Antecedents and consequences of binge-watching for college students
First Monday

Antecedents and consequences of binge-watching for college students by Harsha Gangadharbatla, Colin Ackerman, and Arthur Bamford



Abstract
Binge-watching is a popular practice among college students. While there has been an increased interest in better understanding the phenomenon of binge-watching, there has not been an effort to model the antecedents and consequences of binge-watching for college students. The current study adopts a mixed method (focus group and survey) approach to propose a model for antecedents and consequences of binge-watching and test the hypotheses that link the various constructs in the model. Policy, practical, and theoretical implications are drawn.

Contents

Introduction
Literature review
Study 1 — Focus group
Focus group findings
Study 2 — Survey
Method
Analysis and results
Conclusion
Limitations and implications

 


 

Introduction

We live in an increasingly on-demand world. As with the expectations to have content readily available at all times with the touch or click of a button, the competition for providing the same is also getting intense. Netflix remains the dominant streaming service with over 158 million subscribers worldwide (Netflix, 2019) with a revenue of US$4.19 billion just for the last three months of 2018 (Stevens, 2019). Netflix accounts for 10 percent of all U.S. TV screen time, which it estimates are on for over a billion hours a day (Lynch, 2019). With billions of hours spent by subscribers binge-watching content in extended sessions, it is small wonder that Netflix has announced its biggest streaming price increase ever (Reisinger, 2019). The latest entrant to the streaming market is HBO max offering 10,000 hours of movies, television shows and original content starting May 2020. HBO max will cost US$14.99 a month, which is more expensive than its rivals, Netflix, Apple and Disney. These aggressive pricing strategies of Netflix and HBO suggest that the streaming content market is a lucrative and a growing area. The on-demand nature of streaming services provides users an opportunity to binge-watch among other things.

Binge-watching is the act of watching multiple episodes of a program in rapid succession, typically by means of either online streaming or by means of DVDs. It is estimated that over 70 percent of U.S. consumers binge-watch TV shows with an average of five episodes per each marathon session (Spangler, 2016). This number is even higher among young adults and college students who increasingly subscribe to Netflix or Hulu and consumer streaming content rather than regular television, often referred to as appointment TV (Sabin, 2018). Several studies indicate that young adults are more likely to binge-watch (Rubenking and Bracken, 2018; Sabin, 2018; West, 2014; Wheeler, 2015).

Streaming services like Netflix have been very active in growing the popularity of shows through smart media campaigns. A huge part of this is how shows like Narcos gain popularity and awareness by engaging fans on social media via owned content (Fullerton, 2016). Another example is how Netflix has partnered with media outlets like the Wall Street Journal, New York Times and Atlantic to create highly engaging interactive sponsored content around the themes of some its shows (Wall Street Journal and Narcos, New York Times and Orange is the New Black, and Atlantic and House of Cards). In addition, many brands such as Anheuser-Busch InBev, Samsung, Coca-Cola, Dell and Nike have all etched deals with the streaming giant Netflix to either sponsor content or have their bands placed in their popular shows like House of Cards (O’Brien, 2015). All of this is to say, binge-watching is an increasingly pervading form on entertainment which has implications for all sorts of industries as well as the viewers themselves.

There is an increased interest in better understanding the motivations and consequences of binge-watching, particularly among young adults. For instance, a recent Morning Consult/Hollywood Reporter poll found that young adults are more likely to alter social and personal habits to binge-watch shows such as staying up late, making less healthy choices, canceling social plans, and skipping work among other things (Sabin, 2018). Similarly, a TiVo survey found that 31 percent of their respondents lost sleep to binge-watching habit and another 37 percent said they spent entire weekends staying indoors binge-watching a show (Huddleston, 2015). Based on these industry reports and academic studies in this area, the current study proposes an exploratory model of both the antecedents and consequences of binge-watching. The study is two tiered: Study 1 is a focus group with questions guided by previous literature on binge-watching and study 2 is a survey design informed by the findings from the focus group. Both studies utilize a uses and gratification theoretical framework in investigating antecedents and consequences of binge-watching.

 

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Literature review

Although the term and the practice of binge-watching is relatively new, there have been a number of studies in the recent past examining various aspects of binge-watching. Research on binge-watching can be broadly categorized into the following areas: first, studies that conceptualize, define, assess and measure binge-watching behaviors (Matrix, 2014; Merikivi, et al., 2019; Rubenking and Bracken, 2018; Rubenking, et al., 2018; Vaterlaus, et al., 2019) motivations and factors influencing binge-watching (Flayelle, Canale, et al., 2019; Merrill and Rubenking, 2019; Stamenković, et al., 2018; Steiner and Xu, 2018; Sung, et al., 2018), the consequences of binge-watching (Clarke, 2019; Cousins and Betz, 2019; Granow, et al., 2018; Tefertiller and Maxwell, 2018), information processing, the role of involvement and attentiveness, and the psychology behind binge-watching (Pittman and Steiner, 2019; Shim, et al., 2018; Walton-Pattison, et al., 2018), advertising and the commercial impact of binge-watching (Schweidel and Moe, 2016), critical cultural studies and the political economy of binge-watching (Creeber, 2004; Jenner, 2017a, 2017b; Perks, 2015), and binge-watching examined in cross-cultural or international settings (Flayelle, Castro-Calvo, et al., 2019; Susanno, et al., 2019; Wang and Lobato, 2019).

Given the focus of our study, we will first review the literature on conceptualization and definition of binge-watching and then summarize the studies examining the motivations for binge-watching and the consequences of it. With uses and gratifications as a theoretical framework and based on the results of a qualitative study, we propose a model for both the antecedents and consequences of binge-watching.

