Why Do Users Stop Pleasurable Media Experiences? The Dynamics of Media Experiences and Their Impact on Media Disengagement

Abstract

Given the vast amount of permanently available entertainment content and the high pleasure that viewers derive from it, the question of when and why users disengage from a media entertainment viewing session becomes more pressing. We argue in this paper that communication theories lack a conceptualization of the disengagement part of the communication process. The study presents a novel dynamic view on media use, and argues that specific processes that occur during media exposure contribute to its termination. The assumptions of the theoretical framework are tested with an event-based experience sampling study during TV series viewing sessions among 89 participants (1,952 answered surveys). The findings show that negative and positive response states evolve (partly) independently of each other in the course of entertainment viewing sessions: Despite an increase in negative experiences of goal conflict, guilt, and fatigue, individuals’ level of enjoyment remained stable during a viewing session. These results indicate that negative responses do not necessarily interfere with the experience of enjoyment. The level of enjoyment was the strongest predictor for whether someone stopped a viewing session indicating that hedonic experiences might overrule rational decisions to stop due to being fatigued or having other things to do.

Keywords

entertainment experience sampling multilevel modeling response states

Exposure to entertainment media can be a highly pleasurable experience. People typically choose entertainment media for hedonic reasons, and it is not surprising that exposure has been related to experiences of enjoyment (Vorderer et al., 2004), positive emotions (Wirth & Schramm, 2005), and flow (Sherry, 2004). With the rise of streaming services and online video platforms, a virtually unlimited amount of entertaining media content has become available (Matrix, 2014) allowing users to view many movies or episodes of a show in a row (Tukachinsky & Eyal, 2018). Given the vast amount of permanently available and quickly accessible entertainment content and the high pleasure that viewers derive from it, the question of when and why users disengage from highly pleasurable media entertainment becomes ever more pressing.

Theories focusing on media choice, selection and use, such as uses and gratifications (Katz et al., 1973), mood management (Zillmann, 1988), media entertainment (Vorderer et al., 2004), and media involvement (Tal-Or & Cohen, 2010), explain why and how people engage in media use. These theories provide useful explanations for why, for which reasons, and under which circumstances media are selected and used. However, communication research is remarkably silent about why and when people stop using entertainment media—theoretical reflections as well as empirical evidence are scarce.

Understanding the factors that make individuals discontinue a viewing session is crucial for at least two reasons. First, media use has been conceptualized as a dynamic process (Früh & Schönbach, 1982; Wonneberger et al., 2009), which entails, as any other process, a beginning and an end. Accordingly, any sound understanding of media use requires an analysis of the factors that give rise to and terminate the process. Notably, a dynamic view on media use implies that characteristics of the process itself can contribute to its termination. For instance, continuous changes in media users’ psychological responses may influence when they stop using media. Second, in a media landscape that is characterized by limitless content, understanding the factors that make people end a media session may provide an alternative take on why some people have difficulties to control their media usage (Reinecke & Meier, 2021).

To address this theoretical and empirical shortcoming in the existing literature, the present paper takes a dynamic view on media use and argues that specific processes that occur during media exposure contribute to its termination. Based on theories of hedonic decline (Galak & Redden, 2018), goal conflict (Hofmann et al., 2012; Reinecke & Hofmann, 2016), procrastination (Meier et al., 2016), and mental fatigue (van der Linden, 2011), the study presents an initial framework that conceptualizes factors that may explain why media entertainment viewing sessions are terminated. The assumptions of the theoretical framework will be initially tested using an event-based experience sampling approach during TV series viewing sessions.

Conceptualizations of Disengagement in Existing Media Entertainment Theories

A typical media use situation is a dynamic process over time with a beginning and an end point. Considering the amount of media choice theories (see, e.g., Hartmann, 2009; Wonneberger et al., 2009; Zillmann, 1988), the selection part in this process has arguably received more attention than the disengagement part. However, with the plentitude of entertaining media content readily available at almost every moment in time, the conceptualization of disengagement—the decision to discontinue a specific media use situation—has become a crucial part of the media use situation (e.g., Reinecke & Meier, 2021).

Media choice theories provide useful explanations for why and under which circumstances media are selected and used. For example, the uses and gratifications approach (Katz et al., 1973) assumes that users select media content based on gratifications they seek. Exposure to content may or may not fulfill these gratifications (Palmgreen et al., 1980). However, the theory remains vague about how the satisfaction of gratifications affects whether people continue or stop viewing. For example, if a user selects media content to be entertained, does the viewer stop the moment they feel entertained, or—assuming that being entertained is a highly pleasurable experience—would users rather continue to watch this show? In other words, the question one might pose is when exactly is an entertainment gratification obtained, and what happens thereafter? Similarly, mood management theory assumes that media content is selected to achieve an optimal state of arousal (Zillmann, 1988). With its focus on the selection of media content, the theory is less explicit about what happens once an ideal arousal state is obtained. Will media users then stop their media exposure or will they continue to select other content to keep the mood and arousal states optimal for longer periods of time?

Process models of media exposure (Bilandzic, 2004; Heeter, 1985; Perse, 1998; Wonneberger et al., 2009) go beyond the initial media selection phase and describe typical patterns of “viewing behavior between the decisions to start and to stop watching TV” (Wonneberger et al., 2009, p. 244). These models thus examine how people switch between different contents and address the dynamic nature of viewing sessions (Heeter, 1985; Heeter & Greenberg, 1988; Perse, 1998). This line of theorizing has tried to find answers to the question why TV viewers with an abundance of available content options switch to other content. Explanations that have been put forward in this line of research are, for example, negative reactions toward the program, and a waning interest for a show (Lang et al., 2005). However, these models focus largely on dynamics within viewing sessions, and rarely theorize why someone stops watching altogether.

Employing a utility approach, Fahr and Böcking (2009) have shown that negative emotional reactions toward the content, such as boredom, sadness, and disgust provoke viewers to switch. If these negative reactions (viewing costs) exceed the positive reactions (viewing benefits), viewers will stop using this content. However, it is important to note that these approaches argue that stopping is mainly due to specific content characteristics of the program that evoke negative reactions in viewers. Therefore, these approaches are less suited to explain why viewers stop highly pleasurable viewing experiences. TV series on streaming platforms are characterized by highly enjoyable and entertaining content, and their algorithmic recommender systems provide users with an almost infinite stream of tailored content. Thus, in many viewing sessions, negative reactions to this content might not occur or only on much lower levels.

Importantly, the decision to discontinue a media exposure session might not only be driven by experiences directly related to the media content itself. The recently introduced AMUSE model (Reinecke & Meier, 2021), for example, conceptualizes the continuation or discontinuation of media exposure as a direct function of (1) the entertainment experience itself and (2) self-conscious emotions, such as guilt, that can arise during exposure. Importantly, these self-conscious emotions are not a reaction to the media content itself but to the activity of engaging with media (p. 20). For example, viewers might judge the time they spent watching a series as unproductive and useless which may result in feelings of guilt, even if the series itself was enjoyable. Although entertaining experiences and self-conscious emotions have been conceptualized to arise during media exposure, it has not been conceptualized how these processes evolve over time, and whether they do indeed determine discontinuation of a viewing session.

Toward a Dynamic Model: Dynamic Experiences and the Termination of Media Entertainment Viewing Sessions

We argue that there are four pivotal psychological responses that evolve during media exposure and influence whether users stop their viewing session. More specifically, we propose that hedonic entertainment experiences decline, while perceived goal conflict and guilt—two negative self-conscious emotions—as well as fatigue increase in the course of a viewing session. Moreover, in line with the AMUSE model (Reinecke & Meier, 2021), we suggest that hedonic entertainment experiences prompt users to continue a viewing session, while negative psychological responses prompt users to stop a viewing session. Below, we discuss in more detail the specific trajectories of psychological responses that should emerge during media exposure and their effects on stopping a viewing session.

