Computers in Human Behavior 64 (2016) 65e76 Contents lists available at ScienceDirect Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh Full length article “Facebocrastination”? Predictors of using Facebook for procrastination and its effects on students’ well-being Adrian Meier*, Leonard Reinecke, Christine E. Meltzer Department of Communication, Johannes Gutenberg University Mainz, Germany a r t i c l e i n f o a b s t r a c t Article history: Received 13 January 2016 Received in revised form 30 April 2016 Accepted 12 June 2016 Available online 26 June 2016 Procrastinating with popular online media such as Facebook has been suggested to impair users’ wellbeing, particularly among students. Building on recent procrastination, self-control, and communication literature, we conducted two studies (total N ¼ 699) that examined the predictors of procrastination with Facebook as well as its effects on students’ academic and overall well-being. Results from both studies consistently indicate that low trait self-control, habitual Facebook checking, and high enjoyment of Facebook use predict almost 40 percent of the variance of using Facebook for procrastination. Moreover, results from Study 2 underline that using Facebook for the irrational delay of important tasks increases students’ academic stress levels and contributes to the negative well-being effects of Facebook use beyond the academic domain. The implications of investigating procrastination as a specific pattern of uncontrolled and dysfunctional media use are discussed with regard to research on the uses and effects of ubiquitous online media. © 2016 Elsevier Ltd. All rights reserved. Keywords: Procrastination Social network sites Facebook Self-control Well-being University students 1. Introduction The pervasive access to social media such as Facebook creates new self-control challenges for a growing number of Internet users in different spheres of life (e.g., Hofmann, Vohs, & Baumeister, 2012; Masur, Reinecke, Ziegele, & Quiring, 2014; Panek, 2014; Xu, Wang, & David, 2016). Student users, in particular, report that the social network site (SNS) Facebook ‘makes them’ lose track of time and that they delay tasks they actually intended to get done, such as writing term papers or preparing for final exams, ‘because of Facebook’ (e.g, Rosen, Carrier, & Cheever, 2013). Studies finding a negative relationship between conscientiousness and Facebook use among students suggest that low self-control may be a central driver of this unintended Facebook use (e.g., Lee-Won, Herzog, & Park, 2015; Wilson, Fornasier, & White, 2010). Moreover, research on the uses and gratifications of social media has consistently identified the use of Facebook “to put off something I should be doing” (Quan-Haase & Young, 2010, p. 356) as one of the strongest motives of Facebook use (Papacharissi & Mendelson, 2011; Smock, Ellison, Lampe, & Wohn, 2011). * Corresponding author. Department of Communication, Johannes Gutenberg University Mainz, Jakob-Welder-Weg 12, 55099 Mainz, Germany. E-mail addresses: meier@uni-mainz.de (A. Meier), reineckl@uni-mainz.de (L. Reinecke), meltzer@uni-mainz.de (C.E. Meltzer). http://dx.doi.org/10.1016/j.chb.2016.06.011 0747-5632/© 2016 Elsevier Ltd. All rights reserved. Students, in particular, seem to irrationally delay (i.e., procrastinate) important academic tasks in favor of Facebook use, which has been suggested to be responsible for a large part of the negative relationship between Facebook use and academic performance (Junco, 2012; Kirschner & Karpinski, 2010; Panek, 2014; Rosen et al., 2013; Thompson, 2013). Moreover, initial evidence indicates that procrastination with Facebook is particularly detrimental to students’ well-being (Hinsch & Sheldon, 2013), which is supported by research on the negative consequences of general, non-mediarelated procrastinatory behavior among students (Kim & Seo, 2015; Sirois & Kitner, 2015; Steel, 2007). Although Facebook is among the most widely used online applications around the globe (Alexa, 2015; Facebook, 2015; Pew Research Center, 2015) and several independent lines of research suggest that Facebook is a frequently used, but detrimental “tool for procrastination” (Lavoie & Pychyl, 2001, p. 433) among students, evidence of this practice of ‘Facebocrastination’ is scarce. The present research thus aims at furthering our limited understanding of the uses and effects of procrastination with the popular SNS Facebook. Specifically, the predictors of procrastination with Facebook and its effects on academic and overall well-being will be investigated. In the following section, we will first review evidence on media-related procrastination based on the prevailing understanding of procrastination as irrational task delay (Sirois & 66 A. Meier et al. / Computers in Human Behavior 64 (2016) 65e76 Pychyl, 2013; Steel, 2007). By conceptualizing procrastination with Facebook as a self-control failure (Hofmann, Friese, & Strack, 2009; Steel, 2007), we will then identify dispositional (trait self-control) and Facebook-specific precursors (habitual Facebook checking and enjoyment of Facebook use) that could predict the frequency of procrastination with Facebook. Moreover, we will discuss the potentially detrimental effects of procrastination with Facebook on students’ academic and overall well-being. Based on two studies using data from two student samples, we will subsequently address the predictors (Studies 1 and 2) and effects (Study 2) of procrastination with Facebook. The results will be discussed with regard to their implications for everyday social media use as well as future research on the uses and effects of constantly available online media. 2. Theoretical background 2.1. Media as “tools for procrastination” Consistent with recent procrastination literature, we define procrastination as the “self-regulatory failure of not exerting selfcontrol necessary for task engagement” (Sirois & Pychyl, 2013, p. 116). Essentially, procrastinators give in to pleasant short-term temptations such as checking Facebook instead of engaging in intended, but subjectively aversive tasks such as writing a term paper. According to this typical procrastination scenario, the procrastinatory activity (i.e., checking Facebook) provides the procrastinator with immediate gratifications such as the satisfaction of relatedness needs via Facebook use (Reinecke, Vorderer, & Knop, 2014; Sheldon, Abad, & Hinsch, 2011). In contrast, the procrastinated task (i.e., writing a term paper) is often perceived as stressful, frustrating, or boring and thus increases short-term negative affect during task engagement (Pychyl, Lee, Thibodeau, & Blunt, 2000). Moreover, procrastinated tasks typically provide only distal rewards (e.g., good grades or a higher salary) and are hedonically less attractive in the here and now than proximal competing activities (e.g., ‘just quickly’ checking Facebook, getting coffee, or watching a video clip on YouTube). In line with this conceptualization of procrastination, engaging in an intended, but aversive task requires the exertion of self-control since self-control is a crucial ability drawn on by individuals when they prioritize long-term goals over short-term desires (Hofmann et al., 2009; Sirois & Pychyl, 2013). A further defining characteristic that distinguishes procrastination from more active and strategic forms of delay is its irrational and dysfunctional nature (Steel, 2007). Although procrastinators seem to experience some short-term positive affect from indulging in pleasurable substitute activities such as Facebook use, they typically realize that their delay of important tasks is futile, irrational, and self-harming in the long term (Sirois & Pychyl, 2013). Hence, the short-term affective benefits of irrational delay are outweighed by its long-term costs (e.g., Tice & Baumeister, 1997). We thus understand procrastination with Facebook not as a functional gratification sought by or obtained from Facebook use (cf., Quan-Haase & Young, 2010), but as a dysfunctional behavioral outcome of deficient self-control processes that drive exposure to Facebook. Several researchers have recently identified procrastination as a pervasive pattern of Internet and computer use (e.g., Breems & Basden, 2014; Myrick, 2015). Only few studies, however, have