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Self-Control and Digital Media Addiction: The Mediating Role of Media Multitasking and Time Style

Authors Błachnio A, Przepiorka A, Cudo A , Angeluci A, Ben-Ezra M, Durak M, Kaniasty K, Mazzoni E, Senol-Durak E, Hou WK, Benvenuti M

Received 28 February 2023

Accepted for publication 26 May 2023

Published 21 June 2023 Volume 2023:16 Pages 2283—2296


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Mei-Chun Cheung

Agata Błachnio,1 Aneta Przepiorka,1 Andrzej Cudo,1 Alan Angeluci,2 Menachem Ben-Ezra,3 Mithat Durak,4 Krzysztof Kaniasty,5,6 Elvis Mazzoni,7 Emre Senol-Durak,4 Wai Kai Hou,8 Martina Benvenuti7

1The John Paul II Catholic University of Lublin, Lublin, Poland; 2University of São Paulo, São Paulo, Brasil; 3Ariel University, Ariel, Israel; 4Bolu Abant Izzet Baysal University, Bolu, Turkey; 5Indiana University of Pennsylvania, Indiana, PA, USA; 6Polish Academy of Sciences, Warsaw, Poland; 7University of Bologna, Bologna, Italy; 8The Education University of Hong Kong, Hong Kong, People’s Republic of China

Correspondence: Agata Błachnio, Institute of Psychology, the John Paul II Catholic University of Lublin, Al. Racławickie 14, Lublin, 20-950, Poland, Tel +48 81 445 35 10, Email [email protected]

Introduction: As being an initiating actions and resisting short-term temptations, self-control is negatively related to digital media addiction. However, many studies indicate that there are variables that may mediate this relationship. The present study investigated the mediating role of media multitasking and time style in the relationship between self-control and digital media addiction.
Methods: The study included N= 2193 participants with a mean age of M = 23.26 (SD = 6.98) from seven countries: Brazil, Hong Kong, Israel, Italy, Poland, Turkey, and the United States. The authors used the Brief Self-Control Scale, the Media Multitasking Scale, the Time Styles Scale, the Problematic Smartphone Use Scale, the Problematic Internet Use Scale, and the Problematic Facebook Use Scale.
Results: Results revealed that self-control was negatively related to all assessed types of problematic digital media use, namely problematic Internet use, problematic smartphone use, and problematic Facebook use. Media multitasking was found to be a significant mediator of the relationship between self-control and problematic digital media use.
Discussion: High self-control can be preventative against uncontrolled and automatic social media checking, whereas low self-control fosters the habit of continuously remaining current.

Keywords: self-control, digital media addiction, media multitasking, time style


The present study investigated the mediating role of media multitasking and time style in the relationship between self-control and digital media addiction. Previous studies suggest links between problematic phone use and self-control.1,2 Self-control was also negatively related to social media addictions.3,4 Studies indicate that some variables can mediate the relationship between self-control and new media addiction.5 Servidio found partial mediation of fear of missing out in the relationship between self-control and problematic smartphone use.

Self-Control and Digital Media Addiction

Digital media addiction—which takes different forms, such as addiction to mobile phones, social media, the Internet, television, or video games—is a common problem with negative effects for all age groups worldwide. Nowadays, the use of new technologies connected to the Internet is widespread and can lead to addiction.6 However, the term Internet addiction seems narrow, and the studies suggested that this phenomenon should be considered multidimensionally.7,8 Therefore, using the umbrella term digital media addiction (DMA) seems justified, combining Internet addiction,9 smartphone addiction10 and Facebook addiction.11,12 These phenomena represent other aspects of digital media addiction. Based on previous research, we can define digital media addiction as compulsive use of digital media, eg, tablets, smartphones, laptops, computers, and social media.10,12,13 Digital media addiction manifests symptoms typical for behavioural addictions, difficulty controlling and tolerance, mood modification or withdrawal symptoms.13–15

Self-control, defined as the ability to initiate actions, resist short-term temptations to achieve long-term goals16 and effortful inhibition of destructive behaviors,17 is an important predictor of success and health. In the context of digital media use, low self-control manifests itself in impulsive behavior and frequent risk-taking, which is linked to the risk of addictions such as Internet addiction.3 Lower levels of self-control have been associated with higher levels of problematic mobile phone use1,2 and smartphone addiction.18 It turned out that people with low self-control responded to mobile notifications very quickly after getting a signal.19 Self-control seems to be one of the most essential predictors of problematic phone use.20 Rho and colleagues20 identified five types of problematic smartphone use based on psychiatric symptoms, distinguishing the non-comorbid type and other types with mixed psychiatric symptoms where self-control plays a crucial role. Csibi and collaborators21 analyzed the intensity of addiction components (conflict, salience, mood modification, tolerance, withdrawal syndrome, and relapse) across different age groups. They found that young users spent more time than older ones with their mobile phones and scored higher on the tolerance component, which is closely related to self-control ability and is still in its development stage in adolescence and young adulthood.21