Defining “binge-watching”

Defining binge-watching is a challenge in itself. For example, in a 2013 study conducted by Netflix, a majority of respondents described binge-watching as “watching between two and six episodes of a TV show in one sitting” and reported that the first season of a series can typically be binge-watched in its entirety in a span of four to six days [1]. This raises the important question of whether binge-watching should be defined by the number of episodes watched in one sitting (2–6 episodes) or by the entire span of time (4–6 days) taken to complete a show. Indeed some researchers have defined binge-watching using the number of episodes consumed in one sitting (Walton-Pattinson, et al., 2018) while others have used the time span (Wagner, 2016). In addition, the phenomenon of binge-watching has been examined in a variety of disciplines including but not limited to information systems, marketing, advertising, media studies, communication, economics, medicine, health sciences, and psychology. Understandably, each discipline views and defines binge-watching in its own unique way (e.g., media and communication scholars might define it using attributes such as continuity and control whereas psychology research might approach binge-watching as an addictive behavior).

Despite the challenges of defining binge-watching, there are certain attributes frequently mentioned in literature that separate this phenomenon from previous patterns of television and film consumption. Merikivi, et al. (2019) in an excellent transdisciplinary review of several definitions of binge-watching identify five fundamental attributes that lead to a convergent definition of binge-watching: viewer autonomy, continuity, completion, addiction and immoderacy. Viewer autonomy as it pertains to binge-watching can be traced to the evolution of media technology itself, which began with the introduction of DVD box sets (Jenner, 2017b). DVD box sets and later the advent of DVRs (digital video recorders) in 1999, which allowed viewers to record certain programs, and to skip commercial breaks while watching shows, provide viewers the autonomy that scheduled or appointment TV do not, thereby, making viewer autonomy a necessary and required attribute for something to be classified as binge-watching (Merikivi, et al., 2019).

Next, both continuity and completion are also attributes that distinguishing a binge-watching experience from others. Content is typically viewed without commercial breaks (Steiner and Xu, 2018) and shows are accessed through subscription-based platforms where advertising is increasingly delivered through in-content product placements. The term continuity also refers to the viewing of episodic or serialized [fictional] narratives. The term is not simply used to refer to watching any type of programming for an extended period of time (e.g., sports, news). This is significant because episodic narrative content is designed to end with a cliffhanger in the third act of each episode, then resolve that cliffhanger in the first act of the following episode. This narrative structure makes it uniquely difficult to stop watching at the end of a single episode because episodes often do not provide a dénouement or satisfying sense of resolution for viewers. This is what Pierce-Grove (2017) describes as viewers desire to watch “just one more.” Further, binge-watching is exacerbated by the way in which streaming content providers continue to play episodes sequentially and automatically — a feature that Netflix has dubbed “post-play” (Alters, 2017). Users have to opt-out of continued viewing by stopping the content stream rather than opting-in by actively selecting to view the next episode in a series. Both the narrative content features like cliffhangers and platforms features like auto-play highly encourage and facilitate what Kruglanski and Webster (1996) refer to as the viewers’ desire to know how a television show ends. Although completion does not necessarily require continuity, taken together they represent a distinguishing attribute of binge-watching (Merikivi, et al., 2019).

Lastly, drawn mostly from medical and psychology literature, addiction and immoderacy represent the last two attributes of binge-watching (Merikivi, et al., 2019). These refer to viewers’ feelings of being drawn-in by a show as they watch, which then leads them to view it for much longer than they anticipated or planned. Watching more than a set number of episodes in one sitting that leads to viewers feeling guilty is one of the characteristics of immoderacy (Wagner, 2016). While psychology and medical research might approach binge-watching as an addiction, some researchers argue that the negative consequences of binge-watching are mitigated by the intentional nature of it (Riddle, et al., 2018). Merikivi, et al., (2019) leave out immoderacy and addiction from their definition considering them potential outcomes rather than a fundamental attribute. Given the exhaustive nature of their transdisciplinary review of definitions for binge-watching, we adopt their conceptuatlization of binge-watching in this study, which they ultimately define as “a consumption of more than one episode of the same serialized video content in a single sitting at one’s own time and pace” [2].

Motivations and consequences for binge-watching

Having defined binge-watching, we will now focus the rest of the literature review on studies that examine the motivations and consequences of binge-watching with the goal of proposing an overall model for the antecedents and consequences of binge-watching. As mentioned earlier, there have been numerous studies that examined the factors influencing binge-watching (Flayelle, Canale, et al., 2019; Merrill and Rubenking, 2019; Stamenković, et al., 2018; Steiner and Xu, 2018; Sung, et al., 2018) and the resulting impact of such behaviors (Clarke, 2019; Cousins and Betz, 2019; Granow, et al., 2018; Tefertiller and Maxwell, 2018). Historically, uses and gratifications has been the main theoretical framework to explain behaviors and habits surrounding consumption of all sorts of media including television (Katz, et al., 1973) and internet (Larose, et al., 2001). Given that binge-watching lies at the intersection of both television and the Internet, it is no surprise that many of the aforementioned studies have also linked the motivations of binge-watching to uses and gratifications theory (Panda and Pandey, 2017; Pittman and Sheehan, 2015; Pena, 2015; Riddle, et al., 2018; Rubenking, et al., 2018; Shim and Kim, 2018; Steiner and Xu, 2018).

