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Social Media Overuse and Procrastinatory Social Media Use as Predictors of Social Media Dieting Behaviour and the moderating influence of Trait Self-Control

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Social Media Overuse and Procrastinatory Social Media Use as Predictors of Social Media Dieting Behaviour and the moderating influence of Trait Self-Control

Master Thesis

Graduate School of Communication Persuasive Communication

Marie-Christina Lechtenberg Student no.: 12003921

marielechtenberg@outlook.com Submission date: 25.06.2019 Supervisor: dr. Sanne Schinkel 7540 Words

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Abstract

Social media usage increases year by year, worldwide. University students have an even higher daily usage than the average population. When social media is overused, it can elicit negative consequences and even impair the students’ academic performance. Especially procrastination with social media, meaning the irrational task delay of in this case, for

example, studying or exam completion, is to be blamed for this. In recent years, the “trend” of digital detoxing and social media dieting has emerged, as users become aware of the negative consequences of their social media overuse. The present thesis has been conducted in order to investigate to what extend university students’ social media overuse is related to their social media dieting behaviour, and how this relationship is influenced by trait self-control and procrastinatory social media use. The results of a cross-sectional online-survey partly support the three hypotheses, and reveal significant relationships between the four main variables. They furthermore revealed effects which differ from expectations. Theoretical and practical implications are discussed.

Keywords: Social Media Dieting, Social Media, Procrastination, Trait Self-Control, Overuse, Social Media Addiction.

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Introduction

The average internet user spends around six hours every day using digital devices, with social media accounting for more than two hours every day (Hootsuite & We Are Social, 2019). Compared to the average, emerging adults aged 18 to 34 and especially university or college students use social media even more: According to research, their daily average usage ranges between three to five hours (Knight-McCord, Cleary, Grant, Herron, Lacey,

Livingston et al., 2016; Hootsuite & We Are Social, 2019). Ninety percent of university students indicate that they use social media several times a week, compared to fifty-six percent of the general population (VuMA, 2018). The benefits and downsides of social media ubiquity have been widely discussed in research, showing that social media overuse can lead to depression (Andreassen, Billieux, Griffiths, Kuss, Demetrovics, Mazzoni et al., 2016), is positively related to anxiety (Vannucci, Flannery, & Ohannessian, 2017), impairs general well-being (Marino, Gini, Vieno, & Spada, 2018), decreases efficiency (Brooks, 2015) and is related to disrupted sleep (Van der Schuur, Baumgartner, & Sumter, 2019). Especially

university students are prone to the negative impact of social media misuse or overuse, which has been shown to impair academic performance and to be related to lower GPA (Jacobsen & Forste, 2011; Lau, 2017). There are several aspects that might cause the negative impact of excessive social media use on academic performance, such as social media multitasking (Karpinski, Kirschner, Ozer, Mellott, & Ochwo, 2013), distraction (Maqableh, Rajab,

Quteshat, Masa’deh, Khatib, & Karajeh, 2015), or academic procrastination (Ozer, Karpinski, & Kirschner, 2014).

Research has shown a relationship between social media usage and impaired task performance caused by distraction, especially in university students (Rosen, Carrier, & Cheever, 2013), who are more prone to procrastination and procrastinatory media use

compared to other populations (Perrin, Miller, Haberlin, Ivy, Meindl, & Neef 2011; Solomun & Rothblum, 1984). Meier, Reinecke and Meltzer (2016) identified procrastination with

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social media, meaning the irrational task-delay by using social media, as a predictor for general negative well-being and high academic stress levels in university students, which might result from time-management worries and impaired academic performance (Kim & Seo, 2015).

Furthermore, they have shown that trait self-control is negatively related to the frequency of procrastination with Facebook (Meier et al., 2016). Nevertheless, their study did not address how students cope with this form of procrastination. In connection with self-control theory, Hofmann, Friese and Strack (2009) propose that in reaction to self-self-control failure, in this case giving in to irrational task delay by procrastinating, individuals might form self-control or restraint standards (e.g., keeping a social media diet). Furthermore, students who are more capable of self-regulating and controlling their social media use are able to limit the negative effect on their academic performance (Rouis, Limayem, & Salehi-Sangari, 2011). This can be interpreted as a sign of students becoming aware of the issues that come with excessive social media usage. In a study by Alwagait, Shahzad and Alim (2014), for example, college students name social media use as possible cause for impaired academic performance. As a response to this awareness, the phenomenon of digital detox or social media diets, which describe temporal abstinence from social or digital media, becomes more popular especially among emerging adults (Ofcom, 2016). The most recent Global Digital Report by We Are Social (2019) names the rise of digital detoxing as a future trend, as users shift from being “always on” their devices to a more conscious, intentional usage.

Although there is evidence showing a negative impact of university students social media overuse on their academic performance and increased procrastination with social media, as well as the increase in social media dieting behaviours, the actual connection between these influencing factors and the underlying mechanisms and motivations of engaging in social media diets are hardly mentioned in research. In fact, to date there is no study to be found on this matter. Considering the gap in and general lack of research on social

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media dieting behaviors, the aim of this study is thus to fill this gap by identifying possible underlying mechanisms of social media dieting; particularly the predicting influence of social media overuse on social media dieting behaviours. Furthermore, the association of social media diets as preventive self-control-strategies (Hofmann et al., 2009) with procrastinatory social media use will be investigated. University students are chosen as target population, as they are particularly likely to overuse social media and to procrastinate with (social) media (Meier et al., 2016; Steel & Klingsieck, 2016). This paper will therefore be based on the following research question:

RQ: To what extend is university students’ social media overuse related to their social

media dieting behavior, and how is this relationship influenced by self-control and procrastinatory social media use?