Media Multitasking

The development of media facilitates the simultaneous performance of multiple media tasks. Several studies have demonstrated an increase in this kind of behavior.22,23 At least 90% of TV viewers multitask while watching TV. According to Deloitte Development LLC (2016), millennials (those in the 14–32 age group at the time of the survey) had the highest level of multitasking behavior during TV watching and were involved in up to four additional activities while watching TV, Generation Xers (the 33–49 age group), were involved in up to three additional activities. Those aged 50 and above were involved in up to one additional activity while watching TV. According to this study, it can be said that the number of multitasking varies according to age. Looking at the definition of what the concept of media multitasking is media multitasking refers to engagement in at least two tasks or switching between tasks involving digital media.24,25 Increased media multitasking has been found to be a predictor of depression and social anxiety beyond overall media use and personality traits.26

Some studies indicate cultural differences in media multitasking.27 One of the salient results revealed that American participants spent more time using screen devices and were more engaged in media multitasking than Taiwanese participants, while Taiwanese participants reported higher screen addiction than Americans.27 What is more, one study found cross-cultural differences between American, Kuwaiti, and Russian students in terms of media multitasking behaviors.28 Media multitasking was more prevalent among American and Kuwaiti students than Russian students.28 On the individual level, media use was predicted by media ownership and sensation seeking. In the Kuwaiti and Russian samples, females reported multitasking more often than males, whereas, in the American sample, men multitasked more than women. Differences in the factors predicting multitasking behavior were found on a macro level. For instance, computer ownership, Internet penetration, and press freedom were likely to predict more frequent simultaneous media use.28

Another cross-cultural study by Kononova and Chiang28 examined the differences between the United States and Taiwan in terms of how media and audience factors, such as country of residence, media ownership, and polychronicity, predicted media multitasking behaviors and looked into whether different motivations to multitask mediated the effects of those factors. Its results revealed that ownership, polychronicity, and four motivations (control, entertainment, connection, and addiction) positively predicted media multitasking. Polychronicity interpreted as an individual’s penchant for multitasking, significantly impacts media multitasking. Those who scored high on polychronicity had a stronger tendency to media multitask. Americans reported higher polychronicity and a higher degree of media multitasking than Taiwanians.28 The study by Voorveld and colleagues29 aimed to determine the prevalence of media multitasking across six countries: Germany, the United States, the United Kingdom, and the Netherlands as monochronic countries, and France and Spain as polychronic ones. Differences in media multitasking were found; Americans were the strongest media multitaskers, and the Dutch were the weakest. The cultural factor in predicting the frequency of media multitasking behavior in three contexts based on the nature of media combination was also included in the study by Srivastava, Nakazawa, and Chen.30 Polychronicity at the individual level showed a significant positive association with the frequency of online, offline, and mixed media multitasking behaviors.

Time Style

People differ in their attitude towards time, structure time, and manage their time, mainly related to their personality and culture.31 The literature discusses the approach to time as an element of the silent language32—a concept emphasizing the importance of the nonverbal dimension in intercultural communication. To understand a culture or country, you must better understand how time is organized. The way people structure their time is called time style and can be either economic (marked by a preference for organization) or non-organized.31 Research shows that, as far as time structuring is concerned, some cultures are predominantly oriented towards a clock-based organization of time while others are marked by event-based time structuring.

As defined by Usunier and Valette-Florence,31 a clock-based organization means organizing life according to the clock, setting deadlines, dividing time into segments, formulating plans and designing structures, and acting according to them. Cultures with this approach to time are referred to as clock time cultures. There are also cultures in which time is approached in terms of events rather than plans or deadlines.33,34 The countries where the study was conducted represent cultures with different approaches to time, with a strong (the USA, Hong Kong), moderate (Poland, Israel, Turkey), and weak (Brazil, Italy) tendency to rely on a clock-based structuring of time. Based on previous evidence suggesting a negative association between time management and multitasking,35 we tested whether media multitasking was related to time style.

The Present Study

Ekşi et al5 study revealed that general procrastination was a mediator between self-control and digital media addiction. In a different study, increased media multitasking during cognitive activities was associated with decreased self-control, while media multitasking during recreational activities was associated with high social achievement, normality, and high self-control.36 Therefore, we expected that media multitasking and time style would mediate the relationship between self-control and digital media addiction (Figure 1). We predicted that low self-control would be associated with higher media multitasking (H1) and a more non-organized time style (H2), both of which would subsequently be associated with higher levels of digital medial addition (H3).

