Television and stress – fact or fiction?
How fictional and non-fictional series relate to
perceived levels of stress among emerging adults
Antonie Bassi Student number: 12480320
Master’s Thesis
Graduate School of Communication
Master’s Program Communication Science: Entertainment Communications Supervisor: Prof. dr. J. Peter
2
Abstract
The main objective of this study was to investigate whether consumption of specific content types of fictional and non-fictional television series would correlate to perceived stress levels among emerging adults. The sub-goal was to examine possible underlying mechanisms of this relationship. Two mediation mechanisms were taken into consideration, on the one hand involvement, which was hypothesized to negatively predict perceived stress, and on the other hand guilt, which was hypothesized to positively predict perceived stress. This was tested through a cross-sectional survey among emerging adults (N=242), who self-reported their average consumption of fictional series, non-fictional series and the respective resulting involvement and guilt, as well as their average perceived stress levels. A rigorous set of control variables was included to account for potential spurious relationships. Watching fictional series, as opposed to watching non-fictional series, was found to be positively correlated with perceived stress. Based on mood management theory, four specific content types and their mood-altering potential were analyzed. Overall, none of the tested dimensions of mood management theory were supported by the data. A positive correlation was found between non-fictional romance and guilt, suggesting that this feeling might arise after
content-specific consumption. The obtained findings are mixed but suggest that the realms of fiction and non-fiction do indeed differ. Fiction was found to relate to stress while non-fiction was found to relate to guilt. Possible implications of these findings are discussed and avenues for future research put forward.
3
Introduction
The “Quarter-life Crisis” is a term that has been introduced in rather recent years by the popular press (Robbins & Wilner, 2001) to describe the years of emerging adulthood, and even though it might be a bit extreme to define this period as a “crisis” (Rossi & Mebert, 2011), it is certainly a period characterized by a lot of uncertainties and stress. Emerging adults are just coming out of their teenage years and between the approximate ages of 18 to 29 (Arnett et al., 2014) they are trying to navigate their way into full adulthood. The endless possibilities that emerging adults are facing lead to feelings of instability and the perception of being in between – not identifying as a teenager anymore but not quite yet as an adult either (Arnett, 2000). It is, apart from infancy, the most active and complex stage on the personal, social, emotional, neuroanatomical, and developmental level (Wood et al., 2018). These are also the years in which individuals make important choices for their future and set a base for the rest of their adult life. Qualitative analysis of a group of university students showed that there are multiple aspects in emerging adults’ lives that cause them stress, and while some stated that they managed their stress effectively, others said it negatively affected both their mental and physical health (Peer et al., 2015). The arising question is how these young adults can cope with the regular stress that this period of life often brings with itself. Coping strategies for stress have been examined but have not always been found to have positive or universal outcomes (Bland et al., 2012). Therefore, it is important that emerging adults find an effective way to cope with stress so that they are not hindered in succeeding and further developing into fully independent adults. This study’s social relevance will lie in trying to find a possible solution to help emerging adults effectively manage stress by looking at television consumption.
An excessive use of television, as with any excess, can have harmful effects like unhealthy eating behaviors which lead to being overweight (Pearson et al., 2014), sleep
4
problems (Johnson et al., 2004) and body dissatisfaction (Hargreaves & Tiggemann, 2003). However, in more recent years, research started to focus on the benefits media use may have on aspects of well-being, with research looking at the effects of television, video-games and music consumption on salivary cortisol levels (Nabi et al., 2016), interactive and non-interactive media use on stress recovery (Reinecke et al., 2011) or hedonic and eudemonic entertainment on psychological well-being (Rieger et al., 2014). This study will try and add to this new body of research by investigating the positive effects television consumption specifically may have on individuals’ stress levels.
Nabi and colleagues’ study (2016) analyzed the effects of television consumption on both salivary cortisol and perceived stress and despite not finding consistent results for the main effect of television consumption, they did find some genre specific relationships, suggesting that the content type should be taken into consideration when investigating media effects on stress. This study’s theoretical relevance will therefore consist in extending this research by more specifically looking at content types in the fictional and non-fictional sphere.
The staggering increase in production that occurred in the last years in television series (Koblin, 2020) and the phenomenon of binge-watching, which is quite common among the age group of emerging adults (Richter, 2018), are the main reasons why this research will focus on the effects of television series only and not on movies.
Past research on enjoyment analyzed the different outcomes of fictional and reality-based programs, finding that respondents watched and enjoyed fiction more (Nabi et al., 2006). This study will further investigate the possibility that fictional and non-fictional programming more broadly, might have different effects in the realm of well-being and will do so by categorizing fiction and non-fiction in four main content types. The resulting
5
research question is: To what extent does content specific consumption of fictional and non-fictional television series relate to perceived stress levels among emerging adults?
Recent theoretical advancements, like the Differential Susceptibility to Media Effects Model (Valkenburg & Peter, 2013), highlight the importance of considering underlying mechanisms to be able to grasp a better understanding of the relationship at play. Consequently, the sub goal of this research will be to analyze two potential underlying mechanisms: involvement and guilt.
Theoretical framework
Understanding stress in emerging adulthood
The concept of emerging adulthood has been theorized in recent years as a period of life in between adolescence and adulthood which spans approximately between the ages of 18 and 29 (Arnett et al., 2014). In this developmental stage individuals are faced with quite a few challenges ranging from decisions regarding their living situation, their studies or jobs and romantic relationships. It is an extremely unstructured time, lacking both the structure given by family and school in childhood and the structure of a new family or a stable long-term work which characterizes adulthood (Arnett, 2007).
A large portion of emerging adults in that period of life find themselves pursuing some type of higher education. Studies on college students have shown that this period is often perceived as moderately to highly stressful (Pierceall & Keim, 2007) which in
combination with a behavior of avoidant coping can predict depressive symptoms (Dyson & Renk, 2006). Past research examined five main stress categories which most affected college students, namely academics, family relationships, finances, daily hassles and social
6
The effects that chronic stress has on the brain seem to be mostly reversible if the stress lasts weeks at a time, it is not clear though whether these changes can be reversible after putting a strain on the brain for months or years at a time (McEwen, 2008). It is therefore important that emerging adults find a way to cope with stress in this stage of life since it is a period in which they have to face important choices that will significantly influence their years to come.
Given that stress is a negative mood state, one of the possible techniques to reverse this state might be mood enhancement. The well-known experiment by Bryant and Zillmann (1984) revealed that bored individuals were drawn to more exciting television programs while the results for stressed individuals were not as clear, as they selected a comparable amount of both relaxing and exciting programs.
Mood Management Theory
When it comes to enhancing mood, reducing stress and relaxation through media use, there is one theory that describes these processes best: Mood Management Theory. The basic and main assumption of the theory is that individuals will selectively expose themselves to media messages capable of altering their negative mood states (Zillmann, 1988). The theory puts forward four main dimensions that distinguish media messages and their mood-altering potential: the excitatory potential, the absorption potential, the hedonic valence and the semantic affinity. Semantic affinity occurs when the content of media messages strongly relates to the negative ongoing mood and is unable to modify the affective state compared to messages with a lower semantic affinity (Zillmann, 1988). As previously illustrated, stress in emerging adults’ lives can be caused by multiple factors (school, finances, family
relationships, and personal relationships) which most likely will vary. This study will rely on self-report measures of specific content consumption of the previous month. To be able to
7
check for semantic affinity a whole new set of questions would need to be developed to identify specific causes of stress, therefore for a matter of feasibility the dimension of semantic affinity will not be investigated.