Investigating these associations is scientifically relevant for two reasons. Firstly, it continues procrastination research by linking the concept of self-control standards as reaction to self-control failure to procrastinatory social media use. Secondly, this paper is the first one to investigate the behavioral component of social media fatigue in the form of social media dieting behaviors, which will here be introduced as a self-control standard associated to procrastinatory media use.

Furthermore, the perceived need to take a social media break itself and the negative attitude and affect towards social media, also called social media fatigue, seem to be alarming symptoms for a misuse of these media among students. The association between an increase in academic stress levels of students and procrastinatory social media use (Meier et al., 2016) is an important finding regarding rising numbers of students experiencing burn-out symptoms (Schaufeli, Martinez, Pinto, Salanova, & Bakker, 2002). A further investigation of this

relation could therefore provide valuable information for interventions aiming at improving student’s well-being and stress-management skills with regards to social media overuse. Ideally, the results of this study can increase awareness about negative consequences of social

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media overuse in university students, and provide insights as to how social media abstinence might be a tool to assist university students in increasing their academic performance.

Theoretical Framework Social Media Overuse

Prior studies provide evidence that certain features of social media might trigger symptoms of behavioural addictions in users. Thus, in current research it is frequently made use of the term ‘social media addiction’, which is defined as “the compulsive use of social media sites that manifests itself in behavioural addiction symptoms” (Hawi & Samaha, 2017, p. 577.). Symptoms of behavioural addictions which have been shown in connection with smartphone or social media overuse include compulsive behaviour, loss of self-control,

functional impairment and withdrawal (Lee, Lee, Han, Park, Ju, Choi et al., 2018; Lin, Chang, Lee, Tseng, Kuo, & Chen, 2014). Even though the greatest amount of existing research on addiction to digital media concerns smartphone or internet use, references mentioning a potential social media addiction increase in recent years (Andreassen, Griffiths, Kuss, Mazzoni, Billieux, Demetrovics et al., 2016; Hawi & Samaha, 2017). According to these, social media addiction shows similarities to other behavioural addictions. However, the legitimacy of social media addiction remains open for debate, since only few studies to date have been able to develop and validate measures of some symptoms of behavioural addictions regarding social media use (Griffiths, Kuss, & Demetrovics, 2014; Hawai & Samaha, 2017). It is also difficult to speak of social media addiction, since the term ‘social media’ includes different subcategories of online networks or applications which are used for different purposes and might thus not be addictive as a whole, but each platform or category on its own. Voorveld, van Noort, Muntinga and Bronner (2018) summarize this issue as follows: “Social media, so it seems, is regarded as either an umbrella concept or a specific social medium seen as exemplary for all social media. […] Because this could result in disconnected insights, theory building might be impeded.” (p. 39). However, in this study it will be seen as

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unproblematic to use ‘social media’ as a unified term, given that procrastinating with, overusing or restraining from any platform of social media is possible and that any of the social media platforms mentioned in this study (Facebook, Youtube, Instagram, Snapchat and Pinterest) can potentially be used, as well as overused, for pastime or distraction.

Besides the lack of a universal definition or a standard of how ‘social media addiction’ can be defined to date, it has not been included in the 5th edition of the Diagnostic and

Statistical Manual of Mental Disorders (DSM-5) unlike all other acknowledged mental disorders or disorders of substance abuse, including “internet gaming disorder”. Other terms describing the same issue are, for example, social media overdependence, excessive or problematic social media use and social media overuse. For this paper, the term ‘social media overuse’ will be used to describe problematic social media use, including the empirically proven addiction-like symptoms. Despite the discrepancy in terminology, prior research shows associations between social media overuse and higher levels of technostress, lower happiness, impaired social relationships and general physical and mental well-being (Andreassen et al., 2016; Brooks, 2015; Hawi & Samaha, 2017; Marino et al., 2018). Furthermore, social media overuse has been shown to be predicting both higher

procrastinatory media use including its negative consequences such as higher stress and reduced satisfaction regarding work (Beutel, Klein, Aufenanger, Brähler, Dreier, Müller et al., 2016; Meier et al., 2016) as well as social media fatigue (Anrijs, Bombeke, Durnez, Van Damme, Vanhaelewyn, Conradie et al., 2018; Bright et al., 2015; Dhir, Yossatorn, Kaur, & Chen, 2018). Therefore, this paper expects social media overuse to be associated with

procrastinatory social media use and social media dieting behaviour, which will be described below.

Social Media Dieting Behaviours

In recent years, “social media diet”, “digital detox” or “social media detox” became terms often used in mass media (Eldor, 2018; Farr, 2018; Settembre, 2017), accrediting

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mental health benefits and stress release to the temporary abstinence from digital media in general, or specifically social media. Actress Emma Watson compared her social media usage to actual dieting: “[…] In the same way we think about what we eat, we should think about what we read, what we're seeing, what we're engaging and what we're interacting with everyday” (CNN Business, 2017). Nevertheless, the voluntary and intended abstinence from social media has hardly found attention in research. In fact, no definition of what such social media abstinence entails has been found by the time this thesis has been finished.