explicitly investigated the predictors of procrastination with media content and mediated communication so far. Experience sampling research by Hofmann et al. (2012) demonstrates that people frequently give in to media desires, even though they try to resist them. In fact, engaging in media use despite conflicts with other goals and tasks seems to be one of the most common forms of selfcontrol failure in peoples’ everyday lives, suggesting a high prevalence of media-related task procrastination. Furthermore, Panek (2014) recently investigated the role of self-control for college students’ media use. In his correlational study, low trait self-control was related to increased time spent on leisure media use and decreased time on self-directed learning. From this, he concluded that students often give in to proximal media use opportunities that provide short-term ‘guilty pleasures’ compared to important, but aversive academic tasks. Notably, the use of social media was particularly strongly related to low trait self-control, implying frequent uncontrolled and possibly procrastinatory use of SNS such as Facebook. Research on media multitasking has further underlined that media-induced task-switching during (academic) work is driven by high arousal and hedonic pleasure elicited by the media activity, as well as low cognitive control over the switching behavior (e.g., David, Kim, Brickman, Ran, & Curtis, 2015; Xu et al., 2016; van der Schuur, Baumgartner, Sumter, & Valkenburg, 2015). The findings of this line of research suggest that media in general and social and mobile media in particular are often selected impulsively and in an uncontrolled manner during work sessions, although users intend to work on more important tasks. This ‘media-induced taskswitching’ has been found to significantly impair students’ performance and well-being (e.g., Rosen et al., 2013; Xu et al., 2016). Together, the studies outlined above provide implicit evidence for high levels of media-related procrastination due to low levels of self-control and impulsive selection of hedonically pleasant media stimuli. Two recent studies further corroborate this finding by explicitly linking self-control to procrastination with media content. In a cross-sectional study, Reinecke, Hartmann, and Eden (2014) found that TV and video game use after work was perceived as procrastination when participants reported lower levels of state self-control. Procrastination, in turn, was related to higher levels of guilt about media use. The findings thus suggest that participants perceived their uncontrolled after-work media use as the result of impulsively ‘giving in’ to media temptation instead of pursuing activities that would have been more aligned with their long-term goals (e.g., sports). A recent experience sampling study (Reinecke & Hofmann, 2016) further substantiates the link between media use and self-control failure: Participants reported that media use conflicted with other important goals on more than half of all media use occurrences (61.2%), underlining that media use poses a particularly difficult self-regulatory challenge for many people in day-to-day settings. Moreover, higher trait self-control significantly predicted decreased procrastination with media content, which supports the notion that self-control is a key factor for media-related procrastination (Reinecke & Hofmann, 2016). 2.2. Procrastination with Facebook as self-control failure The available evidence suggests a link between procrastination with both offline and online media and trait self-control, indicating that media use for procrastination is a consequence of self-control failure. Thus, the first goal of our research is to investigate whether low trait self-control also drives the use of Facebook for procrastination. In line with self-control research, we refer to trait selfcontrol as individual differences in the capacity to override or inhibit problematic behavioral tendencies and desires (Hofmann et al., 2009; Tangney, Baumeister, & Boone, 2004). Trait selfcontrol has been found to predict numerous positive behavioral outcomes such as better academic grades, fewer unhealthy eating A. Meier et al. / Computers in Human Behavior 64 (2016) 65e76 behaviors, and less substance abuse (e.g., Tangney et al., 2004; de Ridder, Lensvelt-Mulders, Finkenauer, Stok, & Baumeister, 2011). Individuals that frequently succeed in restraining their problematic desires and in attaining their personal goals also report better psychological adjustment, less psychopathology, more positive momentary affect, and more life satisfaction (Hofmann, Luhmann, Fisher, Vohs, & Baumeister, 2014; Tangney et al., 2004). In conclusion, self-control can be characterized as being “among humankind’s most valuable assets” (Hofmann et al., 2014, p. 265) and as a key dispositional factor for a wide range of self-regulatory outcomes, including frequent procrastination (Sirois & Pychyl, 2013; Steel, 2007). Since prior research has demonstrated that trait selfcontrol is a negative predictor of procrastination with both offline and online media (Reinecke & Hofmann, 2016), we thus predict that trait self-control is negatively related to the frequency of procrastination with Facebook (H1). Beyond dispositional levels of self-control, prior research suggests that impulsive selection of hedonically tempting media activities contributes to uncontrolled and procrastinatory use of these media. Thus, the second goal of our research is to investigate whether impulsive media selection is a further predictor of procrastinatory Facebook use, operating as an antagonist to the selfcontrol efforts of limiting one’s media use when working on intended tasks. Recent research indicates that self-control failure is often driven by strong impulsive desires that overwhelm individuals’ self-control ability (Heatherton & Wagner, 2011; Hofmann & van Dillen, 2012; Hofmann, Kotabe, & Luhmann, 2013). Such impulsive behavior is particularly likely when individuals are confronted with stimuli that elicit strong automatic reactions. According to Hofmann et al. (2009), individuals associate desirable stimuli (e.g., a delicious candy) with automatic impulses. Specifically, individuals develop automatic approachavoidance tendencies towards desired stimuli. The stronger these automatic tendencies are, the more likely the execution of impulsive behavior (e.g., reaching for the candy jar) becomes, even if it is inconsistent with long-term goals (e.g., weight loss) (Hofmann et al., 2009). We propose that this influence of automatic approach reactions on impulsive behavior can be applied to media selection as well. Specifically, we argue that automatic approach reactions predict procrastination with Facebook as a form of self-control failure. The importance of automatic approach reactions for media use is substantiated by research on media habits (e.g., LaRose, 2010; Naab & Schnauber, 2014): In many situations, users do not deliberately ponder over whether or not they should engage in media use (e.g., check their Facebook account). Instead, media exposure is initiated unconsciously through media habits. Media habits thus represent automatic behavioral responses that rely on mental scripts (LaRose, 2010; Naab & Schnauber, 2014) and learned stimulus-response associations (Gardner, 2015). More specifically, habits are characterized by automatic and impulse-driven initiation of behavior (Gardner, 2015; Gardner, Abraham, Lally, & Bruijn, 2012; Naab & Schnauber, 2014). Thus, the more habitually a medium is used, the more likely the medium is selected automatically and impulsively. Several studies underline that Facebook use is a strongly habitual activity (Giannakos, Chorianopoulos, Giotopoulos, & Vlamos, 2013; Papacharissi & Mendelson, 2011; Smock et al., 2011) and that texting applications such as the Facebook messenger are used in a highly automatic and impulsive fashion (Bayer, Dal Cin, Campbell, & Panek, 2016). A recent multi-method study found that Facebook is among the most frequently ‘checked’ mobile applications, suggesting that automatic selection in form of habitual Facebook checking is an integral part of Facebook use (Oulasvirta, Rattenbury, Ma, & Raita, 2012). Since 67 habitually checking Facebook represents an automatic approach reaction that is likely to interfere