Unveiling Contributing Pathways to Problematic Mobile Phone Use: Mediating Effects of FOMO, Cyberloafing, Mobile Phone and Social Media Use

Unveiling Contributing Pathways to Problematic Mobile Phone Use: Mediating Effects of FOMO, Cyberloafing, Mobile Phone and Social Media Use | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unveiling Contributing Pathways to Problematic Mobile Phone Use: Mediating Effects of FOMO, Cyberloafing, Mobile Phone and Social Media Use Zafer Kadirhan, Yunus Alkis, Berkan Celik, Sacip Toker, Soner Yildirim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6871454/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Closely aligned with the pervasive presence of mobile phones in today's society surrounded by technology, this study sets out to examine the mediating roles of respect to cyberloafing, fear of missing out (FoMO), mobile phone use hours (MPUH), and social media use hours (SMUH) in the effects of gender and age on problematic mobile phone use (PMPU) among university students. A correlational research method was employed in this study. The participants were 1,272 university students. Data were collected using paper-pencil questionnaires and analyzed through path analysis. The results showed that cyberloafing, FoMO, mobile phone use hours, and social media use hours significantly contributed to the prediction of PMPU. Furthermore, while age indirectly influenced PMPU via FoMO and mobile phone use hours, gender indirectly influenced PMPU via FoMO, cyberloafing, mobile phone use hours, and social media use hours. The study contributes to a deeper understanding of the interactions between psychological reactions and technological behaviors aligns with the broader goal of promoting healthy technology use in educational environments. Insights from this research can inform interventions and policies aimed at fostering responsible and beneficial technology use among students. The implications of this study are multifaceted and can be significant in addressing issues related to PMPU among university students. Business and commerce/Information systems and information technology Social science/Education Problematic mobile phone use fear of missing out FOMO cyberloafing mobile phone use demographics mediation Figures Figure 1 Figure 2 1. Introduction In today's digitally interconnected world, the widespread use of mobile phones and social media platforms has revolutionized how individuals engage with information, communication, and social interaction. The integration of these technologies into daily life has resulted in significant changes in human behavior, providing researchers with a unique opportunity to investigate the intricate relationships between various psychological constructs and technology-mediated activities. The present study examines the complex relationship between four key constructs: Fear of Missing Out (FoMO), Cyberloafing, Problematic Mobile Phone Use (PMPU), and Social Media Use Hours (SMUH). According to Przybylski et al. (2013), FoMO is the concern that one is missing out on rewarding experiences that others may be having as a result of their use of social media and digital communication. As mobile phones and social media platforms continue to proliferate, researchers, psychologists, and social scientists have directed increasing attention to the concept of FoMO. Numerous studies have linked FoMO to a range of psychological constructs, including increased anxiety, reduced well-being, low self-esteem, and excessive digital engagement (Tandon et al., 2022; Gupta and Sharma, 2021; Barry and Wong, 2020; Przybylski et al., 2013). Additionally, some studies have investigated the underlying factors behind this phenomenon (Dogan, 2019). This paper aims to investigate the impact of FoMO on individuals' use of mobile phones and social media, as well as the possible effects of this concern on their digital behaviors. Cyberloafing, on the other hand, is a phenomenon characterized by the diversion of work-related internet use toward non-work-related activities during working hours (Batabyal & Bhal, 2020; Askew et al., 2019; Lowe-Calverley & Grieve, 2017; Greengard, 2000; Lim et al., 2002; Polito, 1997). Similarly, school-related cyberloafing is defined as students’ use of the Internet during school hours for non-school-related activities (Kalaycı, 2010). Even though cyberloafing has primarily been studied in work-based settings, it has recently begun to attract attention in the field of education. A growing number of studies have explored this phenomenon within educational settings (Alyahya & Alqahtani, 2022; Demirtepe-Saygılı & Metin-Orta, 2020; Saritepeci, 2019). This shift is attributed to the increased prevalence of technology integration and students’ increased access to digital technologies (Akbulut et al., 2016). Given the ubiquity of smartphones and the accessibility of social media platforms in these settings, cyberloafing has become a concern for organizations, researchers, and educators. While previous research has focused on the factors that lead to cyberloafing and its consequences (e.g., Toker & Baturay, 2021), this study aims to investigate whether FoMO plays a significant role in motivating individuals to engage in cyberloafing behaviors, ultimately affecting their productivity and work-related outcomes. In parallel, PMPU has emerged as an essential concern related to the excessive and compulsive utilization of mobile phones, which disrupts everyday activities (Bianchi & Phillips, 2005; Billieux et al., 2015; Shin & Kim, 2022; Tako et al., 2009). As individuals increasingly utilize mobile devices for social media interaction and information consumption, it is crucial to comprehend the role of FOMO in the evolution of PMPU. Previous studies have investigated several factors associated with PMPU, such as psychological distress, desire for social connectedness, and poor academic performance (Grant et al., 2019; Pivetta et al., 2019). The present study expands upon the existing body of literature by examining the potential role of FoMO as a stimulant for PMPU, thereby providing insights into the underlying processes associated with these two variables. Lastly, the amount of time individuals spends on social media platforms, often referred to as SMUH, has become an essential measure in contemporary research on technology use and well-being (Liu et al., 2019; Primack et al., 2017; Twenge, 2019). As excessive social media use has been associated with negative mental health outcomes (Shannon et al., 2021), it is essential to comprehend the relationship between FoMO and SMUH. This study investigates the relationship between FoMO and SMUH, while also exploring the potential mediating and moderating factors that may impact this correlation. In sum, this study examines the complex relationships between FoMO, Cyberloafing, PMPU, and SMUH. By investigating these connections, this study attempts to contribute to a deeper comprehension of the way psychological reactions and technological behaviors interact in the digital age. Such insights hold significant implications for both academic discourse and practical interventions aimed at promoting healthy technology use and well-being in an increasingly digitalized society. With mobile phones and social media platforms now ubiquitous, their impact on student behavior, including cyberloafing during school hours, remains a significant concern for educators and institutions. Understanding how individuals engage with information and communication is particularly relevant to education, where technology integration has become a focal point worldwide. 2. Literature Review and Hypothesis Development 2.1 Age, Cyberloafing, FoMO, MPUH, and SMUH Research studies revealing the relationship between age and cyberloafing have, to date, provided some differing results. To this end, Vitak et al. (2011) found that being younger is associated with cyberloafing variety and frequency. Aybas and Gungor (2020) revealed a significant negative association between age and the prevalence of cyberloafing. However, according to Ozler and Polat (2012), no significant difference was found in relation to cyberloafing behavior based on age. Similarly, Ahmad and Omar (2017) and Koay et al. (2017) indicated that age had no significant influence on cyberloafing. Regarding the relationship between age and FoMO, (Przybylski et al., 2013) revealed a negative correlation, and other research studies have also exposed a significant negative link between age and FoMO (Dogan, 2019; Elhai, McKay, et al., 2021; Giagkou et al., 2018). However, Busch et al. (2021) revealed that the prevalence of FoMO was considered very rare among older adults. Other evidence has shown that FoMO is more related to younger age (Elhai, Yang, & Montag, 2021), with younger people expressing higher levels of FoMO (Abel et al., 2016). According to Bianchi and Phillips (2005), self-reported time spent using mobile phones was shown to be negatively