In contrast to the lack of research regarding social media dieting, the concept of social media fatigue has been increasingly mentioned in research in recent years (Bright et al., 2015; Dhir et al., 2018; Vanman, Baker, & Tobin, 2018). It is described as a user’s tendency to back away from social media when he or she experiences informative, technological or

communicative overload (Bright et al., 2015; Dhir et al., 2018; Zhang, Zhao, Lu, & Yang, 2016). While social media fatigue emphasizes the affective aspect underlying social media diets, meaning the feeling of not wanting to use social media anymore, the actual behavioural outcome has not been researched - even though social media fatigue is expected to result in actual withdrawal from social media (Dhir et al., 2018).

Miksch and Schulz (2018) were the first to investigate motivations and actual behavioural aspects of, in this case, ‘digital detox’, through qualitative semi-structured interviews. They identified five different actions young adults take to limit their digital media usage: Creating Barriers, Creating Structure (in life), Creating Awareness, implementation of Offline Activities and using Offline Media. The underlying motivations for engaging in these actions are Keeping Self-Control, Increasing Performance, Improving Well-Being,

Being in the Moment and Maintaining real life Relationships.

Following Miksch and Schulz (2018) and previous definitions of ‘digital detox’ or ‘digital diets’ from market research (Ofcom, 2016; PwC, 2018), social media dieting

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usage with a specific aim, such as increase of well-being or the opportunity to focus on offline activities.

It is thus assumed that the frequency of an individual’s social media use, especially if it is an overuse causing negative symptoms, is positively related to this individual’s social media dieting behaviour. It is suspected that if an individual becomes overwhelmed with stressors and negative consequences of social media overuse, which might elicit social media fatigue, he or she engages in social media diets in order to be able to achieve one or more specific aim.

Vanman et al. (2018) showed in an experimental design that temporary abstinence from social media indeed decreases cortisol levels, while it at the same time also decreases subjective well-being. Decreased subjective well-being is a known withdrawal symptom in addicts which has been shown to be connected to social media overuse before (Turel, Brevers, & Bechara, 2018). With other behavioural addictions like smoking, withdrawal often results in relapse to the addictive behaviour (Piasecki, Jorenby, Smith, Fiore, & Baker, 2003). Vanman et al. (2018) conclude from their results that once a fatigue occurs, “[…] the occasional Facebook break might happen naturally.” (p. 503). Still, after the break, the

individual will turn back to social media. Based on these findings, it is therefore assumed that higher levels of social media overuse are related to a higher frequency of engaging in social media diets: Overuse is followed by temporal abstinence to satisfy a certain gratification, which is more likely followed by relapse, the more a person generally uses social media. Therefore, an association between the level of social media overuse and the frequency of social media dieting behaviours is proposed in this paper.

H1: An individual’s level of social media overuse is positively related to their frequency of

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Procrastinatory Social Media Use

Procrastination is defined as “the voluntary delay of an intended and necessary and/or [personally] important activity, despite expecting potential negative consequences that outweigh the positive consequences of the delay” (Klingsieck, 2013, p. 26). When

procrastinating, a person gives in to pleasant short-term temptations, such as checking social media, instead of engaging in the planned, but perceived as less pleasant task, such as studying for an exam (Meier et al., 2016). While the procrastinated task is perceived as less hedonically satisfying in the moment than other possible activities, the procrastinating person usually realizes that the long-term consequences of his or her task delay weigh heavier than the short-term benefits gained from procrastination (Sirois & Pychyl, 2013). He or she nevertheless chooses to consciously or subconsciously ignore those (Reinecke & Hofmann, 2016). Procrastination has also been shown to be linked to task aversiveness: “Procrastinators prioritize their short-term desire to terminate the aversive feelings aroused by an upcoming task […]” (Reinecke & Hofmann, 2016, p. 443 f.). This is in line with mood management theory (Zillmann, 1988), which states that an individual’s “selection of messages for

consumption often serves the regulation of mood states” (Zillmann, 1988, p. 327). According to Sirois and Pychyl (2013), short-term mood regulation is a main driver of procrastination.

Research has shown that quite often media is used for this form of irrational task delay (Hofmann, Baumeister, & Vohs, 2012). Especially social media seem suitable for

procrastination and short-term mood optimization for two reasons. Firstly, social media use provides pleasurable experiences and positive affect in the moment of usage (Reinecke, Vorderer, & Knop, 2014). The endlessness of possibilities on social media and the likes, emojis and human interaction online can deliver instant gratifications which elicit dopamine in the brain’s reward-system (Haynes, 2018). Secondly, social media is potentially always available and often used parallel to other media. University students tend to media multitask even while studying (Lau, 2017). Media multitasking does not necessarily have negative

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consequences. But it has shown to decrease academic performance in students, when social media multitasking is used for non-academic purposes (Lau, 2017). By distracting the students, social media naturally decreases their performance as it limits cognitive capacity (Walsh, Fielder, Carey, & Carey, 2013). It can thus be concluded that social media provide a seemingly attractive type of media for procrastination which also entails negative

consequences.