The absorption potential as it has been theorized by Zillmann (1998) states that moods are effectively improved by strong stimulus intervention. Highly absorbing media messages will lead to a cognitive elaboration of these messages, which will suppress the thoughts that induced the negative mood (Reinecke, 2016). If viewers get involved in a program, the attention will shift from what caused the negative mood to whatever they are watching and therefore as a result alter the mood.
The mood-altering potential of hedonic valence results from the more positive tone of media content. A negative state, like stress, will therefore be more likely to be altered by a program with a positive hedonic valence like comedy, than by a negatively valenced one (Zillmann, 1988). Humorous content is the most hedonic form of entertainment media and experimental studies have shown that exposure to comedy decreases stress levels (Toda & Ichikawa, 2012). Comedy as a TV genre is also one of the most popular next to drama (Watson, 2019; Tivo, 2019), therefore fictional and non-fictional humor will be one of the content types analyzed in this context.
Emerging adults have a greater interest in viewing dark, creepy or violent content (Mares et al., 2008), therefore, two of the main content types that will be analyzed are fictional and non-fictional suspense and violence. The excitatory potential has the ability to modify the intensity of moods. Those moods characterized by high excitatory potential, like stress, are best modified by media with a lower excitatory potential (Zillmann, 1998).
8
In television, as well as in written texts, fiction and non-fiction are two separate realms. Fiction can be defined as an imaginative production which isn't necessarily true, even though the surroundings of the story and characters resemble reality (Green et al., 2004). Non-fictional works on the other hand are about facts and real events (Merriam-Webster, n.d.).
Oatley advanced the claim that “fiction might be twice as true as fact" (1999, p. 101), meaning that when a reader is emotionally moved by the fiction consumed, personal insights might occur. This may be interpreted in the sense that individuals might identify more with a fictional story than a non-fictional one, when the narrative creates a feeling of relatedness with one's own problems. Fictional series like "How I Met Your Mother" might be based on fictitious characters and stories, but often depict common problems which might give a strong sense of relatedness.
A study on the effects of non-fictional texts, looked at how students developed
feelings of empathy when reading first-person essays about being bullied (Ansbach, 2012). In this context, the students could presumably relate to these stories as they might have
experienced similar situations, which goes to show that a personal closeness might elicit certain feelings or emotions. When looking at non-fictional television content types however, what is depicted is often somehow further away from everyday life. An example of this might be a serie like "The Bachelor" where a single man tries to find true love by having a group of participants compete to prove they are the best fit for him, which is an unlikely scenario in an emerging adult’s real life. Furthermore, documentary series like "I Am a Killer", featuring stories of prisoners sentenced to death in the United States, will most likely have no overlap with the everyday struggles of an emerging adult.
9
Further differences between fiction and non-fiction have been found in written texts. Positive predictors of fiction in written texts, compared to non-fiction, include social ability, given from empathizing and understanding characters in fictional worlds which then is translated to peer relationships in real life (Mar et al., 2006). This has been found to occur not only in literary fiction but in television fiction as well, showing that independently of the medium, fiction may aid one’s ability to understand others’ minds (Black & Barnes, 2015).
Research on the relationship between television viewing and stress dates back as far as 1959, when Pearlin found that the escapist use of television served as relief from daily strain and unpleasant experiences. In more recent years, a study on reality and fictional programming has shown that fictional programming evoked more positive emotions compared to reality programming, but also that reality programs differed from one another, suggesting that maybe a broader classification of non-fictional content might yield different results (Nabi et al., 2006).
Qualitative research on fiction found that interviewees liked talking about their favorite programs with their peers and that interviewees tried to adjust and shape aspects of the programs to their daily life (Lacalle, 2012). Further reception analysis by Lacalle (2015) on a group of teenagers and young adults, examining the modes of reception of television fiction, found that it was often seen as a fun way to prevent boredom and escape
responsibilities as well as serving as inspiration for facing problems and personal issues.
The distinction between fiction and non-fiction is not often made in research on television, but the two content types are rather analyzed separately resulting in a body of research focusing particularly on reality television (e.g.: Papacharissi & Mendelson, 2007; Reiss & Wiltz, 2004) and some research analyzing specifically fictional content (e.g.: Lacalle, 2015).
10
A different conceptualization is therefore being proposed by analyzing content types respectively for fictional and non-fictional content. Given that television consumption has been associated with stress reduction before (Nabi et al., 2016) it is primarily hypothesized that:
H1a: Fictional and non-fictional content will both be negatively associated with perceived levels of stress.
Considering the positive effects of fictional content compared to non-fiction found in previous research, the following is further hypothesized:
H1b: Fictional content will have a stronger association with perceived levels of stress compared to non-fiction.
The focus of this research will not be on genre, but on content types of fictional and non-fictional television series. Four specific content types have been selected in accordance to emerging adults’ preferences as well as popularity, namely: violence, humor, suspense and romance. These may overlap in certain programs but there usually is the prevalence of one of the content types. Also given that fiction has also been defined a genre (Friend, 2015) it seems that investigating content types may be less problematic.
As already mentioned, according to the hedonic valence of content theorized by Zillmann (1988), it is expected that humorous content will be associated with decreased stress. The following is thus hypothesized:
H2a: Compared to other fictional content types, fictional humorous content will have the strongest negative association with perceived stress levels.
H2b: Compared to other non-fictional content types, non-fictional humorous content will have the strongest negative association with perceived stress levels.
11
The excitatory potential on the other hand predicts higher levels of stress for content types like violence and suspense.
H3a: Fictional violence (1) and suspense (2) will have the strongest positive association with perceived stress levels, compared to other fictional content types.
H3b: Non-fictional violence (1) and suspense (2) will have the strongest positive association with perceived stress levels, compared to other non-fictional content types.
To the best of my knowledge, there is a lack of research on the different effects fictional or non-fictional content types, specifically humor, violence and suspense, may have in the realm of well-being and specifically stress. Accordingly, the following sub research questions are being proposed:
SubRQ1: How do fictional and non-fictional humorous content consumption compare in relation to perceived levels of stress?
SubRQ2: How do fictional and non-fictional violence (1) and suspense (2) content consumption compare in relation to perceived levels of stress?
Involvement
Involvement might be a possible underlying mechanism in the relationship between fiction and stress, considering the experience of being involved in a narrative has been shown to influence feelings of enjoyment (Green et al., 2004).
The concept of involvement seems to be highly connected to the entertainment experience (Wirth, 2006) and is usually described as "more elaborative, self-determined, active, and in-depth acting with and processing of media content" (Wirth, 2008).
12
Television watching has been linked to numerous aspects of involvement like
transportation into the narrative (e.g. Greenwood, 2008), parasocial relationships (e.g. Cole & Leets, 1999) and identification (e.g. Chory, 2013).
With specific regards to television series, a study on the phenomena of binge-watching found that viewers who engage in this activity compared to traditional viewers (watching episodes with weekly intervals) had enhanced parasocial relationships with the characters, while identification and transportation occurred both in long and short media exposure periods (Tukachinsky & Eyal, 2018).
H4a: Fiction and non-fiction will both be positively associated with involvement.
Consuming fictional horror content with an escapist motivation has been shown to predict both cognitive and affective involvement (Lin & Xu, 2017). Furthermore, a study on the role of sadness in movies, found that a fictional tragic movie induced sadness, which proved to be a positive predictor for involvement which, in turn, predicted enjoyment of the movie (Ahn et al., 2012). Studies on reality TV have found that these program types can lead to involvement (Hall, 2009) but there is a lack of literature on the potential involvement with other non-fictional content types.