Figure 1 Theoretical model.

The body of research indicates that it is worth considering the use of new technologies in a cultural context.14,37 W cross-cultural analyses showed that the prevalence of social media addiction depends on the dimension of collectivism – individualism.14 The study in the US and Italy found that the models explaining the Internet and social media are not universal, which is an argument to conduct research with samples from different countries.38 Apart from the varying levels of polychronic, or media multitasking, other decisive factors considered in the selection process were the characteristics critical for explaining digital media addiction: technological development, political freedoms that allow unrestricted circulation of information, and cultural attributes.28,39 The selected countries differ regarding industrial, economic, societal, and technological development (;

What is more, Internet and Facebook penetration play crucial. The prevalence of Internet addiction varies across different countries.40 Furthermore, Internet and Facebook penetration rates in those countries differ (; Moreover, the countries differ in terms of political constraints on information distribution, press freedom ( and level of democratization is different in selected countries ( These discrepancies in access to the Internet and Facebook create different backgrounds for their users. More differences between the countries included in the study are shown in the Inglehart–Welzel cultural map of the world (, which arranges countries in two dimensions: traditional vs secular-rational values and survival vs self-expression values.

The study aimed to test the relationship between self-control and digital media addiction. We indicated the role of media multitasking and time style in this relationship. We tested the model with data from seven countries. According to our knowledge, previous research did not include media multitasking and time style in explaining digital media addiction. The advantage of the presented study is to show the results considering the samples in a cultural context.38

Participants and Procedure

The study included 2193 participants from seven countries, including the United States, Brazil, Hong Kong, Israel, Italy, Poland, and Turkey, with a mean age of M = 23.26 (SD = 6.98) years. Due to missing data, 5.2% of participants (N = 114) were eliminated from the research, resulting in a final sample size of 2079 (1274 females, 61.28%, and 805 males, 38.72%) for further analysis: The following is the distribution of participants by country: 161 from Brazil, 285 from Hong Kong, 298 from Israel, 255 from Italy, 486 from Poland, 331 from Turkey, and 263 from the United States. The characteristics of participants from each country are presented in Table 1. The minimum sample size will be N = 160 in each country. Considering the sample size analysis carried out for the two-level model using the sjstats R package,41–44 the results showed that minimum sample size should be 112 subjects per cluster and 787 total sample size (alpha = 0.05; power = 0.08; effect size = 0.2; cluster number = 7).

Table 1 Characteristics of Participants from Each Country

The study was conducted in local languages. After the electronic version of the questionnaire was prepared, the link to the research site was sent out via the Internet. A convenience sampling approach was used to obtain a large group of respondents that differ in terms of socio-demographic characteristics. The participants volunteered to take part in the study and received no monetary reward. They were assured about the anonymity of the data they provided. The study was approved by the Institute of Psychology ethic committee board.


To measure dispositional self-control, we used the Brief Self-Control Scale,45 which consists of 13 items (eg, “I am good at resisting temptation”). Participants responded on a 5-point Likert-type scale, indicating to what extent they agreed or disagreed with each of the statements (1 = strongly disagree to 5 = strongly agree).

Media multitasking was measured with the 9-item Short Media Multitasking Scale,46 which focused on three media activities: watching TV, using social network sites, and sending messages through phone or computer. Participants rated each item on a 4-point Likert-type scale, indicating how often they engaged in each of the nine activities (1 = never to 4 = very often).

The structuring of time was assessed with the Time Styles Scale (TSS),31 which consists of the Economic Time subscale (eg, “I like to have a definite schedule and stick to it”) and the Non-Organized Time subscale (eg, “I hate following a schedule”). Participants indicated on a 5-point Likert-type scale how strongly they agreed or disagreed with each of the seven statements (1 = strongly disagree to 5 = strongly agree).

To measure digital media addiction, we used three methods: (1) smartphone addiction, we used the Smartphone Addiction Scale—Short Version (SAS-SV),47 which consists of 10 items (eg, “Missing planned work due to smartphone use”) with 6-point rating scales (1 = strongly disagree to 6 = strongly agree). (2) Problematic Internet use was assessed with the Problematic Internet Use Questionnaire—Short Form (PIUQ-SF-6),48 consisting of six questions for participants to respond to on a 5-point Likert-type scale (1 = never to 5 = always / almost always; eg, “How often do you try to conceal the amount of time spent online?”). (3) To measure problematic Facebook use, we administered the 8-item Facebook Intrusion Questionnaire,11 which quantifies participants’ Facebook involvement on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree; eg, “I have been unable to reduce my Facebook use”).