Simply put, according to the uses and gratifications framework, consumers have certain goals or needs which they continually seek to satisfy through mass mediated interactions (Elliott and Quattlebaum, 1979). This framework assumes consumers are not passive and actively seek out media for certain gratifications, consumers are aware of these gratifications sought (making self-reporting to be accurate), and that certain media compete with one another as the preferred source to provide certain gratifications (Katz, et al., 1974, 1973). These assumptions should hold true for newer media such as Internet-based media (and binge-watching platforms); however, certain affordances of the Internet must be taken into account. According to Ruggiero (2000), the Internet differs from traditional media because of its interactivity (more control), demassification (more choice), and asynchroneity (more options of when to consume). This leads to a greater potential of difference in the gratifications sought and the gratifications obtained (LaRose, et al., 2001). This means a person may go to an online platform seeking one gratification but there is a high chance they actually obtain a different one. For the present research, this means a potential imbalance in the creation of model of antecedents and the consequences of binge-watching. The gratifications sought likely may not line up exactly with the gratifications obtained. This complication, of course, does not preclude us from applying the uses and gratifications framework but rather presents us an opportunity to further develop the theory within the context of binge-watching. Indeed, Pittman and Sheehan (2015) suggest the same when they state that, “binge-watching is arguably different from other types of viewing since it gives users a degree of control over their viewing activities that they have never had before.” This newness of digital streaming platforms used for binge-watching presents an opportunity to explore issues related to uses and gratifications theory further.

Sung, et al. (2015a) ran a similar study, determining the seven motivations for binge-watching as social interaction, entertainment, passing time, relaxation, escape, information and habit. Of these, passing time, entertainment and social interaction were found to be the most significant predictors of an individual’s desire to binge-watch. There was also evidence of a correlation between depression and desire to binge-watch to distract or escape. This study also found a positive relationship between binge-watching and media transportation, which is “where a person not only attends to information but also is absorbed into the narrative flow of a story in a pleasurable and active way” [3]. Using a dataset from popular online streaming content service, Hulu.com, Schweidel and Moe (2016) examined the main drivers of binge-watching behavior and found that motivations can be based on user-level traits and/or on the characteristics of previously viewed content. Their results indicate that viewing leads to more viewing, advertising messages during binge-watching sessions discourage binge-watching behaviors, and users with high proclivities to binge-watching are less responsive to advertising (Schweidel and Moe, 2016). Research on other situational and content-related factors indicates that the genre of the programs (i.e., comedies and dramas) was a significant predictor of binge-watching, streaming services and DVRs were most commonly used platforms for binge-watching, and factors such as background noise for multitasking, need to avoid spoilers, desire to maximize social currency and escapism were all found to motivate binge-watching (Wagner, 2016).

Studies in the recent past support many of the findings from earlier ones. For instance, Panda and Pandley (2017) surveyed college students and found “social interaction, escape from reality, easy accessibility to TV content and advertising [all] motivate college students to spend more time binge-watching” [4]. In an online survey of 785 binge-watchers, Shim and Kim (2018) found that individuals perceive binge-watching as means to satisfy their desire for enjoyment, efficiency, control and fandom. Additionally, binge-watching behaviors were found to be influenced by recommendations from others, individuals’ levels of sensation seeking, and their need for cognition (Shim and Kim, 2018). In a survey of 714 individuals from East Asia, a positive association between viewers’ negative attitudes and the extent of their binge-watching was found and personality trait immediate gratification (IG) mediated this relationship such that individuals with high levels of IG with negative attitude toward binge-watching were more likely to binge-watch than their low-IG counterparts (Shim, et al., 2018). Results from a series of focus groups indicate that binge-watching is influenced by four main motivational factors: (a) anticipation induced by both content and technology features, (b) mood management, excitement and arousal, (c) procrastination and escapism and (d) social goals (Rubenking, et al., 2018). Another study found that young adults are more likely to engage in binge-watching behaviors, emotion regulation through viewing is greatly associated with binge-watching, and both habit and suspense/anticipation associated with shows also significantly predict binge-watching (Rubenking and Bracken, 2018).

Lastly, Steiner and Xu (2018) extended Uses and Gratifications theory and found through qualitative semi-structured interviews that the primary motivations for binge-watching are catching up, relaxation, sense of completion, cultural inclusion and improved viewing experience. Flayelle, Canale, et al. (2019) identified seven factors in their development of a Binge-Watching Engagement and Symptoms Questionnaire (BWESQ), which are engagement, positive emotions, desire-savoring, pleasure preservation, binge-watching itself, dependency and loss of control. In another study, it was found that binge-watching frequency was influenced by low self-regulation, greater tendency to use binge-watching as both a reward and a form of procrastination, and lower levels of regret; and binge-watching duration was associated with being female and experiencing higher levels of enjoyment while binging (Merrill and Rubenking, 2019). And most recently, in a study of millennials in Jakarta using an online survey, three factors — escape, social engagement, and attractive price — were identified as antecedents of binge-watching behaviors (Susanno, et al., 2019).

Moving on to research examining the consequences of binge-watching, it is worth noting that these studies are relatively fewer than the ones looking at the antecedents of this phenomenon. This is perhaps due to what Devasagayam (2014) describes as the difficulty associated with determining consequences based on binge-watchers’ feedback due in part to the inability to see immediate changes affecting the body for a binge-watching session. There have been long-term medical studies showing a link between sedentary media behavior, such as binge-watching, and long-term physical health complications (Thorp, et al., 2011) but very little attempt to track psychological consequences; however, there has been ample media coverage suggesting negative psychological and health related implication of binge-watching (Stone, 2014; Karmakar and Kruger, 2016; Landa, 2015).