predicted by age. In a study by Andone et al. (2016), the mobile phone use of participants was tracked for a period of 28 days. The results indicated that younger people used their mobile phones for longer durations than older people. Moreover, the daily average smartphone use duration was shown to be negatively related to age by both Erdem et al. (2017) and Hussain et al. (2017). Worldwide, younger adults have been shown to use and engage in social media more than their older counterparts (Blackwell et al., 2017; Poushter et al., 2018), and they are regular social media users (Villanti et al., 2017). A younger age was shown to predict social networking site participation and therefore, younger individuals reported more frequent use (Chou et al., 2009) and more engagement in social media (Blackwell et al., 2017). Similarly, a younger age was associated with spending a greater amount of time using social media per day and more social media site visits per week (Lin et al., 2016). In other words, age and social media use were revealed to be negatively associated (Correa et al., 2010). Although several research studies have indicated a reverse relationship between age and social media use, only a very limited number have found no association between age and social media use (Vannucci et al., 2017). The following hypothesis has been suggested considering the abovementioned studies. Hypothesis 1: Participants’ age impacts (a) cyberloafing, (b) FoMO, (c) MPUH, and (d) SMUH. 2.2 Gender, Cyberloafing, FoMO, MPUH, and SMUH Accordingly, it was found that gender has a positive impact on cyberloafing, with males cyberloafing more than females (Akbulut et al., 2017; Andreassen et al., 2014; Baturay & Toker, 2015; Dursun & Donmez, 2018; Lim & Chen, 2012; Vitak et al., 2011). Contrary to previous studies, Arabaci (2017) revealed cyberloafing behaviors to be more in favor of female participants than males in their research. As such, the literature still lacks any consensus in this regard. While some studies have found no significant gender differences associated with FoMO (Casale & Fioravanti, 2020), Lo Coco et al. (2020) reported females as having higher levels of FoMO behaviors. The fact that females engage with social media more than males may negatively affect their FoMO behaviors (Oberst et al., 2017), and females have been reported to use social media more often, more actively, and spend much more time using it than males (Burke et al., 2010; Kasahara et al., 2019; Misra et al., 2015). Similarly, other studies have indicated that females prefer, connect, and use social media more frequently than males (Kimbrough et al., 2013; Muscanell & Guadagno, 2012). Excessive use of mobile phones is also associated with female users (Jenaro et al., 2007; Lopez-Fernandez et al., 2017), and this association is probably related to females tending to depend more on their mobile phones than males (Leung, 2008; Lopez-Fernandez et al., 2015, 2017). In light of these previous studies, the following hypothesis has been formed: Hypothesis 2: Participants’ gender impacts (a) cyberloafing, (b) FoMO, (c) MPUH, and (d) SMUH. 2.3 Cyberloafing and PMPU Cyberloafing is associated with PMPU and is considered one of the predictors of PMPU in the literature. PMPU is also known as smartphone/mobile phone addiction (Kim & Byrne, 2011). According to Walsh et al. (2007), dangerous usage (e.g., whilst driving), inappropriate usage (e.g., in the cinema or in the classroom), and overuse are three indicators of PMPU and which are also known causes of smartphone addiction (Chóliz, 2012). In addition, according to Baturay and Toker (2015), males exhibit more cyberloafing behaviors and are also more likely to engage in such behaviors than females. Moreover, Garrett and Danziger (2008a, 2008b) found that the male gender was positively associated with cyberloafing behavior. The results of Gökçearslan et al. (2016) indicated that cyberloafing positively affected mobile phone addiction / PMPU. Accordingly, based on their results, it may be inferred that students’ cyberloafing behavior within the school-based environment increases their tendency towards PMPU, hence the following hypothesis has been stated: Hypothesis 3: Cyberloafing impacts PMPU. 2.4 FoMO and PMPU Research studies have attempted to reveal the links between PMPU and FoMO in addition to mobile phone use and FoMO. Having more FoMO might promote the overuse of mobile phones (Kaspersky Lab, 2016). In their study, Rosen et al. (2018) recorded objective smartphone usage information of 216 college students for a period of at least 21 days. Their results showed that FoMO predicted smartphone usage measured by self-report and real-time application data. Hato (2013) reported a positive link between C-FoMO and smartphone engagement and mobile phone checking frequency. Elhai et al. (2016) investigated a few variables that are conceptually connected with problematic smartphone use and smartphone use frequency in a study with 308 participants. The results showed a significant association between FoMO and problematic smartphone use on the bivariate and multivariate levels. Gokler, Aydin, Unal, and Metintas’s (2016) study with 200 university students revealed a significant positive strong correlation between FoMO and PMPU. Moreover, there was also a significant association between FoMO and the number of social media accounts, and Facebook and Twitter checking frequency. In another study, Wolniewicz et al. (2018) found a strong association between FoMO and problematic smartphone use in a study conducted with 296 college students. In brief, when people examine or interact with their phones more frequently, they tend to elicit increased levels of FoMO due to worrying about potentially missing something they deem important (Kaspersky Lab, 2016). On this, the following hypothesis has been suggested: Hypothesis 4: FoMO impacts PMPU. 2.5 MPUH and PMPU There are a considerable number of studies in the literature in which the duration of MPUH triggers PMPU or smartphone addiction. For example, a study by Gökçearslan et al. (2016) investigated the role of several variables on smartphone addiction and revealed that the duration of mobile phone usage was positively related to smartphone addiction. In another study, Merlo et al. (2013) found that those who use their mobile phones more have higher rates of PMPU. Likewise, in a largescale study involving nearly 5,000 participants, Kim et al. (2016) endeavored to identify personality-based factors that may be indicators of smartphone addiction. The study examined the variables with both smartphone-addicted and non-addicted sample groups and revealed that students from the smartphone-addicted group had more MPUH than those from the non-addicted group. Moreover, in a similar study, Van Deursen et al. (2015) investigated the role of different variables affecting addictive smartphone behavior and concluded that the habitual use of smartphones significantly contributed to smartphone addiction. Furthermore, there have also been research studies published that have indicated the duration of mobile phone usage as an important factor in smartphone addiction (Kwon, Kim, et al., 2013; Kwon, Lee, et al., 2013; Lin et al., 2016). As a result of these various studies, the following hypothesis has been stated: Hypothesis 5: MPUH impacts PMPU. 2.6 SMUH and PMPU Although one of the most significant indicators of smartphone addiction is social networking (Salehan & Negahban, 2013), an insufficient number of studies in the literature have examined the association between social media use and PMPU. Among the limited studies available, the survey study by Salehan and Negahban (2013) aimed to model several social networking variables together with mobile addiction and reported that social intensity or social media usage as a significant predictor of mobile addiction. In another study, Van Deursen et al. (2015) revealed that social usage of smartphones, such as for the purposes of interacting with others, maintaining relationships, and contacting people through social media, increased the risk of smartphone addiction. Similarly, Zhitomirsky-Geffet and Blau (2016) found that the use of WhatsApp, a social application for mobile phones, has a strong influence on smartphone addiction. With regards to this area, the following hypothesis was included in the model: Hypothesis 6: SMUH impacts PMPU. 2.7 Age and PMPU The relationship between age and PMPU has been inconclusive since differing findings have been reported in the literature. In a study published by Long et al. (2016) on the prevalence and correlation of problematic smartphone usage in a large random sample of undergraduate students, it was reported that no significant impact was established for age on problematic smartphone use. In line with this result, Demirhan et al. (2016) reported that age was not a significant predictor of PMPU. The results of Zhitomirsky-Geffet and Blau’s (2016) study found that younger