The reliability of scores on the measures used for each country is reported in Appendix A. The values of Tucker’s phi,49 assessing the cross-cultural equivalence of the analyzed variables,50 are also presented in Appendix A.

Statistical Analyses

Descriptive statistics are presented as means and standard deviations. Additionally, taking into account the possible differences between countries, we used a multilevel Pearson correlation coefficient to calculate the relationships between the analyzed variables. Additionally, in order to determine the differences between countries, we performed one-way ANOVA with post hoc Tamhane’s T2 test. The effect size was calculated using parietal eta squared.

In order to analyze the mediating effects of media multitasking, economic time style, and non-organized time style on the associations between self-control and problematic behaviors (problematic smartphone use, problematic Facebook use, problematic Internet use), we performed multilevel mediation analyses with restricted maximum likelihood (REML) estimator.51 A multilevel mediation analysis was performed separately for each problematic behavior. We applied this technique because of the multilevel nature of the data, which had been collected in different countries, and due to the intraclass correlation coefficient (ICC) results. More precisely, the values of ICC—a measure of the clustering effect—ranged from 0.023 to 0.176 (see Table 2).

Table 2 The Intraclass Correlation Coefficient (ICC) Values for Outcome Variables and Mediators

We conducted the mediation analysis using the 1-(1-1-1)-1 design with random intercepts and slopes52 separately for each problematic behavior. We specified the diagonal covariance matrix for each mediation model and the diagonal residual covariance matrix. Indirect effects were tested using the Monte Carlo method (10,000 samples) with 95% confidence intervals (95% CI). Additionally, age was included as a within-level covariate because of its correlations with the study variables. Due to the small number of clusters relating to multilevel analysis,53 only within fixed-effects with random effects were presented. We computed the following model fit statistics: −2 times the Log Likelihood (−2LL), Akaike’s Information Criterion (AIC), Hurvich and Tsai’s Criterion (AICC), Bozdogan’s Criterion (CAIC), and Schwarz’s Bayesian Criterion (BIC).54 The descriptive statistics were calculated using SPSS 27 and the multilevel correlation analysis was carried out using R software with the correlation package.55 The mediation analyses were carried out using SPSS 27 software with MLmed macros.54


The descriptive statistics and correlations between the examined variables are shown in Table 3. Problematic smartphone use was positively associated with media multitasking and non-organized time style. It was also negatively associated with self-control and age. Problematic Internet use was positively related to media multitasking and non-organized time style, whereas self-control was negatively associated with economic time style, age, and problematic Internet use. Problematic Facebook use was positively related to media multitasking and negatively related to self-control. The three problematic behaviors were positively intercorrelated. Detailed results are shown in Table 3.

Table 3 Descriptive Statistics and Correlations Between the Analyzed Variables (N = 2079)

There were statistically significant differences between countries on the measures of self-control, media multitasking, economic time style, non-organized time style, problematic smartphone use, problematic Facebook use, and problematic Internet use (see Table 4). It should be noted, however, that the effect sizes of these differences were small.

Table 4 Differences Between Countries in the Analyzed Variables

Problematic Smartphone Use

In the multilevel mediation analysis, indirect effects were not calculated due to errors in some of the estimated random effects, namely the random slope between self-control and media multitasking and the random slope between non-organized time style and problematic smartphone use. Consequently, these random effects were excluded and the modified model was recalculated (−2LL = 19,583.70, AIC = 19,617.70, AICC = 19,617.78, CAIC = 19,754.11, BIC = 19,737.11). The multilevel mediation analysis revealed a significant indirect effect between self-control and problematic smartphone use via a non-organized time style. Similarly, there was a significant indirect effect between self-control and problematic smartphone use via economic time style. The indirect effect between self-control and problematic smartphone use via media multitasking was also significant (see Table 5 and Table 6). There was a significant unstandardized direct effect between self-control and problematic smartphone use (direct effect = −0.572, SE = 0.057, p < 0.001, 95% CI [−0.713, −0.431]). These results indicate partial mediation between self-control and problematic smartphone use via media multitasking, economic time style, and non-organized time style.

Table 5 Multilevel Mediation Model of the Relationship Between Self-Control and Problematic Smartphone Use via Media Multitasking, Non-Organized Time, and Economic Time

Table 6 Unstandardized Indirect Effects with 95% Confidence Intervals

Problematic Internet Use

Due to errors in some of the estimated random effects, namely the random slope between self-control and media multitasking, the random slope between age and problematic Internet use, the random slope between economic time style and problematic Internet use, and the random slope between non-organized time style and problematic Internet use, we did not calculate the indirect effects. Consequently, these random effects were excluded and the modified model was reanalyzed (−2LL = 18,690.72, AIC = 18,720.72, AICC = 18,720.78, CAIC = 18,841.08, BIC = 18,826.08). The results of multilevel mediation analysis showed that there was a significant indirect effect only between self-control and problematic Internet use via media multitasking (see Table 6 and Table 7). Given the significant unstandardized direct effect between self-control and problematic Internet use (direct effect = −0.462, SE = 0.048, p < 0.001, 95% CI [−0.583, −0.341]), these results indicate partial mediation between self-control and problematic Internet use via media multitasking.