That said, there has been an increased interest in examining the consequences of binge-watching in the last two years (Ahmed, 2017; Clarke, 2019; Cousins and Betz, 2019; Granow, et al., 2018; Riddle, et al., 2018; Tefertiller and Maxwell, 2018; Vaterlaus, et al., 2019; Walton-Pattison, et al., 2018). A study conducted on a sample of Arab residents in UAE found correlations between binge-watching behaviors and depression but no relationship between binge-watching and loneliness (Ahmed, 2017). Vaterlaus, et al. (2019) conducted a qualitative study to identify the potential consequences of binge-watching and found that participants perceived binge-watching to have adverse physical and mental health consequences and social isolation but also some positive benefits such as leading to making of new friends as some perceive it as a social activity. While not necessarily identified as consequences, Walton-Pattison, et al. (2018) found automaticity, anticipated regret, goal conflict and goal facilitation all to be correlated with binge-watching behaviors.

Some studies from psychology and medicine have also examined the negative consequences of binge-watching. For instance, Clarke (2019) applied both uses and gratifications and escape theory to examine the relationship among binge-drinking, binge-eating and binge-watching with depression, anxiety, and stress among 102 college students ages 18 to 24. Results indicate that participants with low anxiety scores had high scores on binge-watching, which was highly correlated with stress and anxiety among other things. Winland (2015) found that binge-watching was related to negative academic performance with half of the participants in her study reporting that binge-watching distracted them from their academics. On the other hand, Cousins and Betz (2019) failed to find any evidence for binge-watching negatively impacting individuals’ physical activity leading them to conclude that binge-watching does not appear to be preventing college students from doing physical activities so there is a need for more research in this area to determine what binge-watching may be stopping them from doing. And indeed both Sung, et al. (2015b) and Tukachinsky and Eyal (2018) found binge-watching to be related to depression, loneliness and a lack of self-control. However, Tefertiller and Maxwell (2018) failed to find any link between binge-watching behaviors and unhealthy emotional traits in individuals. All of these contradictory and somewhat inconclusive results call for more research into the effects of binge-watching behaviors.

In summary, while there are several studies examining the antecedents and consequences of binge-watching, research has, thus far, been fragmented with studies identifying and focusing on a few motivations or consequences. In addition, many of the studies were conducted on samples drawn from outside U.S. from areas such as Belgium (Flayelle, Castro-Calvo, et al., 2019), China (Wang and Lobata, 2019), Jakarta (Susanno, et al., 2019), South Korea and East Asia (Shim, et al., 2018), UAE (Ahmed, 2017). Furthermore, studies have either used qualitative methods or quantitative methods but not both (i.e., mixed methods). The current study is an exploratory endeavor to address all three of these limitations/gaps in the literature by proposing and testing a model of both antecedents and consequences of binge-watching using a mixed method approach for a sample of U.S. college students. Combining the list of antecedents and consequences identified from the various studies above with a focus group (Study 1) to explore further the various factors and effects, we propose a model and test it using an online survey in Study 2. The following two research questions guide our first study:

RQ1: What are the antecedents for binge-watching?
RQ2: What are the consequences of binge-watching?

 

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Study 1 — Focus group

Study 1 is designed to further our understanding of the factors and effects of binge-watching. A number of antecedents and consequences have already emerged from our exhaustive review of literature in this area but in order to investigate further, we designed a focus group as an in-depth exploratory look at the binge-watching behaviors. Taken together, the results of the focus group and the findings from literature review will answer our research questions. While the results of a focus group cannot be generalized, qualitative research offers numerous advantages that quantitative surveys do not, particularly for understanding new and emerging behaviors such as binge-watching content online. Focus groups are ideal when the goal of a project is to explore and gain an in-depth understanding of feelings, motivations and consequences of certain behaviors. Communication researchers frequently use qualitative methods as they “give us the chance to listen to consumers express their ideas in their own words and the opportunity to connect with their minds, and hence draw insights and explanations from the participants themselves” [5].

Focus group participants were recruited from various classes at a large public university. A total of six individuals — three male and three females — from various class levels including one graduate student participated in the discussion. All participants were over the age of 18 and the study was reviewed and approved by the university’s Institutional Review Board (IRB). The focus group lasted for a little over 60 minutes and the entire discussion was recorded using an audio recording device. The recording was transcribed by one of the authors and double checked for accuracy by another graduate student. The transcribed content was then analyzed by classifying each “unit of thought” into categories and themes that ultimately answered our research questions. A unit of thought is the smallest meaningful set of words that were relevant to the purpose of the study (Rook, 1987). In the following section, we outline this process further and present our findings.

 

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Focus group findings

We started our focus group discussion with some icebreaker questions (e.g., what are your favorite shows? How often do you watch streaming content?) followed by first broad question: What does binge-watching mean to you? We felt it necessary to probe further with “what is binge-watching?” “why do you think ...” etc. type questions to better understand the concept of binge-watching itself. Responses to this question fell into two categories — duration and content. In other words, what was considered binge-watching by our focus group participants depended on how long someone was watching it and what they were watching. There was general agreement on the requirement of a certain number of hours to be spent before something could be deemed binge-watching although the exact number varied from at least two episodes or in terms of hours to over four hours. Surprisingly, as noted by one participant, watching live television (e.g., NCAA football, election results) for an extended period of time does not come to one’s mind when thinking of binge-watching. The content had to be watched in a time-shifted fashion for it to be considered binge-watching. Although, theoretically, one could classify any extending viewing as binge-watching, it was interesting to see participants make distinctions of what could be termed binge-watching and what could not. Overall, what was considered binge-watching or not depended on three factors — how long one watched (duration), what manner (streaming or DVR/DVD or live) and type of content (on-demand and time-shifted) — one watched. All of these are in line with our earlier/guiding definition of binge-watching in the literature review.

