Social media use in lectures mediates the relationship between procrastination and problematic smartphone use

Accepted Manuscript Social media use in lectures mediates the relationship between procrastination and problematic smartphone use

Dmitri Rozgonjuk, Mari Kattago, Karin Täht PII:

S0747-5632(18)30378-9

DOI:

10.1016/j.chb.2018.08.003

Reference:

CHB 5637

To appear in:

Computers in Human Behavior

Received Date:

08 May 2018

Accepted Date:

01 August 2018

Please cite this article as: Dmitri Rozgonjuk, Mari Kattago, Karin Täht, Social media use in lectures mediates the relationship between procrastination and problematic smartphone use, Computers in Human Behavior (2018), doi: 10.1016/j.chb.2018.08.003

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ACCEPTED MANUSCRIPT Social media use in lectures mediates the relationship between procrastination and problematic smartphone use Dmitri Rozgonjuk 1, 2, * Mari Kattago 1 Karin Täht 1

1 2

Institute of Psychology, University of Tartu, Näituse 2, Tartu 50409, Estonia

Department of Psychology, University of Toledo, 2801 W Bancroft St, Toledo, Ohio 43606, USA

Reprint requests can be addressed to Dmitri Rozgonjuk, Institute of Psychology, University of Tartu, Näituse 2, Tartu 50409, Estonia. Email correspondence may be initiated via:
 [email protected] For the Journal Editor and Production Staff: For fastest correspondence from this journal about this submission, e-mail Dmitri Rozgonjuk at: [email protected]

ACCEPTEDAND MANUSCRIPT RUNNING HEAD: PROCRASTINATION PROBLEMATIC SMARTPHONE USE Social media use in lectures mediates the relationship between procrastination and problematic smartphone use ABSTRACT Problematic smartphone use (PSU) has been consistently shown to relate to dysfunctional behaviors and negative daily life outcomes, including in academic context. One explanatory factor could be procrastination - yet it has not been studied how procrastination is related to PSU. The aim of this research was to study that relationship. Participants were 366 Estonian university students who responded to the Estonian Smartphone Addiction Proneness Scale, Aitken Procrastination Inventory, and items regarding social media use in lectures via an online survey. Correlation analysis and structural equation modelling were used to investigate the relationships between procrastination, PSU, and social media use in lectures. The results showed that procrastination and PSU and were positively correlated. Furthermore, social media use in lectures completely mediated that relationship, suggesting that students who tend to procrastinate may engage in more social media use in lectures, and that may be a driver of PSU. In addition to theoretical contribution, this study could contribute to discussions on ICT use in educational context. Keywords: problematic smartphone use; smartphone addiction; social media use; procrastination; higher education

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1. Introduction Smartphones could enhance productivity by providing possibilities to be virtually almost always available online, communicate with others, browse for information, and to use various productivity enhancement applications. Smartphones could also be utilized beneficially in educational contexts (Sung, Chang, & Liu, 2016). However, problematic smartphone use (PSU) has been shown to be negatively related to various daily life outcomes, such as different psychological disorders (Contractor, Weiss, Tull, & Elhai, 2017; Elhai, Dvorak, Levine, & Hall, 2017), sleep quality (Demirci, Akgonul, & Akpinar, 2015), and academic outcomes (Lepp, Barkley, & Karpinski, 2015; Samaha & Hawi, 2016). One of the most popular activities carried out with smartphones is social media use (Lopez-Fernandez et al., 2017; Oulasvirta, Rattenbury, Ma, & Raita, 2011). Nowadays, social media could also be one of the main activities which is related to procrastination (Przepiorka, Błachnio, & DíazMorales, 2016). In this paper, we investigate the relationships between PSU, procrastination, and social media use in lectures. 1.1.