Table 7 Multilevel Mediation Model of the Relationship Between Self-Control and Problematic Internet Use via Media Multitasking, Non-Organized Time, and Economic Time

Problematic Facebook Use

As in the previous multilevel mediation analyses, the indirect effects were not calculated due to errors in estimated random effects, namely the random slope between self-control and media multitasking. Consequently, this random slope was removed from the model and the modified mediation model was reanalyzed (−2LL = 20,328.46, AIC = 20,364.46, AICC = 20,364.54, CAIC = 20,508.89, BIC = 20,490.89). The multilevel mediation analysis revealed an indirect effect between self-control and problematic Facebook use via media multitasking (see Table 6 and Table 7). Additionally, the findings showed that there was a statistically significant indirect effect between self-control and problematic Facebook use via economic time style (see Table 6). However, no statistically significant association was found between economic time style and problematic Facebook use (see Table 8). Consequently, the relationship between self-control and problematic Facebook use via economic time style cannot be considered a mediation effect. Moreover, given the significant unstandardized direct effect between self-control and problematic Facebook use (direct effect = −0.343, SE = 0.051, p < 0.001, 95% CI [−0.463, −0.223]), these results indicate partial mediation between self-control and problematic Facebook use via media multitasking.

Table 8 Multilevel Mediation Model of the Relationship Between Self-Control and Problematic Facebook Use via Media Multitasking, Non-Organized Time, and Economic Time


The study’s main purpose was to investigate the mediating roles of media multitasking and time style in the relationship between self-control and digital media addiction. The present research utilized data collected in seven countries. First of all, taking into account the multilevel nature of data from different countries, we found that self-control was negatively related to all measured digital media addiction types. Social media addiction manifests itself in a compulsion to use social media,56 in a need to be constantly on top of things, and in a desire to be online all the time, which includes automatically checking social media.57 Thus, high self-control functions as a preventative against uncontrolled and automatic social media checking, whereas low self-control fosters the habit of continuously remaining current.58 In this context, it seems necessary to give psycho-education to those who have low self-control strategies to regulate their social media interactions.

We predicted that media multitasking would mediate the relationship between self-control and digital media use operationalized as problematic Internet, smartphone, and Facebook use. The results of our study confirm the prediction that low self-control would translate into higher media multitasking and higher digital media addiction. Lopez and colleagues59 also found that media multitasking was associated with inadequate self-regulation, which manifested itself in a reduced ability to control food stimuli, resulting in weight gain and obesity. This result was explained by an imbalance between brain systems involved in self-regulation and reward.59 A different study indicated, moreover, that multitasking was strongly related to engaging in various online activities, such as using social media, emailing, and listening to music,35 and to addictive phone use,60 leading to addiction to screen devices.27 This can be explained by the fact that media multitasking reduces task performance61 and results in tasks taking longer to perform, which in turn increases the time spent using the media when working or studying in their presence. However, the longer a person utilizes media, the greater their likelihood of becoming dependent on them.60,62

Further, we hypothesized that time style (economic vs non-organized) would play the role of a mediator in the relationship between self-control and digital media use. We found that economic and non-organized time styles partially mediated the associations between self-control, problematic smartphone, and Facebook uses. However, it should be noted that the results for non-organized time styles may not be entirely conclusive of the low reliability of the subscale measuring this construct in some countries. Consequently, this result should be approached with caution. Moreover, self-control was positively related to both economic and non-organized time styles. This means that individuals with higher self-control prefer to plan their time and do not like to act without a plan. Moreover, regardless of a person’s time style, low self-control is accompanied by a high level of mobile phone addiction. Reference can be made to studies showing that time management is one of the aspects of self-control.35 Many studies have demonstrated robust relationships between self-control and problematic new media use.3,63,64

Limitations and Future Research

Despite the numerous exciting results that we have obtained, it must be acknowledged that this study is not free from limitations. Its results should be interpreted in light of these limitations. Firstly, the study had a cross-sectional design, so no causal conclusions should be drawn. In the future, it is recommended to conduct longitudinal studies with repeated measurement or diary studies.

Secondly, the reliability coefficients of the Time Styles Scale in some countries were low, which means conclusions regarding time styles should be approached with caution. This may be due to the poor understanding of the items in these countries, an issue that should be addressed in the future. In future studies, time style should be measured using a better method.