individuals elicit higher emotional dependence on smartphones than older smartphone users. However, the influence of age on addictive behavior can be considered non-linear; that is, the degree of addictive behavior may differ among those from different generations/age groups. In contrast to studies that reported finding no relationships between age and PMPU, Bianchi and Phillips (2005) found age to have a negative influence on PMPU, with young people in their study having higher PMPU scores than other participants. In parallel, Kwon, Lee, et al. (2013) reported that students tend to be more addicted to smartphone use. In a study on modeling habitual and addictive smartphone behavior, Van Deursen et al. (2015) reported that age had a negative effect on both habitual smartphone use and addictive smartphone behavior. Another study focused on the prevalence and prediction of problematic cell phone use, with Smetaniuk (2014) having reported a negative association revealed between age and the degree of PMPU. Similarly, PMPU scores were found to be higher in younger age groups than in other age groups. As a result, younger mobile phone users were found to have more problems related to mobile phone use, whilst older users reported fewer problems. In addition, the literature has revealed that age has an influence over the variables of cyberloafing (Aybas & Güngör, 2020; Vitak et al., 2011), FoMO (Dogan, 2019; Elhai, McKay, et al., 2021; Giagkou et al., 2018; Przybylski et al., 2013), MPUH (Bianchi & Phillips, 2005), and SMUH (Blackwell et al., 2017; Poushter et al., 2018). Moreover, many previous studies have concluded that a relationship exists between PMPU and the variables of cyberloafing (Gökçearslan et al., 2016), FoMO (Elhai et al., 2016; Kaspersky Lab, 2016; Rosen et al., 2018), MPUH (Kim et al., 2016; Merlo et al., 2013), and SMUH (Salehan & Negahban, 2013; Van Deursen et al., 2015). When these findings are evaluated together as a whole, it can be considered that an indirect relationship may exist between age and PMPU mediated by cyberloafing, FoMO, MPUH, and SMUH variables. Based on the findings suggested to date, the following hypothesis has been identified: Hypothesis 7: Age indirectly impacts PMPU via Cyberloafing, FoMO, MPUH, and SMUH. 2.8 Gender and PMPU Gender differences in PMPU have been addressed in many studies, but there has been no definitive consensus formed on the subject. Previous studies have found that gender has a tendency to affect PMPU (Lee et al., 2014; Park & Lee, 2014; Van Deursen et al., 2015). For example, it was found that females are more dependent on using their phones (Billieux et al., 2008; Walsh et al., 2011). Likewise, females use text messaging more concentratedly than males (Geser, 2006; Sánchez-Martínez & Otero, 2009). In contrast, another study showed that males are more likely to become involved in problematic mobile phone behaviors more females. Similarly, another study showed that males have a greater tendency than females to use their mobile phone whilst driving (Billieux et al., 2008). It has been demonstrated that male students have more PMPU than female students (Öztunç, 2013). Another previous research indicated that intensive mobile phone use and mobile phone dependence were associated with the female gender (Sánchez-Martínez & Otero, 2009). Other studies have also indicated that females are more likely to be addicted to and experience more PMPU than males (Kim et al., 2016; Mok et al., 2014; Roser et al., 2016; Takao et al., 2009). On the other hand, it has conclusively been shown that although males experience more problematic use of technology than females, gender does not predict PMPU (Bianchi & Phillips, 2005; Zhitomirsky-Geffet & Blau, 2016). Wolniewicz et al.’s (2018) study also indicated the mediating impact of FoMO between gender and the fear of positive or negative evaluation and problematic smartphone use. It has been reported that males have higher engagement in cyberloafing behaviors than females (Metin-Orta & Demirutku, 2020), so that it may be said that gender has an impact on cyberloafing, while cyberloafing positively impacts upon PMPU (Gozum et al., 2020; Savci et al., 2021). Thus, an indirect path seems to exist between gender and PMPU via cyberloafing. While males exhibit higher FoMO behaviors than females (Gullu & Serin, 2020; Qutishat, 2020), it has been noted that gender has an impact upon FoMO. On the other hand, higher levels of FoMO have been linked with higher PMPU (Li et al., 2020; Santana-Vega et al., 2019). Hence, gender has been indirectly associated with PMPU via FoMO. According to Taywade and Khubalkar (2019), females are more prone to PMPU due to spending greater amounts of time using smartphones than males; revealing that their findings support the indirect effect of gender on PMPU via MPUH. It has also been expressed that females are more active on social media than males, and therefore have a greater inclination towards PMPU/smartphone addiction (Chen et al., 2017; Lee et al., 2018). Gender may be indirectly associated with PMPU behaviors through the mediation of cyberloafing, FoMO, MPUH, and SMUH. Although gender differences and comparisons regarding to PMPU have been addressed in some of the previous studies, mediators and their indirect effect between gender and PMPU have yet to be investigated, hence the following hypothesis was constructed: Hypothesis 8: Gender indirectly impacts PMPU via Cyberloafing, FoMO, MPUH, and SMUH. 3. Method 3.1 Research Method The correlational research method was used in this study. Correlational research, as one of the quantitative research approaches, is used to explore the association between two or more variables (Creswell, 2012). Correlational research, according to Fraenkel et al. (2012), analyzes the possibility of correlations between two or more variables without attempting to affect or alter them. In the current study, the correlational research method was used to determine the degree of relationship between cyberloafing, FoMO, MPUH, SMUH, and PMPU. 3.2 Participants A total of 1,272 students from universities in Ankara, Turkey, took part in the study. Convenience sampling method was used, and participation was voluntary. Of the participants, 521 (41%) were male, with the rest ( n = 751, 59%) being female. The study’s participants consisted of 1,171 (92.1%) undergraduate students and 101 (7.9%) graduate students. Split by study level, the participants consisted of 108 (8.5%) preparatory class students, 243 (19.1%) freshmen, 277 (21.8%) sophomores, 229 (18.0%) juniors, and 314 (24.7%) senior students. Additionally, 65 (5.1%) of the participants were studying for a master’s degree and 36 (2.8%) were PhD students. The participants were studying in 36 different departments, and their ages ranged from 18 to 40 years old ( M = 21.60; SD = 2.74). The daily Internet usage of the participants ranged from between 1 to 16 hours ( M = 5.28; SD = 3.11). Details on the demographics of the participants are presented in Table 1 below. Table 1. Gender, age, and study year distribution of the participants Variable Frequency Percentage Gender Female 751 59.0 Male 521 41.0 Age Range (years) 18-20 472 37.1 21-23 596 46.9 24 or more 204 16.0 Year of Study Preparatory Class 108 8.5 Freshman 243 19.1 Sophomore 277 21.8 Junior 229 18.0 Senior 314 24.7 Graduate 101 7.9 Total 1,272 100.0 3.3 Data Collection Instruments Data were gathered through using a paper-pencil questionnaire that consisted of seven questions related to demographic information and 59 items related to three separate scales. The data questionnaire used to collect data was divided into four sections. The first section included seven questions about the participants' demographics (gender, age, department, grade level, daily average Internet usage, daily average mobile phone usage, and daily average social media usage). The second part consisted of the 26-item PMPU scale, whilst the third part consisted of the 10-item FoMO scale . Lastly, the fourth part consisted of the 23-item cyberloafing scale. Bianchi and Phillips were the first to establish the PMPU scale (2005). Pamuk and Atli (2016) later produced a Turkish-adapted version of the scale. The items were formed in a 5-point, Likert-type scale (1 = not suitable at all , 2 = rarely suitable , 3 = somewhat suitable , 4 = quite suitable , 5 = completely suitable ). The scale had a four-factor structure. Internal consistency of the scale was acceptable (Cronbach's alpha coefficient >.70). Internal consistency of the scale was acceptable (Cronbach's alpha coefficient >.70). Przybylski et al. (2013) established the FoMO scale, which was then modified to the Turkish context by Gökler, Aydin, Ünal, Metintaş, alşmada, et al (2016). The items were organized into a five-point, Likert-type, single-dimension scale (1 = not at all true of me, 2 = slightly true of me, 3 = moderately true of me, 4 = very true of me, 5 = extremely true of me). Internal consistency of the