Problematic smartphone use

Problematic smartphone use (PSU) is characterized by addiction-like excessive smartphone use behavior, associated with detrimental effects on one’s daily life (Billieux, Maurage, Lopez-Fernandez, Kuss, & Griffiths, 2015). This concept is closely related to other conditions that include excessive smartphone use in association with negative outcomes - one may find literature on smartphone addiction (Kwon et al., 2013), proneness to smartphone addiction (Rozgonjuk, Rosenvald, Janno, & Täht, 2016), smartphone overuse (Inal, Demirci, Cetintürk, Akgonül, & Savas, 2015; Lee et al., 2017), and excessive smartphone use (Chen, Liang, Mai, Zhong, & Qu, 2016). Although there may be nuanced differences between these conditions, the common denominator for all is the excessive use of and/or time spent on smartphone use, and detrimental/problematic relationships it may have with other everyday

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life constructs. Even though earlier studies applied more addiction terminology (e.g., Internet or smartphone addiction), the problematic technology use terminology has been used to refer to less severe or emergent states of excessive technology use (Tokunaga, 2015). Addiction terminology may be more prevalent in clinical research where, e.g., Internet addiction has been treated as pathological form of Internet use with serious negative effects in different life-domains (Müller, Dreier, & Wölfling, 2017). Yet, because there is conceptual confusion with the construct and there are no standardized diagnostic criteria of smartphone addiction, using the term problematic smartphone use has been suggested (Panova & Carbonell, 2018). In this study, we will conform with this suggestion. Even though some studies have provided prevalence statistics for smartphone addiction, e.g., Aljomaa, Al.Qudah, Albursan, Bakhiet, and Abduljabbar (2016) reported that 48% of their sample was smartphone addicted, and Billieux et al. (2015) have provided a brief overview of studies with relatively heterogeneous prevalence statistics, ranging from 0% to 35%, this information may be confusing for the reasons mentioned above (e.g., no standardized criteria). Additionally, what counts as a behavioral addiction, and whether PSU could be regarded as an addiction within such a theoretical framework, is still a matter of debate (Billieux et al., 2017; Griffiths, 2017; Kardefelt-Winther et al., 2017; Sussman, Rozgonjuk, & van den Eijnden, 2017). Notwithstanding the above, the literature suggests the phenomenon is likely prevalent (excessive) smartphone use and detrimental daily life outcomes have come from both South and North America (Khoury et al., 2017; Wolniewicz, Tiamiyu, Weeks, & Elhai, 2018), Europe (Haug et al., 2015; Lopez-Fernandez, 2017; Lopez-Fernandez et al., 2017), and Asia (Aljomaa et al., 2016; Kwon et al., 2013; Lian, You, Huang, & Yang, 2016), suggesting that PSU may be a cross-cultural phenomenon.

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Typical findings with regards to PSU are the detrimental relationships with different psychopathologies or emotional problems, such as depression and anxiety (Elhai et al., 2017; Elhai, Levine, Dvorak, & Hall, 2016), social phobia (Enez Darcin et al., 2016), and aggressive behavior (Lee et al., 2016); those with higher PSU tend to be more impulsive (Kim et al., 2016) and inattentive (Marty-Dugas, Ralph, Oakman, & Smilek, 2017). In addition, it has been shown that PSU is negatively related to academic outcomes (Lepp et al., 2015; Samaha & Hawi, 2016) and higher levels of PSU are linked to more superficial approach to studying (Rozgonjuk, Saal, & Täht, 2018). 1.2.

Procrastination

In general, procrastination has been thought of as an irrational and problematic behavior where executing important tasks is delayed until the subjective feeling of discomfort (Aitken, 1982; Solomon & Rothblum, 1984; Steel & Klingsieck, 2016). It is characterized by three components: (a) the activity has to be related to delaying an activity or a decision, (b) the delay is non-intentional, and (c) it results in poorer performance or execution of the task (Milgram, Marshevsky, & Sadeh, 1995; Steel, 2007). In addition to poorer attainment in tasks, procrastination is associated with additional stress and depression (Sirois, 2007; Steel & Ferrari, 2013). Students with higher trait procrastination tend to have poorer academic outcomes (Lay & Schouwenburg, 1993), suggesting that academic settings may be one of the environments where trait procrastination may matter. Several studies have also shown that academic procrastination, or procrastinatory activities related to academic activities, is negatively associated with learning, academic achievements (Balkis & Duru, 2015; Kim & Seo, 2015; Klassen & Kuzucu, 2009; Motie, Heidari, & Sadeghi, 2012), and self-regulation in academic settings (Park & Sperling, 2012). More procrastination has been associated with higher impulsivity (Dewitte & Schouwenburg, 2002). In addition, nature of the task is relevant, with more boring, difficult