with important tasks at hand (Hofmann et al., 2009), we believe that it is a key driver of procrastinatory Facebook use. We thus predict that habitually checking Facebook is positively related to the frequency of procrastination with Facebook (H2). In addition to automatic approach reactions such as habits, we further propose that associating enjoyment with specific media activities is an important predictor of irrational and impulsive selection of these media despite facing important tasks at hand. Procrastination research (Sirois & Pychyl, 2013) has recently proposed that procrastinators often prioritize the positive affect elicited by hedonically pleasant procrastinatory activities (e.g., meeting friends or watching TV) over the attainment of long-term goals. Thus, associating pleasurable experiences and enjoyment with a certain activity (e.g., Facebook use) increases the probability that individuals are tempted by this activity when they simultaneously face an aversive task (Sirois & Pychyl, 2013). Notably, an early study on Internet use and procrastination by Lavoie and Pychyl (2001) supports this notion of the pivotal role of enjoyment for procrastination. In their cross-sectional survey, the authors found that participants who perceived Internet use as more entertaining and enjoyable also reported higher levels of online procrastination. Accordingly, they concluded that “Internet users appear to be entertaining themselves into task postponement” (Lavoie & Pychyl, 2001, p. 441). Recent research further indicates that Facebook use is a highly enjoyable activity that satisfies basic psychological needs through online entertainment and social interaction (Reinecke, Vorderer et al., 2014; Sheldon et al., 2011). Combining these results on the enjoyment of Facebook use with the predictions made by procrastination research (Sirois & Pychyl, 2013), we argue that higher enjoyment of Facebook use should increase the risk of being tempted by Facebook and, hence, impulsively selecting Facebook even if it conflicts with more important activities. We thus predict that the enjoyment of Facebook use is positively related to the frequency of procrastination with Facebook (H3). In this section, we have argued that the frequency of procrastination with Facebook can be predicted by low trait self-control and impulsive selection of Facebook due to strong checking habits and high Facebook enjoyment. Initial evidence further suggests that procrastination with Facebook affects the well-being of Facebook users (e.g., Hinsch & Sheldon, 2013), particularly in the academic domain (Kim & Seo, 2015; Sirois & Kitner, 2015; Steel, 2007). However, prior research on the consequences of procrastination with media content and mediated communication is scarce. We thus aim at extending prior research by investigating the link between procrastination with Facebook and students’ well-being. In the following section, we will first review the available evidence on the consequences of procrastination and then link these results to recent research suggesting negative effects of procrastinatory Facebook use on well-being. 2.3. Consequences of procrastination with Facebook Research has identified several detrimental consequences of procrastination for task performance and well-being. A specific focus of this research has been on the consequences of dilatory behavior in the academic domain (Kim & Seo, 2015; Steel, 2007). Procrastination is particularly prevalent among university students, who typically face complex tasks in self-directed learning settings that leave a lot of leeway for ‘slacking’ and irrational delay. Moreover, Facebook seems to be a particularly prominent tool for procrastination among students (Hinsch & Sheldon, 2013; Quan-Haase & Young, 2010). In the present investigation, we thus focus on the 68 A. Meier et al. / Computers in Human Behavior 64 (2016) 65e76 negative effects of procrastination with Facebook in the college student population.1 A large body of research underlines the negative consequences of procrastination for academic performance (Kim & Seo, 2015; Steel, 2007). Delaying intended academic tasks can result in poorer performance on subsequent trials to complete the tasks, for example, because time pressure rises (Ferrari, 2001). As students become aware of their decreasing performance due to procrastination, they start to ruminate (Flett, Stainton, Hewitt, Sherry, & Lay, €ber & Joormann, 2001) about their delay, 2012) and worry (Sto which leads to increased anxiety (Lay & Schouwenburg, 1993) and feelings of guilt (Pychyl et al., 2000). These negative selfevaluations greatly impair procrastinators’ psychological wellbeing. Specifically, several studies show a strong link between procrastination and stress: As procrastinators finally tackle their postponed work, they typically struggle with the drawbacks of decreased time for task completion as well as negative selfevaluative thoughts and emotions, which increases levels of stress (e.g., Flett et al., 2012; Sirois & Kitner, 2015; Sirois, 2014). Furthermore, in a longitudinal study conducted among students (Tice & Baumeister, 1997), procrastination of academic tasks was not only related to stress as the deadline got closer during the semester, but also to symptoms of stress-related illness. Taken together, the results from procrastination research clearly demonstrate the negative consequences of procrastinatory behavior for students’ academic performance and well-being (Kim & Seo, 2015; Sirois & Kitner, 2015). We propose that procrastination with Facebook will have similar detrimental effects in the academic domain and that these effects extend to students’ overall well-being. This rationale is supported by communication research linking Facebook use to decreased academic performance (e.g., Junco, 2012; Kirschner & Karpinski, 2010; Rosen et al., 2013), which suggests that students frequently and irrationally turn to Facebook instead of their academic tasks (i.e., procrastinate with Facebook). Due to the resulting decrease in academic performance, procrastination with Facebook could thus elicit strong stress reactions in the academic domain (e.g., feeling ‘crushed’ or ‘overwhelmed’ by the academic work that is piling up due to procrastination). Accordingly, we predict that the frequency of procrastination with Facebook is positively related to academic stress (H4). Beyond these effects of procrastination with Facebook on domain-specific academic stress, procrastination research underlines the detrimental consequences of procrastination for students overall well-being (i.e., their well-being beyond the academic domain) (Steel, 2007). Initial evidence from cross-sectional and experience sampling research has also linked Facebook use to impaired overall well-being among students (Kross et al., 2013; Satici & Uysal, 2015). Experimental research by Sagioglou and Greitemeyer (2014) further indicates that Facebook use impairs affective well-being (e.g., current mood) because it is perceived as “less meaningful, less useful, and more of a waste of time” than other (online) activities (p. 361). Since procrastination is a highly 1 Although more prevalent among students, procrastination is also a highly relevant and detrimental behavior in the general population. Studies using multinational samples underline that procrastinatory behavior can become chronic and problematic for about 20% of the adult population (Ferrari, Diaz-Morales, O’Callaghan, Diaz, & Argumedo, 2007; Steel & Ferrari, 2013). Accordingly, we believe that procrastination with widely popular online media such as Facebook is also relevant in the general population. However, for the purpose of this early research on the psychological predictors and effects of procrastination with media use, we focus on students as a population that is particularly likely to procrastinate with online tools such as Facebook (Hinsch & Sheldon, 2013; Quan-Haase & Young, 2010; Steel, 2007) dysfunctional activity that is essentially characterized by a meaningless ‘wasting of time’, these results clearly suggest negative effects of procrastination with Facebook on affective well-being. Moreover, a recent intervention study found that decreases in social media use were associated with decreased procrastination and increased cognitive well-being (i.e., life