It can thus be argued that one of the potentials of fiction lies in being able to evoke emotions (e.g.: Oatley, 2002; Sperduti et al., 2016), therefore it would be plausible to assume that fictional series might create a higher involvement compared to non-fictional series. It is therefore hypothesized, that:
H4b: Fiction will lead to higher levels of involvement compared to non-fiction.
Watching television seems to be a more effective way to diminish stress, compared to other alternative activities, due to its potential of involving and absorbing viewers (Zillmann, 1991). It has also been theorized that involvement plays a role in viewers’ consideration of
13
whether to continue watching a program but is not a decisive aspect in choosing what to watch (Vorderer, 1991). Additionally, according to the absorption potential, highly absorbing messages should effectively improve negative moods. It is therefore further hypothesized, that:
H4c: Involvement with fiction, compared to non-fiction, will be associated with lower levels of perceived stress.
Guilt
In the context of media use and more specifically television use, guilt is not an uncommon emotion experienced by viewers (Panek, 2014). This feeling of guilt might consequently negatively affect well-being and specifically perceived stress levels; thus, the mediating role of guilt will be analyzed.
Guilt is defined as a “self-conscious emotion characterized by a painful appraisal of having done (or thought) something that is wrong” (American Psychological Association, n.d.). The feeling can arise as a consequence of perceiving media use as a procrastinatory activity and can lead to an ineffective recovery experience that could otherwise be achieved through media use (Reinecke et al., 2014). Procrastination has been linked to short term mood and emotion regulation (Sirois & Pychyl, 2013), as well as being associated with feelings of guilt (Blunt & Pychyl, 2005). Feelings of guilt in the context of media use are often also associated with perceived lack of self-control. The inability to self-control one’s media use results in perceiving it as an activity which conflicts with other tasks and
responsibilities, turning it therefore into a "guilty pleasure" activity and causing negative emotions (Hofmann et al., 2017). Furthermore, a study on college students' binge-watching behaviors found that guilt is commonly experienced after watching too many television series
14
(Vaterlaus et al., 2019). Given the association between television series watching and guilt, the following is hypothesized:
H5a: Both fiction and non-fiction will be positively associated with guilt.
Emerging adults are in a period of life exploration and one central aspect is that of romantic relationships, which compared to adolescence involve a deeper degree of intimacy (Arnett, 2000), thus fictional and non-fictional romance will be the fourth content type analyzed. Interpersonal relationships have also been shown to be a cause of stress for emerging adults (e.g. Chow & Ruhl, 2014; Nieder & Krenke, 2001).
Qualitative analysis on why individuals watch what they themselves define as "bad" and "trash" television and how they manage the normative contradiction they experience from it, found that one way was to frame their viewing as a “guilty pleasure” (McCoy & Scarborough, 2014). Most of the interviewees of this former study identified reality television as their guilty pleasure, but specifically the viewing of romance reality is often seen as
something to be ashamed of (Albertini, 2003). Romantic reality TV-shows like "The Bachelor" are ubiquitously acknowledged as “guilty-pleasure” programs (Higgs, 2019). Furthermore, a study on daily life guilt found it to be positively correlated with aversive arousal states (Baumeister et al., 1995).
Given the fact that romantic non-fiction is the only content type which contains mainly reality tv shows in addition to the general negative view held of romance reality shows, it is hypothesized that:
H5b: Non-fictional romance will have the strongest positive association with guilt, compared to all other content types.
Previous research on media and stress found a significant correlation between romance-oriented media and perceived stress (Nabi et al., 2016). Considering that guilt is a
15
negative emotion and its association with ineffective recovery experiences (Reinecke et al., 2014), the following is hypothesized:
H5c: Guilt from non-fictional romance, compared to guilt from other content types, will be associated with higher levels of perceived stress.
Control variables
A series of other variables were measured to account for potential spurious relationships between the key concepts of this study. Emerging adults are not faced with many responsibilities that come with adulthood such as full-time employment and children, and are often students, therefore these three aspects will be controlled for among participants. With regards to stress another factor that might alleviate this negative state is physical
exercise (Hamer et al., 2012). Frequent social media use (van der Schuur et al., 2019) as well as heavy television consumption (Frey et al., 2007) also have been associated with well-being and stress.
It’s also important to note that this study has been carried out during the worldwide pandemic of Coronavirus, which might have affected the general well-being of participants. Accordingly, two spheres of well-being in addition to perceived stress were measured, namely: loneliness, depression and anxiety. Additionally, respondents were asked to what extent the Coronavirus pandemic impacted their usual levels of stress.
Method
Design
The scope of this research was to compare specific fictional and non-fictional content types with levels of stress within the specific target group of emerging adults. Due to matters of time and feasibility the best suited method was deemed to be a cross-sectional survey.
16
Firstly, on the grounds that a higher number of concepts could be measured and given the rather delicate nature of some of the concepts (e.g.: perceived stress, loneliness) it was deemed easier for participants to answer these in a private setting.Secondly, a larger sample size could be obtained through a survey, which is valuable as it is a better estimate of the population and will reduce sampling error (Field, 2018). While mood management theory is predominantly investigated through experimental designs, it has also been successfully tested through survey studies (e.g.: Meadowcroft & Zillmann, 1987). Given the lack of existing literature on the comparison of fictional and non-fictional content, a preliminary correlational study was deemed more valuable to explore first possible relationships between the variables, before making a causal inference. Additionally, this type of method yields higher external validity (Bryman, 2012).
Procedure
The survey was programmed in Qualtrics (https://www.qualtrics.com/) and data was collected between 20th April and 11th May. Through a link the survey was distributed on
social media platforms as well as the survey exchange platform Surveyswap
(https://surveyswap.io/). A convenience sampling method was therefore used, since a
probability sampling technique was not feasible due to time constraints and accessibility to a larger population. Given that the target audience was emerging adults a filter question was implemented after the consent form asking for participants age.
Participants
Online data collection resulted in 260 responses; however, 15 participants were excluded from the research due to not having completed the survey and three participants were excluded due to not meeting the targets audience age. The classic definition of emerging adults does tend to describe individuals who do not have to deal with the responsibilities of
17
adulthood like children or a full-time employment and suggests they are often still students. To not exclude any participants, a set of analyses was run to assess whether participants, which met the above outlined characteristics, would significantly differ in terms of stress levels and consumption of fiction and non-fiction. The results of these analyses are not reported here given that they are not central to the research, but no statistically significant differences were found and no further participants were excluded.
The final sample of 242 participants had an age range between 19 and 29 years old (M=23.68, SD=2.31) and a skewed gender distribution (71.9% female, 27.3% male, 0.8% preferred not to disclose gender). The sample seemed to be highly educated, with 54.5% already having completed a Bachelor’s degree. With regards to country of origin, 26% of participants indicated they were from the Netherlands, 13.2% from Italy and 12% from Germany.
Measurements
Fictional and non-fictional series
Fictional and non-fictional series consumption was assessed based on a self-report of four main content types: violence, comedy, suspense and romance. For each content type there were three examples of specific series (e.g.: Narcos for fictional violence, The Bachelor for non-fictional romance. A complete overview of all series can be found in Appendix A.) which were chosen based on three main criteria. Firstly, the IMDb database
(https://www.imdb.com/) to assess content type and most viewed series, secondly the Kijkwijzer (https://www.kijkwijzer.nl/), which is a Dutch content rating system, to asses to what extent the programs contained violence or fear (for the categories of violence and suspense) and thirdly the release year was considered to not include outdated series. The answer categories ranged from 1 (Never) to 5 (Very Often). In order to test some of the
18
hypotheses a mean score was created for fictional (M=2.94, SD=0.72) and for non-fictional (M=2.36, SD=0.79) content. Given that the use of fiction and non-fiction are both manifest and not latent concepts, no factor or reliability analysis were performed. This has been done before, for instance to group the viewing frequency of TV programs with horror content (Lin & Xu, 2017) as well as the use of different social media platforms (Panek, 2014).