Hedonic Decline During Media Exposure

One of the main motivations to use streaming services is enjoyment (Karuza Podgorelec, 2020; Tukachinsky & Eyal, 2018). This is not surprising as it has been argued that enjoyment lies at the core of the hedonic entertainment experience (Vorderer et al., 2004) and that individuals seek out entertainment content to experience enjoyment (Oliver & Raney, 2011). If we consider media exposure as a dynamic process, entertainment experiences are likely to constantly change during media exposure (Früh & Schönbach, 1982). As explicated in the AMUSE model (Reinecke & Meier, 2021), and in entertainment theories (e.g., Oliver & Raney, 2011; Vorderer et al., 2004) entertainment experiences might change as a function of the content to which someone is exposed, and the reappraisals of the experience. Moreover, we argue that enjoyment might also develop as a function of the length of a viewing session, even if the content itself remains enjoyable.

There is substantive evidence for the phenomenon that hedonic responses to a stimulus decrease over time, even if the stimulus itself does not change (for a review, see Galak & Redden, 2018). This phenomenon is called hedonic decline. Hedonic decline refers to a decline in positive emotional responses to a stimulus after repeated exposure (Galak & Redden, 2018). Hedonic decline has been shown for various stimuli, such as food, art, pictures, and music (see Galak & Redden, 2018). The general pattern is that the hedonic response to a highly pleasurable stimulus declines after repeated exposure to it. For example, listening to the same song repeatedly, leads to a decreased pleasure response over time.

The main explanation for this effect is adaptation (Frederick & Loewenstein, 1999). Extant research has shown that people adapt to pleasurable and unpleasurable experiences, making both less intense over time (for reviews see: Luhmann & Intelisano, 2018; Lyubomirsky, 2011). Galak and Redden (2018) explain that: “the core notion of this mechanism is that stimuli are perceived relative to an adaptation level that reflects past exposures. For instance, eating a sweet chocolate bar now makes every other food seem a little less sweet by comparison” (p. 8). Thus, every pleasurable experience is set against a reference point. However, with repeated exposure to a pleasant stimulus this reference point increases. For example, listening to an enjoyable song repeatedly raises the reference point of what an enjoyable song is, leading to a hedonic decline over time (Kahnx et al., 1997).

Although hedonic decline has also been shown for short videos (Nelson et al., 2009), simple video games (Galak et al., 2013), music (Kahnx et al., 1997), and pictures (Yangmei et al., 2018), the concept has been less frequently adapted to explain hedonic experiences during exposure to more complex and more enduring media content. It is therefore less clear whether hedonic decline occurs during exposure to pleasurable media content. Theories on hedonic decline are based on the idea that exposure to the exact same stimulus is repeated, and that this leads to an adaptation of the original response over time. Thus, the same movie watched twice or thrice loses its hedonic appeal over time. But what happens when people experience prolonged exposure to media content that is not fully redundant (e.g., different episodes of the same show, different content on social media)?

On the one hand, we might assume that in these contexts hedonic decline does not occur because entertaining media content is characterized by a higher variety and complexity than the stimuli used in typical hedonic decline paradigms. Variety and complexity have been shown to slow down or even impede hedonic decline (Galak & Redden, 2018). Thus, the hedonic decline should occur less strongly if several episodes of the same show are watched in comparison to watching the same episode over and over again (Cox & Cox, 2002). Similarly, Yangmei et al. (2018) have shown that the hedonic response declines faster for hedonic than eudaimonic photographs. Thus, during exposure to more complex media stimuli hedonic decline might not occur. Moreover, producers of entertaining media content might include strategies into their productions such as cliffhangers, complex and parallel narratives, as well as unexpected turns of events that might counteract hedonic decline (Rubenking & Bracken, 2018).

On the other hand, a certain amount of hedonic decline is still plausible in these contexts. Episodes of the same TV series are characterized by recurring features, similar structure, and recurring characters (Potter, 2009). These recurring features might also lead to some form of adaptation. For example, let’s consider watching a comedy series like “Friends.” Watching the first episode might set the reference point for enjoyment and might make other previous comedy shows seem less funny. However, this reference point might increase with each following episode of this same show, as it is compared to the previous one. Thus, there might be an adaptation to the show, and thus a decline in the hedonic response over time. This might partly explain why many series become less successful over time and are terminated after a few seasons.

We thus argue that there will be a hedonic decline during prolonged media exposure (even if this decline is likely slower than for repeated exposure to the exact same stimuli).

H1a: During a prolonged media entertainment viewing session, the hedonic response declines (i.e., less enjoyment over time).

Hedonic decline during media exposure might provide an explanation for why people stop watching at a given moment. Since entertainment media are sought out for experiencing enjoyment, it is likely to assume that low levels of enjoyment make people terminate a viewing session. Thus, if the entertainment need is no longer fulfilled, viewers might turn to another activity instead. This reasoning is also in line with the assumptions of the AMUSE model that conceptualize hedonic entertainment as a direct precursor for the decision to continue or discontinue media exposure (Reinecke & Meier, 2021). We thus expect that enjoyment of the media content at a specific point during media exposure predicts whether someone discontinues the session. More specifically, we propose:

H1b: The higher the level of enjoyment, the lower the likelihood that someone terminates a media entertainment viewing session.

Self-Conscious Emotions: Goal Conflict and Feelings of Guilt

Media exposure might not only elicit positive emotions, but can also at times elicit negative self-conscious emotions, such as guilt. As explicated in the AMUSE model (Reinecke & Meier, 2021), guilt can arise as a direct consequence of engaging in a media activity. First, self-conscious emotions can arise as a reaction to the media content itself if the content one is viewing is perceived as meaningless and a “guilty pleasure.” The feeling of guilt in this case is based on meta-appraisals of the emotional response in that situation (Schramm & Wirth, 2010). Second, guilt can also be elicited as a consequence of the media activity per se, unrelated to the content. It has been argued that particularly the length of an exposure session is important in this respect. As time is a limited resource, media activities oftentimes conflict with other personal goals someone wants to achieve (Meier et al., 2016). For example, it has been shown that media activities are frequently used to procrastinate, that is to adjourn other pressing activities, such as studying (Meier et al., 2016). Due to the highly pleasurable experiences elicited during media exposure, it is difficult for individuals to resist these activities (Hofmann et al., 2012), and thus media use often conflicts with other personal goals. It is thus likely to assume that during a prolonged media session, the salience of goal conflicts increases.

Two recent studies on binge watching have found correlational evidence for a relationship between the frequency of binge watching and perceived goal conflict (Erdmann & Dienlin, 2022; Granow et al., 2018). Thus, individuals engaging more frequently in binge watching reported experiencing goal conflicts more often. Similar, Hofmann et al. (2012) have shown that the desire to use media frequently conflicts with other goals in everyday life. Although these studies provide evidence that media use is related to goal conflicts, it is not clear yet how goal conflict develops during a media exposure session. We expect that goal conflict should be relatively low at the beginning of a viewing session. Over time, perceptions of goal conflict should increase as goal conflicts are likely to become more apparent the longer someone watches.