Heavy social media and internet users in general are more prone to irrationally delay a task in order to spend more time online (Hinsch & Sheldon, 2013; Meier et al., 2016;

Thatcher, Wretschko, & Fridijhon, 2008). As mentioned before, university students are using social media more than the average population, tend to media multitask and procrastinate with social media more often. Given that they are also likely to experience the negative long-term consequences of procrastination, such as decreased academic performance (Lau, 2017; Walsh, et al., 2013) it is expected that they might have the intention to temporarily restrain from social media, the more they use it. Furthermore, the self-regulatory failure a student

experiences when procrastinating (Sirois & Pychyl, 2013) might cause him or her to reflect on his or her behaviour. He or she might form self-control standards to counteract the impulsive behaviour such as social media checking, for example, by implementing restraint standards (Hofmann et al., 2009), such as a temporary social media abstinence. The association between social media overuse and social media dieting is thus expected to be mediated by

procrastinatory social media use. Social media overuse leads to higher procrastinatory social media use, as the positive short-term affect weighs heavier for overusers. However, higher procrastinatory social media use leads to greater negative long-term consequences of procrastination and thus increases the frequency of social media dieting behaviours.

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H2: The association between social media overuse and social media dieting behaviour is

mediated by procrastinatory social media use, meaning that higher levels of social media overuse increase the frequency of procrastinatory social media use, which increases the frequency of engaging in social media dieting behaviours.

Trait Self-Control

Trait self-control describes an individual’s capability to override or withstand problematic desires or impulses (Hofmann et al., 2009; Tangney, Baumeister, & Boone, 2004). The term “desire” is defined as a motivation to approach “certain stimuli in our environment and engage in activities with them that provide us with a relative gain in immediate pleasure” (Hofmann, Reinecke, & Meier, 2016, p.235).

Procrastination research has found a relationship between procrastinatory media use and trait self-control, showing that irrational task delay with social media is a consequence of self-control failure (Du, Koningsbruggen, & Kerkhof, 2018; Meier et al., 2016). Sirois and Pychyl (2013) include trait self-control in their definition of procrastination and describe it as “self-regulatory failure of not exerting self-control necessary for task engagement” (p. 116). Giving in to the immediate pleasure of (social) media use is a common form of self-control failure (Hofmann et al., 2016). Its likeliness can be reasoned by the immediate gratifications social media provide, the degree of habitualization of social media use, the ubiquitous availability of social media and the attentional demands onto media users such as push notifications (Hofmann et al., 2016). Especially the habitualization could play an important role in procrastinatory social media use for task averseness. Habitualizing procrastinatory social media use happens, when an individual learns to associate positive affect with social media and develops a behavioural preference to approach it (Hofmann et al., 2016). Once this behaviour becomes an automatic impulsive response, its execution becomes more likely and increases the risk of self-control failure (Hofmann et al., 2009). Given the habitualization,

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ubiquity, immediate gratifications and attentional demands, procrastinating with social media might thus become more tempting than engaging in a less rewarding activity, at least in the short-term perspective.

But this does not necessarily mean that the initial loss of self-control is irreversible. In connection with this, it is important to look at the source for the initial self-control-loss. Within trait self-control, Hofmann et al. (2016) distinguish between a capacity-based (not being able to control oneself anymore) and a motivational (not wanting to control oneself) self-control. With the short-term positive affect, it can be suspected that task averse procrastinatory social media use is a rather motivational loss of self-control, meaning not wanting to control media use in this moment. Nevertheless, research shows that these positive emotions quickly fade once a user realizes the negative long-term consequences, such as lower task performance, and might even change to feelings of guilt (Meier et al., 2016), also called “spoiled-pleasure” effect (Hofmann, Kotabe, & Luhmann, 2013).

Although social media overuse might lead to procrastinatory social media use, it is thus possible that not all individuals with excessive social media use react on it in the same way. Trait self-control might buffer this effect in the way that individuals with higher levels of trait self-control might be better at limiting the impact their social media overuse has on task performance (Lian, Sun, Zhou, Fan, Niu, & Liu, 2018). University students who are aware of their long-term goals, such as assignment completion or studying, might realize that procrastinatory social media use poses a threat to achieving those goals. Even when overusing social media, they would be able to weigh between short-term and long-term goal

achievement, depending on the assigned importance of the goal. This would reflect the activation of motivational self-control. Different levels of self-control might thus influence the association between social media overuse and procrastinatory social media use in the sense that high levels of trait self-control weaken the relationship between social media overuse and procrastinatory social media use. A student with high trait self-control would be

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able to limit the impact of social media overuse on procrastinatory social media use. This leads to the following hypothesis:

H3: The mediated relationship between social media overuse and social media dieting

behaviour is moderated by trait self-control, in the sense that the positive relationship between an individual’s level of social media overuse and his or her frequency of engaging in procrastinatory social media use will be weaker for an individual with higher levels of trait self-control.

The interaction between the four variables is displayed in a conceptual model in Figure 1.

Figure 1. Conceptual model.

Method Design and Sample

A cross-sectional online survey using Qualtrics has been conducted between April 16th and May 6th 2019. The target population for this study were enrolled university students who use at least one social media platform. Non-probability convenience sampling has been chosen. The link to the online survey has been distributed through the social media channels of the author, in which most contacts are students. Furthermore, the survey has been published on an online platform on which students can exchange links to their surveys to generate responses. After informed consent has been obtained, the participants self-indicated their opinion and attitudes regarding the different variables in the survey individually, in their own

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preferred setting. After indicating their age, gender and country of residence, they were first presented the question regarding their self-control before indicating their level of social media overuse. To ease the questionnaire, participants then indicated their subject of studies and daily social media use in hours before answering questions regarding their procrastinatory social media use and finally social media dieting behaviour.