Cognitive Involvement
The extent to which participants felt cognitively involved with fictional and non-fictional content was assessed through the 5-item elaboration scale from Perse (1990). One of the items was removed given the unlikelihood of it applying to the target audience of emerging adults (“… I thought about what the program meant to me and my family.”). The answer categories ranged from 5 (Strongly agree) to 1 (Strongly disagree). The concept was measured for both fiction and non-fiction. Two separate principal axis factoring (PAF) analyses were carried out. Analysis on cognitive involvement with fiction revealed that all items scored on one component which held an eigenvalue of 1.57 which explained 52.41% of the variance. The scale proved a rather low reliability (α=.54) but given no possibility of improvement by deleting any of the items, a mean score was created nonetheless (M=3.22, SD=0.75). PAF analysis on cognitive involvement with non-fiction revealed one component (eigenvalue 2.06) which explained 68.70% of the variance. The scale proved to be
sufficiently reliable and a mean score was created (M=3.19, SD=0.89, α=.77).
Guilt
Guilt was measured with an adaptation of the state shame and guilt scale (Marschall et al., 1994) already used in another study (Reinecke et al., 2014) which proved high reliability. The concept was measured both for fiction and non-fiction on a scale from Very Often (5) to Never (1). PAF analysis on guilt and fiction showed all items scored on one component
19
(eigenvalue 3.25) which explained 64.91% of the variance. The scale had a good reliability, therefore an average variable for guilt for fiction was created (M=1.72, SD=0.71, α=.86).
The second PAF analysis for non-fiction revealed that all 5 items scored on one component which had an eigenvalue of 3.52 which explained 70.48% of the variance. The reliability of the scale was good and a mean score was created for guilt after non-fiction (M=1.83, SD=0.85, α=.89).
Perceived stress
The concept of perceived levels of stress was measured with the PSS-4 scale (Cohen et al., 1983) with response categories ranging from 1 (Never) to 5 (Very Often). Principal axis factor analysis showed that the 4 items formed a uni-dimensional scale with one component yielding an Eigenvalue of 2.09 which explained 52.30% of the variance and a clear inflexion point in the scree plot after the first component. After reverse coding two of the items to have all items be negatively framed, the scale proved to be reasonably reliable and an average perceived stress scale was created with values between 1 and 5 (M=2.81, SD=0.65, α=.69).
Control variables
To control for loneliness the UCLA 3-item loneliness scale was used (Hughes et al., 2004) with answer categories ranging from “Hardly ever” (1) to “Often” (3). PAF analysis was performed and the three items scored on one factor which had an eigenvalue above 1 (eigenvalue 2.10) which explained 69.49% of the variance. The reliability of the scale was sufficient, and the three items were added together, as suggested by the creators of the scale, to create a possible range of scores from 3 to 9 (M=5.54, SD=1.75, α=.78).
Depression and anxiety were measured with the PHQ 4-item depression and anxiety scale (Kroenke et al., 2009) with response categories ranging from “Not at all” (0) to “Nearly
20
every day” (3). All items scored on one component with an eigenvalue of 2.64 which explained 65.95% of the variance, as shown by PAF analysis. The items formed a good reliability and were therefore added up into one variable ranging from 0 to 12 (M=4.44, SD=2.84, α=.83).
Physical exercise was measured with a single question (“In the past month, how often on the average did you do intense physical exercise for 20 min or more?”) on a 4-item scale (1 = Not at all, 4 = 3 or more times a week; M=3.14, SD=0.99).
Television consumption (“How many hours a day do you watch television, or videos in your leisure time?”) was measured with two items on a 6-point scale (1= None, 6= 4 or more hours) asking participants how much time they spend watching television on an average weekday and on an average weekend-day. The items for weekday were multiplied by five and the weekend-day by two, added together and then divided by seven to create an average daily television consumption variable ranging from 1 to 6 (M=3.71, SD=1.15).
Social media use was measured for five main platforms (Facebook, Instagram, YouTube, Snapchat and Twitter) on a scale ranging from “Never” (1) to “6 times or more” (5) which was averaged into a general social media usage variable (M=2.47, SD=0.68).
The impact of coronavirus, specifically on stress, was measured with one item (“What impact did the Coronavirus Pandemic have on your life, in terms of stress?”) with response categories ranging from “Extremely good” (1) to “Extremely bad” (5). Out of all the
participants, 52.8% indicated that it had a negative impact on their stress, 17.8% said it had a positive impact and 29.3% indicated a neither good nor bad impact (M=3.43, SD=0.98)
Data analysis
To test the hypotheses in this study correlational analyses and multiple regression analyses were carried out. Before testing the hypotheses, a correlation analysis was
21
performed to test the influence of six control variables on the key variables (perceived stress, fiction and non-fiction) to see if there were any significant relationships. Almost all of the control variables significantly correlated with perceived stress (Table 1), while only a few significant correlations appeared for fiction and even less for non-fiction. Given the significant correlations all control variables were included in the analysis.
Table 1 – Correlations between key and control variables
1 2 3 4 5 6 7 8 9 1 Perceived stress 1 2 Fiction ,144* 1 3 Non-fiction -,003 ,396** 1 4 Physical exercise -,190** ,083 ,119 1 5 Coronavirus ,245** ,110 ,000 -,022 1 6 TV consumption ,224** ,171** ,051 -,209** ,102 1 7 Social media -,048 ,131* ,209** -,020 ,025 ,259** 1 8 Loneliness ,446** ,020 ,096 -,137* ,100 ,153* ,074 1 9 PHQ4 ,628** ,075 ,047 -,153* ,313** ,152* ,043 ,487** 1
*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
Results
Fiction, non-fiction and perceived stress
H1a and H1b hypothesized that both fiction and non-fiction would be associated with lower perceived levels of stress, with fiction having a stronger effect than non-fiction. The initial correlation analyses already revealed that fiction was significantly correlated with perceived stress and that non-fiction wasn’t (Table 1; column 1, row 2 and 3). A multiple regression analysis showed that fiction significantly predicted an increase in perceived stress while non-fiction didn’t yield significant results (Table 2, column 1). Both hypotheses are therefore rejected.
22
Table 2 – Regression analyses on perceived stress, involvement and guilt.
Dependent
variable Perceived stress Involvement Guilt
Hypotheses tested
H1a, H1b H2a, H3a H2b, H3b SubRQ1 SubRQ2 H4a, H4b H5a H5b
Column 1 2 3 4 5 6 8 9 N = 242 B SE (B) B SE (B) B SE (B) B SE (B) B SE (B) B SE (B) B SE (B) B SE (B) Fiction .11* .05 .20** .07 .10 .06 R2 .07* .08** Non-fiction -.06 .04 .46*** .07 .24** .07 R2 .47*** .24*** .11*** F. violence .02 .03 .01 .03 .03 .04 F. humor .02 .03 .04 .03 -.02 .04 F. romance .02 .02 .05 .03 F. suspense .04 .03 .04 .03 .03 .04 R2 .46*** .09* NF. violence .05 .03 .04 .03 .07 .06 NF. humor -.01 .03 -.03 .03 .03 .04 NF. romance -.04 .03 .08* .04 NF. suspense -.01 .03 -.03 .03 .08 .05 R2 .47*** .46*** .47*** .06**
Note: All models were controlled for TV consumption, Social media use, physical exercise, coronavirus impact, loneliness and depression and anxiety. Pertinent regression coefficients are not displayed for legibility reasons. F. = fictional, NF. = non-fictional *p < .05. **p <.01. ***p<.001 (two-tailed).