One immediate consequence of these goal conflicts during media use are feelings of guilt (Reinecke & Meier, 2021). If someone feels the necessity to engage in another activity but still finds it difficult to terminate a media session, it is likely that this person experiences guilt as a result. Thus, even if someone enjoys the content of a show, meta-appraisals about the current situation might lead to the meta-emotional experience of guilt (Schramm & Wirth, 2010). Several correlational studies indicated that goal conflict and feelings of guilt are related. This has been shown in the context of binge watching (Erdmann & Dienlin, 2022; Granow et al., 2018), general media use (Reinecke et al., 2014), as well as in the context of procrastination with social media (Meier et al., 2016). The dynamics of feelings of guilt during media exposure have not yet been investigated, though. In line with the predictions of the AMUSE model (Reinecke & Meier, 2021), we expect guilt to increase as a function of viewing length. Feelings of guilt should thus show a similar trajectory to those of goal conflict during media exposures, with low levels of guilt at the beginning of a session, and increasing levels throughout the session. We, thus, expect:

H2a: During a prolonged media entertainment viewing session, perceptions of goal conflict increase.

H3a: During a prolonged media entertainment viewing session, feelings of guilt increase.

Both perceptions of goal conflict and feelings of guilt are aversive experiences that people may want to avoid. Hofmann et al. (2012) have shown that goal conflict signals individuals that one’s desire to engage in an activity is discordant with other goals. Thus if goal conflicts arise, individuals may try to resist their desire to engage in media use (Hofmann et al., 2012). Similarly, the AMUSE model predicts that feelings of guilt are a direct precursor of the decision to continue or discontinue media use. Thus, it is likely to assume that at a certain level of goal conflict and guilt, the likelihood that someone stops watching increases.

H2b: The higher the level of perceived goal conflict, the higher the likelihood that someone ends a media entertainment viewing session.

H3b: The higher the level of guilt, the higher the likelihood that someone ends a media entertainment viewing session.

Mental Fatigue

Mental fatigue typically refers to a feeling of tiredness and exhaustion that people can experience after cognitive effortful tasks (Boksem & Tops, 2008). Although entertainment media are frequently selected to relax from stressful events, to wind down, and to recover from fatigue (Reinecke & Eden, 2017; Rieger et al., 2014, 2017), it has been argued that the restorative ability of watching television is questionable (Basu et al., 2019). Cognitive effort is needed to process media messages (Atkin, 1985; Huskey et al., 2020; Lang, 2000; Lang et al., 2013), and this is particularly so for complex audio-visual stimuli (Lang, 2000; Lang et al., 2013; Thorson et al., 1985). Moreover, processing of media stimuli requires directed and sustained attention. According to Attention Restoration Theory (ART, Kaplan, 1995), prolonged sustained attention leads to mental fatigue (Staats et al., 2003). Thus, due to the cognitive effortful nature of many media stimuli, processing of entertainment media should over time lead to mental fatigue. Interestingly, ART suggests that media entertainment such as watching videos can be characterized as “hard fascination” activities that fully occupy the mind without leaving mental capacities for contemplation. These activities are typically less restorative than “soft fascination activities” (e.g., walking in nature) but are still chosen to recover as they are falsely perceived as being restorative (Basu et al., 2019).

Recent studies have shown that prolonged media use is related to exhaustion (Gangadharbatla et al., 2019). Although the effect on exhaustion might be stronger for complex media entertainment, even simple, hedonic videos might still increase fatigue as they require mental effort that then is not available for mental restoration. For example, subjective fatigue already increased after a 30-min entertaining video (Koo et al., 2018) Thus, it seems that although entertainment media are frequently selected to recover and relax, individuals might not achieve this goal, but instead might feel more fatigued after media use (Basu et al., 2019). Empirical evidence on the dynamics of fatigue during a media exposure situation is lacking. We argue that due to the cognitive effort related to message processing, fatigue should increase during media sessions.

H4a: During a prolonged media entertainment viewing session, fatigue increases.

From the literature on task engagement, it is known that one of the driving forces to stop a cognitive effortful activity, is mental fatigue (Hopstaken et al., 2015). van der Linden (2011) has conceptualized mental fatigue as a “stop emotion” suggesting that mental fatigue predominantly explains why someone feels the urge to stop a cognitive activity and switch to another activity. In this sense, mental fatigue can be seen as adaptive as it precludes the organism from spending too much energy on the same activity (van der Linden, 2011). Whether fatigue is also a driving force in explaining why someone terminates a media session is still unknown. However, qualitative reports on the use of video games tap into this by indicating that players continued playing even after the game ceased to be fun, and only stopped playing when they felt physically or mentally fatigued (King & Delfabbro, 2009). As fatigue is a strongly aversive state, it is likely to assume that it increases the likelihood of ending a media use session.

H4b: The higher the level of fatigue, the higher the likelihood that someone ends a media entertainment viewing session.

The Current Study

In the current study, we employ an event-based experience sampling approach during media entertainment sessions. This approach allows us to examine the dynamic processes of positive as well as negative experiences that occur during media entertainment sessions in real-life. We propose that four psychological responses—enjoyment, perceived goal conflict, guilt, and mental fatigue—evolve during media exposure and influence when users stop their viewing session. More specifically, we expect that during exposure, positive as well as negative experiences develop dynamically over time. We argue that due to processes of hedonic decline, enjoyment should decline during a viewing session, while at the same time, negative self-conscious emotions, as well as mental fatigue increase. Moreover, we expect that these positive and negative experiences predict whether someone continues with or disengages from a viewing session.

Method

Sample and Procedure

To test the hypotheses, an event-based experience sampling study was conducted. Ethical approval was received from the ethics board of the Faculty of Social and Behavioral Sciences from the University of Amsterdam (ethics project number: 2018-CS-9701). Participants were recruited via the University’s subject pool. After agreeing to participate, participants filled in an entry survey which included detailed instructions about the study and the research app (MyPanel). Participants downloaded the app on their smartphones (Android and iOS) through which they could fill in short surveys. They were instructed to fill in a survey each time they had watched an episode of a show (TV or online) for the coming 3 weeks. More specifically, the instructions stated: “Please fill in the short survey EACH time after you have watched an episode of a TV series. Let’s say you are watching three episodes of a TV series in a row, then please fill in the short mobile survey three times (once after each episode). Please do this every time you watch a TV series in the coming 3 weeks.” Of the 132 students who agreed to participate, 92 filled in at least one event-based survey. Three participants who had event-based data did not participate in the entry survey and were thus excluded from the analyses. The final sample thus consisted of 89 students (78 female; M_age = 21.07, SD_age = 2.67) who filled in a total of 1,952 surveys over a period of 3 weeks. On average participants filled in 22 surveys (M = 21.93, SD = 26.42; median = 16 responses; range = 1–209). Participants could choose to either receive research credits or a voucher worth 10€ for participation.

Measures

Experiences During Media Exposure

We first asked participants to mention the show they had just watched in an open text box. Information about the shows that were watched can be found in Table A7 on the OSF (https://osf.io/dwn5c). Each survey included single item measures of participants’ experiences during exposure. In line with many experience sampling studies, we decided to use single items in order to keep the surveys as short as possible and to increase compliance. Responses were recorded on scales ranging from 0 (not at all) to 10 (very much). To assess participants’ enjoyment, participants were asked to rate how much they enjoyed viewing the episode they had just finished watching (“How much did you enjoy viewing this episode?”). To assess participants level of fatigue, respondents reported how tired they felt (“How tired do you feel right now?”). Feelings of guilt and perceived goal conflict were measured with the items, “Do you feel bad about watching TV series right now?,” and “At this moment, do you feel that watching series conflicts with other activities (e.g., working, sleeping, exercising, academic activities, etc.)?” Descriptive analyses of users’ mean responses indicate high levels of enjoyment (M = 7.76, SD = 1.02) and comparatively low levels of fatigue (M = 4.00, SD = 1.66), feelings of guilt (M = 2.32, SD = 1.54), and perceived goal conflict (M = 3.35, SD = 1.90).