satisfaction) over time (Hinsch & Sheldon, 2013). Together, the available evidence suggests that procrastination with social media may play a significant role in the negative relationship between general Facebook use and overall well-being. Accordingly, we propose that procrastination with Facebook contributes to the strains that Facebook use places on students’ overall well-being beyond the academic domain. We thus hypothesize that the frequency of procrastination with Facebook is positively related to Facebook-induced strains on students’ overall well-being (H5). To address the hypothesized predictors (H1eH3) and detrimental effects (H4eH5) of procrastination with Facebook, we conducted two studies. Study 1 was designed to establish, whether Facebook is a frequently used ‘tool’ for procrastination among students and whether the frequency of procrastination with Facebook can be predicted by trait self-control (H1), habitually checking Facebook (H2), and the enjoyment of Facebook use (H3). Study 2 was designed as a follow-up study with two primary goals: (a) to test whether the hypothesized predictors of procrastination with Facebook can be replicated in a second student sample and (b) to examine whether procrastination with Facebook is positively associated with academic stress (H4) and Facebook-induced strains (H5). Additionally, Study 2 addressed some of the methodological limitations of Study 1. 3. Study 1 3.1. Method 3.1.1. Participants In Study 1, hypotheses H1eH3 were tested with data from a convenience sample of student Facebook users. Participants were recruited by 30 undergraduate students through their online social networks on Facebook. Our student recruiters were enrolled in the communication program at a large University in Germany. Recruiters were asked to distribute the link to an open online survey among their friends on Facebook via public status updates, by posting in Facebook groups, or by writing personal messages. When following the link, participants were first provided with general information about the aim of the study and then asked for their consent. Afterwards they completed the measures reported below as well as additional questions about their general social media use. Finally, participants provided demographic information and were asked whether they studied at a German university. Seven hundred seventy-one individuals started the survey, 480 of which completed the questionnaire (62%). Of those 480 respondents, 120 were excluded because they were not students and another five cases were excluded due to missing data. Thus, our final sample consisted of N ¼ 354 student Facebook users (71.2% female, Mage ¼ 22.89, SD ¼ 2.51). Frequent use of Facebook was a common activity in the sample: Seventy-eight percent of participants reported that they use Facebook on six or seven days of a typical week. On average, participants estimated their use of Facebook at about 73 min per day (SD ¼ 77). Compared to the results of a representative study on German Internet users (Busemann, 2013), these usage patterns closely resemble those of the German population between the ages 14 to 29. In this population, 75 percent report daily social media use and an average of 87 min spent on social media per day. A. Meier et al. / Computers in Human Behavior 64 (2016) 65e76 69 Table 1 Means, standard deviations, scales, reliabilities, and zero-order correlations for SEM variables (Study 1). 1. 2. 3. 4. Trait self-control FB checking habit FB enjoyment Frequency of procrastination with FB M SD Range a CR 1. 2. 3. 4. 2.94 3.99 3.73 3.43 0.72 1.74 0.92 0.98 1e5 1e7 1e5 1e5 0.82 0.87 0.75 0.90 0.82 0.87 0.75 0.90 e 0.17** 0.04 0.40*** e 0.21** 0.36*** e 0.25*** e Note. Based on N ¼ 354 participants and two-tailed significance tests. High values (5 or 7) represent high levels for each construct. FB ¼ Facebook. CR ¼ composite reliability. * p < 0.05, **p < 0.01, ***p < 0.001. 3.1.2. Measures All constructs were measured using multi-item Likert scales, which showed satisfactory internal consistencies (Cronbach’s alpha) and composite reliabilities (CR) > 0.70 (see Table 1 for details on reliabilities, means, and standard deviations).2 We conducted a series of exploratory factor analyses (EFA) with varimax rotation on all scales in Study 1 to first test the structure of each construct before conducting further analyses. Trait self-control was measured with eight items from the Brief Self-Control Scale (Tangney et al., 2004), which assesses the individual capacity to resist temptations and control unwanted urges. The items (e.g., “I am good at resisting temptation”) were measured on a scale from 1 (does not apply at all) to 5 (fully applies). The scale showed a unidimensional structure (eigenvalue ¼ 2.93) and acceptable factor loadings ranging from 0.48 to 0.74, which corresponds with the results of a recent revision of the scales psychometric properties (Lindner, Nagy, & Retelsdorf, 2015). Participants’ Facebook checking habit was assessed with four items from the Self-Report Habit Index (Verplanken & Orbell, 2003). These four items (“I often check my Facebook account without having to consciously remember”, “Using Facebook is something I do without thinking.”, “Sometimes I start using Facebook before I realize I’m doing it.”, “While logging in on Facebook, I think about completely different things.”) reflect automatic activation of Facebook checking behavior (Gardner et al., 2012) and were measured on a scale from 1 (does not apply at all) to 7 (fully applies). The scale showed a unidimensional structure (eigenvalue ¼ 2.51) with factor loadings exceeding 0.65. Enjoyment of Facebook use was measured with two items that were adapted from studies by Quan-Haase and Young (2010) and Smock et al. (2011). Participants responded to the items “I use Facebook because it is fun” and “I use Facebook because it is entertaining” on a scale ranging from 1 (does not apply at all) to 5 (fully applies). The scale had a unidimensional structure (eigenvalue ¼ 1.20) with factor loadings of 0.77. Finally, participants reported the frequency of their procrastination with Facebook by responding to four items from the Procrastination Scale (Tuckman, 1991), which was already used to measure procrastination with media use in previous research (Reinecke, Hartmann et al., 2014). The items were adapted to measure Facebook use as irrational procrastinatory behavior (“I used Facebook although I had more important things to do”, “I used Facebook although I had more important things to do.”, “I used Facebook although I knew that I had an important task to complete.”, “I used Facebook although I had planned to get something done.”) on a scale ranging from 1 (never) to 5 (very often). Participants were asked to estimate the frequency of procrastination with Facebook by thinking about their Facebook use in the last half year when working at a computer. The scale showed a unidimensional structure (eigenvalue ¼ 2.79) with factor loadings exceeding 0.79. 2 All measures used in Studies 1 and 2 can be obtained from the first author upon request. 