scale was acceptable (Cronbach's alpha coefficient >.70). Blanchard and Henle created the first cyberloafing scale (2008). Kalaycı (2010) altered it for the Turkish context, and Yasar later updated it (2013). Within a four-factor structure, the scale's items were developed as a 5-point, Likert-type scale (1 = severely disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, 5 = strongly agree). Internal consistency of the scale was acceptable (Cronbach's alpha coefficient >.70). Table 2 summarizes the three measurement scales employed in the current study. Table 2. Summary of measurement scales Scale Developed by (in English) Adapted by (into Turkish) Number of Items Format for Measurement Reliability PMPU Bianchi & Phillips (2005) Pamuk & Atli (2016) 26 5-Point Likert-type .93 FoMO Przybylski et al. (2013) Gökler, Aydin, Ünal, Metintaş, et al. (2016) 10 5-Point Likert-type .81 Cyberloafing Blanchard & Henle (2008) Kalaycı (2010) Yasar (2013) 23 5-Point Likert-type .82 3.4 Data Collection Procedures Data were collected from university students using paper-pencil surveys. Before the study, an ethical approval was taken from the Human Subjects Committee. The participants were advised that their participation in the study was entirely voluntary, and their consent was obtained to that end. Moreover, informed consent was obtained from all individual participants included in the study. The information from the paper-and-pencil questionnaires was entered into a Microsoft Excel spreadsheet. Following a preliminary inspection, the data was exported into SPSS. 3.5 Data Analysis Multivariate normality tests were performed in order to check the distribution of data related to normality prior to starting the statistical analysis. Then, descriptive statistics were conducted to explore the sample characteristics. Path analysis was performed on possible associations between variables using structural equation modeling. IBM's SPSS 26 and AMOS 24 tools were used to examine the data. Several mediation analyses were also carried out to investigate the potential influence of cyberloafing, FoMO, MPUH, and SMUH on the relationship between age and gender and PMPU. In some cases, a mediation variable stands for full mediation, meaning that the causation variable cannot directly impact the outcome variable when the mediator was present. To examine mediation, The following four steps were proposed by Baron and Kenny (1986): The causation variable must be significantly related to the outcome variable. The mediator variable must be significantly related to the result variable. The mediator variable must be significantly related to the causation variable. The mediator variable requires that the causation variable not be significantly related with the outcome variable. When all four of these steps are true, the outcome may be inferred as evidence for mediation. Based on the information provided in the literature review; the following direct-effect hypotheses were developed, which are also illustrated in Figure 1. The study sample was compromised of participants selected via convenience sampling. Many social science researchers are not able to use random sampling given the lack of resources such as money and time (Wallen & Fraenkel, 2001). In the current investigation, we used the power analysis particular to structural equation modeling (SEM) (MacCallum et al., 1996), which produced 825 individuals for.99 power, df = 5, RMSEA-H0 =.10 (mis-fit), and RMSEA-H1 =.035 (near to good-fit), p =.05. We had 1,272 participants at the end of the data collection. Furthermore, according to Boomsma (1985) and Kline (1985), the minimal or moderate size of the sample for SEM might range between 100 and 200. (1998). More specifically, Bentler and Chou (1987) and Bollen (1989) indicated that five to 10 observations per estimated parameter should be reached, whilst Nunnally (1967) stated 10 cases per variable would be sufficient for SEM. According to other experimentally validated evidence, the minimum required sample size can range from 30 to 80. (Wolf et al., 2013). Additionally, Wolf et al. (2013) noted how determining sample size may be affected by the model's complexity, and that a larger sample size does not always lead to improved results. The current study exceeded the sample size more than the recommended as a result of the power analysis, hence the results were deemed interpretable. However, because of the sampling strategy used, the study's generalizability should be approached with caution. 4. Results Prior to proceeding with the data analysis step, we assessed univariate and multivariate normality, a crucial assumption of SEM (Arbuckle, 2007). Kurtosis values are thought to be more important to analyze than skewness for variance- and covariance-based analysis (DeCarlo, 1997); thus, while skewness influences means-based tests, kurtosis has a significant impact on variance- and covariance-based tests. Because SEM is based on covariance matrices, we focused on the kurtosis value. Table 3 shows the current study's univariate and multivariate normal distribution assessments. Table 3. Assessment of Normality Variable Skewness Critical Ratio Kurtosis Critical Ratio SMUH 1.814 26.407 4.140 30.142 Cyberloafing -0.245 -3.566 -0.338 -2.463 MPUH 1.475 21.475 2.675 19.472 FoMO 0.547 7.963 -0.124 -0.904 PMPU 0.711 10.358 0.038 0.274 Multivariate 20.099 31.930 The findings show a violation of normality because the critical ratio values for kurtosis were greater than 7.0 (West et al., 1995) and the critical ratio value for multivariate distribution was greater than 5.0. (Byrne, 2013). In these situations, Byrne (2013) recommended bootstrapping and calculating confidence intervals, and then comparing the model’s significance values with the bootstrapped confidence intervals. We applied this recommendation in the current study in order to ensure the significance values. Table 4 shows bivariate correlations for all variables in the study. As can be seen, PMPU was shown to be positively related with gender, cyberloafing, FoMO, MPUH, and SMUH, and negatively related with age. The magnitudes of relationship with cyberloafing, FoMO, MPUH, and SMUH were close to medium, whilst, in contrast, they were low for the association with age and gender. Cyberloafing was shown to be positively related with all variables, and their magnitudes were low. FoMO was negatively related to age; however, it was positively correlated with gender, MPUH, and SMUH. Table 4. Bivariate Correlations of All Variables 1 2 3 4 5 6 7 Age - Gender -.078** - Cyberloafing .026 .063* - FoMO -.158** .074** .211** - MPUH -.087** .169** .107** .161** - SMUH -.064* .143** .151** .250** .622** - PMPU -.101** .118** .321** .464** .329** .347** - Note. N = 1,272; Dashes indicate that the value of the cells is 1.00; * p < .05, ** p < .01 In the first set of hypotheses, the results confirmed that the participants’ age negatively impacted their FoMO and MPUH. Where the participants were older, their FoMO and MPUH were shown to be less than that of the younger participants. Age was unable to significantly explain cyberloafing and SMUH. The second set of hypotheses also confirmed that gender positively impacted upon cyberloafing, FoMO, MPUH, and SMUH. Females were shown to have been cyberloafing more than males and had greater FoMO than males. Moreover, they used mobile phones and social media longer than males. For the third, fourth, fifth, and sixth hypotheses, cyberloafing, FoMO, MPUH, and SMUH positively predicted PMPU. For the seventh and eighth hypotheses, there was an indirect effect of age on PMPU via FoMO and MPUH; while gender had an indirect effect on PMPU via cyberloafing, FoMO, MPUH, and SMUH. Table 5 presents the unstandardized, standardized, and critical values for the significance testing. Table 5. Unstandardized and Standardized Regression Weights Hypotheses Unstandardized Estimates Standardized Estimates SE t Status Participants’ age impacts Cyberloafing, .009 .031 .009 1.090 Not approved FoMO, -.034 -.153 .006 -5.527** Approved MPUH, -.100 -.074 .037 -2.675** Approved SMUH. -.054 -.053 .028 -1.906 Not approved Participants’ gender impacts Cyberloafing, .114 .065 .049 2.334* Approved FoMO, .078 .062 .035 2.253* Approved MPUH, 1.252 .163 .213 5.886** Approved SMUH. .797 .139 .160 4.983** Approved Cyberloafing impacts PMPU. .208 .208 .023 8.842** Approved FoMO impacts PMPU. .501 .363 .033 15.102** Approved MPUH impacts PMPU .040 .176 .007 6.030** Approved SMUH impacts PMPU .035 .115 .009 3.864** Approved Age indirectly impacts PMPU use via Cyberloafing, FoMO, MPUH, and SMUH. a -.021 -.068 .005 [-.029. -.013]** Partially approved b Gender indirectly impacts PMPU use Cyberloafing, FoMO, MPUH, and SMUH. a .140 .081 [.093. .185] ** Approved Note. The estimates were also bootstrapped (N = 1,000) to ensure consistency and confidence intervals, and that there no differences between them. R 2 values of predicted values were: 0.5%, cyberloafing; 2.9%, FoMO; 3.4%, MPUH; 