Before publishing the final survey, it has been tested in a pilot to clear out possible misunderstandings in the formulation of the questions. Seventeen students participated in this pilot test. Since there were no complaints, the original questionnaire was used in the final study and data from the pilot test has been included in the final sample.

The survey generated a total of 188 responses, of which 161 remained after data cleaning. Twenty-seven responses were removed either because the respondents did not complete the survey, indicated that they do not use any form of social media or because they did not answer a control question, which has been included to ensure reliability of responses. 82.6% of respondents were female and the age of all respondents ranged from 19 to 56 (M=25.19, SD=4.32). The respondents indicated to currently reside in 17 different countries, with the large majority living in Germany (N=93), The Netherlands (N=35) or Austria (N=10). Regarding the subject of studies, students from 13 different fields participated in the survey, most of them studying Communication, Journalism or Media (N=52), Business or Economics (N=45) or Psychology (N=30). Students in this sample spend on average 2.17 hours using social media every day (SD = 1.27, range: 0 to 8.5 hours) with their preferred platforms being Instagram (80.7%), Facebook (80.1%) and YouTube (50.3%).

Measurements

Social media overuse. In order to measure the level of the independent variable, social media overuse, a validated scale from the Smartphone Overuse Screening

Questionnaire (SOS-Q) (Lee et al., 2018) has been used and adapted to social media use. This

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smartphone during class or work” on which respondents indicate their answer on a 5-point Likert scale with options being “never”, “rarely”, “sometimes”, “often” and “all the time”. In order to adjust the measurement to social media use, 11 items of the scale have been selected for this study. Other items have been excluded, as they did not fit the context of social media or because they are very similar in wording to the ones that are chosen. The selected items have been adapted in wording to social media instead of smartphone use. For example: “I try to reduce my smartphone usage” was adapted to “I try to reduce my social media usage”. A principal component factor analysis using varimax rotation revealed that the 11 items load on one factor. The Kaiser-Mayer-Oblimin measure adequacy is .880 and Bartlett’s test of

sphericity is significant (χ2(55)=766.231, p <.000), allowing for factor analysis with the data. The extracted factor explains 57.53% of the total variance and has an eigenvalue of 5.16. Factor loadings range from .519 to .809. Internal reliability of the scale is deemed good (α=.89). A new variable has been computed by mean scoring the items on the scale (M=2.92,

SD=0.71). In order to separate the group of “overusers” from those with low to average use, a

median split has been performed and the newly created dummy variable has been used for analysis.

Social media dieting behaviour. Given the limited research on social media dieting behaviours, no existing or validated scale could be used for this study. The scale to measure the dependent variable has been obtained by applying results by Miksch and Schulz (2018), who identified five different actions participants take in order to limit the interaction with digital technologies, namely Creating Barriers, Creating Structure, Creating Awareness, implementation of Offline Activities and using Offline Media instead. Thirteen items for the social media dieting behaviour scale are created based on indications for each of these categories. In the online survey, respondents indicated their own social media dieting behaviour on a 5-point Likert scale again ranging from “never” to “all the time”, with items completing the sentence “In order to limit my social media usage, I…”, for example, “... turn

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my phone around to avoid seeing the screen”. A principal component factor analysis using varimax rotation revealed that items load on one factor. The Kaiser-Mayer-Oblimin measure adequacy is .771 and Bartlett’s test of sphericity is significant (χ2(28)=340.428, p <.000), showing that factor analysis is useful. The factor explains 55.12% of the total variance and has an eigenvalue of 3.29. Factor loading ranges from .464 to .759. Scale reliability is deemed good (α=.79). Thus, internal reliability can be assumed. A new variable has been computed by mean scoring the items on the scale (M=2.47, SD=0.79).

Procrastinatory social media use. Respondents indicated the frequency of their procrastinatory social media use by responding to four items from the Procrastination scale used by Meier et al. (2016). The items to measure the mediating variable have been adapted to measure social media use as procrastinatory behaviour, with participants responding to four items on a 5-point Liker scale ranging from “Never” to “All the time”. A principal component factor analysis using varimax rotation revealed that the four items load on one factor. The Kaiser-Mayer-Oblimin measure adequacy is .826 and Bartlett’s test of sphericity is significant (χ2(6)=477.345, p <.000). The extracted factor explains 80.43% of the total variance and has an eigenvalue of 3.22. All factors load above the value of .871. Internal reliability can be assumed good (α=.92). A new variable has been computed by mean scoring the items on the scale (M=3.15, SD=0.88).

Trait Self-Control. Respondents indicated their individual tendency to be disciplined and abrogate impulses on nine items from the 5-point Brief Self-Control Scale (Tangney et al., 2004), ranging from 1 “strongly disagree” to 5 “strongly agree” after being asked how much each of the statements applies to them. The scale previously has been shown to be internally reliable (α=0.83). Four out of the original 13 items have been excluded either because they were very similar in wording to other included items or did not seem appropriate for measuring the student’s individual perception of their self-control (e.g. “People would say that I have iron self- discipline”). Two items have been reversed before analysis. A principal

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component factor analysis using varimax rotation revealed that the nine items load on one factor. The Kaiser-Mayer-Oblimin measure adequacy is .828 and Bartlett’s test of sphericity is significant (χ2(36)=353.810, p <.000), allowing for interpretation of the factor analysis. The extracted factor explains 51.14% of the total variance and has an eigenvalue of 3.51. Factor loadings range from .419 to .730. The scale shows to have good reliability (α=.80). A new variable has been computed by mean scoring the items on the scale (M=3.03, SD=0.67).