3.2. Results In order to establish whether Facebook is used as a tool for procrastination as suggested by prior research, we first conducted a descriptive analysis of the reported frequency of procrastination with Facebook. Including all four items used to measure procrastination with Facebook in a mean index and using a conservative estimate (M < 2.00 on a scale from 1 to 5), only nine percent of participants (n ¼ 31) reported that, on average, they had “never” procrastinated with Facebook in the last half year. In fact, procrastination with Facebook was found to be a frequent behavior in our student sample (M ¼ 3.43, SD ¼ 0.98, scale 1e5). Before testing our hypotheses, we then investigated the convergent and discriminant validity of all constructs based on the average variance extracted (AVE), the maximum shared variance (MSV), and the average shared variance (ASV). Convergent validity was satisfactory for all scales with AVEs > 0.50, except for trait selfcontrol (AVE ¼ 0.37). However, all variables, including trait selfcontrol, showed satisfactory discriminant validity with AVEs considerably larger than the MSV and ASV values. The suboptimal level of convergent validity for trait self-control corresponds with the moderate factor loadings found in our previous exploratory factor analysis (see 3.1.2 Measures). Although these values demonstrate room for improvement, they mirror the findings of a recent investigation of the psychometric properties of the Brief Self-Control Scale (cf., Lindner et al., 2015). This low convergent validity is indicative of the complexity of trait self-control, which is expressed in the diversity of the items of the Brief Self-Control Scale (Tangney et al., 2004). However, a unidimensional structure of the scale seems to outperform alternative two-dimensional structures (Lindner et al., 2015). Since the Brief Self-Control Scale is a frequently used and prevalidated measure of trait self-control that shows convergent validity with other self-control measures (Duckworth & Kern, 2011) and connects to a large body of existing research (de Ridder et al., 2011), we decided to retain this variable in our model in its current form. We thus continued our analysis by testing the hypothesized relationships. To test H1eH3, we computed a structural equation model (SEM) using the AMOS 22 software package. Additionally, means, standard deviations, and zero-order correlations for all SEM variables were calculated using SPSS 22 (see Table 1 for details). In Model 1 (Fig. 1), trait self-control, Facebook checking habit, and Facebook enjoyment were included as exogenous variables and the frequency of procrastination with Facebook was included as the endogenous variable. Two covariances between exogenous variables (checking habit and trait self-control as well as checking habit and enjoyment) were included in the model due to moderate zeroorder correlations (see Table 1). The maximum likelihood (ML) method was chosen for model estimation. Although the data were not normally distributed (Mardia’s multivariate kurtosis estimate ¼ 28.109), the ML method was chosen as it is comparatively robust against variations in kurtosis (Olsson, Foss, Troye, & Howell, 2000). To cope with non-normality, however, all hypotheses were tested using the bootstrapping 70 A. Meier et al. / Computers in Human Behavior 64 (2016) 65e76 Trait SelfControl -.17 -.41 * *** e1 R²=.40 FB Checking Habit ** .29 ** Procrastination with FB *** *** .26 .27 FB Enjoyment Fig. 1. SEM for trait self-control, Facebook checking habit, and Facebook enjoyment as predictors of procrastination with Facebook (Study 1) Note. Observed structural equation model based on N ¼ 354 participants. Fit indices are c2 ¼ 316.460, df ¼ 130, p < 0.001, c2/df ¼ 2.434, CFI ¼ 0.932, RMSEA ¼ 0.064 (90% C.I.: 0.055, 0.073), SRMR ¼ 0.060. Scores in the figure represent standardized path coefficients. Significance levels are based on 5000 bootstrap samples with replacement and 95% bias-corrected confidence intervals. FB ¼ Facebook. *p < 0.05, **p < 0.01, ***p < 0.001. method. Based on 5000 bootstrap samples with replacement, 95% bias-corrected confidence intervals were computed for significance testing of all parameters in Model 1 (Fig. 1). The model showed an acceptable fit (Little, 2013, pp. 109e117) to the data (c2 ¼ 316.460, df ¼ 130, p < 0.001, c2/df ¼ 2.434, CFI ¼ 0.932, RMSEA ¼ 0.064 (90% C.I.: 0.055, 0.073), and SRMR ¼ 0.060) and confirmed hypotheses H1eH3 (see Fig. 1). As expected (H1), trait self-control was negatively and significantly related to the frequency of procrastination with Facebook (b ¼ 0.41, p < 0.001). In turn, habitually checking Facebook (b ¼ 0.29, p < 0.01) and enjoyment of Facebook use (b ¼ 0.26, p < 0.001) were positively and significantly related to the frequency of procrastination with Facebook, confirming H2 and H3. Together, the three predictors explained 40% of the variance in the frequency of procrastination with Facebook (R2 ¼ 0.40, p < 0.01). 3.3. Discussion Study 1 was designed to assess whether the proposed set of predictors (H1eH3) can explain the frequency of procrastination with Facebook. The observed structural relationships provided considerable support for the three hypothesized predictors of procrastination with Facebook (trait self-control, habitual Facebook checking, and enjoyment of Facebook use). However, in order to reduce the possibility that these results were specific to the sample under investigation, we aimed at replicating this model in a second sample. In addition to establishing the validity of our hypothesized predictors, Study 2 also aimed at addressing the possible consequences of procrastination with Facebook (H4 and H5), thus significantly extending the findings of Study 1. 4. Study 2 4.1. Method 4.1.1. Participants We conducted a second online survey with data from another convenience sample of student Facebook users from Germany. Analogously to Study 1, participants were recruited via undergraduates’ social networks on Facebook. Our 28 student recruiters were enrolled in the communication program at the same University as in Study 1 and followed the same procedure as in Study 1. In contrast to Study1, however, non-student participants were filtered out at the beginning of the survey, rather than excluded from data analyses subsequently. Five-hundred sixty-one individuals started the survey, 369 of which reported to be students and completed the questionnaire (66%). A total of 35 cases were excluded due to missing data, resulting in a final sample of N ¼ 345 student Facebook users (62.3% female, Mage ¼ 21.17, SD ¼ 1.98). Intensity of Facebook use was comparable to Study 1 (M ¼ 73 min Facebook use on a typical day, SD ¼ 90). 4.1.2. Measures Trait self-control and Facebook checking habit were measured with the scales outlined in Study 1. A series of EFAs with varimax rotation were conducted on all scales in Study 2 to test the structure of each construct before conducting further analyses. The scale measuring trait self-control again showed a unidimensional structure (eigenvalue ¼ 2.84) and factor loadings ranging from 0.43 to 0.74 (cf., Lindner et al., 2015). The measure of participants’ Facebook checking habit also showed a unidimensional structure (eigenvalue ¼ 2.09) and factor loadings ranging from 0.55 to 0.83. The frequency of procrastination with Facebook was measured with the same items used in Study 1. However, we extended the scope of the procrastination measure to include all forms of procrastinatory Facebook use. Thus, participants in Study 2 were asked to base their estimates of the frequency of procrastination with Facebook on their overall Facebook use (as compared to their Facebook use in the last half year in Study 1), including mobile access to Facebook via smartphones and tablets. Again, the scale showed a unidimensional structure (eigenvalue ¼ 3.13) and factor loadings exceeding 0.86. Furthermore, to avoid problems with Heywood cases in the subsequent structural equation modelling (Chen, Bollen, Paxton, A. Meier et al. / Computers in Human Behavior 64 (2016) 65e76 Curran, & Kirby, 2001), enjoyment of Facebook use was measured with three items in Study 2 (as compared to only two items in Study 1). The items (“I enjoy using Facebook”, “Using Facebook is fun.”, “I feel entertained by the use of Facebook.”) were taken from a recent study that specifically focused on the enjoyment of Facebook use (Reinecke, Vorderer et al., 2014). The scale had a unidimensional structure (eigenvalue ¼ 1.56) with factor loadings exceeding 0.60. To assess negative consequences of procrastination with Facebook, two additional variables were included in Study 2: First, participants reported their level of perceived academic stress on three items adapted from the Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983). The items (“I feel very stressed when I try to get work for my studies done”, “It feels as if the difficulties in my studies are piling up so high that I cannot overcome them.”, “I feel unable to cope with all the upcoming task in my studies.”) were rated on a scale from 1 (never) to 6 (very often). The scale showed a unidimensional structure (eigenvalue ¼ 2.04) with factor loadings exceeding 0.80. As a second measure of well-being, participants reported how often they perceived strains in different spheres of life as a consequence of their overall Facebook use on a scale from 1 (never) to 6 (very often). Six items measuring Facebook-induced strains on wellbeing (“My Facebook use …”: “… impairs my general well-being.”, “… puts strains on my personal relationships.”, “… leads to stress in my day to day life.”, “… makes it more difficult to relax in my day to day life.”, “… impairs my temporary moods.”, “… hinders my personal growth.”) were developed based on prior research (e.g., Beutel et al., 2011; Kross et al., 2013; Sagioglou & Greitemeyer, 2014) that assessed negative consequences of Internet use on different well-being dimensions (e.g., global, affective, or social well-being, Huta & Waterman, 2014). The scale showed a unidimensional structure (eigenvalue ¼ 3.34) and factor loadings exceeding 0.61. All scales in Study 2 showed satisfactory internal consistencies (Cronbach’s alpha) and composite reliabilities (CRs) > 0.70 (see Table 2 for details). 