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or unpleasant tasks leading to more delays in execution (Milgram et al., 1995). The context of the task is also relevant. It has been found that people who do not usually delay the execution of their tasks in one setting (e.g., at home) may do so in an academic setting (Milgram et al., 1995; Moon & Illingworth, 2005). Dewitte and Schouwenburg (2002) have also found that more procrastination and poorer concentration are associated with social distractors, such as friends, media, and social media among others. Although procrastination has not been extensively researched in the context of (problematic) smartphone use, there are studies that have linked procrastination to other (problematic) technology use, such as Internet and social media. Studies have relatively consistently demonstrated that problematic Internet use is positively correlated with procrastination across different samples, such as college students (Davis, Flett, & Besser, 2002), adolescents (Reinecke et al., 2018), and adults (Reinecke et al., 2016), even suggesting procrastination to be a central symptom of problematic technology use (Davis et al., 2002). Though a study by Odaci (2011) has looked into the relationship between academic procrastination and problematic Internet use (finding no significant correlation between these constructs), it has not been investigated how PSU could be related to procrastination. 1.3. Social media use in relation to procrastination and PSU Social media could be defined as Web 2.0 based computer-mediated applications with user-generated content, including user-specific profile (Obar & Wildman, 2015). Social media is designed and maintained by social media services that also facilitate the development of online social networking by connecting users' content and profiles (Obar & Wildman, 2015). Among the more popular social media platforms are Facebook, Twitter, Instagram, YouTube, and blogs. Social media is typically accessible over devices with Internet access, such as smartphones. Therefore, it may not be surprising that social media

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use is one of the most common activities in smartphones (Jeong, Kim, Yum, & Hwang, 2016; Kwon et al., 2013) and has been considered as a vulnerability factor for forming PSU (LopezFernandez et al., 2017). Hofmann, Reinecke, and Meier (2017) have proposed the design features that may drive higher social media engagement: immediate gratifications, habitualized usage, ubiquitous availability, and attentional demands. Social media provides immediate gratifications by satisfying people's basic needs, such as social connectedness (Ryan & Deci, 2000). Constant availability, that may be further enhanced by smartphones with Internet access, may add an additional challenge to abstain from checking smartphones for social media notifications, especially in individuals with lower self-control (Hofmann et al., 2017). Such checking behavior maybe be strongly habitual (Oulasvirta et al., 2011), potentially further reinforcing the behavior. Finally, instant messages and push notifications are designed to demand the attention of the user, and this may further engage the person with their device and social media (Hofmann et al., 2017). These central features of social media may be attractive for procrastinatory behavior. Media use in general has been linked to procrastination in several studies (Panek, 2013; Reinecke, Hartmann, & Eden, 2014; Reinecke & Hofmann, 2016; Schnauber-Stockmann, Meier, & Reinecke, 2018). In addition, research has suggested that procrastination is specifically related to social media use (Hinsch & Sheldon, 2013; Meier, Reinecke, & Meltzer, 2016; Myrick, 2015). As mentioned earlier, more procrastination is associated with poorer academic achievement (Balkis & Duru, 2015; Kim & Seo, 2015; Klassen & Kuzucu, 2009; Motie et al., 2012), and more social media use, too, has been associated with detrimental academic outcomes (Baturay & Toker, 2016; Junco, 2012b; Kirschner & Karpinski, 2010; Rozgonjuk, Saal, et al., 2018). These findings may indicate that higher procrastinators engage in more social media use that, in turn, may result in poorer educational