23
The second and third set of hypotheses (H2a – H3b) suggested that specific content types would have a higher or lower association with perceived stress. Sub research questions 1 and 2 meant to explore the possibility whether these differences would have a stronger effect for fictional or non-fictional content. Regression analyses revealed no significant predictors for fictional or non-fictional content, therefore H2a, H3a (Table 2, column 2), H2b and H3b (Table 2, column 3) are rejected. Sub research question 1 revealed no significant predictors for stress (Table 2, column 4) and also sub research question 2 had no significant predictors for stress (Table 2, column 5).
Fiction, non-fiction, involvement and perceived stress
Hypothesis H4a indicated that both fiction and non-fiction would be positively
associated with involvement, while H4b suggested a stronger association for fiction than non-fiction. As already mentioned, involvement was measured twice, once for involvement with fiction and once for non-fiction. Both correlation and regression analyses yielded significant results, revealing a positive association for involvement with fiction (r(240) = .23 p < .001; Table 2, column 6) and involvement with non-fiction (r(240) = .44, p < .001; Table 2, column 6), accordingly H4a will be accepted, while H4b will be rejected given that
involvement with non-fiction seemed to be stronger.
H4c was a mediation hypothesis tested with Andrew Hayes PROCESS extension for SPSS. Neither the direct effect of fiction (b= .08, SE= .05 p = .067), nor the effect of
involvement (b= .03, SE=.04 p = .438) on stress were significant. Fiction did however significantly predict involvement (b= .20, SE= .07 p = .003). No significant mediation effect was found in the model (b= .007, CI 95% [-0.01; 0.03]) leading to the hypothesis being rejected.
24
H5a hypothesized that both fiction and non-fiction would be positively associated with guilt, with H5b further suggesting that non-fictional romance would be the content type with the highest levels of guilt compared to other content types. Guilt was measured
separately for fiction and non-fiction and the analysis showed only a significant association for non-fiction (r(240) = .24, p < .001; Table 2, column 8) and not for fiction (r(240) = .07, p = .312; Table 2, column 8), meaning H5a was only supported for non-fiction.
Regression analysis on both fictional and non-fictional content types revealed non-fictional romance as the only significant predictor of guilt (Table 2, column 9), therefore H5b will be only partially supported, as the other predictors were not significant (Table 2, column 9).
H5c was a mediation hypothesis and was tested with PROCESS. The analysis (5.000 bootstrap samples) showed that non-fictional romance significantly predicted feelings of guilt (b= .10, SE = .39 p =.015). Neither non-fictional romance (b= -.04, SE = .02 p =.10) nor guilt (b= .03, SE = .04 p =.456) significantly predicted stress. Unsurprisingly, no significant mediation was found (b= .003., CI 95% [-0.005; 0.01]), thus the hypothesis was rejected.
Discussion
The goal of this study was to extend the current research on the different relationship fictional and non-fictional content types might have with levels of stress in emerging
adulthood. Thus far, studies have shown that media use can have positive effects on well-being (e.g. Nabi et al., 2016; Reinecke et al., 2011; Rieger et al., 2014). Additionally, fictional programming has been linked to experiencing more positive emotions (Nabi et al., 2006) and also serving as inspiration for solving personal problems (Lacalle, 2015). The more specific difference between fictional and non-fictional content and their relationship to perceived stress remains unclear, which this study aimed to explore.
25
The results obtained are mixed, as only some of the hypotheses were supported. One of the findings was that fiction significantly predicted an increase in perceived stress levels. Given that this study has been carried out while the coronavirus pandemic was at its peak in Europe as well as in other countries, these results might be somehow influenced by this situation. When normal life came to a stand and everybody had to cope with the stress that the pandemic brought with it, a lot of people turned to entertainment media (Koeze & Popper, 2020), possibly to find relief from boredom and stress. More than half of the respondents (52.8%) of this survey indicated that the pandemic had a negative or somewhat negative impact on their usual stress levels. But, given that this is a correlational study, it might also be plausible to assume that the higher levels of stress caused by the coronavirus pandemic might have increased the consumption of fictional content, but not of non-fictional content, simply because it served as a better escape, explaining why one was related to stress while the other was not.
In any case, given that fictional content was a significant predictor of stress and non-fiction was not, this still reveals that there might be a difference between the two concepts. While this research started to explore the possible differences between fictional and non-fictional television content, further qualitative analyses should explore the differences in perception, and further experimental studies should be done to try and establish a causal relationship between the concepts of fiction and non-fiction and stress.
None of the assumptions of mood management theory were confirmed by the data. Mood management theory in the past has not always been confirmed by empirical evidence (e.g.: Mares & Cantor, 1992) so this study is not in itself an exception. In response to these mixed results, some theoretical extensions have been proposed over the years. The two most plausible explanations in this case are individual differences and emotional utility (Reinecke, 2016). Individual differences could be gender-specific content preferences (Zillmann, 2000)
26
as well as personal characteristics or personality traits, which could influence the predilection for certain types of content and therefore play a role in the mood-altering process. Emotional utility has been analysed through the concept of meta-emotions to clarify the emotional benefit of sad media content (Oliver, 1993). This directly contradicts the premises of mood management, which posits that hedonic forms of media content are best at alleviating negative moods. Another possible explanation might be that many hedonic humorous series tend to have a light and rather uncomplex narrative which might not be sufficiently involving to contrast a negative mood state, while a more complex narrative would require more
attention and this shift in focus might then be more effective in lowering stress levels. This latter interpretation would however need to be tested in future research.
In addition to the direct relationship between fiction and non-fiction and perceived stress, the second goal of this study was to identify underlying mechanisms to this
relationship. The hypothesized underlying mechanism of involvement between fiction and perceived stress was not supported by the data. The concept of involvement has been analyzed by various reviews (e.g. Zaichkowsky, 1986; Wirth, 2006) but there is still no universally accepted definition. The concept is also often analyzed jointly with viewing motives. The reason for not analyzing whether the viewing motives were ritualistic or
instrumental, was due to this study looking at TV-series, which would have made difficult the distinction between these two concepts. Future research may need to include other television formats like movies and take into consideration different conceptualizations of involvement and eventually also viewing motivations.
The second underlying effect analyzed in this study was feelings of guilt following the viewing of fictional and non-fictional content. Experiences of guilt after the consumption of entertaining media use has been shown to negatively relate to recovery experiences (Reinecke et al., 2014). This research however only found non-fiction to be a positive significant
27
predictor of guilt, meaning that the consumption of non-fiction predicted an increase in feelings of guilt. This is an unexpected but interesting finding, which should be addressed further in future research to better understand what specific aspects of non-fictional content lead to feelings of guilt. Specifically, the category of non-fictional romance, which was mainly romantic reality television programs, proved to be the only significant predictor of guilt when analyzing the content types individually. This confirms how reality television is often seen as a “guilty pleasure” (McCoy & Scarborough, 2014). The fact that this specific content type has been associated before with aversive arousal states (Baumeister et al., 1995) as well as with perceived stress (Nabi et al., 2016) and now has been linked specifically to feelings of guilt goes to show that it affects various realms of well-being. The extent to which these aspects are correlated to each other and the further implications that derive from this should be further explored. Nevertheless, the mediation of guilt did not prove to significantly predict perceived levels of stress. Yet, once more the fact that only non-fiction was a
significant predictor and fiction wasn’t, demonstrates that the two concepts can lead to different outcomes.