Media Disengagement

At the end of each short survey, participants had to indicate whether they will stop or continue viewing. Specifically, participants could indicate that they stopped; that they continued viewing the same show; or that they continued viewing another show. Based on this variable, a dichotomous variable was created which indicated whether participants continued or stopped.

Moreover, a variable was created which reflected the position of an episode within one viewing session (e.g., third episode of a viewing session). If participants indicated after watching an episode that they continued viewing the same show or any other series, the counter was increased by one (i.e., after watching the first episode, the counter was set to 1; after watching a second episode, the counter was set to 2, etc.). Because some participants forgot to fill in that they stopped a viewing session, we included the following safeguard: If the interval between the submission of two subsequent surveys was longer than 4 hr, we counted this as a new viewing session, rather than a continuation of the same session. On average, a viewing session was 1.85 episodes long (SD = 1.63).

Results

All analyses were conducted in R (version 4.2.2; R Core Team, 2022). The hypotheses were tested through multilevel modeling, given that the repeated measurements of response states (level 1) were nested within participants (level 2). The models were estimated using the lme4 package (version 1.1-34; Bates et al., 2015). Tests of significance for regression coefficients were conducted with the lmerTest package (version 3.1-3; Kuznetsova et al., 2017). The conditional R² was estimated using the partR2 package (version 0.9.1; Stoffel et al., 2021).

Linear multilevel models were estimated using maximum likelihood estimation to test how response states evolved during prolonged viewing.¹ In each model, one response state (i.e., enjoyment, fatigue, guilt, or perceived goal conflict) was included as the dependent variable. The order of the viewed show within a viewing session was included as the independent variable (conceptually, a level-1 predictor). Thus, parameter estimates for the effect of “order” on a given response state reflect how the response state changes when participants watched an increasing number of episodes. Age and sex of the participant (1 = male, 2 = female) functioned as control variables and were added as additional predictors (conceptually, level-2 predictors of the random intercept). Results of the linear multilevel models are depicted in Table 1. The results show that prolonged viewing (i.e., the order of a viewed show) did not affect enjoyment (b = 0.03, p = .122). This finding does not support H1a which predicted a hedonic decline, and thus a decrease in enjoyment over time. In line with expectations, however, when watching an increasing number of episodes, people experienced higher levels of goal conflict (b = 0.16, p < .001) and guilt (b = 0.16, p < .001), and they became more fatigued (b = 0.15, p < .001). As indicated by the unstandardized regression coefficients, the predicted changes in the psychological responses, which were measured on a scale ranging from 0 to 10, were relatively small. In sum, hypotheses H2a, H3a, and H4a are supported.

Table 1.

Changes in Response States During Viewing Session (Linear Multilevel Model).

	Enjoyment	Goal Conflict	Guilt	Fatigue
Fixed effects
Intercept	9.09 (1.03)^***	5.69 (1.90)^**	3.51 (1.55)^*	4.60 (1.72)^**
Sex	0.21 (0.32)	−0.91 (0.58)	−0.44 (0.48)	−0.53 (0.53)
Age	−0.08 (0.04)^*	−0.04 (0.07)	−0.03 (0.06)	0.01 (0.07)
Order of viewed show	0.03 (0.02)	0.16 (0.03)^***	0.16 (0.03)^***	0.15 (0.03)^***
Random effects
Intercept	0.80 (0.89)	2.71 (1.65)	1.82 (1.35)	2.17 (1.47)
Residual	1.91 (1.38)	5.97 (2.44)	4.02 (2.01)	5.57 (2.36)
Intraclass correlation	0.31	0.31	0.31	0.27
Deviance (−2LL)	6,974.8	9,210.2	8,437.4	9,062.1
Conditional R²	.31	.32	.33	.29

Note. N = 1,952 responses nested in 89 individuals. Unstandardized effects are depicted with standard errors of fixed effects and standard deviations of random effects in brackets.

p < .05. ^**p < .01. ^***p < .001.

To test whether the response states predicted stopping a viewing session, a logistic multilevel model was estimated using maximum likelihood estimation. The dependent variable was whether participants stopped their viewing session after an episode. Enjoyment, goal conflicts, guilt, and fatigue after viewing a show were person-mean-centered (Hamaker & Muthén, 2020) and included as independent variables (level-1 predictors). Moreover, the order of the viewed show within a viewing session (level-1 predictor), as well as age and sex were entered as independent variables (level-2 predictors of random intercept). When estimating the model with the default optimizer implemented in lme4 (i.e., a combination of the Nelder-Mead- and bobyqa-algorithm), the model did not converge. When re-estimating the model using the bobyqa-algorithm, the model converged. The results are depicted in Table 2. The results show that—in line with H1b, H2b, and H4b—enjoyment negatively predicted stopping a viewing session (p < .001), whereas experiencing goal conflicts (p = .003) and being fatigued (p < .001) positively predicted stopping. Experiencing guilt did not significantly predict stopping (p = .495), thus not supporting H3b. The odds ratios indicate that enjoyment decreased the odds of stopping by 16% (e^b = 0.84), whereas experiencing goal conflicts (e^b = 1.09) and fatigue (e^b = 1.08) increased the odds of stopping by 9% and 8% respectively.

Table 2.

Effects of Response States on Media Disengagement (Logistic Multilevel Model).

	b	SE	e ^b	p-Value
Fixed effects
Intercept	1.28	0.94	3.60	.173
Sex	−0.98	0.30	0.37	.001
Age	0.04	0.03	1.04	.236
Order of viewed show	−0.04	0.03	0.96	.246
Enjoyment	−0.18	0.04	0.84	<.001
Goal conflict	0.08	0.03	1.09	.003
Guilt	0.02	0.03	1.02	.495
Fatigue	0.08	0.02	1.08	<.001
Random effects
Intercept	0.40 (0.63)
Intraclass correlation	0.12
Deviance (−2LL)	2,511.3
Conditional R²	.14

Note. N = 1,952 responses nested in 89 individuals. Intraclass-correlation for logistic multilevel models according to Sommet and Morselli (2017). b = expected change in the logit (log odds); SE = standard errors of b; e^b = expected change in odds (odds ratio); p-Value = significance test of b.

Additional Analyses

We inspected correlations between response states. We estimated multilevel correlations using the correlation package (version 0.8.3; Makowski et al., 2020) to account for the nesting of repeated measurements within participants. Enjoyment was negatively correlated with fatigue (r = −.09, p < .001), but neither associated with perceived goal conflict (r = .03, p = .42) nor with guilt (r = .00, p = .985). Moreover, perceived goal conflict was positively correlated with guilt (r = .68, p < .001) and fatigue (r = .13, <0.001). Guilt was positively correlated with fatigue (r = .12, p < .001).