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outcomes because procrastinators may miss out on learning content due to procrastination on social media. Because smartphones allow for ubiquitous availability of social media use, this may, in turn, drive the engagement with smartphones, possibly reaching problematic levels of technology use. 1.4. Aims Research on PSU has recently demonstrated that PSU is associated with several detrimental outcomes, such as more severe psychopathology symptoms (e.g., depression and anxiety, reviewed in Elhai et al., 2017) and poorer academic outcomes (Lepp et al., 2015; Samaha & Hawi, 2016). However, thus far, it has not been investigated how procrastination and PSU are related to each other. Therefore, the current study aims to provide insights into (1) if and how are procrastination and PSU related, (2) how procrastination and social media use in lectures are associated, and, finally, (3) if social media use in lectures mediates the relationship between procrastination and PSU. Investigating these relationships may provide additional knowledge to PSU studies, as procrastination may be one explanatory driving factor of PSU. In addition, including social media use in lectures into this relationship may further specify the mechanism in the association. 1.5. Hypotheses and Research Model We have posed three hypotheses, derived from the previous empirical findings. Recent literature has consistently demonstrated that PSU is associated with maladaptive and dysfunctional affective, cognitive, and behavioral tendencies and outcomes, such as psychopathologies and transdiadnostic constructs (Elhai et al., 2017; Elhai, Levine, O’Brien, & Armour, 2018; Hoffner & Lee, 2015), but also poorer academic outcomes (Lepp et al., 2015; Samaha & Hawi, 2016) and more surface approach to studying (Rozgonjuk, Saal, et al., 2018). Procrastination has been regarded as a maladaptive behavior and trait, commonly associated with emotional discomfort, including depression, guilt, and anxiety (Kim & Seo, 2015; Schouwenburg &

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Lay, 1995; Solomon & Rothblum, 1984). In the context of academic achievement, procrastination has previously been shown to be associated with poorer academic outcomes (Kim & Seo, 2015). In addition, several studies have demonstrated that procrastination is related to problematic uses of other technologies, such as Internet (Davis et al., 2002; Reinecke et al., 2016; Reinecke et al., 2018).Therefore, we have posed the following hypothesis: H1: Procrastination and problematic smartphone use in lectures should be correlated. Studies have demonstrated that media use in general (Panek, 2013; Reinecke et al., 2014; Reinecke & Hofmann, 2016; Schnauber-Stockmann et al., 2018), but also social media use in specific (Panek, 2013; Reinecke et al., 2014; Reinecke & Hofmann, 2016; Schnauber-Stockmann et al., 2018) are associated with procrastinatory activities. Social media design features, such as providing instant gratifications, may propagate higher social media engagement (Hofmann et al., 2017). Our second hypothesis, therefore, is: H2: Procrastination and social media use in lectures should be correlated. Finally, we expect that higher propensity for procrastination may lead to more consumption of social media in lectures, and excessively engaging in that activity may reflect in higher levels of PSU. This model is novel, as research on how PSU and procrastination should be related, and what drives that relationship, is scarce. Because smartphones allow for almost constant access to social media, one may be inclined to use their smartphone for using social media that has typically been found to be one of the most popular activities in people with higher levels of PSU (Jeong et al., 2016; Kwon et al., 2013). In fact, social media use has been suggested as a vulnerability factor for PSU (Lopez-Fernandez et al., 2017). This leads to our third hypothesis: H3: Social media use in lectures should mediate the relationship between the levels of procrastination and problematic smartphone use. Based on these hypotheses, we propose a research model where procrastination is a predictor of PSU, and social media use in lectures mediates this relationship. In addition, we

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have also included age and gender as covariates of PSU, as studies have previously demonstrated that younger age and female gender may predict smartphone use engagement (Rozgonjuk et al., 2016; van Deursen, Bolle, Hegner, & Kommers, 2015). The graphical representation of this model is presented in Figure 1. We used observed summed scores for procrastination and social media use in lectures, and age and gender as covariates predicting a latent higher-order factor score for PSU. Figure 1 The hypothesized research model

2. Method 2.1.

Sample and Procedure

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Three hundred and sixty-six Estonian university students (age ranging from 19 to 55, Mage = 25.75 ± 7.70 years; 78.69% were female) took part in a web survey. The participants were recruited by sending the information about the study to different Estonian universities’ mailing lists. The participants were asked about their general socio-demographics, including age and gender. Additionally, they filled out the Estonian Smartphone Addiction Proneness Scale (Rozgonjuk et al., 2016), Aitken Procrastination Inventory (Aitken, 1982), and they responded to questions regarding the frequency of social media use in lectures. The study was in accordance with the Helsinki Declaration, and the research project was approved by the board of research of a psychology department in a major Estonian university. 2.2.