The next set of discussion questions all involved an exploration of the motivations for binge-watching and the consequences of doing so (RQs). Participants were asked why they binge-watched, what motivated them to binge-watch, what does binge-watching do to and for you, how do you feel during and after binge-watching, how important is binge-watching and a variety of such questions all aimed at uncovering the antecedents and consequences of binge-watching. Throughout the discussion, the moderator probed the participants by asking why and to explain further ample times. An analysis of all the responses resulted in multiple themes that were classified as antecedents or motivations for binge-watching and another set of themes that were classified as consequences of binge-watching. Figure 1 below is a model of the antecedents and consequences of binge-watching as described by our focus group participants.

 

A model of antecedents and consequences of binge-watching
 
Figure 1: A model of antecedents and consequences of binge-watching.

 

To elaborate on the antecedents and consequences, the factors that influenced binge-watching included Family, Friends and Peers, Relaxation, Procrastination, Entertainment, Addition, Content and Environmental Factors. Many of these are self-explanatory. For instance, under family fell reasons like, “my sister calls me to say she’s on episode eight and I have to catch up” (Emily) and “my friend’s family is obsessed with the show I Love Lucy so they sit on the couch as a family and binge-watch that show” (Stephanie). Similarly, peer pressure and keeping up with friends was another big reason why people binge-watched. Binge-watching helped people relax, “keeps me occupied and busy” (Katie) or “it calms me down especially after a long day at school” (Stephanie) and it was also a “way to fall asleep every night” (Stephanie). Some mentioned binge-watching as a way of procrastination. One factor that emerged from many participants’ comments is the addictive nature of binge-watching. “I literally spent an entire day binge-watching,” said one participant (Emily). For many, it seemed like once a binge-watching session starts it is really hard to stop. And the last set of factors that are content-related and environment-related may be also contributing to the addiction of binge-watching. The content-related factors include shows that end with cliffhangers that make it really hard to not continue on. For instance, said one participant (Katie), “some shows are more conducive to binge-watching e.g., Shonda Rhimes’ shows, which always end with a cliffhanger and you have to know.” Similarly, platforms like Netflix have a “ticking time countdown for the next episode which makes it really hard to stop. I guess I am watching the next episode now is how I feel” (Nick). Other environmental factors could include the type and time of day (weekends were mentioned as binge-watching days), weather-related, platform and on-demand nature of shows now. “It’s right there available and it’s all on-demand, all the episodes,” said another (Sam).

The consequences of binge-watching can be broadly classified as physical and emotional along with other impacts like falling grades, missing school and work. The primary physical consequences of binge-watching include headaches, exhaustion, being tired, eyes hurting and feel falling asleep, feeling hungry, lethargic and lazy after extended sessions of binge-watching. A lack of hygiene and taking care of oneself also came up with one participant (Nick) saying, “my roommate doesn’t shower ... sits there for hours watching the same show.” Among the emotional impacts were feeling sad (“finishing a show is the worst” (Emily) and in some cases depressed. “It’s really rough when you finish a show,” (Stephanie) and “I feel really sad when shows end ... sometimes it’s because of how they end.” (Katie). Other emotional effects include being mentally drained, lonely and feeling caged and wanting to just go outside.

 

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Study 2 — Survey

Having identified the antecedents and consequences of binge-watching from a thorough literature review and from the findings of Study 1, we turn our attention to testing the model proposed in Figure 1. It should be noted that the proposed model is consistent with some of the previous research on binge-watching and the uses and gratifications framework, which has been widely used in understanding the motivations for media consumption. As a recap, the uses and gratification approach explains how and why people use media to gratify their needs and by extension the different patterns of media use and behavior (Katz, et al., 1974; Rubin, 2009). Uses and gratification assumes that media users choices of media and behavior around it are goal-oriented and motivated by wanting to satisfy some sort of need. The approach stresses an active use and choice of certain media platforms and content (Baran and Davis, 2006; Katz, et al., 1973).

Applying uses and gratification to the new technology of binge-watching also is in line with the call from Swanson (1979) that research should strive for “the continuing evolution of the uses and gratification approach to mass communication research” [6].

Based on the proposed model for antecedents and consequences in Figure 1 from the findings of Study 1 (focus group) and the literature review above, we posit the following hypotheses:

H1: A factor analysis will result in the antecedents and consequences of binge-watching loading as distinct components as suggested by uses and gratifications literature and our findings from the focus groups (see Figure 1).
H2: The antecedents will predict individuals’ attitudes toward binge-watching and their willingness to binge-watch.

 

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Method

To test our hypotheses, an online survey was designed, and participants were recruited through various undergraduate courses at a large public university. The convenience nature of the sample prevents generalization to entire college student population, but our goal is to test the various links among antecedents and consequences with binge-watching in an exploratory fashion. Participants were sent a link to the survey, which contained an informed consent on its first page along with a description of the study. Respondents were told that the purpose of the study was to better understand individuals’ binge-watching behaviors and that the entire survey should take no more than 10–15 minutes. They were also informed that their participation is voluntary, and that no identifying information was being collected.

Upon agreeing to participate in the study, respondents were taken to the first set of questions: (1) In your opinion, a minimum how many of hours of watching is needed for something to be classified as binge-watching? and (2) On average, how often do you binge-watch? Next, a series of 13-item Likert scale statements on the antecedents of binge-watching were presented asking respondents to rate how strongly they agreed or disagreed with each. Individuals’ attitudes toward binge-watching and their willingness to binge-watch was measured using six Likert scale items. Following that a series of 20-item Likert scale statements on the consequences of binge-watching were presented to respondents. And lastly, the survey concluded with some questions that gathered the demographic information of the respondents. All scales used were five-point Likert scales.