4.2. Results We began our analysis by testing the convergent and discriminant validity of all constructs based on their AVE, MSV, and ASV values. As in Study 1, convergent validity was satisfactory for all scales with AVEs > 0.50, except for trait self-control (AVE ¼ 0.36). All variables, including trait self-control, showed satisfactory discriminant validity with AVEs considerably larger than the MSV and ASV values. Consistent with the arguments presented in Study 1 (see 3.2 Results), we decided to continue our analysis albeit recognizing the limited convergent validity of the trait self-control measure. To replicate the results of Study 1 (H1eH3) and to test the hypothesized relationships in H4 and H5, a second SEM was computed. The model (Model 2, Fig. 2) included the same exogenous variables, endogenous variables, and covariances as in Study 1. Additionally, academic stress (H4) and Facebook-induced strains (H5) were included as endogenous variables, reflecting the consequences of frequent procrastination with Facebook (see Table 2 for details on SEM variables). The data in Study 2 was not normally distributed (Mardia’s multivariate kurtosis estimate ¼ 80.919). Thus, again, the model was estimated with the ML method and significance of all parameters was tested with the bootstrapping method. The model showed a good fit to the data (c2 ¼ 507.654, df ¼ 343, p < 0.001, c2/df ¼ 1.480, CFI ¼ 0.962, RMSEA ¼ 0.037 (90% C.I.: 0.030, 0.044), and SRMR ¼ 0.070) and supported hypotheses H1eH5 (see Fig. 2). Confirming the results of Study 1, trait selfcontrol was also negatively and significantly related to the 71 frequency of procrastination with Facebook in Study 2 (b ¼ 0.33, p < 0.001). Moreover, habitually checking Facebook (b ¼ 0.31, p < 0.001) and enjoyment of Facebook use (b ¼ 0.31, p < 0.001) were again positively related to the frequency of procrastination with Facebook. Thus, all hypothesized relationships (H1eH3) for the predictors of procrastination with Facebook (R2 ¼ 0.37, p < 0.01) were replicated in Study 2. Furthermore, as hypothesized in H4 and H5, the frequency of procrastination with Facebook was positively related both with academic stress (b ¼ 0.28, p < 0.001) and FBinduced strains on well-being (b ¼ 0.44, p < 0.001). Procrastination with Facebook explained significant portions of variance in both variables (R2 ¼ 0.08 and R2 ¼ 0.19, respectively, both p < 0.001). As we found substantial direct effects for all hypothesized relationships in Study 2, we additionally tested the indirect effects of trait self-control, checking habit, and Facebook enjoyment on users’ well-being via procrastination with Facebook. To compare possible indirect effects with respective direct effects, we computed an alternative version of the model depicted in Fig. 2 that additionally included six direct paths from our three exogenous variables (i.e., trait self-control, checking habit, and Facebook enjoyment) to the two endogenous variables academic stress and Facebook-induced strains. Significance levels of standardized direct and indirect effects were based on 5000 bootstrap samples with replacement and 95% bias-corrected confidence intervals (see Table 3 for details). The model showed a good fit to the data (c2 ¼ 443.991, df ¼ 337, p < 0.001, c2/df ¼ 1.317, CFI ¼ 0.975, RMSEA ¼ 0.030 (90% C.I.: 0.022, 0.038), and SRMR ¼ 0.048) and results indicated substantial indirect effects. The frequency of procrastination with Facebook significantly mediated the effects of trait self-control (b ¼ 0.09, p < 0.001), Facebook checking habit (b ¼ 0.09, p < 0.001), and Facebook enjoyment (b ¼ 0.09, p < 0.001) on academic stress as well as the effects of trait self-control (b ¼ 0.12, p < 0.001), Facebook checking habit (b ¼ 0.11, p < 0.001), and Facebook enjoyment (b ¼ 0.12, p < 0.001) on Facebook-induced strains on students’ overall well-being.3 Results also indicated several significant direct effects between the exogenous variables and the two well-being indicators, which are largely consistent with our general argumentation (see Table 3). Notably, however, Facebook enjoyment showed small, but significant negative direct effects on both academic stress (b ¼ 0.17, p < 0.05) and Facebook-induced strains (b ¼ 0.20, p < 0.01), working in opposite direction to the positive indirect effects and resulting in nonsignificant total effects (see zero-order correlations in Table 2). 4.3. Discussion Study 2 was designed to replicate the relationships investigated in Study 1 (H1eH3) and to extend the hypothesized model by including possible effects of procrastination with Facebook on users’ well-being (H4 and H5). The results of Study 2 underline that trait self-control, habitually checking Facebook, and enjoyment of Facebook use distinctly contribute to the frequency of procrastination with Facebook. Notably, the effects found for the hypothesized relationships showed remarkable equivalence in standardized size, significance, and explained variance in both studies (see Figs. 1 and 2 for details). In Study 2, we also assessed the frequency of procrastination with Facebook as a predictor of perceived academic stress and 3 Please note that the equivalence in size of the beta coefficients for the indirect effects is due to the almost equivalent effect sizes for the paths from trait selfcontrol, Facebook checking habit, and Facebook enjoyment to procrastination with Facebook as well as subsequent rounding errors (see Fig. 2 for details). 72 A. Meier et al. / Computers in Human Behavior 64 (2016) 65e76 Table 2 Means, standard deviations, scales, reliabilities, and zero-order correlations for SEM variables (Study 2). 1. 2. 3. 4. 5. 6. Trait self-control FB checking habit FB enjoyment Frequency of procrastination with FB Academic stress FB-induced strains M SD Range a CR 1. 2. 3. 4. 5. 6. 2.87 3.87 3.27 3.45 3.36 2.00 0.71 1.60 0.74 1.42 1.20 0.97 1e5 1e7 1e5 1e6 1e6 1e6 0.81 0.81 0.75 0.94 0.86 0.88 0.81 0.81 0.76 0.93 0.86 0.88 e 0.17** 0.08 0.36*** 0.29*** 0.19*** e 0.16** 0.36*** 0.02 0.38*** e 0.33*** 0.04 0.01 e 0.25*** 0.40*** e 0.19*** e Note. Based on N ¼ 345 participants and two-tailed significance tests. High values (5, 6, or 7) represent high levels for each construct. FB ¼ Facebook. CR ¼ composite reliability. * p < 0.05, **p < 0.01, ***p < 0.001. Trait SelfControl e2 R²=.08 -.17 -.33 * *** e1 *** R²=.37 FB Checking Habit *** ** .28 Academic Stress Procrastination with FB .31 *** e3 R²=.19 *** *** * .44 .31 .16 *** FB-induced Strains FB Enjoyment Fig. 2. SEM for predictors of procrastination with Facebook and effects on academic stress and FB-induced strains on well-being (Study 2) Note. Observed structural equation model based on N ¼ 345 participants. Fit indices are c2 ¼ 507.654, df ¼ 343, p < 0.001, c2/df ¼ 1.480, CFI ¼ 0.962, RMSEA ¼ 0.037 (90% C.I.: 0.030, 0.044), SRMR ¼ 0.070. Scores in the figure represent standardized path coefficients. Significance levels are based on 5000 bootstrap samples with replacement and 95% bias-corrected confidence intervals. FB ¼ Facebook. * p < 0.05, **p < 0.01, ***p < 0.001. Table 3 Standardized direct effects of trait self-control, Facebook checking habit, and Facebook enjoyment on academic stress and Facebook-induced strains and respective indirect effects via procrastination with Facebook (Study 2). Academic stress Direct effect 1. Trait self-control 2. FB checking habit 3. FB enjoyment ** 0.26 [0.40, 0.12] 0.11 [0.25, 0.02] 0.17* [0.31, 0.03] FB-induced strains Indirect effect *** 0.09 [0.16, 0.05] 0.09*** [0.04, 0.16] 0.09*** [0.04, 0.13] Facebook-induced strains on overall well-being. The results support the notion that procrastination with Facebook is associated with higher academic stress and more Facebook-induced strains. Moreover, the frequency of procrastination with Facebook mediated the effects of all three predictors in our model (H1eH3) on academic stress and Facebook-induced strains on well-being. Together, the significant direct and indirect effects found in our SEM analysis provide strong support for our proposed theoretical model. Intriguingly, our supplemental mediation analysis also revealed an ambiguous role of Facebook enjoyment for students’ well-being: Enjoyment indirectly increased academic stress and Facebook-induced strains by increasing procrastination with Facebook. Simultaneously, however, enjoyment of Facebook use directly decreased participants’ self-reported academic stress and Direct effect Indirect effect 0.01 [0.14, 0.12] 0.33*** [0.20, 0.45] 0.20** [0.32, 0.08] 0.12*** [0.19, 0.07] 0.11*** [0.07, 0.18] 0.12*** [0.07, 0.19] Facebook-induced strains. 