Measures

We asked about participants' demographics, such as age and gender, and, in addition, they filled out the following questionnaires: The Estonian Smartphone Addiction Proneness Scale (E-SAPS18) was developed by Rozgonjuk et al. (2016) and is based on the work by Kwon et al. (2013). The E-SAPS18 is a five-dimensional questionnaire that consists of 18 items measuring the severity of PSU-related symptoms on a 6-point Likert scale (ranging from 1 = strongly disagree to 6 = strongly agree). It consists of five factors (tolerance, positive anticipation, cyberspace-oriented relationships, withdrawal, and physical symptoms), its internal reliability is very good (Cronbach’s α = .87; α = .91 for the sample of this study), and its validity has been tested against other measures of problematic internet and smartphone use. Aitken Procrastination Inventory (API). The API was developed by Aitken (1982) to differentiate between high- and low-procrastinating students. It measures the extent of trait procrastination (Aitken, 1982; Kim & Seo, 2015). The scale consists of 19 items measured on a 5-point Likert-type scale (ranging from 1 = strongly disagree to 6 = strongly agree). The

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internal reliability has been reported as good, with coefficient alpha = .82 ( = .89 for the sample of this study). Social media use in lectures. Participants were asked two questions about social media use in lectures (Junco, 2012a; Rozgonjuk, Saal, et al., 2018): 1) How frequently do you keep track of social media in lectures? and 2) How often do you communicate with your friends using social networking sites (Facebook, Twitter, etc.) in lectures? The response scale ranged from 1 = never to 5 = all the time. These items were highly correlated (r = .753, p < .001) and were summed into one index reflecting social media use in lectures. 2.3.

Analysis

RStudio version 3.2.3 (R Core Team, 2017) was used for descriptive statistics and correlation analysis. Pearson product-moment correlation was used to analyze bivariate relationships between the variables. Mplus version 8 (Muthén & Muthén, 2017) was used for confirmatory factor analysis, structural equation modelling and mediation analysis. Firstly, confirmatory factor analysis was conducted for the E-SAPS18, and its five factors were modeled with a higher-order factor of PSU. Weighted least squares estimation with a mean- and varianceadjusted chi-square (WLSMV) was used, treating the items of these scales as ordinal/categorical, thus involving a polychoric covariance matrix and probit regression coefficients (DiStefano & Morgan, 2014). Similar procedure was applied for API. To compute the mediation effect, the cross-products of direct effects and the Delta method for computing indirect effect standard errors, with non-parametric bootstrapping across 1000 samples, were used(MacKinnon, 2008). Goodness of fit was judged by standard parameters: (a) Comparative Fit Index (CFI)  .90, (b) Tucker-Lewis Index (TLI)  .90, and (c) root mean square error of approximation (RMSEA)  .08 (Hu & Bentler, 1999). 3. Results 3.1.

Descriptive statistics and correlations

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The descriptive statistics and correlations between measures are presented in Table 1. Table 1 Descriptive statistics and Pearson correlations between the measures Variable

M

SD

1

7.70

-

1. Age

25.75

2. Problematic smartphone use

33.58

12.12 -.329***

3. Procrastination

52.97

12.50 -.062

4. Social media use in lectures

5.55

2.00 -.376***

2

3

.153**

-

.352***

.232***

Notes. N = 366. * p < .05, ** p < .01, *** p < .001.

It can be observed that PSU, procrastination, and social media use in lectures are significantly positively correlated. The association between PSU and procrastination yields a small effect, as is the case in the relationship between social media use in lectures and procrastination. PSU and social media use in lectures are moderately correlated. Age is moderately negatively correlated to both PSU and social media use in lectures, but not to procrastination. 3.2.

Structural equation model and mediation analysis

The results of confirmatory factor analysis showed that a five-factor solution of the ESAPS18 with a higher-order factor of problematic smartphone use provided adequate fit, χ2 (130, N = 366) = 441.846, CFI = .959, TLI = .952, RMSEA = .081 [90% CI: .073 to .089]. Because API did not show a very good fit, , χ2 (152, N = 366) = 1394.119, CFI = .837, TLI = .816, RMSEA = .149 [90% CI: .142 to .157], we used a summed score for API. Structural regression analysis was used to investigate the hypothesis that social media use in lectures mediates the relationship between procrastination and PSU. Age and gender were used as covariates for PSU. The model showed a good fit, χ2 (203, N = 366) = 487.889,

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CFI = .964, TLI = .960, and RMSEA = .062 [90% CI: .055 to .069]. The standardized coefficients and bootstrapped standard errors (over 1000 samples) are presented in Figure 2. Figure 2 Structural equation model