2.3%, SMUH; and, 33.4%, PMPU. a For these hypotheses, indirect standardized estimates were computed using bootstrapping in AMOS 24. Because bootstrapping findings in AMOS 24 do not provide exact t-values, confidence intervals are given for indirect effects. b Mediation analysis Step 1 for H7 was provided by H1a and H1d as not significant; hence, cyberloafing and SMUH were unable to mediate the relationship between age and PMPU. ** p < .01, * p < .05 The model fit values indicated that a good fit was obtained for the model under consideration, as shown in Table 6. Table 6. The Model Fit Evaluation Fit Index Model Value Criteria for Good Fit Resource χ 2 ( df ) 3.172, p = .205 Low χ 2 value and p > .05 Hooper et al. (2008) χ 2 / df 1.586 χ 2 / df < 3 Wheaton et al. (1977) Tabachnick and Fidell (2007) RMSEA .021 RMSEA < .05 Hu and Bentler (1999) Steiger (2007) SRMR .0078 SRMR ≤ .05 Byrne (2013) Diamantopoulos and Siguaw (2006) GFI .999 .95 ≤ GFI ≤ 1 Tabachnick and Fidell (2007) Miles and Shevlin (2007) AGFI .990 .85 ≤ AGFI ≤ 1 Tabachnick and Fidell (2007) CFI .995 .97 ≤ CFI ≤ 1 Hu and Bentler (1999) IFI .999 .95 ≤ IFI ≤ 1 Miles and Shevlin (2007) NNFI .998 .97 ≤ NNFI ≤ 1 Hu and Bentler (1999) Fan et al. (1999) Note. Bootstrapping was employed to check the consistency of the model fit values (N = 1,000), and no difference was discovered. For the seventh hypothesis, we applied Baron and Kenny’s (1986) mediation steps within the hypothesized model, which is explained as follows: The causation variable should have a strong relationship with the outcome variable. The causation variable in the current study was age, while the outcome variable was PMPU. Age had a significant and negative impact on PMPU, with a regression weight of -.028, = -.093, p.01; Step 1 was confirmed. The mediator variables, cyberloafing, FoMO, MPUH, and SMUH, should be significantly associated with the causation variable (age). Age was found to be significantly connected with FoMO and MPUH in Hypotheses 1a, 1b, 1c, and 1d (see Table 5), but not with cyberloafing and SMUH. Because Step 2 was approved for FoMO and MPUH, Steps 3 and 4 take these variables into account. The mediators (FoMO and MPUH) should have a significant influence on the outcome variable (PMPU). Significant correlations were found, as indicated in Hypotheses 4 and 5 (see Table 5). Step 3 for FoMO and MPUH was approved. The impact of the causation variable (age) on the outcome variable (PMPU) ought not to be significant when the mediators (FoMO and MPUH) are present. To test this step, we introduced a direct relationship from the causal to the outcome variable in the hypothesized model shown in Figure 1 and re-ran the analysis. When FoMO and MPUH were present, we discovered that the direct impact of age on PMPU was not significant; regression weight was -.008, = -.026, p =.273. To differentiate and compare the impact of the mediators, FoMO and MPUH, we estimated specific indirect effects using the approach suggested by Gaskin and Lim (2018). The results are illustrated in Table 7. Table 7. Specific indirect effects for mediation relationships for Age and PMPU via FoMO and MPUH Indirect Path Unstandardized Estimate LB UB β p Age --> FoMO --> PMPU -0.016 -0.022 -0.012 -0.054 .001 Age --> MPUH --> PMPU -0.002 -0.004 -0.001 -0.007 .025 LB: Lower Bound, UB: Upper Bound Table 7 demonstrates that FoMO (β = -.054) had a stronger mediation role compared to MPUH (β = -.007). The four-step technique of Baron and Kenny (1986) demonstrated that FoMO and MPUH mediated the association between age and PMPU. On the other hand, cyberloafing and SMUH were unable to mediate this relationship. The indirect effect of age on PMPU via FoMO was -.021, β = -.068, p < .01. In other words, due to the indirect (mediated) effect of age on PMPU, when age increases by 1 year, PMPU decreases by -.021 points. The proportion variance explained by mediation was 42.23% of the total effect variance, which indicates partial mediation (Hair et al., 2016). More specifically, 37.38% of variance was accounted for by FoMO, and 4.85% was explained by MPUH. For the eighth hypothesis, we repeated Baron and Kenny's (1986) mediation procedures within the theorized model, as follows: The causation variable should have a significant relation with the outcome variable. Gender was the causation variable in this study, and PMPU was the outcome variable. Gender had a significant and significant impact on PMPU, with a regression weight of.191, =.111, p.01; Step 1 was accepted. The mediator variables (cyberloafing, FoMO, MPUH, and SMUH) should be significantly associated with the causation variable (gender). Gender had a significant impact on all variables, as hypotheses 2a, 2b, 2c, and 2d suggested. Step 2 has been confirmed. The mediators (cyberloafing, FOMO, MPUH, and SMUH) should have a significant impact on the outcome variable (PMPU). All variables had a significant impact on PMPU, as demonstrated by Hypotheses 3, 4, 5, and 6. Step 3 has been confirmed. The impact of the causal variable (gender) on the outcome variable (PMPU) ought not to be significant when the mediators are present. To test this step, we introduced a direct relationship from the causation to the outcome variable in the hypothetical model shown in Figure 1 and re-ran the analysis. We discovered that when the mediators were present, the direct impact of gender on PMPU was not significant; regression weight was.054, =.031, p =.182. To differentiate and compare the impact of the mediators MPUH, cyberloafing, FoMO, and SMUH, we estimated specific indirect effects using the approach suggested by Gaskin and Lim (2018). The results are illustrated in Table 8. Table 8. Specific indirect effects for mediation relationships for gender and PMPU via MPUH, cyberloafing, FoMO, and SMUH Indirect Path Unstandardized Estimate LB UB β p Gender --> MPUH --> PMPU 0.050 0.031 0.075 0.029 .001 Gender --> Cyberloafing --> PMPU 0.023 0.005 0.043 0.013 .024 Gender --> FoMO --> PMPU 0.039 0.011 0.068 0.023 .019 Gender --> SMUH --> PMPU 0.028 0.014 0.048 0.016 .001 LB: Lower Bound, UB: Upper Bound Table 8 demonstrates that the MPUH (β = .029) was the strongest mediator, whilst cyberloafing was the least impactful (β = .013). The four-step technique of Baron and Kenny (1986) demonstrated that there were mediations of cyberloafing, FoMO, MPUH, and SMUH on the association between gender and PMPU. Gender's indirect influence on PMPU via these mediators was.140, =.081, p.01. In other words, because of the indirect (mediated) influence of gender on PMPU, females had.140 points greater PMPU than males.The proportional variance explained by mediation was 42.19% of the total effect variance, which indicates partial mediation (Hair et al., 2016). MPUH accounted for 15.11% of mediated variance, while FoMO accounted for 11.98%, SMUH accounted for 8.34%, and cyberloafing accounted for 6.77% of mediated variance. 5. Discussion This study aimed to examine the predictors of problematic mobile phone use (PMPU) with respect to cyberloafing, fear of missing out (FoMO), mobile phone use hours (MPUH), and social media use hours (SMUH) with the effects of gender and age on these variables among university students. The study aimed to bring new insights into the associations of age and gender on PMPU, providing the opportunity to better understand what types of behaviors might result in PMPU via observing any increase in cyberloafing, FoMO, MPUH, and SMUH. 5.1 Age & Gender > FoMO > PMPU The study’s results have shown that while FoMO mediated relations between age and PMPU, it was negatively correlated with age, but positively correlated with PMPU. In addition, whilst FoMO mediated relations between gender and PMPU, it was positively correlated with both gender and PMPU. Likewise, FoMO has previously been shown to be associated positively with PMPU (Alt, 2015; Elhai, Gallinari, et al., 2020; Lo Coco et al., 2020; Przybylski et al., 2013; Wolniewicz et al., 2018), which is similar and in line with the current study’s findings. Similarly, a positive and significant relationship was observed between FoMO and PMPU variables, which also support previous work (Fuster et al., 2017; Gil et al., 2016; Santana-Vega et al., 2019). As females attach much more importance to interpersonal relationships than males (Lopez-Fernandez, 2017), this may result in females experiencing feelings of FoMO more, and thus exhibit more PMPU behavior. Consistent with the current study’s results, it may also be said that FoMO can trigger and result in PMPU, by drawing a path from FoMO to PMPU related to the direction of this association (Chotpitayasunondh & Douglas, 2016). The current study’s findings fit with Elhai, Yang, et al.’s (2020) findings in that FoMO is related to problematic smartphone use severity. The findings of the current study suggest that age and gender causal variables have an indirect association with PMPU outcome variable through FoMO as a mediator variable. According to