To check the stability of the results of the linear and the logistic multilevel models, we re-estimated the models with minor modifications. Three sets of additional multilevel models were estimated. First, we ran both models without covariates. Second, in the original analyses we defined viewing sessions from when someone started watching until someone stopped watching altogether. Thus, when someone watched two different shows, this was still considered as one viewing session. Therefore, we additionally analyzed models in which viewing sessions were defined as the prolonged viewing of the same show. Third, as it could make a difference whether someone watched three very short episodes (e.g., 20 min-episodes) versus three longer episodes (e.g., 60-min episodes), we also estimated models in which the length of a viewing session was taken into consideration. For this we categorized the length of each show (1 = up to 30 min; 2 = 31 to 45 min; 3 = 46 to 60, 4 = 60+ min) and weighted each episode accordingly. The results of these additional analyses are highly comparable to the original analyses. One difference was that when only sessions with the same show are taken into account, enjoyment slightly increased during viewing. The specific findings of all additional analyses are available on the OSF (https://osf.io/dwn5c).

Discussion

The present study investigated the dynamics of positive and negative response states during media entertainment viewing sessions, and how these response states are related to terminating a viewing session. We theorized that positive as well as negative response states dynamically change over the course of a viewing session. More specifically, we expected that during a prolonged viewing session, negative experiences increase while positive experiences decrease. In line with expectations, self-conscious emotions did indeed increase over the course of a viewing session. More specifically, people felt increasingly guilty during a viewing session, and experienced increased levels of goal conflict. Moreover, they felt increasingly tired. Interestingly—and in contrast to theoretical expectations—enjoyment did not decrease during a prolonged entertainment session. Together, these findings are intriguing because they indicate that during entertainment exposure negative and positive response states evolve partly independently of each other: Despite an increase in negative responses of goal conflict, guilt, and fatigue, individuals’ levels of enjoyment remained stable during a viewing session indicating that negative responses do not necessarily interfere with the experienced enjoyment of the viewer.

On a first glance, these findings seem to be in contrast to theoretical assumptions on the relationship between negative experiences, such as goal conflict and guilt, and positive affect such as enjoyment. For example, Hofmann et al. (2013) provided evidence for a “spoiled pleasure effect” when people enacted on pleasurable temptations that were in contrast to their long-term goals. No evidence for this spoiled pleasure effect for viewing pleasurable TV series was found in the current study as the findings show that enjoyment remained stable despite of an increase in guilt. Similar findings are reported in the study by Granow et al. (2018) and Erdmann and Dienlin (2022) that also did not find a direct relationship between guilt and enjoyment of binge watching. It thus seems that self-conscious emotions such as guilt do not necessarily interfere with media enjoyment, however, they may interfere with other positive feelings such as general happiness, satisfaction and recovery which were not assessed in the present study.

In contrast to expectations, the present study did not find support for a hedonic decline during TV series watching. This finding has implications for the generalizability of adaptation theory. Although hedonic decline has been found for a variety of stimuli, the present findings are in line with recent studies indicating that hedonic decline is slowed for more complex (Cox & Cox, 2002), eudaimonic (Yangmei et al., 2018), or sentimental (Yang & Galak, 2015) stimuli. Overall, these findings indicate that stimuli addressing higher cognitive functions may lead to less hedonic decline (see Galak & Redden, 2018). Interestingly, we found that when participants watched only episodes of the same show in one viewing session, enjoyment even increased during viewing. This might be explained with entertainment content, notably TV shows, being typically designed to maintain the audience’s interest and attention, for instance, through cliffhangers or multiple story lines with non-simultaneous conclusions (Allrath et al., 2005). Such narrative devices may reduce hedonic decline. In addition, it might be that only when individuals experienced high levels of enjoyment did they continue to watch. Since hedonic decline has been consistently shown for a variety of other stimuli and life experiences, hedonic decline during series watching deserves further research attention. Ideally future studies should be conducted in more controlled settings in which individuals are exposed to a larger number of episodes of the same show, and in which content as well as contextual factors can be manipulated (e.g., in the lab or controlled field study).

The viewing sessions in the present study were rather short with only 1.85 episodes on average, it is thus important to further examine whether hedonic decline occurs in longer viewing sessions. Interestingly, previous studies have shown that interrupted consumption counteracts hedonic decline (Nelson et al., 2009; Nelson & Meyvis, 2008). For example, Nelson and Meyvis (2008) have shown that interrupting pleasurable media experiences, such as watching a video, impeded the hedonic decline, and increased the pleasure response after the break. This has been shown to occur for interrupting TV shows and video games. Viewers enjoyed the content better after a short break, but were reluctant to choose a break during the experience. However, in these studies, simple stimuli were used, and it is thus interesting to understand whether and when hedonic decline occurs for highly complex and well-designed TV series, and whether binge watching a show actually leads to less enjoyment than watching it at a slower pace.

The findings of this study show that next to guilt and goal conflict also fatigue increased during viewing. To our knowledge this study is the first to investigate the development of fatigue during real-life viewing sessions. The finding that fatigue increases is intriguing because previous cross-sectional studies indicated that entertainment media are frequently selected as a means to recover from strain (Reinecke, 2009). Thus our findings might suggest that although individuals seek out entertainment media to recover, they might not necessarily achieve this goal (see also Basu et al., 2019). Future studies should replicate these findings in more controlled settings to further investigate the restorative versus tiring nature of entertainment media by also assessing the different components of fatigue (e.g., mental vs. physical fatigue and feelings of recovery).

The present study also showed that response states during media exposure are linked to whether people stop or continue a viewing session. Interestingly, the study found that the number of episodes that someone watched did not predict termination. This implies that experiential states or other contextual factors are more important in predicting disengagement than the time someone spends watching. However, considering that individuals in our study tended to watch on average only a small number of episodes, this might also indicate that watching is oftentimes habitual (e.g., “I always watch two episodes”), and that during longer viewing sessions, the number of episodes might indeed predict discontinuation. The strongest predictor for termination was enjoyment in that viewers were more likely to stop a viewing session when enjoyment was lower. This is problematic because enjoyment remained on a rather high level over the course of a viewing session making it difficult to stop the longer someone watches. This finding is in line with a previous cross-sectional study that found a relationship between enjoyment and binge watching duration (Merrill & Rubenking, 2019). Enjoyment seemed to be a stronger predictor than fatigue and experienced goal conflict indicating that hedonic experiences might overrule rational decisions to stop due to being tired or having other things to do. These findings highlight the importance for self-regulation in this process: Media users need to find a balance between enacting on their need for hedonic pleasure and long-term goals (e.g., Hofmann et al., 2016; Reinecke & Meier, 2021). Interestingly, and in contrast to predictions, guilt did not predict stopping indicating that viewers seem to tolerate negative feelings to some extent as long as their experienced enjoyment is high.

By taking a dynamic view, we theorized that experiences during entertainment exposure determine the termination of an entertainment session, and that these mechanisms are partly independent of the content of the shows. However, the processes examined in this study reflect only a selection of a multitude of processes that might be at work, and it is important to understand the boundary conditions for these response states to develop. As previous theories state, the content itself plays a role in termination (Fahr & Böcking, 2009; Heeter, 1985). For example, high levels of suspense or drama might interfere with the salience of goal conflicts and guilt as viewers are highly absorbed in the narrative. In addition, it is important to consider personality factors that might influence these responses. For example, in line with the AMUSE model, it is likely to expect that self-control plays a role in these dynamics (Reinecke & Meier, 2021). For individuals high in self-control, goal conflict might be a stronger predictor of discontinuation than for individuals low in self-control. Future studies might thus benefit from studying the boundary conditions under which these processes develop. Although we found that goal conflict, guilt, and fatigue increased on average during viewing, it is likely to assume that the strength of this increase depends on other situational or person-related factors.