 

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Analysis and results

First, data was cleaned and participants who straight-lined and/or completed the survey in a duration less than one-third the median time it took all respondents to complete the survey were removed from the dataset. In addition, respondents who provided subpar answers such as nonsense words in the open entry text question at the end were also eliminated from the sample. In all, this process of data cleaning resulted in a total of N=203 valid completed responses after eliminating speeders, straight-liners and other sources of corrupt data.

A total of 21 respondents chose not to respond to the demographic questions. Of the ones that responded, the breakdown of sample was 57 males (28 percent) and 126 females (62 percent) with 76 percent identifying as White, 1 percent African American, 5 percent Hispanic, 3 percent Asian, and 4 percent in the other category. The median age was 23 with a range of 18 to 65 years and 57 percent belonging to 18–24 age group and 90 percent of the sample being less than 40 years.

In response to the question that asked how many hours they thought made something a binge-watching session, responses ranged from 6 percent selecting 1–2 hours, 59 percent selecting 3–4 hours, 28 percent selecting 5–6 hours, 5 percent selecting 7–8 hours and another 2 percent selecting over 8 hours. In other words, over 95 percent of our sample selected something over three hours, which appears to be the cutoff point for something to be considered binge-watching. When asked how often they binge-watch according to their own definition, 5 percent said more than four times a week, 20 percent said 2–3 times a week, 18 percent said once a week, 16 percent said 2–3 times a month, 14 percent once a month, 8 percent once every two months, 4 percent once every four months, 5 percent once every six months and 3 percent once a year. About 5 percent of our sample said they never binge-watch. When people who do not binge-watch are taken out, the distribution of binge-watchers represents a bell curve heavily skewed to the right i.e., heavy binge-watchers. Over 92 percent of our sample reported binge-watching at least once every six months and over 73 percent at least once a month.

To test our hypotheses, we first conducted a factor analysis using a Principal Component Analysis as extraction method and Varimax with Kaiser normalization as rotation method of the 13 items that represented antecedents, which individuals responded to on a Likert scale. Five distinct components emerged that loaded over 1 on Eigenvalue. Tables 1.1 and 1.2 below depict the results of the factor analysis. The Bartlett’s test for sphericity was significant at p< 0.001.

 

Table 1.1: Total variance explained.
 Initial EigenvaluesExtraction Sums of Squared LoadingsRotation Sums of Squared Loadings
ComponentTotal% of VarianceCumulative %Total% of VarianceCumulative %Total% of VarianceCumulative %
13.1223.9923.993.1223.9923.992.5219.4119.41
22.0515.8439.832.0515.8439.832.1416.5135.92
31.6012.3152.151.6012.3152.151.6012.3448.26
41.108.5260.671.108.5260.671.5011.5959.86
51.037.9668.641.037.9668.641.148.7868.64
6.755.8274.47      
7.705.3879.86      
8.614.7684.62      
9.534.0988.71      
10.503.8892.59      
11.392.9995.59      
12.332.5698.16      
13.231.83100.00      
Extraction Method: Principal Component Analysis. 

 

 

Table 1.2: Rotated component matrix.
 Component
 12345
I binge-watch because I want to know what happens in the next episode..83-.00-.00.07.15
I binge-watch because I become invested in the content..74-.02.05-.04.34
I binge-watch because all the episodes are all available instantly on demand..67.08.00.32-.24
I binge-watch to entertain myself..72.07.17-.07-.13
I binge-watch to relax..48.22.37.13-.41
I binge-watch to spend time with my friends..00.86.08.03.19
I binge-watch to spend time with my peers..02.81.20-.00.26
I binge-watch to spend time with my family..11.72-.27.04-.20
I binge-watch to procrastinate.-.00.08.79.24-.14
I binge-watch because I am bored..19-.05.80.03.13
I am addicted to binge-watching..02.16.20.76-.00
Once I start, I can’t stop watching shows..09-.09.05.84.07
I binge-watch because I don’t want to be left out of social conversations..08.32.01.10.74

Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.

 

 

As seen in the rotated component matrix table above, five somewhat distinct components emerged, which we termed: content and entertainment, friends and family, procrastination, addictive and social capital. These antecedents line up well with our focus group findings, where all of the above factors were brought up as reasons why individuals binge-watch.

For testing the second hypothesis that stated that antecedents (the five that emerged) will in turn predict individuals’ attitudes toward binge-watching and their willingness to binge-watch, we conducted a series of regression analyses with attitudes as dependent variable in one and willingness to binge-watch as dependent in the second. The results of the regression analyses are presented in Tables 2.1 and 2.2 below. The regression model was statistically significant R2 = .42, F(5,198) = 8.58, p<0.001. The R-square of the model was .42, which meant the variance explained by predictors was about 42 percent. Of the five predictors in the model three were statistically significant. Friends, peers and family, additive nature of binge-watching and fear of being left out of social conversations were significant predictors of individuals’ attitudes toward binge-watching.