Finally, the number of countries was insufficient to allow for calculations with relative country-level models. It is estimated that a couple of dozen to fifty countries are needed for such analyses.53,65 The model can therefore be analyzed at the individual level, with country error variance taken into account. It should be noted that even though the countries included in the study are located on different continents and have different cultural backgrounds, they represent only a fraction of the cultures that could be considered, giving a broader picture of the phenomenon under investigation.

The results of this study have wide implications, both theoretical and practical. Digital media have been developing for many years, and the way they are used continues to change, which means new data is needed regarding their users. The results presented here may inspire further research on the subject. They advance knowledge on digital media addiction. They can also be useful in the development of education and training programs dealing with human-computer interactions and directed against behavioral addictions. Understanding this topic will help determine the future directions of therapeutic activities. The presented research is of great practical significance, as any successful addiction treatment requires identifying useful and effective focal points for intervention.


All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual adult participants included in the study.


This study was funded by a grant from the National Science Centre, Poland NCN No. 2017/25/B/HS6/01517.


The authors declare that they have no conflict of interest.


1. West R, Ash C, Dapore A, Kirby B, Malley K, Zhu S. Problematic smartphone use: the role of reward processing, depressive symptoms and self-control. Addict Behav. 2021;122:107015.

2. Şakiroğlu M. The effect of self control and emotion regulation difficulties on the problematic smart phone use of young people. Uluslararasi Egitim Programlari Ve Ögretim Çalismalari Derg. 2019;9(2):301–308. doi:10.31704/ijocis.2019.013

3. Błachnio A, Przepiorka A. Dysfunction of Self-Regulation and Self-Control in Facebook Addiction. Psychiatr Q. 2016;87(3):493–500. doi:10.1007/s11126-015-9403-1

4. Cudo A, Torój M, Demczuk M, Francuz P. Dysfunction of self-control in Facebook addiction: impulsivity is the key. Psychiatr Q. 2020;91(1):91–101. doi:10.1007/s11126-019-09683-8

5. Ekşi H, Tugba T, Erdem S. The mediating role of general procrastination behaviors in the relationship between self-control and social media addiction in university students. Addicta Turkish J Addict. 2019;6(3):717–745. doi:10.15805/addicta.2019.6.3.0069

6. Henzel V, Håkansson A. Hooked on virtual social life. Problematic social media use and associations with mental distress and addictive disorders. PLoS One. 2021;16(4):e0248406. doi:10.1371/journal.pone.0248406

7. Meng S-Q, Cheng J-L, Y-Y L, et al. Global prevalence of digital addiction in general population: a systematic review and meta-analysis. Clin Psychol Rev. 2022;92:102128.

8. Reichert RA, Martins GDG, da Silva AMB, et al. New forms of addiction: digital media. Psychol Subst Abus Psychother Clin Manag Soc Interv. 2021;43–53.

9. Lozano-Blasco R, Robres AQ, Sánchez AS. Internet addiction in young adults: a meta-analysis and systematic review. Comput Human Behav. 2022;130:107201. doi:10.1016/j.chb.2022.107201

10. Olson JA, Sandra DA, Colucci ÉS, et al. Smartphone addiction is increasing across the world: a meta-analysis of 24 countries. Comput Human Behav. 2022;129:107138.

11. Elphinston RA, Noller P. Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychol Behav Soc Netw. 2011;14(11):631–635. doi:10.1089/cyber.2010.0318

12. Brailovskaia J, Rohmann E, Bierhoff HW, Schillack H, Margraf J. The relationship between daily stress, social support and Facebook addiction disorder. Psychiatry Res. 2019;276(May):167–174. doi:10.1016/j.psychres.2019.05.014

13. Gutiérrez J D-S, Rodríguez de Fonseca F, Rubio G. Cell-phone addiction: a review. Front Psychiatry. 2016;7(October):175. doi:10.3389/fpsyt.2016.00175

14. Cheng C, Lau Y, Chan L, Luk JW. Prevalence of social media addiction across 32 nations: meta-analysis with subgroup analysis of classification schemes and cultural values. Addict Behav. 2021;117:106845. doi:10.1016/j.addbeh.2021.106845

15. D’Arienzo MC, Boursier V, Griffiths MD. Addiction to social media and attachment styles: a systematic literature review. Int J Ment Health Addict. 2019;17(4):1094–1118. doi:10.1007/s11469-019-00082-5

16. Yang C, Zhou Y, Cao Q, Xia M, An J. The relationship between self-control and self-efficacy among patients with substance use disorders: resilience and self-esteem as mediators. Front Psychiatry. 2019;10:388.