The length of a viewing session might also be strongly influenced by viewing habits. Previous research has shown that media use is highly determined by habits (LaRose, 2010). Through repetitions, viewing behavior may take habitual forms (Schnauber-Stockmann & Naab, 2019), for example, in that some viewers are used to spend the same amount of time on watching series every evening. The termination of a viewing session in that case might be determined by the formed habit of always watching a certain amount of episodes per session, or by a habit of going to bed at the same time each evening. Integrating views on habitual viewing and dynamic experiences might be a fruitful endeavor for future studies.

Limitations and Directions for Future Research

The present study was the first to investigate response states during entertainment viewing sessions and how they influence the termination of a viewing session. By employing an event-based experience sampling design, the study was able to assess response states as they occurred during viewing sessions in the everyday lives of participants. However, this design lacks full control over the actual viewing context and session length. For example, if a participant filled in three surveys on one evening, it is not clear whether this participant did indeed watch three episodes or whether they just forgot to answer more surveys after the third episode. Therefore, a replication in a more controlled setting would be desirable. However, given our focus on response states and their influence on stopping a viewing session, ecological validity is likely to be violated in a laboratory study: Feelings of guilt or goal conflict are unlikely to arise in a laboratory study, in which the duration of participation is typically predetermined (see Halfmann et al., 2023). Thus, the integration of streaming and survey software in an experience sampling study (e.g., the questionnaire pops up after each episode) may be a better solution to warrant reliable measurement.

A further shortcoming of the present study is that it assessed dynamic processes by measuring responses via self-reports after each episode. The granularity of the analytical approach could be improved by measuring these responses continuously throughout the viewing session. Notably, physiological measurements can provide detailed insights into changes in response states during one episode. However, physiological measures are limited in their capability to distinguish specific response states, such as guilt and perceived goal conflict. Thus, physiological measures may only be able to complement, but not replace self-reports. In addition, we examined the development of these processes independently from each other. Thus, future studies might benefit from extending the framework by exploring the dynamic interrelations between the constructs over time. For example, as predicted by the AMUSE model (Reinecke & Meier, 2021), goal conflict should influence feelings of guilt which in turn should decrease enjoyment over time. In addition, the framework might be extended to other experiences that dynamically evolve during media exposure (e.g., involvement, suspense).

Moreover, the experience sampling approach required us to focus on a limited selection of concepts and indicators. For example, we focused in this study on hedonic enjoyment and ignored other entertainment experiences, such as eudaimonic entertainment, or suspense. It is likely to assume that eudaimonic entertainment experiences may play an important role in series streaming, and will also show unique dynamic patterns. In addition, to increase compliance of participants we kept the mobile surveys short and used single-item measures. Although single item measures have been criticized for having psychometric disadvantages, such as a higher sensitivity to random measurement error (e.g., Gogol et al., 2014), they are frequently used in experience sampling studies, and evidence is accumulating that in many cases single-item measures perform equally well than multi-item measures (e.g., Bergkvist & Rossiter, 2007; Matthews et al., 2022).

Finally, it is important to note that the present study tested the assumptions of the theoretical framework in one specific context of entertainment media, namely TV series, and within a student sample. Although TV series are a popular form of media entertainment, it would be an interesting endeavor for future studies to examine this in other contexts, for example, while people use social media. Although, we expect similar processes to be at work during the use of other types of media, testing these assumptions in other contexts would help to extend the generalizability of the present findings. Similarly, we expect the processes to be generalizable to other populations, however, it could be that the initial level as well as the developmental trajectory of the response states differ. For example, initial levels of guilt were rather low in this sample. Guilt and goal conflict might be higher and increase more strongly during viewing for other populations that have more fixed work schedules or care responsibilities. For future studies it might thus be fruitful to take other populations into account.

Conceptualizing Dynamics and Termination in the Media Entertainment Process

Classical communication science theories on media selection and use were developed in a fundamentally different media environment, characterized by limited choice, comparatively slow publication cycles, and intermittent availability of entertaining media content. In contrast, in today’s media landscape, a plethora of media entertainment is produced, rapidly published, and made permanently available, which allows audiences, in principle, to continuously use this content. Given the reduction of structural barriers to using media entertainment, the question of why and when users terminate their entertainment exposure becomes prevalent.

We have emphasized that a dynamic view, which considers changes in psychological responses during entertainment exposure, is particularly fruitful to answer this question: Momentary responses, such as enjoyment, perceived goal conflict, and fatigue are essential in understanding when exposure is terminated. The broader theoretical implications of a truly dynamic view on entertainment exposure (and media exposure in general) are noteworthy. As mentioned above, a dynamic view allows for the possibility that psychological changes during exposure and, thus, integral characteristics of the process itself influence when the process is terminated. In addition, a dynamic view allows examining within-person processes. Specifically, in our study we found that fatigue, goal conflict and guilt increased during exposure. In contrast, a static view, which does not account for temporal changes in responses, is far more limited in its capability to analyze media use and its termination. For example, by studying these processes cross-sectionally, only between-person differences can be detected (e.g., those who binge-watch more have more goal conflict). Thus our dynamic view provides a more detailed picture about experiential states during the media exposure process. If we focus on dynamics, properties of processes and relationships between processes and relevant outcomes play a crucial role. Although we are not the first to remark on the importance of a dynamic view on media use (e.g., Wang, 2014), dynamic processes are still relatively rarely studied. The present study contributes to the theoretical reflection on the temporal dynamics during media use.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Susanne E. Baumgartner

Notes

Author Biographies

Susanne E. Baumgartner (PhD) is an Associate Professor at the Amsterdam School of Communication Research (ASCoR), at the University of Amsterdam. She is a member of the Center for Research on Children, Adolescents, and the Media, and the Digital Communication Methods Lab. Her work focuses on the effects of digital media.

Rinaldo Kühne (Ph.D. University of Zürich, Switzerland) is Senior Lecturer at the Department of Communication Science at the University of Amsterdam, the Netherlands. His research interests include media psychology and effects and methods of communication science.

References

Allrath

Gymnich

Surkamp

(2005). Introduction: Towards a narratology of TV series. In Allrath

Gymnich

(Eds.), Narrative strategies in television series (pp. 1–43). Palgrave Macmillan.

Atkin

(1985). Informational utility and selective exposure to entertainment media. In Zillmann

Bryant

(Eds.), Selective exposure to communication (pp. 63–91). Erlbaum.

Basu

Duvall

Kaplan

(2019). Attention restoration theory: Exploring the role of soft fascination and mental bandwidth. Environment and Behavior, 51(9–10), 1055–1081. https://doi.org/10.1177/0013916518774400

Bates

Mächler

Bolker

Walker

(2015). Fitting linear mixed-effects models usinglme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01

Bergkvist

Rossiter

J. R.

(2007). The predictive validity of multiple-item versus single-item measures of the same constructs. JMR, Journal of Marketing Research, 44(2), 175–184. https://doi.org/10.1509/jmkr.44.2.175

Bilandzic

(2004). Synchrone Programmauswahl: Der Einfluss formaler und inhaltlicher Merkmale der Fernsehbotschaft auf die Fernsehnutzung. Verlag Reinhard Fischer.

Boksem

M. A.

Tops

(2008). Mental fatigue: Costs and benefits. Brain Research Reviews, 59(1), 125–139. https://doi.org/10.1016/j.brainresrev.2008.07.001

Cox

A. D.

(2002). Beyond first impressions: The effects of repeated exposure on consumer liking of visually complex and simple product designs. Journal of the Academy of Marketing Science, 30, 119–130. https://doi.org/10.1177/03079459994371

Erdmann

Dienlin

(2022). Binge-watching, self-determination, and well-being: A partially successful direct replication and extension of granow et al. Journal of Media Psychology Theories Methods and Applications, 34(6), 383–394. https://doi.org/10.1027/1864-1105/a0003342018.