Lastly this study implemented a rather rigorous set of non-media control variables (e.g. loneliness, physical exercise) which might simply have a stronger relationship with perceived stress than media related aspects.
Limitations and future research
First and foremost, it is important to note that this research was carried out during the unusual circumstances of the Coronavirus pandemic which implies that the research results might be somewhat influenced by this, in this research specifically by increasing levels of stress among more than half of the respondents. A control variable has been implemented to
28
account for this, but a replication of this study in less unique circumstances might therefore lead to distinct results.
Apart from this external factor, another limitation may lie in the specificity of this study in analyzing only television series and in only focusing on four main content types. To be able to further generalize the effects of fiction and non-fiction, other types of content and genres have to be examined, as well as perhaps extending the focus also on movies. Other content types have been linked to aspects of well-being before, like sports (Phua, 2010), crime (Hollis et al., 2017) and specific reality-television programs, like surgery makeover programs (Mazzeo et al., 2007) or weight loss programs (Bourn et al., 2015). The depiction of these conceptualized as fictional and non-fictional might reveal a difference in terms of reception and effects.
Another limitation has to be mentioned with regards to hypothesis H5b as it tested the specific relationship between non-fictional romance content and guilt. However, feelings of guilt were measured in general for all non-fictional content, meaning that the results of this hypothesis have to be interpreted with caution.
It is possible that the number of respondents (N=242) was not quite sufficient in terms of statistical power to generate significant results, future research replicating this study should therefore consider sampling a larger number of respondents.
A further issue, and something that should be addressed in future research, is that participants were not asked how they consume each content type. The rise of binge-watching behaviours, which have been linked both to positive and negative outcomes on well-being (Granow et al., 2018), might give further insight into how fiction and non-fiction might differ.
29
Finally, given that this study was correlational, no causal inferences can be drawn, meaning that the relationship between fiction and stress may also be reversed, suggesting that more stressed individuals might be more inclined to watch more fiction.
30
Reference list
Ahn, D., Jin, S. A. A., & Ritterfeld, U. (2012). ”Sad movies don’t always make me cry” The cognitive and affective processes underpinning enjoyment of tragedy. Journal of Media Psychology, 24(1), 9–18. https://doi.org/10.1027/1864-1105/a000058
Albertini, B. (2003). So wrong it’s right: the guilty pleasure of reality television. Iris: A Journal About Women, (47).
American Psychological Association. (n.d.). Guilt. In APA dictionary of psychology. Retrieved May 3, 2020, from https://dictionary.apa.org/guilt
Ansbach, J. (2012). Long-term effects of bullying: Promoting empathy with nonfiction. English Journal, 101(6), 87-92.
Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469–
480. https://doi.org/10.1037/0003-066X.55.5.469
Arnett, J. J. (2007). Suffering, selfish, slackers? Myths and reality about emerging adults. Journal of youth and adolescence, 36(1), 23-29.
https://doi.org/10.1007/s10964-006-9157-z
Arnett, J. J., Žukauskienė, R., & Sugimura, K. (2014). The new life stage of emerging adulthood at ages 18–29 years: Implications for mental health. The Lancet Psychiatry, 1(7), 569-576. https://doi.org/10.1016/S2215-0366(14)00080-7
Baumeister, R. F., Reis, H. T., & Delespaul, P. A. E. G. (1995). Subjective and experiential correlates of guilt in daily life. Personality and Social Psychology Bulletin, 21(12), 1256–1268. https://doi.org/10.1177/01461672952112002
31
Black, J., & Barnes, J. L. (2015). Fiction and Social Cognition: The Effect of Viewing Award-Winning Television Dramas on Theory of Mind. Psychology of Aesthetics, Creativity, and the Arts, 9(4), 423–429. http://dx.doi.org/10.1037/aca0000031
Bland, H. W., Melton, B. F., Welle, P., & Bigham, L. (2012). Stress Tolerance: New
Challenges for Millennial College Students. College Student Journal, 46(2), 362–375.
Blunt, A., & Pychyl, T. A. (2005). Project systems of procrastinators: A personal project-analytic and action control perspective. Personality and Individual Differences, 38(8), 1771–1780. https://doi.org/10.1016/j.paid.2004.11.019
Bourn, R., Prichard, I., Hutchinson, A. D., & Wilson, C. (2015). Watching reality weight loss TV. The effects on body satisfaction, mood, and snack food consumption. Appetite, 9, 351-356. https://doi.org/10.1016/j.appet.2015.04.070
Brougham, R. R., Zail, C. M., Mendoza, C. M., & Miller, J. R. (2009). Stress, sex
differences, and coping strategies among college students. Current psychology, 28(2), 85-97. https://doi.org/10.1007/s12144-009-9047-0
Bryant, J., & Zillmann, D. (1984). Using television to alleviate boredom and stress: Selective exposure as a function of induced excitational states. Journal of Broadcasting & Electronic Media, 28(1), 1-20. https://doi.org/10.1080/08838158409386511
Bryman, A. (2012). Social Research Methods (4th ed.). Oxford University Press.
Chory, R. M. (2013). Differences in Television Viewers’ Involvement: Identification with and Attraction to Liked, Disliked, and Neutral Characters. Communication Research Reports, 30(4), 293–305. https://doi.org/10.1080/08824096.2013.837041
Chow, C. M., & Ruhl, H. (2014). Friendship and romantic stressors and depression in emerging adulthood: Mediating and moderating roles of attachment
32
representations. Journal of Adult Development, 21(2), 106-115. https://doi.org/10.1007/s10804-014-9184-z
Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of health and social behavior, 385-396.
Cole, T., & Leets, L. (1999). Attachment styles and intimate television viewing: Insecurely forming relationships in a parasocial way. Journal of Social and Personal
Relationships, 16(4), 495–511. https://doi.org/10.1177/0265407599164005
Dyson, R., & Renk, K. (2006). Freshmen adaptation to university life: Depressive symptoms, stress, and coping. Journal of clinical psychology, 62(10), 1231-1244.
https://doi.org/10.1002/jclp.20295
Field, A. (2018). Discovering statistics using IBM SPSS Statistics. (5th ed.). SAGE.
Frey, B. S., Benesch, C., & Stutzer, A. (2007). Does watching TV make us happy?. Journal of Economic psychology, 28(3), 283-313. https://doi.org/10.1016/j.joep.2007.02.001
Friend, S. (2015). Fiction as a Genre. Proceedings of the Aristotelian Society, 112(2), 179-209. https://doi.org/10.1111/j.1467-9264.2012.00331.x
Granow, V. C., Reinecke, L., & Ziegele, M. (2018). Binge-watching and psychological well-being: Media use between lack of control and perceived autonomy. Communication Research Reports, 35(5), 392-401. https://doi.org/10.1080/08824096.2018.1525347
Green, M. C., Brock, T. C., & Kaufman, G. F. (2004). Understanding media enjoyment: The role of transportation into narrative worlds. Communication theory, 14(4), 311-327. https://doi.org/10.1111/j.1468-2885.2004.tb00317.x
Green, M. C., Garst, J., & Brock, T. C. (2004). The power of fiction: Determinants and boundaries. In L. J. Shrum (Ed.), The psychology of entertainment media: Blurring
33
the lines between entertainment and persuasion (pp. 161–176). Lawrence Erlbaum Associates Publishers.