Control variables. In addition to the central four concepts, other variables have been included in the survey. Next to age in years (open question) and gender (male, female or other), the participants have been asked to indicate their subject of studies out of fifteen options and their daily media use in hours by choosing between 18 options ranging in 30 minute steps from “0” to “more than 8 hours” on a dropdown list.

Analysis

The proposed effects in the hypotheses were tested using SPSS and SPSS PROCESS macro by Andrew F. Hayes. The direct predicting effects of social media use on social media dieting behaviour (H1) has been tested using linear regression analysis. The mediation and moderated mediation as proposed in H2 and H3 were tested using regression analysis with 5,000 bootstrap samples with PROCESS model 4 for mediation analysis and model 7 for moderated mediation analysis (Hayes, 2013). All regression analyses have been controlled for age, gender, subject of studies and social media use in hours. After primary analysis,

additional analysis has been performed.

Results Hypothesis testing

Hypothesis 1 predicts that an individual’s level of social media overuse is positively related to his or her frequency of engaging in social media dieting behaviours. A significant regression equation was found, (F(5, 135)=6.83, p<..000, R2=.20). The results indicated that social media overuse (b=.58, p<.000) significantly predicts social media dieting behaviours.

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This shows that students in the group of social media overusers report a higher frequency of social media dieting behaviours than students who do not overuse social media. H1 is thus supported.

According to Hypothesis 2, the association between social media overuse and social media dieting behaviour is mediated by procrastinatory social media use, meaning that higher levels of social media overuse increase the frequency of procrastinatory social media use, which increases the frequency of engaging in social media dieting behaviours. A regression analysis with mediation using model 4 of the PROCESS macro for SPSS has been conducted. The resulting total effects model is significant (F(5, 135)=6.83, p <.000, R²=.20).

The regression analysis for direct effects on procrastinatory social media use (F(5,135)=28.00, p<.000, R²=.51) revealed that social media overuse (b=1.07, p<.000) significantly predicts procrastinatory social media use. This shows that respondents in the group of overusing social media procrastinate with social media more.

However, procrastinatory social media use does not seem to predict social media dieting behaviour (b=.13, p=.163) when controlling for the other included variables, as this effect is not significant.

Regression analysis (F(6, 134)=6.01, p<.000, R²=.21) furthermore showed that compared to the direct effect of social media overuse on social media dieting behaviours (b=.58, p<.000), the indirect effect is only slightly smaller and still significant (b=.44,

p=.007), which shows that mediation does not seem to exist. Thus, the results find partial

support for H2.

Hypothesis 3 predicts that the mediated relationship between social media overuse and social media dieting behaviour is moderated by trait self-control, in the sense that the positive relationship between an individual’s level of social media overuse and his or her frequency of engaging in procrastinatory social media use will be weaker for an individual with higher levels of trait self-control. A regression analysis with 5,000 bootstrap samples

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with moderated mediation using model 7 of the PROCESS macro for SPSS has been

conducted. The resulting total effects model is significant (F(6, 134)=6.06, p<.000, R²=.21). The results of H1 and H2 remained the same after including the moderator, which is visualized in Figure 2.

The interaction effect of social media overuse and trait self-control on procrastinatory social media use is negative, as expected in H3, and marginally significant (b=-.32, p=.062). When looking at the conditional effects, it can be observed that social media overuse increases procrastinatory social media use more for lower levels of trait self-control (see Table 1).

Table 1

Conditional Effects of Social Media Overuse on Procrastinatory Social Media Use at different levels of Trait-Self Control

Trait Self-Control b p LLCI ULCI

-1 SD 1.01 <.000 .77 1.43

M .89 <.000 .67 1.12

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This would support the assumption that the positive effect of social media overuse on procrastinatory social media use becomes smaller for higher levels of trait self-control.

However, the index for moderated mediation with a 95% bootstrap confidence interval is only marginally different from zero with -.04 (BCa CI [-.18,.02]). These results find partial support for H3 with low generalizability, since the effects are only marginally significant.

Additional Analysis

Hypothesis testing did find support for H1 but only partial support for H2 and H3. However, it did show that there are associations between the variables. Given these findings, additional analysis has been conducted.

Prior analysis revealed that procrastinatory social media use does not significantly predict social media dieting behaviour (b=.13, p=.163). However, it is predicted by social media overuse (b=.58, p<.000) and changes the effect of social media overuse on social media dieting behaviour. Thus, moderation analysis has been conducted using model 1 in SPSS Process with 5,000 bootstraps, including age, gender, subject of studies and social media use in hours as control variables. Since procrastinatory social media use is a continuous variable, it has been mean centred prior to the analysis. The resulting model is significant (F(7, 133)=5.92, p<.000, R²=.24). Results show that the effect between social media overuse and social media dieting behaviour becomes smaller at higher levels of procrastinatory social media use (b=-.38, p =.042). When looking at the conditional effects, it can be observed that social media overuse significantly increases social media dieting behaviour when

procrastination is low to average (see Table 2). Thus, the effect of social media overuse on social media dieting is especially high for people who tend to not procrastinate with social media.