10.

Fahr

Böcking

(2009). Media choice as avoidance behavior: Avoidance motivations during television use. In Hartmann

(Ed.), Media choice: A theoretical and empirical overview (pp. 185–202). Routledge.

11.

Frederick

Loewenstein

(1999). Hedonic adaptation. In Kahnemann

Diener

Scharz

(Eds.), Well-being: The foundations of hedonic psychology (pp. 302–329). Russell Sage Foundation.

12.

Früh

Schönbach

(1982). Der dynamisch-transaktionale ansatz: Ein neues Paradigma der Medienwirkungen [The dynamic-transactional approach: A new paradigm of media effects]. Publizistik, 27, 74–88.

13.

Galak

Kruger

Loewenstein

(2013). Slow down! Insensitivity to rate of consumption leads to avoidable satiation. Journal of Consumer Research, 39(5), 993–1009. https://doi.org/10.1086/666597

14.

Galak

Redden

J. P.

(2018). The properties and antecedents of hedonic decline. Annual Review of Psychology, 69(1), 1–25. https://doi.org/10.1146/annurev-psych-122216-011542

15.

Gangadharbatla

Ackerman

Bamford

(2019). Antecedents and consequences of binge-watching for college students. First Monday, 24(12). https://doi.org/10.5210/fm.v24i12.9667

16.

Gogol

Brunner

Goetz

Martin

Ugen

Keller

Fischbach

Preckel

(2014). “My questionnaire is too long!” the assessments of motivational-affective constructs with three-item and single-item measures. Contemporary Educational Psychology, 39(3), 188–205. https://doi.org/10.1016/j.cedpsych.2014.04.002

17.

Granow

V. C.

Reinecke

Ziegele

(2018). Binge-watching and psychological well-being: Media use between lack of control and perceived autonomy. Communication Research Reports, 35, 392–401. https://doi.org/10.1080/08824096.2018.1525347

18.

Halfmann

Meier

Reinecke

(2023). Trapped between goal conflict and availability norm? How users’ mobile messaging behavior during task engagement influences negative self-conscious emotions. Journal of Media Psychology: Theories Methods and Applications. Advance online publication. https://doi.org/10.1027/1864-1105/a000381

19.

Hamaker

E. L.

Muthén

(2020). The fixed versus random effects debate and how it relates to centering in multilevel modeling. Psychological Methods, 25(3), 365–379. https://doi.org/10.1037/met0000239

20.

Hartmann

(2009). Media choice: A theoretical and empirical overview. Routledge.

21.

Heeter

(1985). Program selection with abundance of choice: A process model. Human Communication Research, 12, 126–152. https://doi.org/10.1111/j.1468-2958.1985.tb00070.x

22.

Heeter

Greenberg

B. S.

(1988). A theoretical overview of the program choice process. In Heeter

Greenbert

B. S.

(Eds.), Cableviewing (pp. 33–50). Ablex.

23.

Hofmann

Baumeister

R. F.

Förster

Vohs

K. D.

(2012). Everyday temptations: An experience sampling study of desire, conflict, and self-control. Journal of Personality and Social Psychology, 102(6), 1318–1335. https://doi.org/10.1037/a0026545

24.

Hofmann

Kotabe

Luhmann

(2013). The spoiled pleasure of giving in to temptation. Motivation and Emotion, 37(4), 733–742. https://doi.org/10.1007/s11031-013-9355-4

25.

Hofmann

Reinecke

Meier

(2016). Of sweet temptations and bitter aftertaste. Self-control as a moderator of the effects of media use on well-being. In Reinecke

Oliver

M. B.

(Eds.), The Routledge handbook of media use and well-being: International perspectives on theory and research on positive media effects (pp. 211–222). Routledge.

26.

Hopstaken

J. F.

van der Linden

Bakker

A. B.

Kompier

M. A.

(2015). A multifaceted investigation of the link between mental fatigue and task disengagement. Psychophysiology, 52, 305–315. https://doi.org/10.1111/psyp.12339

27.

Huskey

Wilcox

Clayton

R. B.

Keene

J. R.

(2020). The limited capacity model of motivated mediated message processing: Meta-analytically summarizing two decades of research. Annals of the International Communication Association, 44(4), 322–349. https://doi.org/10.1080/23808985.2020.1839939

28.

Kahnx

Ratner

Kahneman

(1997). Patterns of hedonic consumption over time. Marketing Letters, 8(1), 85–96. https://doi.org/10.1023/A:1007941428628

29.

Kaplan

(1995). The restorative benefits of nature: Toward an integrative framework. Journal of Environmental Psychology, 15(3), 169–182. https://doi.org/10.1016/0272-4944(95)90001-2

30.

Karuza Podgorelec

. (2020). Why binge-Watching? The prominent motives and analysis of the motivating hedonic and eudaimonic elements of emotional gratification in a binge-watching experience. Medijske Studije, 11(21), 3–23. https://doi.org/10.20901/ms.11.21.1

31.

Katz

Blumler

J. G.

Gurevitch

(1973). Uses and gratifications research. Public Opinion Quarterly, 37(4), 509. https://doi.org/10.1086/268109

32.

King

D. L.

Delfabbro

(2009). Understanding and assisting excessive players of video games: A community psychology perspective. Australian Community Psychologist, 21(1), 62–74.

33.

Koo

B. Y.

Jang

M. H.

Kim

Y. C.

Mah

K. C.

(2018). Changes in the subjective fatigue and pupil diameters induced by watching LED TVs. Optik, 164, 701–710. https://doi.org/10.1016/j.ijleo.2018.03.077

34.

Kuznetsova

Brockhoff

P. B.

Christensen

R. H. B.

(2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13), 1–26. https://doi.org/10.18637/jss.v082.i13

35.

Lang

(2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46–70. https://doi.org/10.1093/joc/50.1.46

36.

Lang

Kurita

Gao

Rubenking

(2013). Measuring television message complexity as available processing resources: Dimensions of information and cognitive load. Media Psychology, 16(2), 129–153. https://doi.org/10.1080/15213269.2013.764707

37.

Lang

Shin

Bradley

S. D.

Wang

Lee

Potter

(2005). Wait! Don’t turn that dial! More excitement to come! The effects of story length and production pacing in local television news on channel changing behavior and information processing in a free choice environment. Journal of Broadcasting & Electronic Media, 49(1), 3–22. https://doi.org/10.1207/s15506878jobem4901_2

38.

LaRose

(2010). The problem of media habits. Communication Theory, 20(2), 194–222. https://doi.org/10.1111/j.1468-2885.2010.01360.x

39.

Luhmann

Intelisano

(2018). Hedonic adaptation and the set point for subjective well-being. In Diener

Oishi

Tay

(Eds.), Handbook of well-being (p. 26). DEF Publishers.

40.

Lyubomirsky

(2011). Hedonic adaptation to positive and negative experiences. Oxford University Press.

41.

Makowski

Ben-Shachar

Patil

Lüdecke

(2020). Methods and algorithms for correlation analysis in R. Journal of Open Source Software, 5(51), 2306. https://doi.org/10.21105/joss.02306

42.

Matrix

(2014). The Netflix effect: Teens, binge watching, and on-demand digital media trends. Jeunesse Young People Texts Cultures, 6(1), 119–138. https://doi.org/10.1353/jeu.2014.0002

43.