Notes. N = 366. *** p < .001. Standardized coefficients and bootstrapped standard errors are presented. Procrastination was a significant predictor of social media use in lectures, and social media use in lectures statistically significantly predicted the levels of PSU. These results are in support for the mediational hypothesis. In addition, PSU was controlled for age and gender; while younger age was statistically significantly predicting higher levels of PSU, gender was not a significant covariate. Though procrastination was correlated to the levels of PSU (as shown in Table 1), procrastination was not a significant predictor of the levels of PSU after controlling for social

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media use in lectures, consistent with full mediation. The indirect effect was tested using a bootstrap estimation approach with 1000 samples (Shrout & Bolger, 2002). The results show that the indirect coefficient was statistically significant,  = .080, SE = .022, z = 3.715, p < .001. 4. Discussion The aim of this study was to investigate how procrastination. Problematic smartphone use (PSU), and social media use in lectures are associated. Specifically, we posed three hypotheses. We expected that procrastination and PSU were correlated (H1), procrastination and social media use in lectures were correlated (H2), and social media use in lectures should mediate the relationship between procrastination and PSU (H3). 4.1.

Primary findings

Correlation analyses showed that procrastination and levels of PSU were positively correlated - although, the effect size was rather small. This finding supports our first hypothesis (H1), and might be logical, because PSU has been found to be negatively related to academic outcomes (Lepp et al., 2015; Samaha & Hawi, 2016) and more surface approach to studying (Rozgonjuk, Saal, et al., 2018). In addition, procrastination has also been associated with poorer academic performance (Kim & Seo, 2015). PSU as a maladaptive coping method is expected to be correlated to other dysfunctional behaviors (Elhai, Tiamiyu, & Weeks, 2018), such as procrastination which is also considered a maladaptive coping strategy (Kim & Seo, 2015; Schouwenburg & Lay, 1995; Solomon & Rothblum, 1984). In addition, studies on problematic Internet use and addiction have demonstrated that procrastination and problematic technology use are associated (Davis et al., 2002; Reinecke et al., 2016; Reinecke et al., 2018), even suggesting procrastination to be the central symptom of problematic technology use (Davis et al., 2002). The current study is the first to demonstrate that these findings also hold in the context of PSU.

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According to our second hypothesis (H2), we expected that procrastination would be associated with social media use in lectures, and this hypothesis found support from the data. Design features and formed usage patterns (e.g., instant gratifications, habitualized usage, ubiquitous availability, and attentional demands) may drive higher social media engagement and may also provide a platform for procrastinatory activities (Hofmann et al., 2017). In fact, it has been shown that higher social media engagement is related to more procrastinatory activities (Panek, 2013; Reinecke et al., 2014; Reinecke & Hofmann, 2016; SchnauberStockmann et al., 2018). Therefore, this hypothesis is in line with previous findings, suggesting that procrastination is also related with social media use in lectures. In addition, the bivariate correlation analyses showed that age is inversely related to both social media use in lectures and levels of PSU. These results are consistent with previous findings where it has been shown that younger people tend to exhibit more excessive technology use behavior (Elhai et al., 2017; Kuss & Griffiths, 2011; Rozgonjuk et al., 2016; van Deursen et al., 2015). However, gender was not a statistically significant covariate predicting the levels of PSU. We also proposed a model where procrastination tendency, considered to be as a trait characteristic (Kim & Seo, 2015) would predict the levels of PSU, and the extent of social media use in lectures would mediate the relationship (H3). Social media use is among the most popular activities that engage their users (Jeong et al., 2016; Kwon et al., 2013), and social media use has been considered as a vulnerability factor for PSU (Lopez-Fernandez et al., 2017). As smartphones enable the ubiquity of social media use, it seems logical to assume that those who tend to procrastinate more, engage in higher levels of social media use in lectures, and that drives the users' levels of PSU. Indeed, our results were in support for this model. In fact, the findings suggest that social media use in lectures completely mediates the relationship between procrastination and PSU. Therefore, it seems that social media use may

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be an essential variable explaining the bivariate relationship between procrastination and PSU. Additionally, research has previously demonstrated that several dysfunctional coping strategies with negative affect are related to higher levels of PSU. These findings include associations between PSU and distress tolerance (Elhai, Levine, et al., 2018), emotion regulation (Hoffner & Lee, 2015), ruminative thought style (Elhai, Tiamiyu, et al., 2018), and fear of missing out (Wolniewicz et al., 2018). It could be that social media use may further specify these relationships as a potential driver of PSU. 4.2.