Gugushvili et al. (2020), FoMO is a key predictor and indicator of PMPU. In this respect, the current study addressed this association by directly testing the mediating role of FoMO by indicating pathways. Furthermore, consistent with previous studies in the literature, PMPU differed according to gender, with PMPU being found to be higher among females than males. In relation, some studies in the literature also found that age and gender were related to PMPU, and that both female gender (Wang et al., 2015) and a lower age (Lu et al., 2011; Van Deursen et al., 2015) were associated with PMPU. FoMO has also been previously associated with certain demographic characteristics such as age and gender (Elhai et al., 2018). The current study’s findings may therefore be said to be in line with recent studies, which found FoMO to be related more to those of a younger age (Błachnio & Przepiórka, 2018; Blackwell et al., 2017), and again, for the female gender rather than male (Beyens et al., 2016; Stead & Bibby, 2017). As such, FoMO can be considered a consequential variable that accounts for mediated relations among age, gender, and PMPU. Accordingly, the current study’s findings indicate that positive and negative associations between causal and mediator variables can lead to PMPU. 5.2 Age & Gender > Cyberloafing > PMPU There exists a huge body of research regarding the relationship between cyberloafing and PMPU. Almost all of these studies have found a positive relationship between these two variables under investigation (Gökçearslan et al., 2016; Rehman et al., 2019; Saritepeci, 2019). Consistent with these studies, the current study revealed a positive impact of cyberloafing on PMPU, indicating that the more cyberloafing behaviors students have, the more they tend to exhibit PMPU behaviors. In other words, students’ cyberloafing behaviors within a school-based environment increase their tendency towards PMPU. Contrary to many studies in the literature that showed males exhibiting more cyberloafing behaviors than females (Baturay & Toker, 2015; Garrett & Danziger, 2008b), the current study found that it was the female participants rather than the males who displayed cyberloafing behaviors. This result may be related to the sample group determined within the scope of the current study, as well as to recently noted changes in the smartphone usage habits of users, whereby females have been shown to use smartphones more intensely than males (Taywade & Khubalkar, 2019). The current study’s results may therefore be due to this change in the profile of smartphone users. This change could also be the reason behind why females were found to exhibit greater levels of PMPU behaviors than their male counterparts. 5.3 Age & Gender > MPUH & SMUH > PMPU The current study has shown that while age was negatively associated with MPUH and PMPU, it was not found to be associated with SMUH. In addition, MPUH was found to have an indirect association between age and PMPU. There are numerous studies in the literature that have examined these variables. Although Demirhan et al. (2016) could not find any relationship between age and PMPU, many studies have revealed a negative relationship between these two variables, which parallels the results obtained from the current study (Augner & Hacker, 2012; Bianchi & Phillips, 2005; Smetaniuk, 2014). On the other hand, we found in the current study that gender had a positive association with MPUH, SMUH, and PMPU. Accordingly, females tend to exhibit greater PMPU behaviors than males. Further, both MPUH and SMUH were shown to have indirect associations between gender and PMPU. Even though some studies (Ahmed & Fiaz Qazi, 2011; Bianchi & Phillips, 2005; Demirhan et al., 2016; Dixit et al., 2010; Yen et al., 2009) failed to establish a significant relationship between gender and PMPU, a huge body of research in the literature supports the results obtained in the current study, in that females exhibit PMPU behaviors more than males (Augner & Hacker, 2012; Demirci et al., 2015; Jenaro et al., 2007; Jiang & Zhao, 2016; Lee et al., 2014; Lopez-Fernandez et al., 2014; Nahas et al., 2018; Takao, 2014; Toda et al., 2015). The reason for there being no relationship identified between these two variables in some studies may be due to mobile phone technologies being accepted equally by females and males, as stated by Bianchi and Phillips (2005). In contrast, females exhibiting more PMPU behaviors than males may be because females attach more importance to interpersonal communication (Lopez-Fernandez, 2017). Moreover, the current study’s results revealed that PMPU was positively associated with both MPUH and SMUH. There are many studies in the literature supporting the current study’s results (Lee et al., 2014; Lopez-Fernandez, 2017; Vally & El Hichami, 2019). Furthermore, based on some previous studies, increased time spent using mobile phone and social media has an association with depression (Brunborg & Burdzovic, 2019; Ikeda & Nakamura, 2014), and that people diagnosed with depression are more likely to exhibit PMPU behaviors (Yen et al., 2009). Although these results are not directly related to the current study’s findings, they help provide a meaningful basis to explain the current study’s results. 6. Conclusion The study aimed to investigate the relationships between age, gender, cyberloafing, FoMO, MPUH, SMUH, and their impacts on PMPU. It can be inferred from the study’s results that demographic variables such as gender and age can play a critical role in PMPU, with the mediation of cyberloafing, FoMO, MPUH, and SMUH. The findings also revealed that cyberloafing, FoMO, MPUH, and SMUH all played a substantial role in predicting PMPU. Additionally, while age has a direct impact upon FoMO and MPUH, it indirectly impacts on PMPU through the mediations of FoMO and MPUH. Moreover, gender directly impacts upon cyberloafing, FoMO, MPUH, and SMUH, and indirectly impacts upon PMPU through the mediations of FoMO, cyberloafing, MPUH, and SMUH. There are, however, several limitations to the current study. The first was using the total scores obtained from the instruments. Future studies could consider using the sub-dimensions of each measure and to assess the associations among the variables in more sophisticated detail. Second, the study used participants’ self-reported data for MPUH, SMUH, and cyberloafing. Future research could apply an objective measure of these variables for a better reflection of the associations. Based on the findings of the current study, additional demographic factors should be addressed in order to better explain the variance of mediating factors. Based on the study’s findings, other factors also need to be addressed in order to predict cyberloafing activities and behaviors to more effectively explain the variance. 7. Implications The study's findings hold several significant pedagogical implications for educational institutions and educators. Given the omnipresence of mobile phones and the influence of social media platforms among student populations, it is paramount for educational institutions to incorporate digital literacy education proactively into their curricula. Such programs should encompass responsible mobile phone and social media usage, strategies to address PMPU concerns, and promote positive online behaviors. Moreover, educators should consider designing awareness programs specifically tailored to tackle issues like FoMO and cyberloafing practices, equipping students with the knowledge necessary to understand the potential adverse consequences of these behaviors on their academic performance and overall well-being. Additionally, educators can foster a balanced perspective on technology use by encouraging the establishment of guidelines for mobile phone and social media use during study or instructional time and providing instruction on effective time management and self-regulation. Educational institutions can also harness the power of technology for educational purposes, leveraging mobile applications, social media platforms, and internet resources to enhance the learning process while promoting responsible and purposeful usage. Furthermore, they should offer support services, such as counseling, workshops, and peer support groups, to assist students in managing problematic mobile phone use. Faculty and teachers can benefit from professional development and training programs emphasizing effective technology integration in the classroom and strategies to address and mitigate PMPU issues. Collaborative efforts with parents can also be fruitful in regulating technology usage among children, both at school and in domestic environments, by providing parents with information and awareness about the potential hazards associated with excessive mobile phone and social media usage. By addressing these pedagogical implications, educational institutions can proactively tackle the challenges related to problematic mobile phone use, foster responsible