 

Table 2.1: Regression analysis — Dependent variable is attitudes toward binge-watching.
 Unstandardized coefficientsStandardized coefficients 
BStd. errorBetatSig.
1(Constant)1.41.35 4.00<.001
 Content and entertainment factors.11.08.091.45.14
 Friends, peers and family.13*.05.162.44.01
 Procrastination-.08.05-.11-1.62.10
 Addiction.12*.05.162.38.01
 I binge-watch because I don’t want to be left out of social conversations..16*.04.253.80<.001
a. Dependent variable: Attitude toward binge-watching 

 

Another regression presented in the Table 2.2 below with “willingness to binge-watch” as the dependent variable was conducted. This model was statistically significant R2 = .63, F(5,198) = 25.7, p<0.001. An R-square of .63 meant that 63 percent of the variance in the dependent variable was explained by the predictors in the model. Surprisingly, the variables that were statistically significant were content and entertainment factors, and addiction. In other words, while attitudes toward binge-watching are predicted by friends, family, peers, addiction and fear of being left out of social conversations, their willingness to actually binge-watch was predicted by content and entertainment factors and the addictive nature of binge-watching. These findings suggest a disconnect between attitudes and willingness or at least a difference in the variables that predict both. Attitudes and intentions are, of course, different constructs so it is expected that there will be some differences in the variables that predict each. This is also in line with Shim, et al.’s (2018) finding that higher levels of binge-watching were actually associated with negative attitudes toward the phenomenon, and this discrepancy can perhaps be explained by our results where different factors predicted attitudes and behavioral intentions.

 

Table 2.2: Regression analysis — Dependent variable is willingness to binge-watch.
 Unstandardized coefficientsStandardized coefficients 
BStd. errorBetatSig.
1(Constant).48.33 1.45.14
 Content and entertainment factors.61*.07.467.94<.001
 Friends, peers and family.07.05.091.52.12
 Procrastination-.01.05-.01-.19.84
 Addiction.28*.05.315.42<.001
 I binge-watch because I don’t want to be left out of social conversations.-.02.04-.03-.52.59
a. Dependent variable: Willingness to binge-watch 

 

The potential consequences of binge-watching were measured with a battery of 20-item Likert scale statements. A factor analysis of those 20 items using a Principal Component Analysis as extraction method and Varimax with Kaiser normalization as rotation method was conducted. In this case, only three distinct components emerged that loaded over 1 on Eigenvalue. Tables 3.1 and 3.2 below depict the results of the factor analysis.

 

Table 3.1: Total variance explained.
 Initial EigenvaluesExtraction Sums of Squared LoadingsRotation Sums of Squared Loadings
ComponentTotal% of VarianceCumulative %Total% of VarianceCumulative %Total% of VarianceCumulative %
18.2741.3741.378.2741.3741.374.4622.3022.30
21.728.6450.011.728.6450.013.7218.6140.92
31.226.0956.111.226.0956.113.0315.1956.11
4.954.7760.89      
5.924.6365.52      
6.793.9469.47      
7.733.6673.13      
8.623.1276.26      
9.592.9879.24      
10.552.7581.99      
11.522.6384.63      
12.462.3486.98      
13.432.1589.13      
14.412.0691.19      
15.351.7892.98      
16.331.6794.66      
17.301.5296.19      
18.281.4097.59      
19.261.3298.91      
20.211.08100.00      
Extraction Method: Principal Component Analysis. 

 

 

Table 3.2: Rotated component matrix.
 Component
 123
People like me less because I binge-watch..71.28-.00
After binge-watching I feel sad..73.11.38
binge-watching makes me feel like a loser..76.14.21
After binge-watching I feel lonely..64.08.42
binge-watching makes me feel like a caged animal..67.25.08
binge-watching has affected my relationships negatively..61.44.10
After I am done with binge-watching, I feel depressed..60.09.50
binge-watching is negatively impacting my life..55.51.23
After binge-watching I usually get a headache..51.10.43
binge-watching has interfered with my school work..11.73.29
I have gotten bad grades because of binge-watching..24.74.10
binge-watching has interfered with my work..07.77.14
I am always behind on things because of binge-watching..23.74.19
binge-watching causes me to sleep less..13.43.27
binge-watching makes me anti-social..37.42.41
binge-watching causes my personal hygiene to suffer..35.46.19
After binge-watching I usually feel exhausted..24.27.70
binge-watching makes me hungry.-.01.26.61
After binge-watching parts of my body hurt (e.g., eyes, back, legs).23.20.68
After binge-watching I feel mentally drained..32.17.60

Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.

 

 

As seen in the rotated component matrix table, the three components that emerged that could be considered important when it comes to binge-watching. We called these: (1) negative impact on self and relationships (i.e., depression, loneliness and bad relationships); (2) negative impact on school and work; and (3) physical and mental exhaustion. Again, these three factors mirror that of our proposed model that resulted from our Study 1 (focus group) in Figure 1.

Unfortunately, testing the actual effects or consequences of binge-watching on a survey is difficult as it is hard to capture the actual binge-watching behavior of individuals on a survey. To truly test the consequences of binge-watching, one would have to conduct a laboratory experiment where individuals are randomly placed in either treatment (binge-watching) or control condition (no binge-watching). We hope our current findings in terms of potential consequences serves as a starting point for future studies that can examine the actual effects of binge-watching in an experimental study.

 

++++++++++

Conclusion

There has been a great deal of interest in better understanding the phenomenon of binge-watching in the last five years. Several studies have identified both motivations and effects of binge-watching. However, these studies are all somewhat fragmented in that none have attempted to propose a comprehensive model of both the antecedents and consequences of binge-watching. To that end, we conducted a mixed method but exploratory study of binge-watching to model and test the various antecedents and consequences. Our first study, a qualitative focus group, identified several antecedents and consequences, which are in line with the findings from previous studies in this area. However, there were also a few new items that we uncovered to include in our comprehensive model. To sum up our main findings from Study 1, several factors such as family, friends, relaxation, procrastination, entertainment, addiction, content-related and environmental factors were all cited as the driving factors for binge-watching. Similarly, the consequences of binge-watching include missing school and work, falling grades and a host of physical (headaches, exhaustion, feeling lethargic and lazy, feeling hungry, loss of sleep, hurting body parts (eyes, feet and back pain) and general lack of hygiene) and emotional effects (sadness, depression, mentally drained, feeling of loneliness and feeling caged indoors). Based on the results of our first study and the list of antecedents and consequences frequently mentioned in literature, we proposed and tested a model for binge-watching behaviors through an online survey. Using factor analyses, we found that five distinct antecedents were related to binge-watching behaviors: (1) content and entertainment; (2) friends and family; (3) procrastination; (4) addictive; and (5) social capital, and three broad consequences: (1) negative impact on self and relationships (e.g., depression, loneliness and bad relationships); (2) negative impact on school and work; and (3) physical and mental exhaustion resulted from binge-watching behaviors. Of the five antecedents, three were found to predict attitudes and two were found to predict intentions to binge-watch. More precisely, the factors that predict individuals’ willingness to engage in binge-watching behaviors are content and entertainment factors and the addictive nature of it. According to our data, these factors are almost half of the reasons why someone binge-watches. Modeling and testing the various links between antecedents/consequences and attitudes toward binge-watching has not received much research attention and this mixed method study is a first step toward furthering our understanding on this latest media phenomenon.