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Table 2

Conditional Effects of Social Media Overuse on Social Media Dieting at different levels of Procrastinatory Social Media Use

Procrastinatory social media use b p LLCI ULCI

-1 SD .94 .002 .36 1.52

M .59 .001 .24 .94

+1 SD .24 .198 -.13 .61

Prior analysis has furthermore revealed that social media use in hours (b=.48, p<.000) significantly predicts procrastinatory social media use. Social media use in hours is

furthermore highly correlated to all the other included variables (see Table 3). Table 3

Pearson Correlation

Measure 1 2 3 4 5 6 7 M SD

1. Procrastination - 3.15 .88

2. Self-Control .55** - 3.03 .67

3. Social media use .79** .46** - 2.92 .71

4. Social media dieting .34** .10 .46** - 2.47 .79

5. Age -.10 .04 -.15 -.20* - 25.19 4.32

6. Social media use in hours .43** .24** .46** .17* -.18* - 2.17 1.27 ** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

It does also seem likely that actual social media usage compared to perceived overuse might be an indicator of procrastinatory social media use and social media dieting. The assumption from H3 - namely that the positive relationship between an individual’s level of social media overuse and his or her frequency of engaging in procrastinatory social media use will be weaker for an individual with higher levels of trait self-control – has thus been tested with actual social media use in hours as independent variable. Trait self-control and social media use in hours have been mean centred before analysis. A regression model 1 for

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moderation with SPSS Process with 5,000 bootstrap samples has been performed and showed to be significant (F(6, 142)=19.10, p<.000, R² =.45). None of the included control variables (subject of studies, age and gender) is significantly related to procrastinatory social media use. As expected, both social media use in hours (b=.24, p< .000) and trait self-control (b=.71,

p<.000) positively predict procrastinatory social media use. The interaction effect of the two

variables on procrastinatory social media use is negative and significant (b=-.17, p=.024), showing that the effect between social media use in hours and procrastinatory social media use is more positive for lower levels of trait self-control. This would reflect the assumption made in H3, in the way that people with lower trait self-control are more likely to

procrastinate when the more they use social media.

Discussion and Conclusion

This research has been conducted in order to investigate to what extend university students’ social media overuse is related to their social media dieting behaviour, and how this relationship is influenced by trait self-control and procrastinatory social media use. The results of a cross-sectional online-survey partly support the three hypotheses, and revealed significant relationships between the four main variables. They furthermore revealed effects which differ from expectations.

Firstly, it has been found that social media overuse indeed positively predicts social media dieting behaviour, which is in line with H1. This provides empirical evidence to

support the findings of the qualitative study by Miksch and Schulz (2018), according to which a digital abstinence is performed because the overuse of digital media inhibits the

achievement of certain gratifications.

Secondly, H2 has found partial support. Results show that social media overuse leads to higher procrastinatory social media use. This is in line with previous findings in

procrastination research (Hinsch & Sheldon, 2013; Meier et al., 2016; Thatcher et al., 2008) which showed a connection between the preference for short-term positive affect and media

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overuse. However, unlike expected in H2, the assumption that procrastinatory social media use in university students would lead to a higher frequency of social media dieting behaviours has not found support in the results. Procrastinatory social media use does not seem to

mediate the effect of social media overuse on social media dieting behaviour; instead, it seems do moderate this effect.

Results show that the effect of social media overuse on social media dieting is especially high for people who tend to not procrastinate with social media. This contradicts H2 and seems paradox, given that social media overuse positively predicts both

procrastinatory media use and social media dieting behaviour. A possible explanation could be found in an article by Reinecke and Hofmann (2016) who claim that procrastinators prioritize short-term desires to terminate negative feelings aroused by an upcoming task. When looking at the conditional effects of procrastinatory social media use, the question arises whether there might be a cut-off point, as in a point of no-return. Could it be that especially for students with high social media overuse the satisfaction of short-term desires through procrastinatory social media use depends more on their level of capacity-based trait self-control (Hofmann et al., 2016)? A student with a low capacity in self-control and high social media overuse might not be able anymore to engage in social media dieting behaviour, as he can no longer weigh between negative long-term and positive short-term consequences – independent of his or her motivational self-control. This would resemble withdrawal-symptoms as in behavioural addictions (Piasecki et al., 2003; Turel et al., 2018), as low capacity-based self-control might trigger relapse.

This connection would find further support in the third finding of this study. Results revealed a marginally significant moderating influence of trait self-control on the effect of social media overuse on procrastinatory social media use, as expected in H3. This supports the assumption that for university students with higher levels of trait self-control the effect of social media overuse on procrastinatory social media use is smaller, which is also in line with

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previous research (Hofmann et al., 2016; Lian et al., 2018). Additionally, the reproduction of H3 with social media use in hours as independent variable also revealed a significant

interaction effect between social media use in hours and trait self-control on procrastinatory social media use. This provides further evidence for trait self-control as a moderating influence on the relationship of students’ social media use and their procrastinatory social media use.

In sum, the findings in this study show complex interactions between social media overuse, use in hours, procrastionatory social media use and trait self-control that also impact social media dieting behaviours. Considering the results and findings regarding H2, a three-way interaction between social media overuse, procrastinatory social media use and trait self-control on social media dieting seems possible. However, the sample in this study is too small to further explore this association. It might thus be an interesting track to follow in further research.

This study adds to the literature for several reasons. Firstly, it is the first one to conceptualize social media dieting behaviour and to relate it to social media overuse. Secondly, it contributes to procrastination research by identifying procrastinatory social media use as a moderating influence on social media dieting behaviour. Thirdly, it identifies social media dieting as a possible restraint standard associated to self-control and social media overuse and shows that university students’ self-control has a moderating effect on their procrastinatory social media use.