Greenwood, D. N. (2008). Television as escape from self: Psychological predictors of media involvement. Personality and Individual Differences, 44(2), 414–424.
https://doi.org/10.1016/j.paid.2007.09.001
Hall, A. E. (2009). Perceptions of the authenticity of reality programs and their relationships to audience involvement, enjoyment, and perceived learning. Journal of Broadcasting and Electronic Media, 53(4), 515–531. https://doi.org/10.1080/08838150903310468
Hamer, M., Endrighi, R., & Poole, L. (2012). Physical activity, stress reduction, and mood: insight into immunological mechanisms. In: Q. Yan
(Ed.) Psychoneuroimmunology (pp. 89-102). Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-071-7_5
Hargreaves, D., & Tiggemann, M. (2003). Longer‐term implications of responsiveness to ‘thin‐ideal’ television: support for a cumulative hypothesis of body image
disturbance? European Eating Disorders Review, 11(6), 465–477. https://doi.org/10.1002/erv.509
Higgs, M. M. (2019, July 1). ‘Guilty’ Pleasures? No Such Thing. The New York Times. https://nyti.ms/2LvfWK7
Hofmann, W., Reinecke, L., & Meier, A. (2017). Self-Control as a Moderator of the Effects of Media Use on Well-Being. In L. Reinecke & M. B. Oliver (Eds.), The Routledge handbook of media use and well-being: International perspectives on theory and research on positive media effects (pp.211–222). Routledge.
34
Hollis, M. E., Downey, S., del Carmen, A., & Dobbs, R. R. (2017). The relationship between media portrayals and crime: perceptions of fear of crime among citizens. Crime prevention and Community Safety, 19, 46-60. https://doi.org/10.1057/s41300-017-0015-6
Hughes, M. E., Waite, L. J., Hawkley, L. C., & Cacioppo, J. T. (2004). A short scale for measuring loneliness in large surveys: Results from two population-based studies. Research on aging, 26(6), 655-672.
https://doi.org/10.1177%2F0164027504268574
Johnson, J. G., Cohen, P., Kasen, S., First, M. B., & Brook, J. S. (2004). Association Between Television Viewing and Sleep Problems During Adolescence and Early Adulthood. Archives of pediatrics & adolescent medicine, 158(6), 562–568. https://doi.org/10.1001/archpedi.158.6.562
Koblin, J. (2020, January 9). Peak TV Hits a New Peak, With 532 Scripted Shows. The New York Times. https://nyti.ms/2T8amRK
Koeze, E., & Popper, N. (2020, April 7). The Virus Changed the Way We Internet. The New York Times. https://nyti.ms/2XePBWp
Kroenke, K., Spitzer, R. L., Williams, J. B., & Löwe, B. (2009). An ultra-brief screening scale for anxiety and depression: the PHQ–4. Psychosomatics, 50(6), 613-621. https://doi.org/10.1016/S0033-3182(09)70864-3
Lacalle, C. (2015). Young people and television fiction. Reception
analysis. Communications, 40(2), 237-255. https://doi.org/10.1515/commun-2015-0006
35
Lacalle, C. (2012). Genre and age in the reception of television fiction. Revista Comunicar, 20(39), 111-118. https://doi.org/10.3916/C39-2012-03-01
Lin, C. A., & Xu, Z. (2017). Watching TV Series with Horror Content: Audience Attributes, Motivations, Involvement and Enjoyment. Journal of Broadcasting and Electronic Media, 61(4), 638–657. https://doi.org/10.1080/08838151.2017.1375503
Mar, R. A., Oatley, K., Hirsh, J., dela Paz, J., & Peterson, J. B. (2006). Bookworms versus nerds: Exposure to fiction versus non-fiction, divergent associations with social ability, and the simulation of fictional social worlds. Journal of Research in Personality, 40(5), 694–712. https://doi.org/10.1016/j.jrp.2005.08.002
Mares, M. L., & Cantor, J. (1992). Elderly viewers’ responses to televised portrayals of old age: Empathy and mood management versus social comparison. Communication Research, 19, 459–478. https://doi.org/10.1111/j.1460-2466.2006.00313.x
Mares, M., Oliver, M. B., & Cantor, J. (2008). Age Differences in Adults' Emotional Motivations for Exposure to Films. Media Psychology, 11(4), 488–511. https://doi.org/10.1080/15213260802492026
Marschall, D., Sanftner, J., & Tangney, J. P. (1994). The state shame and guilt scale. Fairfax, VA: George Mason University.
Mazzeo, S. E., Trace, S. E., Mitchell, K. S., & Gow, R. W. (2007). Effects of a reality TV cosmetic surgery makeover program on eating disordered attitudes and behaviors. Eating Behaviors, 8(3), 390-397. https://doi.org/10.1016/j.eatbeh.2006.11.016
McCoy, C. A., & Scarborough, R. C. (2014). Watching “bad” television: Ironic consumption, camp, and guilty pleasures. Poetics, 47, 41-59.
36
McEwen, B. S. (2008). Central effects of stress hormones in health and disease: Understanding the protective and damaging effects of stress and stress mediators. European journal of pharmacology, 583(2-3), 174-185. https://doi.org/10.1016/j.ejphar.2007.11.071
Meadowcroft, J. M., & Zillmann, D. (1987). Women's comedy preferences during the menstrual cycle. Communication Research, 14(2), 204–218.
https://doi.org/10.1177/009365087014002004
Merriam-Webster. (n.d.). Nonfiction. In Merriam-Webster.com dictionary. Retrieved May 3, 2020, from https://www.merriam-webster.com/dictionary/nonfiction
Nabi, R. L., Prestin, A., & So, J. (2016). Could Watching TV Be Good for You? Examining How Media Consumption Patterns Relate to Salivary Cortisol. Health
Communication, 31(11), 1345–1355. https://doi.org/10.1080/10410236.2015.1061309
Nabi, R. L., Stitt, C. R., Halford, J., & Finnerty, K. L. (2006). Emotional and Cognitive Predictors of the Enjoyment of Reality-Based and Fictional Television Programming: An Elaboration of the Uses and Gratifications Perspective. Media Psychology, 8(4), 421–447. https://doi.org/10.1207/s1532785xmep0804_5
Nieder, T., & Seiffge-Krenke, I. N. G. E. (2001). Coping with stress in different phases of romantic development. Journal of adolescence, 24(3), 297-311.
https://doi.org/10.1006/jado.2001.0407
Oatley, K. (1999). Why fiction may be twice as true as fact: Fiction as cognitive and emotional simulation. Review of general psychology, 3(2), 101-117.
37
Oatley, K. (2002). Emotions and the story worlds of fiction. In M. C. Green, J. J. Strange, & T. C. Brock (Eds.), Narrative impact: Social and cognitive foundations (pp. 39–69). Lawrence Erlbaum Associates Publishers.
Oliver, M. B. (1993). Exploring the paradox of the enjoyment of sad films. Human Communication Research, 19(3), 315-342. https://doi.org/10.1111/j.1468-2958.1993.tb00304.x
Panek, E. (2014). Left to their own devices: College students “guilty pleasure” media use and time management. Communication Research, 41(4), 561-577.
https://doi.org/10.1177/0093650213499657
Papacharissi, Z., & Mendelson, A. L. (2007). An exploratory study of reality appeal: Uses and gratifications of reality TV shows. Journal of Broadcasting & Electronic Media, 51(2), 355-370. https://doi.org/10.1080/08838150701307152
Pearlin, L. I. (1959). Social and personal stress and escape television viewing. Public opinion quarterly, 23(2), 255-259.
