciting television content, which may explain why it is a common act among college students. In the current investigation, about 89.4% of the participants reported that they engage in binge watching. The prevalence of binge watching among college students might also suggest that binge watching is more likely to be viewed as a norm and not a risky behavior.

3.1. Limitations and Future Research

There are a few limitations of the current investigation that should be addressed. First, there is a lack of variance explained in both regression models. Although four different variables successfully explained 19.6% of the variance in the binge watching frequency regression model, this can be improved. In the binge watching duration regression model, only 7.7% of the variance was explained. Future research should explore more variables that might predict binge watching frequency and binge watching duration.

The convenience sample used is another limitation of this study, as the participants were all college students that were predominantly Caucasian/White, female, and around the same age. Thus, the restricted social variables represented in the sample may have an influence on the situational individual difference variables. That is, rewarding oneself might be grounded in social foundations such that a college student would reward themselves differently than a middle-aged working individual. Thus, future research should utilize a more representative sample. In doing so, other social variables, such as level of education, could be utilized as potential predictors of binge watching frequency or duration and researchers could potentially control for confounding variables.

Lastly, some of the measures utilized in the current investigation function as a limitation. First, more strategic binge watching measures should be developed. Previous researchers have used different measures to operationalize binge watching frequency. Thus, it is difficult to test appropriately. Future research should create clearer measures of both binge watching frequency and duration to ensure consistency among scholars. Second, it would be interesting to investigate the casual relationship between binge watching and enjoyment. Future researchers should develop more appropriate and separate measures of these variables to better test this relationship.

3.2. Implications and Contributions

Overall, the findings presented in this study have theoretical and practical applications. Scholars should reconceptualize binge watching to include measures on both binge watching frequency and duration. The non-overlapping predictors of these aspects of binge watching suggest that there are different variables that explain binge watching behavior.

This study also builds upon the theoretical concepts of self-regulation and self-control. Self-regulation plays an important part in how frequently one engages in binge watching. With lower levels of self-regulation, individuals might binge watch as a form of procrastination, which would then engender some regret towards the binge watching experience. With higher levels of self-regulation, individuals can then complete initial tasks in a timelier manner. In doing so, individuals may then reward themselves with binge watching to celebrate their progress/accomplishments. Thus, individuals should set goals to complete primary tasks in a timelier manner and then reward themselves with binge watching so that their experience can be a positive one. However, the current investigation suggests a different outlook on the role self-control has on binge watching. Self-control has often been associated with risk-taking behaviors (Gottfredson and Hirschi 2003). Since self-control did not predict binge watching frequency or duration, it is likely that binge watching might not be viewed as a risk-taking behavior.

4. Materials and Methods

4.1. Participants and Procedure

Undergraduate college students (N = 651) from a university in the southeastern United States were recruited for this study. Eligible participants received either partial credit or course credit for participating. Participants were aged between 18 and 54 (M = 20.45, SD = 3.65) and 63.6% (N = 414) of the sample were female. Of the participants, 52.7% identified as White, 20.4% as Hispanic or Latino, 10.6% as Black or African American, 8.8% as Asian/Pacific Islander, 6.0% as Biracial or Multiracial (of two or more races), 0.5% as Native American or American Indian, and 1.1% as Other.

All participants completed an online survey that was hosted by Qualtrics. Measures on self-regulation and self-control were presented first, in random order, which were followed by binge watching frequency and binge watching duration items. Then, participants responded to enjoyment, reward watching, procrastination, and regret measures in random order. Lastly, the participants completed a few demographic measures. All measures and procedures were approved by the university’s institutional review board.

4.2. Measures

4.2.1. Self-Regulation

Pintrich et al. (1991) metacognitive self-regulation subscale of the Motivated Strategies for Learning Questionnaire (MSLQ) was utilized to measure self-regulation. Participants responded to 12 items on a 7-point scale (M = 4.38, SD = 0.86, α = 0.771), ranging from “Not at all true of me” to “Very true of me.” Greater scores indicate greater levels of self-regulation.

4.2.2. Self-Control

We utilized the Brief Self-Control Scale developed by Tangney et al. (2004) to measure an individual’s level of self-control. Participants responded to 13 statements on a 5-point scale (M = 3.21, SD = 0.65, α = 0.818), ranging from “Not at all” to “Very much.” Greater scores indicate greater levels of self-control.

4.2.3. Enjoyment

A modified version of the Oliver and Bartsch (2010) Enjoyment Audience Response Scale was utilized to measure enjoyment of binge watching. Participants referred to their last binge watching session when responding to 12 statements, measured on a 7-point scale (M = 4.51, SD = 1.21, α = 0.929).

4.2.4. Reward Watching

Utilizing binge watching as a reward, or reward watching, was measured with two items that asked how often individuals rewarded themselves with binge watching after and while completing academic tasks. Responses were on a 5-point scale (M = 2.27, SD = 0.95, α = 0.725), ranging from “Never” to “Always.”

4.2.5. Procrastination

Procrastination was measured with a modified subscale from the Binge Watching Motivation Scale developed by Rubenking et al. (2018). Participants responded to three items that asked how likely they were to use binge watching as a form of procrastination, which was measured on a 7-point scale (M = 4.46, SD = 1.65, α = 0.879) that ranged from “Not at all likely” to “Extremely likely.”

4.2.6. Regret

We measured regret with two items that asked whether a person felt regretful when engaging in binge watching. Participants responded to a 7-point Likert-type scale of agreement (M = 3.36, SD = 1.56, α = 0.756), ranging from “Strongly disagree” to “Strongly agree.”

4.2.7. Binge Watching Frequency

Binge watching frequency was measured based on a previous scale by Rubenking and Bracken (2018). Participants indicated how often they binge watched on a 9-point scale ranging from “Never” to “For a large part of every day.” About 10.6% of the participants stated that they never binge watched. The scale (M = 3.64, SD = 1.96) found that 29.5% of participants binge watched every few months, 12.0% monthly, 14.7% several times per month, 13.5% weekly, 11.4% 2–3 times per week, 3.7% 4–6 times per week, 2.8% daily, and 0.8% for a large part of every day.

4.2.8. Binge Watching Duration

Binge watching duration was measured by having the participants refer to their last binge watching session. They were asked to report, in hours, rounding to the nearest half hour, how long the binge watching session was. The scale (M = 4.02, SD = 2.58) ranged from 0 to 15 h.

5. Conclusions

Binge watching frequency and binge watching duration are predicted by different non-overlapping variables. It is suggested that the frequency of binge viewing sessions is driven by self-regulation and situational individual differences of reward watching, procrastination, and regret, whereas the duration of binges is driven by sex and enjoyment. Thus, individuals are binge watching more frequently as a form of procrastination. This is a product of lower levels of self-regulation, as these individuals have a harder time staying on task and completing an initial goal. When individuals are not using binge watching as a form of procrastination, they are rewarding themselves with frequent binge watching. Additionally, the less regretful these individuals feel, the more they binge watch. On the other hand, individuals are enjoying the content they are watching, which is why they are binge watching for longer periods of time. The results suggest that future research should continue to conceptualize binge watching as both a measure of frequency and duration, as no variable in this study was a predictor of both.