However, this study has a few limitations. Given the novelty of social media dieting behaviour as a variable, the concept and operationalization of the variable need to be reviewed and rebuilt in further research. The generalizability of the results in this study are limited to its population, university students, and therefore leave out possible differences in the effect for other populations. Most students in the sample were students of Communication Science and Business, who might naturally have higher social media affinity than other students and

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populations. Furthermore, the sample size is quite small for testing a moderated mediation model. Hence, the results should be interpreted with care. Also, the fact that both social media overuse and social media use in hours predict social media dieting behaviour show that the operationalization of the independent variable might be open to question. While the social media overuse scale directly asks the respondent for a perceived possible overuse in different aspects of life, social media use in hours could be a more objective measurement. For further research it might be interesting to use both scales and compare differences. How problematic the students perceive their social media usage might strongly influence the outcome of data analysis when the valence of the variable differs for each student.

Nevertheless, this research provides valuable insights into the recent and modern phenomenon of social media dieting behaviours and its association with social media overuse, and the possibly moderating influence of procrastinatory social media use and trait

self-control. It constitutes an important first approach to further empirical research in this field. The importance of further investigation is undeniable as the perceived need to take a social media break among students seems to be alarming symptoms for a misuse of these media. Especially since impaired well-being and increased stress have been related to social media overuse and procrastinatory social media use (Meier et al., 2016; Vannucci et al., 2017) and numbers of university students experiencing burn-out symptoms are rising (Schaufeli et al., 2002). The findings regarding H2, indicating a cut-off point for high social media overuse and low trait self-control, might support assumptions for social media addiction. Given that social media addiction has not been officially defined as a behavioural addiction, further research in this direction seems necessary.

A further investigation of the relation between social media overuse and social media dieting behaviour could therefore provide valuable information for interventions aiming at improving student’s well-being and stress-management skills with regards to social media overuse. The results of this study can increase awareness about university students’

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ambivalent relationship with social media, which they on the one hand overuse and on the other hand restrain from. They furthermore show that there might be differences in the effects of varying levels of usage, procrastination and self-control on university students stress levels which should be further investigated. Ideally, when research in this field is carried forward, the results can provide insights as to how social media abstinence might be a tool to assist university students who are more vulnerable to social media overuse and procrastinatory social media use in increasing their academic performance.

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APPENDIX Online Survey

Q1

I would like to start by asking you a few general questions about yourself.

What is your age in years?

________________________________________________________________ Q2

What is your gender?

o

Female (1)

o

Male (2)

o

Other (3) Q3

In which country do you currently reside? ▼ Afghanistan (1) ... Zimbabwe (1357)

Q4

Are you currently a student at a university, college or similar form of higher education?

o

Yes

o

No Q5

Below you find a collection of statements about yourself. Using the 1-5 scale below, please indicate to what extent you agree that each statement applies to you. There are no right or wrong answers. Please answer according to what really reflects your personality rather than what you think the right answer should be.

Strongly disagree Somewhat disagree Neither agree nor disagree Somewhat agree Strongly agree I am good at resisting temptation.

o

o

o

o

o

I have a hard time breaking bad

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habits.

I am lazy.

o

o

o

o

o

I do certain things that are bad

for me, if they are fun.

o

o

o

o

o

I wish I had more

self-discipline.

o

o

o

o

o

Pleasure and fun sometimes keep me from getting work done.

o

o

o

o

o

I have trouble concentrating.

o

o

o

o

o

I am able to work effectively

toward long-term goals.

o

o

o

o

o

I often act without thinking

through all the alternatives.

o

o

o

o

o

Q6

Below is a collection of statements about your everyday experience with social media. Using the 1-5 scale below, please indicate how frequently or infrequently you currently have each experience. There are no right or wrong answers. Please answer according to what really reflects your experience rather than what you think your experience should be.

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Never Rarely Sometimes Often All the time I use social media whenever I get the

chance to do so.

o

o

o

o

o

I check social media during class or

work.

o

o

o

o

o

When I'm not able to use social media, I become anxious about whether there are new posts, alarms, messages, etc.

o

o

o

o

o

I try to reduce my social media usage.

o

o

o

o

o

I end up using social media for a longer

period of time than originally intended.

o

o

o

o

o

My friends and/or family point out that I

spend a lot of time on social media.

o

o

o

o

o

I underreport the amount of time I spend

on social media.

o

o

o

o

o

I use social media instead of doing

things I need to do (e.g. homework).

o

o

o

o

o

When I use social media, I lose track of

how much time has passed.

o

o

o

o

o

I think about changing my social media

usage habits.

o

o

o

o

o

I think that I use social media

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Q7

Which one of the following options best describes your field of study? ▼ Art / Music (13) ... Other (15)

Q8

Which of the following social media platforms do you use on a daily basis? Multiple answers are possible. Please choose all that apply.

o

Facebook

o

Instagram

o

Pinterest

o

Snapchat

o

Youtube

o

Other

o

Neither Q9

How many hours per day do you spend using social media? Please estimate the average amount of hours. You could also consult screentime apps if you use them.

▼ 0 ... More than 8 hours

Q10

You will now see a collection of statements about your social media usage in connection with task completion. Using the 1-5 scale below, please again indicate how frequently or

infrequently you have each experience. Please answer according to what really reflects your experience rather than what you think your experience should be.

Never Rarely Sometimes Often All the time

I use social media although I have more

important things to do.

o

o

o

o

o

I use social media while putting off

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