5. General discussion Several lines of research suggest that Facebook is a frequently used ‘tool for procrastination’, which could be particularly detrimental to the well-being of students. The central aim of this study was to integrate and extend previous findings on the procrastinatory use of Facebook. Specifically, our goal was to a) identify important predictors of procrastination with popular online media such as Facebook and b) investigate whether frequent procrastination with Facebook affects students’ psychological well-being in the academic domain and beyond. The findings from two consecutive studies confirm that students’ frequently turn to A. Meier et al. / Computers in Human Behavior 64 (2016) 65e76 Facebook to procrastinate and consistently support our theoretical assumptions on the predictors and effects of procrastination with Facebook. The first part of our hypothesized model addressed the predictors of procrastination with Facebook and highlighted the pivotal role of self-regulation processes for using Facebook as a means of irrational task delay (i.e., procrastination). Our review suggested that trait self-control as well as precursors of impulsive media selectiondspecifically, habitual Facebook checking and high Facebook enjoymentdpredict the frequency of procrastination with online media such as Facebook. The results from our two studies consistently confirm our assumptions (H1eH3) and underline that procrastination with Facebook is a form of “quintessential self-regulatory failure” (Steel, 2007, p. 65) driven by low trait self-control, strong Facebook checking habits, and high enjoyment of Facebook use. The second part of our model addressed the potentially detrimental effects of procrastination with Facebook on users’ wellbeing. We specifically focused our investigation on students’ academic stress and overall well-being, as prior research suggests that students are among the most likely individuals to be affected by procrastination with Facebook. The results from Study 2 support our notion that procrastination with Facebook contributes to both the detrimental consequences of Facebook use in the academic domain (H4) and to students’ overall well-being (H5). The more frequently students procrastinated with Facebook, the more their academic stress increased and the more they reported strains resulting from their overall Facebook use on several indicators of well-being (e.g., temporary mood, personal relationships, and personal growth). The present research thus significantly extends prior work on media-related procrastination. Specifically, our studies have two main implications for research on the uses and effects of (social) online media. First, our research integrates the fragmentary evidence on media-related procrastination and provides a coherent model of procrastination with media content and mediated communication as an outcome of deficient self-regulatory processes. The results underline that not only media users’ capacity for general self-control but also their tendency to impulsively and unintentionally select specific media activities due to strong habits and high media enjoyment drive procrastinatory media use. Together, our set of predictors significantly and consistently explained almost 40 percent of the variance in the frequency of procrastination with Facebook in both studies. Our work thus extends prior communication research by demonstrating that users’ unique patterns (i.e., habitualization) and appraisal (i.e., enjoyment) of media use crucially influence the prevalence of procrastination with these media beyond the mere quantity of media use (cf., Hinsch & Sheldon, 2013). Second, our results underline that these specific patterns of Facebook use affect users’ well-being: As indicated by the significant mediation effects in Study 2, trait self-control, enjoyment, and habitualization of Facebook use were linked to increased academic stress and Facebook-induced strains by increasing the frequency of procrastination with Facebook. Beyond confirming the central role of self-control for the consequences of procrastination with Facebook, our results thus advance prior research by revealing the crucial role of enjoyment and habitualization in the interplay of procrastinatory Facebook use and well-being: At first sight, the enjoyment of Facebook use may seem to represent a primarily functional Facebook experience that satisfies the user’s need for affective well-being and mood repair. However, our results indicate that appraising media use as a highly enjoyable activity can also drive dysfunctional forms of media use due to increased procrastination that subsequently impairs users’ well-being. Moreover, the 73 link between habitual Facebook checking, increased procrastination with Facebook, and decreased well-being supports the notion that being permanently online and permanently connected (Vorderer & Kohring, 2013) can have negative consequences on users’ everyday lives. Individuals that frequently and habitually check Facebook to obtain new (social) information and social interaction seem to be specifically prone to use Facebook even in situations where its usage conflicts with more important upcoming tasks, which reduces their performance and well-being. Although the results from our two studies provide consistent support for the hypothesized model, the present research comes with a number of limitations. The first limitation concerns the cross-sectional nature of our data, which confines our structural model to correlational interpretations. Several of the proposed relationships could also show a reversed direction of effects: The negative affect associated with stress and impaired well-being, for example, could also predict students’ procrastination with Facebook as it might make students more susceptible to hedonically pleasant activities such as Facebook use, even though they have important work to complete (Sirois & Pychyl, 2013). However, the evidence provided by prior research supports the causal directions implied by our model. Academic stress, for example, seems to be a consequence rather than a predictor of procrastination, as demonstrated by research using longitudinal designs (e.g., Tice & Baumeister, 1997). Moreover, our hypothesized predictors represent either relatively stable dispositional variables (trait selfcontrol) or patterns of overall Facebook use (Facebook checking habit and Facebook enjoyment) that should influence the more narrowly defined use of Facebook for procrastination and not vice versa. Nonetheless, the reciprocal effects of well-being, self-control, and procrastination with media content should be investigated by future research using longitudinal or experimental designs. A second methodological limitation pertains to the measure we used to assess participants’ level of trait self-control in both studies. Although the Brief Self-Control Scale (Tangney et al., 2004) is a well-established and commonly used measure in self-control research (de Ridder et al., 2011) that has shown to be predictive of procrastination with media content (Reinecke & Hofmann, 2016), our analyses indicated limited convergent validity in two student samples. Future research should thus test whether the connection between trait self-control and procrastination with Facebook can be replicated with different