Contribution, limitations, and future directions

There are two main theoretical contributions that this study provides. Although procrastination has been studied in relation to Internet addiction and problematic Internet use (Reinecke et al., 2016; Reinecke et al., 2018) and social media use (Hinsch & Sheldon, 2013; Meier et al., 2016; Myrick, 2015), research including PSU is scarce. Our study showed that as with other forms of problematic technology use, PSU, too, is associated with procrastination, adding to the body of evidence that demonstrate how dysfunctional behaviors are consistently related to higher levels of PSU (Elhai, Tiamiyu, et al., 2018). In addition, we proposed a novel model where the aim was to investigate how social media use in lectures could explain the bivariate findings. Social media use in lectures, in fact, fully mediates the relationship between procrastination and PSU, suggesting that students who are prone to procrastinate more may do that on social media, and that behavior may manifest in higher levels of PSU. Of course, we acknowledge that our measures were self-reported and crosssectional, as discussed below. Nevertheless, there is some basis for the credibility of this proposed model - it may be logical to assume that trait characteristic, such as procrastination in this case, may be influencing one's behavior, e.g., engagement with digital technologies. In addition, it could be that social media design features may motivate higher engagement in checking for notifications and updates due to relatively instantly gratifying the user's needs

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(e.g., social), and smartphones allow for constant connectedness. Therefore, smartphones may be used as tools for social media access, possibly making smartphone use engagement contingent on social media access. However, these causal directions ought to be further validated in subsequent studies with objectively measured behavioral data. Among the more practical implications, this study could be used in discussions on ICT use in academic contexts. Students could be informed that higher procrastination tendency may drive higher engagement with social media and smartphones. Spending time in social media while in a lecture may take away attention from study materials, and, extrapolated from other research (Baturay & Toker, 2016; Junco, 2012b; Kirschner & Karpinski, 2010), that may result in poorer academic outcomes. In addition to educating students about these relationships, students could also be introduced to methods for preventing and/or coping with procrastinatory activities. Training self-regulation skills, creating realistic plans for prioritized tasks, strengthening one's volition, and limiting distractors and access to them (e.g., by limiting social media and/or smartphone use in lectures) could be helpful in overcoming procrastination (Van Eerde, 2000). While social media could be useful in academic settings by providing a contemporary platform for communicating with peers (Manca & Ranieri, 2016; Roblyer, McDaniel, Webb, Herman, & Witty, 2010) and discussing the lecture materials (Gikas & Grant, 2013; Gregory, Gregory, & Eddy, 2014; Legaree, 2015), it may also facilitate procrastinatory activities that distract the student from learning objectives (Ragan, Jennings, Massey, & Doolittle, 2014). Therefore, finding out which activities are beneficial and which are detrimental for academic activities could further promote the understanding of the interplay between procrastination, technology use, and academic outcomes. A central question that may arise from the results of this study is if smartphones and social media use should be banned from classroom. Although we did demonstrate that

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procrastination, social media use in lectures, and PSU are inter-related, this study does not directly suggest banning in-class digital technology use - even though this inference might be tempting. It is not likely that digital technology use in classroom is going to decline - instead, there are many opportunities that could be provided by the application of online environments, social media and smartphones (Dabbagh & Kitsantas, 2012; DeAndrea, Ellison, LaRose, Steinfield, & Fiore, 2012). Rather, we suggest informing students, teachers, and education policy makers about these results, and, additionally, providing knowledge about coping with procrastination. Regarding future research, it would be interesting to know about why procrastinators engage in more technology use, and if there are alternative ways to cope with stimuli (or the absence of them) that onset procrastination-related activities. Although the aim of the study was not looking into the motivational factors behind procrastination behavior, it would be nevertheless helpful for that information to accompany the findings of this study. One potential motivator for engaging in procrastinatory activities may be boredom. Boredom proneness has been linked to higher levels of procrastination (Blunt & Pychyl, 1998; Zhou & Kam, 2017) and PSU (Elhai, Vasquez, Lustgarten, Levine, & Hall, in press), so it could be hypothesized that experiencing boredom may onset higher digital technology engagement in individuals with higher procrastination tendency. Boredom could also have positive effects, such as increasing curiosity (Hunter, Abraham, Hunter, Goldberg, & Eastwood, 2016), which may have an additional influence on social media engagement, e.g., seeking for additional information, that may actually be beneficials for academic outcomes. Another relevant psychological construct that may be helpful in explaining the relationships between procrastination, social media use and PSU is self-regulation, or the extent of motivation and self-control needed for achieving one's desired goals (Bandura, 1991). Lower self-regulation has been associated with higher engagement in smartphone (van Deursen et al., 2015), social