17. Gillebaart M. The ‘operational’definition of self-control. Front Psychol. 2018;9:1231.

18. Kim H-J, Min J-Y, Min K-B, Lee T-J, Yoo S. Relationship among family environment, self-control, friendship quality, and adolescents’ smartphone addiction in South Korea: findings from nationwide data. PLoS One. 2018;13(2):e0190896.

19. Berger S, Wyss AM, Knoch D. Low self-control capacity is associated with immediate responses to smartphone signals. Comput Human Behav. 2018;86:45–51.

20. Rho MJ, Park J, Na E, et al. Types of problematic smartphone use based on psychiatric symptoms. Psychiatry Res. 2019;275:46–52.

21. Csibi S, Griffiths MD, Demetrovics Z, Szabo A. Analysis of problematic smartphone use across different age groups within the ‘components model of addiction. Int J Ment Health Addict. 2021;19:616–631.

22. Zhou Y, Deng L. A systematic review of media multitasking in educational contexts: trends, gaps, and antecedents. Interact Learn Environ. 2022;1–16.

23. Drody AC, Pereira EJ, Smilek D. A desire for distraction: uncovering the rates of media multitasking during online research studies. Sci Rep. 2023;13(1):781.

24. Aagaard J. Multitasking as distraction: a conceptual analysis of media multitasking research. Theory Psychol. 2019;29(1):87–99. doi:10.1177/0959354318815766

25. Ophir E, Nass C, Wagner AD. Cognitive control in media multitaskers. Proc Natl Acad Sci U S A. 2009;106(37):15583–15587. doi:10.1073/pnas.0903620106

26. Becker MW, Alzahabi R, Hopwood CJ. Media multitasking is associated with symptoms of depression and social anxiety. Cyberpsychol Behav Soc Netw. 2013;16(2):132–135.

27. Lin TTC, Kononova A, Chiang Y-H. Screen addiction and media multitasking among American and Taiwanese users. J Comput Inf Syst. 2020;60(6):583–592.

28. Kononova A. Multitasking across borders: a cross-national study of media multitasking behaviors, its antecedents, and outcomes. Int J Commun. 2013;7:1688–1710.

29. Voorveld HA, Segijn CM, Ketelaar PE, Smit EG. Investigating the prevalence and predictors of media multitasking across countries. Int J Commun. 2014;8:2755–2777.

30. Srivastava J, Nakazawa M, Chen Y. Online, mixed, and offline media multitasking: role of cultural, socio-demographic, and media factors. Comput Human Behav. 2016;62:720–729. doi:10.1016/j.chb.2016.04.040

31. Usunier J, Valette-Florence P. The Time Styles Scale. Time Soc. 2007;16(2–3):333–366. doi:10.1177/0961463X07080272

32. Hall ET. The Dance of Life: The Other Dimension of Time. Anchor; 1984.

33. Levine RV. Time and culture. Calif State Univ Fresno. 2013;1:654.

34. Tarkowska E. Review: Time in Contemporary Culture. Polish Sociological Review. 1997;118:191–195.

35. Yang X, Zhu L. Predictors of media multitasking in Chinese adolescents. Int J Psychol. 2016;51(6):430–438. doi:10.1002/ijop.12187

36. Xu S, Wang Z, David P. Media multitasking and well-being of university students. Comput Human Behav. 2016;55:242–250. doi:10.1016/j.chb.2015.08.040

37. Błachnio A, Przepiórka A, Gorbaniuk O, et al. Cultural correlates of internet addiction. Cyberpsychol Behav Soc Netw. 2019;22(4):258–263.

38. Błachnio A, Przepiorka A, Benvenuti M, Mazzoni E, Seidman G. Relations between Facebook intrusion, Internet addiction, life satisfaction, and self-esteem: a study in Italy and the USA. Int J Ment Health Addict. 2019;17:793–805.

39. Kononova A, Zasorina T, Diveeva N, Kokoeva A, Chelokyan A. Multitasking goes global: multitasking with traditional and new electronic media and attention to media messages among college students in Kuwait, Russia, and the USA. Int Commun Gaz. 2014;76(8):617–640. doi:10.1177/1748048514548533

40. Cheng C, Li AY-L. Internet addiction prevalence and quality of (real) life: a meta-analysis of 31 nations across seven world regions. Cyberpsychol Behav Soc Netw. 2014;17(12):755–760. doi:10.1089/cyber.2014.0317

41. Lüdecke, D _sjstats: Statistical Functions for Regression Models (Version 0.18.2) 2022 Accessed 19 June 2023

42. Cohen J. Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ, USA: Lawrence Erlbaum Associates; 1988.

43. Hsieh FY, Lavori PW, Cohen HJ, Feussner JR. An Overview of Variance Inflation Factors for Sample-Size Calculation. Eval Health Prof. 2003;26(3):239–257.doi: 10.1177/0163278703255230

44. Snijders TAB. Power and Sample Size in Multilevel Linear Models. In: Everitt BS, Howell DC (Hrsg.). Encyclopedia of Statistics in Behavioral Science. Chichester, UK: John Wiley and Sons, Ltd; 2005.