measures of self-control capacity, such as delay of gratification tasks or peer ratings (Duckworth & Kern, 2011). A third limitation concerns our focus on students. Procrastination is particularly common and detrimental in the academic context, because students have considerable leeway in switching between self-directed learning sessions and unstructured leisure time (Kim & Seo, 2015). Thus, students’ performance and wellbeing is particularly dependent on their self-regulatory skills, specifically, the ability to resist ubiquitously available online and offline leisure temptations such as media use (Hofmann et al., 2012). It is unclear and an important task for future research to investigate whether our theoretical model of online procrastination with Facebook can be replicated in the general population. Notably, research on procrastination with media content has consistently confirmed frequent use of online media for procrastination among diverse adult samples (e.g., Lavoie & Pychyl, 2001; Myrick, 2015; Reinecke & Hofmann, 2016). We are thus confident that the same basic psychological self-control processes identified in the present study apply to procrastination with online media in the general population. A final limitation concerns our focus on the negative outcomes of procrastination with Facebook. As suggested by recent research (Sirois & Pychyl, 2013), procrastination is driven by procrastinators’ 74 A. Meier et al. / Computers in Human Behavior 64 (2016) 65e76 desire for short-term increases in positive affect and their need for distraction from aversive tasks. However, the results from our mediation analysis in Study 2 revealed that positive affect associated with Facebook use (i.e., Facebook enjoyment) did not only increase participants’ procrastination with Facebook, but also served as a protective factor that seemed to reduce participants’ academic stress levels and the frequency with which they perceived strains on their well-being as a consequence of their Facebook use. Thus, it would be particularly interesting to further investigate whether procrastination with Facebook results in (short-term) affective well-being benefits, for example, through need satisfaction (Reinecke, Vorderer et al., 2014), self-affirmation (Toma & Hancock, 2013), or social sharing on Facebook (Choi & Toma, 2014). A recent study found that social support via Facebook can serve as a stress buffer (Nabi, Prestin, & So, 2013), which, in turn, may counteract the negative well-being effects of irrational procrastination with Facebook. Consequently, we believe that future research can benefit from a more fine-grained perspective on the uses and effects of online media such as Facebook by addressing the dynamics between short-term benefits and longterm costs of Facebook use for well-being, particularly in the context of procrastination. While our research provides several key insights into the nature of procrastination with Facebook as well as the role of self-control processes in media use, several open questions remain. Specifically, the generalizability of our proposed predictor model should be investigated with regard to other offline and online media. It could be a promising task for future research to compare how the unique affordances of popular interpersonal (e.g., Instagram, Snapchat, or WhatsApp), interactive (e.g., browser games), and non-interactive (e.g., Netflix) forms of social and entertaining online media use shape the prevalence and consequences of procrastination. Anecdotal evidence further suggests that non-hedonic online media such as news websites or Wikipedia, or less demanding online tasks such as checking one’s email, are the preferred tools for procrastination among some individuals. It could be argued that these forms of procrastination are easier to justify and rationalize as they are more likely to be perceived as ‘meaningful’ activities, leaving procrastinators with less detrimental or even positive effects on their well-being (cf., Sagioglou & Greitemeyer, 2014). We encourage other researchers to explore the potentially complex interplay between the selection of hedonic vs. non-hedonic and ‘meaningful’ vs. ‘meaningless’ media activities for procrastination and its effects on different dimensions of well-being (e.g., hedonic vs. eudaimonic well-being, Huta & Waterman, 2014; Oliver & Bartsch, 2010). Beyond these more general avenues for future research, we would like to outline two particular benefits of a) investigating procrastination as a specific and reoccurring pattern of media use and of b) integrating self-control theory into research on the uses and effects of media. We believe that our results provide valuable impulses for research on the effects of online media use in learning environments (e.g., Thompson, 2013) as well as research on media multitasking (e.g., van der Schuur et al., 2015). Specifically, the evidence reviewed and presented in this paper suggests that procrastination is both an important moderator and mediator for the effects of online media use on students’ academic performance and well-being: Students showing a relatively stable tendency to procrastinate (i.e., high trait procrastination, Steel, 2007) should suffer from negative consequences of dilatory Facebook use more frequently, suggesting that procrastinatory tendencies moderate the negative relationship between overall Facebook use and academic performance and well-being. Considering the tendency to procrastinate as a moderator could thus help to clarify some of the conflicting evidence on the outcomes of social media use (e.g., Thompson, 2013). Moreover, procrastination with online media such as Facebook may mediate the negative effects of media multitasking on performance and well-being. When students frequently switch from academic offline tasks to online media activities due to impulsive media selection, their media use can easily extend to the point that it conflicts with more important tasks. The frequency of “media-induced task switching” (Rosen et al., 2013, p. 948) could thus increase the frequency of reported procrastination with media, which, in turn, should mediate the negative effects of task-switching on performance and well-being. Moreover, multitasking research could significantly benefit from investigating whether students’ (trait and state) capacity for self-control operates as a moderator of these effects. Finally, our study did not address how individuals cope with procrastinatory media use that interferes with their long-term goals and psychological well-being. Hofmann et al. (2009) propose that in reaction to detrimental self-control failure, individuals reflect on their behavior and form deliberate self-control standards. Specifically, they argue that implementing restraint standards (e.g., keeping a diet) should counteract automatic impulsive reactions such as habitual media selection. Media users struggling with frequent media-related procrastination could thus attempt to implement restraint standards that reduce their dilatory media use. Recent research suggests that behavioral interventions, for example, temporary reductions in Facebook use, are effective in decreasing overall procrastination and in increasing life satisfaction among students (Hinsch & Sheldon, 2013). Moreover, many users seem to voluntarily commit to such “Facebook vacations” during periods of increased work demand (Rainie, Smith, & Duggan, 2013). Further investigating ‘media diets’ and ‘media hiatus’ as preventive and interventive self-control strategies (Hofmann & Kotabe, 2012) aimed at reducing procrastination thus seems to be a fruitful endeavor for future research. 6. Conclusion Overall, the present research furthers our understanding of uncontrolled and potentially detrimental media use by investigating what drives students’ use of Facebook for procrastination. The results from our two studies crucially extend prior research on media use and procrastination by demonstrating that trait selfcontrol and users’ specific patterns and appraisal of Facebook use (i.e., habitualization and enjoyment) are crucial predictors of procrastination with Facebook. Moreover, our results support the notion that ‘Facebocrastination’ significantly impairs students’ academic and overall well-being. 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