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media (Holmgren & Coyne, 2017) and Internet use (LaRose, Lin, & Eastin, 2003). In addition, self-regulation has been viewed as one of the central psychological features in procrastination (Ferrari, 2001; Senécal, Koestner, & Vallerand, 1995; Van Eerde, 2000). Based on previous research, it could be hypothesized that because of design features that promote procrastinatory behavior (Hofmann et al., 2017), individuals with lower self-control would engage in more technology use. Therefore, it would be interesting to investigate how the constructs studied in this paper are related to self-regulation - or if the findings of the study would hold when controlled for self-regulation. However, these hypotheses should be addressed in future studies. There are also limitations that need to be addressed. First, our study was cross-sectional and that, therefore, cautions for mindful interpretation of our proposed structural equation model (SEM). Because SEM may be bound to bivariate correlations, further research is needed to validate the directions of causality proposed in this study. However, our rationale for the model stems in the theoretical reasoning where procrastination is considered to be a trait characteristic (Aitken, 1982; Kim & Seo, 2015), and where, logically, more social media use contributes to more PSU (Lopez-Fernandez et al., 2017). Longitudinal and repeatedmeasures studies could be a valuable contribution to further test our proposed mediation model. Second, our results rely on self-reports rather than objective measures. Further research could involve objectively measured social media and smartphone use data, as recent studies have demonstrated that relationships with psychological constructs may depend on differences in smartphone usage patterns (Elhai, Tiamiyu, et al., in press; Rozgonjuk, Levine, Hall, & Elhai, 2018; Wilcockson, Ellis, & Shaw, 2018). Even though the aim of the current study was not to investigate the relationships between our key constructs and academic outcomes (e.g., grades, test and exam scores), objective data on academic outcomes (e.g., from schools' and/or other educational institutions' registries) could further develop the

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understanding of the associations between procrastination, social media use in lectures, PSU, and academic outcomes. Third, we did not account for different motivational factors behind procrastination. It would be necessary to understand why exactly students procrastinate and how that has an impact on engagement in social media and PSU. Investigating the contribution of related psychological constructs, such as boredom proneness and selfregulation, might provide a further insight and a better understanding on why procrastination is related to higher social media and smartphone engagement. Finally, we used a university students' sample which may not be representable regarding the population and may limit the extent of generalizability to other population groups, such as students on other educational levels. It would be interesting to see if the results of this study were replicable in lower secondary and secondary level students. With these limitations in mind, our proposed model could serve as an indicator to future directions in the studies concerning problematic technology use and procrastination. 5. Conclusions The aim of this study was to investigate the relationships between procrastination, problematic smartphone use (PSU), and social media use in lectures. The results suggest that these constructs are inter-related, with higher levels of social media use in lectures and PSU being positively correlated to procrastination. We also found that social media use in lectures mediates the relationship between procrastination and problematic smartphone use. These findings are novel and original, and they also contribute to theoretical understanding of problematic technology use. References Aitken, M. E. (1982). A personality profile of the college student procrastinator. (PhD Doctoral dissertation), University of Pittsburgh, University of Pittsburgh. ( Dissertation Abstracts International, 43, 722A–723A.)

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ACCEPTED MANUSCRIPT Research highlights 

Relation between procrastination and problematic smartphone use (PSU) was studied.

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In addition, social media use in lectures (SMUL) was included.

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Procrastination, PSU and SMUL were positively correlated.

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SMUL completely mediated the association between PSU and procrastination.

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This is the first study to analyze these relationships and to propose this model.