45. Tangney JP, Baumeister RF, Boone AL. High Self-Control Predicts Good Adjustment, Less Pathology, Better Grades, and Interpersonal Success. J Pers. 2004;72(2):271–324. doi:10.1111/j.0022-3506.2004.00263.x

46. Baumgartner SE, Weeda WD, van der Heijden LL, Huizinga M. The relationship between media multitasking and executive function in early adolescents. J Early Adolesc. 2014;34(8):1120–1144. doi:10.1177/0272431614523133

47. Kwon M, Lee J, Won W, et al. Development and Validation of a Smartphone Addiction Scale (SAS). PLoS One. 2013;8(2):e56936. doi:10.1371/journal.pone.0056936

48. Demetrovics Z, Király O, Koronczai B, et al. Psychometric properties of the problematic internet use questionnaire short-form (PIUQ-SF-6) in a nationally representative sample of adolescents. PLoS One. 2016;11(8):e0159409. doi:10.1371/journal.pone.0159409

49. Tucker LR. A Method for Synthesis of Factor Analysis Studies. Educational Testing Service Princeton Nj; 1951.

50. Lorenzo-Seva U, Berge JMF. Tucker’ s congruence coefficient as a meaningful index of factor similarity. Methodology. 2006;2(2):57–64. doi:10.1027/1614-1881.2.2.57

51. Hayes AF, Rockwood NJ. Conditional process analysis: concepts, computation, and advances in the modeling of the contingencies of mechanisms. Am Behav Sci. 2020;64(1):19–54.

52. Zhang Z, Zyphur MJ, Preacher KJ. Testing multilevel mediation using hierarchical linear models. Organ Res Methods. 2009;12(4):695–719. doi:10.1177/1094428108327450

53. McNeish DM, Stapleton LM. The effect of small sample size on two-level model estimates: a review and illustration. Educ Psychol Rev. 2016;28(2):295–314. doi:10.1007/s10648-014-9287-x

54. Rockwood NJ, Hayes AF MLmed: an SPSS macro for multilevel mediation and conditional process analysis. In: Poster Presented at the Annual Meeting of the Association of Psychological Science (APS), Boston, MA; 2017.

55. Makowski D, Ben-Shachar M, Patil I, Lüdecke D. Methods and algorithms for correlation analysis in R. J Open Source Softw. 2020;5(51):2306. doi:10.21105/joss.02306

56. Hou Y, Xiong D, Jiang T, Song L, Wang Q. Social media addiction: its impact, mediation, and intervention. Soc Media Addict Its Impact, Mediat Interv. 2019;13(1):54.

57. Andreassen CS, Pallesen S. Social network site addiction - an overview. Curr Pharm Des. 2014;20(25):4053–4061.

58. Mylonopoulos N, Theoharakis V, Mylonopoulos N. Are you keeping your Facebook passions and habit under control ? A dual-system perspective on Facebook addiction-like symptoms Are you keeping your Facebook passions and habit under symptoms. Int J Electron Commer. 2021;25(2):181–203. doi:10.1080/10864415.2021.1887697

59. Lopez RB, Heatherton TF, Wagner DD. Media multitasking is associated with higher risk for obesity and increased responsiveness to rewarding food stimuli. Brain Imaging Behav. 2020;14:1050–1061.

60. Domoff SE, Foley RP, Ferkel R. Addictive phone use and academic performance in adolescents. Hum Behav Emerg Technol. 2020;2(1):33–38. doi:10.1002/hbe2.171

61. May KE, Elder AD. Efficient, helpful, or distracting? A literature review of media multitasking in relation to academic performance. Int J Educ Technol High Educ. 2018;15(1):1–17.

62. Bachnio A, Przepiórka A. The more you use Facebook, the more you risk becoming addicted to it?: a study report. Neuropsychiatry. 2016;6(3):80–84.

63. Jiang Z, Zhao X. Self-control and problematic mobile phone use in Chinese college students: the mediating role of mobile phone use patterns. BMC Psychiatry. 2016;16(1):416. doi:10.1186/s12888-016-1131-z

64. Sagar ME. Predictive role of cognitive flexibility and self-control on social media addiction in university students. Int Educ Stud. 2021;14(4):1–10.

65. Bryan ML, Jenkins SP. Multilevel modelling of country effects: a cautionary tale. Eur Sociol Rev. 2016;32(1):3–22.

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