Pearson, N., Biddle, S. J. H., Williams, L., Worsley, A., Crawford, D., & Ball, K. (2014). Adolescent television viewing and unhealthy snack food consumption: the mediating role of home availability of unhealthy snack foods. Public Health Nutrition, 17(2), 317–323. https://doi.org/10.1017/S1368980012005204
Peer, J. W., Hillman, S. B., & Van Hoet, E. (2015). The Effects of Stress on the Lives of Emerging Adult College Students: An Exploratory Analysis. ADULTSPAN Journal, 14(2), 90–99. https://doi.org/10.1002/adsp.12007
38
Perse, E. M. (1990). Involvement with local television news: Cognitive and emotional dimensions. Human Communication Research, 16(4), 556-581.
https://doi.org/10.1111/j.1468-2958.1990.tb00222.x
Phua, J. J. (2010). Sports Fans and Media Use: Influence on Sports Fan Identification and Collective Self-Esteem. International Journal of Sports Communication, 3(2), 190-206. https://doi.org/10.1123/ijsc.3.2.190
Pierceall, E. A., & Keim, M. C. (2007). Stress and coping strategies among community college students. Community College Journal of Research and Practice, 31(9), 703-712. https://doi.org/10.1080/10668920600866579
Reinecke, L. (2016). Mood management. In P. Rössler (Ed.), The International Encyclopedia of Media Effects (pp. 1271-1284). Wiley-Blackwell.
https://doi.org/10.1002/9781118783764.wbieme0085
Reinecke, L., Hartmann, T., & Eden, A. (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
Reinecke, L., Klatt, J., & Krämer, N. C. (2011). Entertaining Media Use and the Satisfaction of Recovery Needs: Recovery Outcomes Associated With the Use of Interactive and Noninteractive Entertaining Media. Media Psychology, 14(2), 192–215.
https://doi.org/10.1080/15213269.2011.573466
Reiss, S., & Wiltz, J. (2004). Why people watch reality TV. Media psychology, 6(4), 363-378. https://doi.org/10.1207/s1532785xmep0604_3
Richter, F. (2018, November 7). Young Adults Keep Pressing Play. Statista. https://www.statista.com/chart/16023/binge-watching-young-adults/
39
Rieger, D, Reinecke, L., Frischlich, L., & Bente, G. (2014). Media Entertainment and Well-Being – Linking Hedonic and Eudaimonic Entertainment Experience to Media-Induced Recovery and Vitality. Journal of Communication, 64(3), 456–478. https://doi.org/10.1111/jcom.12097
Robbins, A., & Wilner, A. (2001). Quarterlife crisis: The unique challenges of life in your twenties. Penguin.
Rossi, N. E., & Mebert, C. J. (2011). Does a Quarterlife Crisis Exist? The Journal of Genetic Psychology, 172(2), 141–161. https://doi.org/10.1080/00221325.2010.521784
Sirois, F., & Pychyl, T. (2013). Procrastination and the priority of short‐term mood regulation: Consequences for future self. Social and personality psychology compass, 7(2), 115-127. https://doi.org/10.1111/spc3.12011
Sperduti, M., Arcangeli, M., Makowski, D., Wantzen, P., Zalla, T., Lemaire, S., Dokic, J., Pelletier, J., & Piolino, P. (2016). The paradox of fiction: Emotional response toward fiction and the modulatory role of self-relevance. Acta psychologica, 165, 53-59. https://doi.org/10.1016/j.actpsy.2016.02.003
Tivo. (2019, October 24). What TV Content Genres Do Viewers Prefer?
https://www.marketingcharts.com/television/tv-audiences-and-consumption-110704
Toda, M., & Ichikawa, H. (2012). Effect of laughter on salivary flow rates and levels of chromogranin A in young adults and elderly people. Environmental Health and Preventive Medicine, 17(6), 494–499. https://doi.org/10.1007/s12199-012-0279-5
Tukachinsky, R., & Eyal, K. (2018). The Psychology of Marathon Television Viewing: Antecedents and Viewer Involvement. Mass Communication and Society, 21(3), 275– 295. https://doi.org/10.1080/15205436.2017.1422765
40
Valkenburg, P. M., & Peter, J. (2013). The differential susceptibility to media effects model. Journal of Communication, 63(2), 221-243.
https://doi.org/10.1111/jcom.12024
van der Schuur, W. A., Baumgartner, S. E., & Sumter, S. R. (2019). Social media use, social media stress, and sleep: Examining cross-sectional and longitudinal relationships in adolescents. Health communication, 34(5), 552-559.
https://doi.org/10.1080/10410236.2017.1422101
Vaterlaus, J. M., Spruance, L. A., Frantz, K., & Kruger, J. S. (2019). College student television binge watching: Conceptualization, gratifications, and perceived consequences. The Social Science Journal, 56(4), 470-479.
https://doi.org/10.1016/j.soscij.2018.10.004
Vorderer, P. (1991). Fern-Sehen Oder Mit-Leben? Spielfilmrezeption zwischen Interesse und Involvement. SPIEL: eine Zeitschrift zur Medienkultur, 10(1), 161–189.
Watson, A. (2019, August 9). Genre breakdown of the top 250 TV programs in the United States in 2017. Statista. https://www.statista.com/statistics/201565/most-popular-genres-in-us-primetime-tv/
Wirth, W. (2006). Involvement. In J. Bryant & P. Vorderer (Eds.), Psychology of entertainment (pp. 199–212). Lawrence Erlbaum Associates Publishers.
Wirth, W. (2008). Involvement with Media Content. In W. Donsbach (Ed.), The International Encyclopedia of Communication (pp. 2532–2534). Wiley Publishing.
https://doi.org/10.1002/9781405186407.wbieci090
Wood, D., Crapnell, T., Lau, L., Bennett, A., Lotstein, D., Ferris, M., & Kuo, A. (2018). Emerging Adulthood as a Critical Stage in the Life Course. In N. Halfon, C. B
41
Forrest., R. M Lerner, & R. M. Faustman (Eds.), Handbook of Life Course Health Development (pp. 123–143). Springer. https://doi.org/10.1007/978-3-319-47143-3_27
Zaichkowsky, J. L. (1986). Conceptualizing involvement. Journal of Advertising, 15(2), 4– 34. https://doi.org/10.1080/00913367.1986.10672999
Zillmann, D. (1988). Mood Management Through Communication Choices. Behavioral Scientist, 31(3), 327–340. https://doi.org/10.1177/000276488031003005
Zillmann, D. (1991). Television Viewing and Physiological Arousal. In J. Bryant, & D. Zillmand (Eds.), Responding to the screen: Reception and reactions processes (pp. 103-133). Routledge.
Zillmann, D. (2000). Mood management in the context of selective exposure theory. In M. E. Roloff (Ed.), Communication Yearbook 23 (pp. 103–123). Sage.
42
Appendix A – Fictional and non-fictional series examples for each content type.
Fictional violence Narcos, Breaking Bad, Gomorra
Fictional comedy How I Met Your Mother, The Office, Shameless Fictional romance Grey’s Anatomy, Outlander, This Is Us
Fictional suspense The Walking Dead, Locke & Key, Stranger Things Non-fictional violence Who Killed Malcolm X, I am a Killer, Evil Genius
Non-fictional comedy Saturday Night Live, The Ellen DeGeneres Show, Comedians in Cars Getting Coffee
Non-fictional romance Love is Blind, The Bachelor, Love Island
Non-fictional suspense Haunted, Don’t F**k with Cats: Haunting an Internet Killer, The Jinx