 

++++++++++

Limitations and implications

Despite interesting findings and being a study that proposes a comprehensive model of antecedents and consequences of binge-watching through a mixed method approach, it should be noted that our study has several limitations. First and foremost, it is designed as an exploratory investigation into the antecedents and consequences. While the results of the focus group, although qualitative in nature, provide solid basis for a model, the hypotheses and findings from the second study present some limitations in terms of external validity. This is mostly because of our small convenience sample size. Despite the small sample size, our results were statistically significant indicating that a larger sample size will only strengthen them further. Furthermore, our sample was college students, and despite many studies suggesting that young adults are more likely to engage in binge-watching (Rubenking and Bracken, 2018; Sabin, 2018; West, 2014; Wheeler, 2015), it is a convenience sample and severely limits the external validity of our findings. It is worth noting that our objective with our second study was not to find generalizable results but to rather establish a link between the antecedents/consequences and attitudes/intentions that can be further tested via quantitative approaches like structural equation modeling with larger representative samples. In other words, our study provides a framework for future quantitative methods where individuals’ motivations for binge-watching and the consequences of doing so could be empirically tested. Our model (Figure 1) is the first step in that direction and our second study provides initial evidence for the existence of a link between antecedents/consequences and attitudes/intentions. Further, the true effects of binge-watching can only be studied through an experiment and a survey method is not the most ideal for that.

Our study has numerous theoretical and managerial implications. Among the theoretical implications, an exploration of the antecedents and consequences of binge-watching is the first step toward testing a model of binge-watching behavior. The various antecedents could be hypothesized to have an either positive or negative impact on attitudes toward binge-watching, which using a theory of planned behavior, can be hypothesized to influence their behavior intentions, which in turn impact their ultimate binge-watching behavior. Similarly, the various consequences can be linked to binge-watching behavior to estimate the empirical impact of each. Future studies can estimate the coefficients of all these impacts in a hypothesized model using structural equation modeling (SEM) and software like AMOS.

People are getting used to watching TV without interrupting ads; having access to on-demand content is something everyone seems to be used to and even expect. One practical implication of our study’s finding is that the factors that drive increased consumption seem to be content-related, entertainment, social and addictive factors. For example, services like Netflix could add social elements that might increase consumption. Letting others know that one is watching something on Netflix seems to be more desirable than one would otherwise imagine. In terms of content, recommending users to similar shows might actually be very effective in increasing consumption as well. Users seem to value the recommendations and come to rely on it for finding their future favorite shows. Our study’s results also explain the rationale behind some of the features already used by streaming services like Netflix to facilitate binge-watching. For instance, streaming services take full advantage of the addictive nature of binge-watching by counting down to next episode making it much easier to continue the binge-watching session with little or no input from the user. Netflix recently added a “smart download” feature on its app, which automatically downloads episodes of the shows one is watching and queues them up for easier binge-watching sessions (Keach, 2019). Lastly, while an obvious finding, our study supports the fact that the more entertaining the content, the more likely people are to engage in binge-watching. It is no surprise that many of the shows with multiple episodes “dropped” on Netflix are all consumed by a huge segment of the subscribers within 24–48 hours. Overall, binge-watching will continue to be a popular phenomenon and any investigation to further our understanding of what drives binge-watching and what the effects of it are is important and much-needed. End of article

 

About the authors

Harsha Gangadharbatla is Associate Professor in Advertising, Public Relations, and Media Design at the University of Colorado.
E-mail: gharsha [at] colorado [dot] edu

Colin Ackerman is a graduate student in Media, Research, and Practices at the University of Colorado.
E-mail: coac1001 [at] colorado [dot] edu

Arthur Bamford is a Ph.D. student in media studies at the University of Colorado.
E-mail: Arthur [dot] Bamford [at] colorado [dot] edu

 

Notes

1. Alters, 2017, p. 211.

2. Merikivi, et al., 2019, p. 6.

3. Wang and Calder, 2006, p. 151.

4. Panda and Pandley, 2017, p. 1.

5. Davis, 1997, p. 108.

6. Swanson, 1979, p. 3.

 

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Editorial history

Received 12 February 2019; revised 14 November 2019; accepted 20 November 2019.


Copyright © 2019, Harsha Gangadharbatla, Colin Ackerman, and Arthur Bamford. All Rights Reserved.

Antecedents and consequences of binge-watching for college students
by Harsha Gangadharbatla, Colin Ackerman, and Arthur Bamford.
First Monday, Volume 24, Number 12 - 2 December 2019
https://journals.uic.edu/ojs/index.php/fm/article/view/9667/8296
doi: http://dx.doi.org/10.5210/fm.v24i12.9667





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