Matthews

R. A.

Pineault

Hong

Y. H.

(2022). Normalizing the use of single-item measures: Validation of the single-item compendium for organizational psychology. Journal of Business and Psychology, 37, 639–673. https://doi.org/10.1007/s10869-022-09813-3

44.

Meier

Reinecke

Meltzer

C. E.

(2016). “Facebocrastination”? Predictors of using Facebook for procrastination and its effects on students’ well-being. Computers in Human Behavior, 64, 65–76. https://doi.org/10.1016/j.chb.2016.06.011

45.

Merrill

Jr Rubenking

(2019). Go long or go often: Influences on binge watching frequency and duration among college students. Social Science, 8(1), 10. https://doi.org/10.3390/socsci8010010

46.

Nelson

L. D.

Meyvis

(2008). Interrupted consumption: Disrupting adaptation to hedonic experiences. JMR, Journal of Marketing Research, 45(6), 654–664. https://doi.org/10.1509/jmkr.45.6.654

47.

Nelson

L. D.

Meyvis

Galak

(2009). Enhancing the television-viewing experience through commercial interruptions. Journal of Consumer Research, 36(2), 160–172. https://doi.org/10.1086/597030

48.

Oliver

M. B.

Raney

A. A.

(2011). Entertainment as pleasurable and meaningful: Identifying hedonic and eudaimonic motivations for entertainment consumption. Journal of Communication, 61(5), 984–1004. https://doi.org/10.1111/j.1460-2466.2011.01585.x

49.

Palmgreen

Wenner

L. A.

Rayburn

J. D.

(1980). Relations between gratifications sought and obtained: A study of television news. Communication Research, 7(2), 161–192. https://doi.org/10.1177/009365028000700202

50.

Perse

(1998). Implications of cognitive and affective involvement for channel changing. Journal of Communication, 48(3), 49–68. https://doi.org/10.1093/joc/48.3.49

51.

Potter

W. J.

(2009). Arguing for a general framework for mass media scholarship. Sage.

52.

R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

53.

Reinecke

(2009). Games and recovery: The use of video and computer games to recuperate from stress and strain. Journal of Media Psychology, 21(3), 126–142. https://doi.org/10.1027/1864-1105.21.3.126

54.

Reinecke

Eden

(2017). Media use and recreation: Media-induced recovery as a link between media exposure and well-being. In Reinecke

Oliver

M. B.

(Eds.), The Routledge handbook of media use and well-being. International perspectives on theory and research on positive media effects (pp. 106–117). Routledge.

55.

Reinecke

Hartmann

Eden

(2014). The guilty couch potato: The role of ego depletion in reducing recovery through media use. Journal of Communication, 64(4), 569–589. https://doi.org/10.1111/jcom.12107

56.

Reinecke

Hofmann

(2016). Slacking off or winding down? An experience sampling study on the drivers and consequences of media use for recovery versus procrastination. Human Communication Research, 42, 441–461. https://doi.org/10.1111/hcre.12082

57.

Reinecke

Meier

(2021). Media entertainment as guilty pleasure? In Klimmt

Vorderer

(Eds.), The Oxford handbook of entertainment theory (pp. 205–230). Oxford University Press.

58.

Rieger

Reinecke

Bente

(2017). Media-induced recovery: The effects of positive versus negative media stimuli on recovery experience, cognitive performance, and energetic arousal. Psychology of Popular Media Culture, 6, 174–191. https://doi.org/10.1037/ppm0000075

59.

Rieger

Reinecke

Frischlich

Bente

(2014). Media entertainment and well-being-linking hedonic and eudaimonic entertainment experience to media-induced recovery and vitality. Journal of Communication, 64, 456–478. https://doi.org/10.1111/jcom.12097

60.

Rubenking

Bracken

C. C.

(2018). Binge-watching: A suspenseful, emotional, habit. Communication Research Reports, 35(5), 381–391. https://doi.org/10.1080/08824096.2018.1525346

61.

Schnauber-Stockmann

Naab

T. K.

(2019). The process of forming a mobile media habit: Results of a longitudinal study in a real-world setting. Media Psychology, 22(5), 714–742. https://doi.org/10.1080/15213269.2018.1513850

62.

Schramm

Wirth

(2010). Exploring the paradox of sad-film enjoyment: The role of multiple appraisals and meta-appraisals. Poetics, 38(3), 319–335. https://doi.org/10.1016/j.poetic.2010.03.002

63.

Sherry

J. L.

(2004). Flow and media enjoyment. Communication Theory, 14(4), 328–347. https://doi.org/10.1111/j.1468-2885.2004.tb00318.x

64.

Sommet

Morselli

(2017). Keep calm and learn multilevel logistic modeling: A simplified three-step procedure using stata, R, mplus, and SPSS. International Review of Social Psychology, 30(1), 203–218. https://doi.org/10.5334/irsp.90

65.

Staats

Kieviet

Hartig

(2003). Where to recover from attentional fatigue: An expectancy-value analysis of environmental preference. Journal of Environmental Psychology, 23(2), 147–157. https://doi.org/10.1016/s0272-4944(02)00112-3

66.

Stoffel

M. A.

Nakagawa

Schielzeth

(2021). partR2: Partitioning R² in generalized linear mixed models. PeerJ, 9, e11414. https://doi.org/10.7717/peerj.11414

67.

Tal-Or

Cohen

(2010). Understanding audience involvement: Conceptualizing and manipulating identification and transportation. Poetics, 38(4), 402–418. https://doi.org/10.1016/j.poetic.2010.05.004

68.

Thorson

Reeves

Schleuder

(1985). Message complexity and attention to television. Communication Research, 12, 427–454. https://doi.org/10.1177/009365085012004001

69.

Tukachinsky

Eyal

(2018). The psychology of marathon television viewing: Antecedents and viewer involvement. Mass Communication & Society, 21(3), 275–295. https://doi.org/10.1080/15205436.2017.1422765

70.

van der Linden

. (2011). The urge to stop: The cognitive and biological nature of acute mental fatigue. In Ackerman

P. L.

(Ed.), Cognitive fatigue: Multidisciplinary perspectives on current research and future applications (pp. 149–160). APA Press.

71.

Vorderer

Klimmt

Ritterfeld

(2004). Enjoyment: At the heart of media entertainment. Communication Theory, 14(4), 388–408. https://doi.org/10.1111/j.1468-2885.2004.tb00321.x

72.

Wang

(2014). Bridging media processing and selective exposure: A dynamic motivational model of media choices and choice response time. Communication Research, 41(8), 1064–1087. https://doi.org/10.1177/0093650214534963

73.

Wirth

Schramm

(2005). Media and emotions. Communication Research Trends, 24(3), 3–39. http://cscc.scu.edu/trends/v24/v24_3.pdf

74.

Wonneberger

Schoenbach

van Meurs

(2009). Dynamics of individual television viewing behavior: Models, empirical evidence, and a research program. Communication Studies, 60(3), 235–252. https://doi.org/10.1080/10510970902955992

75.

Yangmei

Fan

M. O.

Xuhai

Hongda

Xuqun

(2018). The habituation of hedonic and eudaimonic affect. Acta Psychologica Sinica, 50(9), 985–996. https://doi.org/10.3724/SP.J.1041.2018.00985

76.

Yang

Galak

(2015). Sentimental value and its influence on hedonic adaptation. Journal of Personality and Social Psychology, 109(5), 767–790. https://doi.org/10.1037/pspa0000036

77.

Zillmann

(1988). Mood management through communication choices. American Behavioral Scientist, 31(3), 327–340. https://doi.org/10.1177/000276488031003005