and well-balanced technology usage among students, and ultimately enhance their overall educational experience. The implications drawn from this study also have significant relevance for both academic theory and practical applications. Firstly, the path model employed in this research successfully identifies a range of significant factors associated with PMPU, laying the groundwork for a deeper understanding of this phenomenon. These findings encourage scholars to delve further into the intricate network of influences contributing to PMPU, ultimately enriching the academic discourse surrounding this topic. Secondly, the insights derived from this study have practical implications for developing interventions to address PMPU. By targeting the specific characteristics highlighted in the path model, practitioners can effectively tailor their strategies to address the root causes and mediators of PMPU. This targeted approach can potentially improve the design and implementation of treatments and interventions, ultimately assisting individuals in managing and mitigating problematic mobile phone use behaviors. In conclusion, this study's path model and findings offer a valuable foundation for advancing academic understanding and practical solutions to problematic mobile phone use. The complexity and multidimensionality of PMPU underscore the need for comprehensive research and tailored interventions. Future research endeavors can build upon these insights to construct more holistic frameworks and develop pragmatic approaches to tackle the growing issue of PMPU in our digitally connected world. Declarations Author contributions ZK-YA-BC-Conceptualization, literature review, formulating research questions, writing and editing the manuscript; ST- Data analysis, writing and editing the manuscript; SY-Conceptualization, data collection, critical review of the manuscript, and guidance of the research process. Competing interests The authors declare no competing interests. Ethical approval Approval was obtained from the Applied Ethics Research Center of the Middle East Technical University (Approval ID: 2016-EGT-165, dated 05 December 2016). All procedures involving human participants were conducted in accordance with the ethical standards of the Declaration of Helsinki and relevant national/institutional guidelines. The approval covered all aspects of the study, including recruitment, data collection, storage, and analysis. Informed consent Informed consent was obtained in written form from all participants prior to data collection. The consent process was carried out face-to-face between 10 and 20 December 2016, during which participants were informed about the purpose of the study, the voluntary nature of participation, the right to withdraw at any time without consequences, and how their data would be used and stored. Only individuals who provided written consent were included in the study. Data availability The data of this study are not publicly available. However, the anonymized datasets analyzed during the current study are available from the corresponding author on reasonable request. All authors had full access to all data used in the analysis and approved the final manuscript. References Abel, J. P., Buff, C. L., & Burr, S. A. (2016). Social Media and the Fear of Missing Out: Scale Development and Assessment. Journal of Business & Economics Research (JBER) , 14 (1), 33-44. https://doi.org/10.19030/jber.v14i1.9554 Ahmad, A., & Omar, Z. (2017). Understanding who cyberloafs from the self-control perspective: A study in the public service sector. International Journal of ADVANCED AND APPLIED SCIENCES , 4 (8), 123-128. https://doi.org/10.21833/ijaas.2017.08.017 Ahmed, I., & Fiaz Qazi, T. (2011). A look out for academic impacts of social networking sites (SNSs): A student-based perspective. African Journal of Business Management , 5 (12), 5022-5031. https://doi.org/10.5897/AJBM11.595 Akbulut, Y., Dönmez, O., & Dursun, Ö. Ö. (2017). Cyberloafing and social desirability bias among students and employees. Computers in Human Behavior , 72 , 87-95. https://doi.org/10.1016/j.chb.2017.02.043 Akbulut, Y., Dursun, Ö. Ö., Dönmez, O., & Şahin, Y. L. (2016). In search of a measure to investigate cyberloafing in educational settings. Computers in Human Behavior , 55 , 616-625. https://doi.org/10.1016/j.chb.2015.11.002 Alt, D. (2015). College students’ academic motivation, media engagement and fear of missing out. Computers in Human Behavior , 49 , 111-119. https://doi.org/10.1016/j.chb.2015.02.057 Alyahya, S., & Alqahtani, A. (2022). Cyberloafing in Educational Settings: A Systematic Literature Review. International Journal of Interactive Mobile Technologies (IJIM), 16 (16), 113–141. https://doi.org/10.3991/ijim.v16i16.32285 Andone, I., Błaszkiewicz, K., Eibes, M., Trendafilov, B., Montag, C., & Markowetz, A. (2016). How age and gender affect smartphone usage. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (pp. 9-12). ACM. https://doi.org/10.1145/2968219.2971451 Andreassen, C. S., Torsheim, T., & Pallesen, S. (2014). Predictors of use of social network sites at work - a specific type of cyberloafing. Journal of Computer-Mediated Communication , 19 (4), 906-921. https://doi.org/10.1111/jcc4.12085 Arabaci, I. B. (2017). Investigation faculty of education students’ cyberloafing behaviors in terms of various variables. Turkish Online Journal of Educational Technology , 16 (1), 72-82. http://www.tojet.net/articles/v16i1/1617.pdf Arbuckle, J. L. (2007). Amos TM 16 user’s guide . SPSS. Askew, K. L., Ilie, A., Bauer, J. A., Simonet, D. V., Buckner, J. E., & Robertson, T. A. (2019). Disentangling How Coworkers and Supervisors Influence Employee Cyberloafing: What Normative Information Are Employees Attending To? Journal of Leadership & Organizational Studies , 26(4), 526–544. https://doi.org/10.1177/1548051818813091 Augner, C., & Hacker, G. W. (2012). Associations between problematic mobile phone use and psychological parameters in young adults. International Journal of Public Health , 57 (2), 437-441. https://doi.org/10.1007/s00038-011-0234-z Aybas, M., & Gungor, A. Y. (2020). Does Cyberloafing reduce academic performance? A comparative study between Turkey and Poland. Revista Argentina De Clinica Psicologica , XXIX (5), 1060-1072. https://doi.org/10.24205/03276716.2020.1101 Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology , 51 (6), 1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173 Barry, C. T., & Wong, M. Y. (2020). Fear of missing out (FoMO): A generational phenomenon or an individual difference? Journal of Social and Personal Relationships , 37(12), 2952–2966. https://doi.org/10.1177/0265407520945394 Batabyal, S. K., & Bhal, K. T. (2020). Traditional cyberloafing, mobile cyberloafing and personal mobile-internet loafing in business organizations. Journal of Information, Communication and Ethics in Society , 18(4), 631–647. https://doi.org/10.1108/JICES-07-2019-0081 Baturay, M. H., & Toker, S. (2015). An investigation of the impact of demographics on cyberloafing from an educational setting angle. Computers in Human Behavior , 50 , 358-366. https://doi.org/10.1016/j.chb.2015.03.081 Bentler, P. M., & Chou, C.-P. (1987). Practical Issues in Structural Modeling. Sociological Methods & Research , 16 (1), 78-117. https://doi.org/10.1177/0049124187016001004 Beyens, I., Frison, E., & Eggermont, S. (2016). “I don’t want to miss a thing”: Adolescents’ fear of missing out and its relationship to adolescents’ social needs, Facebook use, and Facebook related stress. Computers in Human Behavior , 64 , 1-8. https://doi.org/10.1016/j.chb.2016.05.083 Bianchi, A., & Phillips, J. G. (2005). Psychological predictors of problem mobile phone use. Cyberpsychology and Behavior , 8 (1), 39-52. https://doi.org/10.1089/cpb.2005.8.39 Billieux, J. (2012). Problematic Use of the Mobile Phone: A Literature Review and a Pathways Model. Current Psychiatry Reviews , 8 (4), 299-307. https://doi.org/10.2174/157340012803520522 Billieux, J., Van der Linden, M., & Rochat, L. (2008). The role of impulsivity in actual and problematic use of the mobile phone. Applied Cognitive Psychology , 22 (9), 1195-1210. https://doi.org/10.1002/acp.1429 Błachnio, A., & Przepiórka, A. (2018). Facebook intrusion, fear of missing out, narcissism, and life satisfaction: A cross-sectional study. Psychiatry Research , 259 , 514-519. https://doi.org/10.1016/j.psychres.2017.11.012 Blackwell, D., Leaman, C., Tramposch, R., Osborne, C., & Liss, M. (2017). Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Personality and Individual Differences , 116 , 69-72. https://doi.org/10.1016/j.paid.2017.04.039 Blanchard, A. L., & Henle, C. A. (2008). Correlates of different forms of cyberloafing: The role of norms and external locus of control. Computers in Human Behavior , 24 (3), 1067-1084. https://doi.org/10.1016/j.chb.2007.03.008 Bollen, K. A. (1989). Structural Equations with Latent Variables . Wiley. https://doi.org/10.1002/9781118619179 Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values in lisrel maximum likelihood estimation. Psychometrika , 50 (2), 229-242. https://doi.org/10.1007/BF02294248 Brunborg, G. S., & Burdzovic Andreas, J. (2019). Increase in time spent on social media is associated with modest increase in depression, conduct problems, and episodic heavy drinking. Journal of Adolescence , 74(1) , 201-209. https://doi.org/10.1016/j.adolescence.2019.06.013 Burke, M., Marlow, C., & Lento, T. (2010). Social network activity and social well-being. In CHI ’10, Proceedings of the 28th International Conference on Human Factors in Computing Systems (pp. 1909-1912). ACM. https://doi.org/10.1145/1753326.1753613 Busch, P. A., Hausvik, G. I., Ropstad, O. K., & Pettersen, D. (2021). Smartphone usage among older adults. Computers in Human Behavior , 121 , Article 106783. https://doi.org/10.1016/j.chb.2021.106783 Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming . Routledge. Casale, S., & Fioravanti, G. (2020). Factor structure and psychometric properties of the Italian version of the fear of missing out scale in emerging adults and adolescents. Addictive Behaviors , 102 , Article 106179. https://doi.org/10.1016/j.addbeh.2019.106179 Chen, B., Liu, F., Ding, S., Ying, X., Wang, L., & Wen, Y. (2017). Gender differences in factors associated with smartphone addiction: a cross-sectional study among medical college students. BMC Psychiatry , 17 (1), Article 341. https://doi.org/10.1186/s12888-017-1503-z Chóliz, M. (2012). No1 Test Mobile Phone Addiction 33. Progress in Health Sciences, 2 (1), 33-44. https://progressinhealthsciences.publisherspanel.com/resources/html/article/details?id=112500 Chotpitayasunondh, V., & Douglas, K. M. (2016). How “phubbing” becomes the norm: The antecedents and consequences of snubbing via smartphone. Computers in Human Behavior , 63 , 9-18. https://doi.org/10.1016/j.chb.2016.05.018 Chou, W.-Y. S., Hunt, Y. M., Beckjord, E. B., Moser, R. P., & Hesse, B. W. (2009). Social media use in the United States: Implications for health communication. Journal of Medical Internet Research , 11 (4), Article e48. https://doi.org/10.2196/jmir.1249 Correa, T., Hinsley, A. W., & de Zúñiga, H. G. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior , 26 (2), 247-253. https://doi.org/10.1016/j.chb.2009.09.003 Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson. DeCarlo, L. T. (1997). On the meaning and use of kurtosis. Psychological Methods , 2 (3), 292-307. https://doi.org/10.1037/1082-989X.2.3.292 Demirci, K., Akgönül, M., & Akpinar, A. (2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of Behavioral Addictions , 4 (2), 85-92. https://doi.org/10.1556/2006.4.2015.010 Demirhan, E., Randler, C., & Horzum, M. B. (2016). Is problematic mobile phone use explained by chronotype and personality? Chronobiology International , 33 (7), 821-831. https://doi.org/10.3109/07420528.2016.1171232 Demirtepe-Saygılı, D., & Metin-Orta, I. (2021). An Investigation of Cyberloafing in Relation to Coping Styles and Psychological Symptoms in an Educational Setting. Psychological Reports , 124(4), 1559–1587. https://doi.org/10.1177/0033294120950299 Diamantopoulos, A., & Siguaw, J. A. (2006). Formative Versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration. British Journal of Management , 17 (4), 263-282. https://doi.org/10.1111/j.1467-8551.2006.00500.x Dixit, S., Shukla, H., Bhagwat, A., Bindal, A., Goyal, A., Zaidi, A., & Shrivastava, A. (2010). A study to evaluate mobile phone dependence among students of a medical college and associated hospital of central India. Indian Journal of Community Medicine , 35 (2), 339-341. https://doi.org/10.4103/0970-0218.66878 Dogan, V. (2019). Why Do People Experience the Fear of Missing Out (FoMO)? Exposing the Link Between the Self and the FoMO Through Self-Construal. Journal of Cross-Cultural Psychology , 50 (4), 524-538. https://doi.org/10.1177/0022022119839145 Dursun, O. O., & Donmez, O. (2018). Predictors of Cyberloafing among Preservice Information Technology Teachers. Contemporary Educational Technology, 9 (1), 22-41. https://dergipark.org.tr/en/pub/cet/issue/34282/378820 Elhai, J. D., Gallinari, E. F., Rozgonjuk, D., & Yang, H. (2020). Depression, anxiety and fear of missing out as correlates of social, non-social and problematic smartphone use. Addictive Behaviors , 105 , Article 106335. https://doi.org/10.1016/j.addbeh.2020.106335 Elhai, J. D., Levine, J. C., Alghraibeh, A. M., Alafnan, A. A., Aldraiweesh, A. A., & Hall, B. J. (2018). Fear of missing out: Testing relationships with negative affectivity, online social engagement, and problematic smartphone use. Computers in Human Behavior , 89 , 289-298. https://doi.org/10.1016/j.chb.2018.08.020 Elhai, J. D., Levine, J. C., Dvorak, R. D., & Hall, B. J. (2016). Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use. Computers in Human Behavior , 63 , 509-516. https://doi.org/10.1016/j.chb.2016.05.079 Elhai, J. D., McKay, D., Yang, H., Minaya, C., Montag, C., & Asmundson, G. J. G. (2021). Health anxiety related to problematic smartphone use and gaming disorder severity during COVID-19: Fear of missing out as a mediator. Human Behavior and Emerging Technologies , 3 (1), 137-146. https://doi.org/10.1002/hbe2.227 Elhai, J. D., Yang, H., Fang, J., Bai, X., & Hall, B. J. (2020). Depression and anxiety symptoms are related to problematic smartphone use severity in Chinese young adults: Fear of missing out as a mediator. Addictive Behaviors , 101 , Article 105962. https://doi.org/10.1016/j.addbeh.2019.04.020 Elhai, J. D., Yang, H., & Montag, C. (2021). Fear of missing out (FOMO): overview, theoretical underpinnings, and literature review on relations with severity of negative affectivity and problematic technology use. Revista Brasileira de Psiquiatria, 43 (2), 203-209). https://doi.org/10.1590/1516-4446-2020-0870 Erdem, H., Türen, U., & Kalkin, G. (2017). Mobil Telefon Yoksunluğu Korkusu (Nomofobi) Yayılımı: Türkiye’den Üniversite Öğrencileri ve Kamu Çalışanları Örneklemi [No Mobile Phone Phobia (Nomophobia) Prevalence: Samples of Undergraduate Students and Public Employees from Turkey]. Bilişim Teknolojileri Dergisi , 10 (1), 1-12. https://dergipark.org.tr/en/pub/gazibtd/issue/27536/289670 Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling: A Multidisciplinary Journal , 6 (1), 56-83. https://doi.org/10.1080/10705519909540119 Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education . McGraw Hill. Fuster, H., Chamarro, A., & Oberst, U. (2017). Fear of Missing Out, online social networking and mobile phone addiction: A latent profile approach. Aloma: Revista de Psicologia, Ciències de l’Educació i de l’Esport , 35 (1), 22-30. https://doi.org/10.51698/aloma.2017.35.1.22-30 Garrett, R. K., & Danziger, J. N. (2008a). Disaffection or expected outcomes: Understanding personal Internet use during work. Journal of Computer-Mediated Communication , 13 (4), 937-958. https://doi.org/10.1111/j.1083-6101.2008.00425.x Garrett, R. K., & Danziger, J. N. (2008b). On cyberslacking: Workplace status and personal Internet use at work. Cyberpsychology and Behavior , 11 (3), 287-292. https://doi.org/10.1089/cpb.2007.0146 Gaskin, J., & Lim, J. (2018). Merge SRW Tables, AMOS Plugin . Gaskination’s StatWiki. Geser, H. (2006). Are girls (even) more addicted? Some gender patterns of cell phone usage. In Sociology in Switzerland: Sociology of the Mobile Phone . University of Zurich. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-327002 Giagkou, S., Hussain, Z., & Pontes, H. (2018). Exploring the Interplay Between Passive Following on Facebook, Fear of Missing out, Self-esteem, Social Comparison, Age, and Life Satisfaction in a Community-based Sample. International Journal of Psychology & Behavior Analysis , 4 (2), Article 149. https://doi.org/10.15344/2455-3867/2018/149 Gil, F., Chamarro, A., & Oberst, U. (2016). Addiction to online social networks: A question of “Fear of missing out”? Journal of Behavioral Addictions , 4 , 51. Gökçearslan, Ş., Mumcu, F. K., Haşlaman, T., & Çevik, Y. D. (2016). Modelling smartphone addiction: The role of smartphone usage, self-regulation, general self-efficacy and cyberloafing in university students. Computers in Human Behavior , 63 , 639-649. https://doi.org/10.1016/j.chb.2016.05.091 Gokler, M., Aydin, R., Unal, E., & Metintas, S. (2016). “Fear of Missing Out” in university students in western area of Turkey. European Journal of Public Health , 26 (suppl_1), 478. https://doi.org/10.1093/eurpub/ckw175.118 Gökler, M. E., Aydin, R., Ünal, E., Metintaş, S., Çalışmada, A., Kaçırma, G., Ölçeğinin, K., & Türkçe, G. (2016). Sosyal Ortamlarda Gelişmeleri Kaçırma Korkusu Ölçeğinin Türkçe sürümünün geçerlilik ve güvenilirliğinin değerlendirilmesi [Determining validity and reliability of Turkish version of Fear of Missing out Scale]. Anadolu Psikiyatri Dergisi, 17 (1), 53-59. https://doi.org/10.5455/apd.195843 Gozum, A. İ. C., Erkul, R., & Aksoy, N. (2020). Use of Smartphones in Class: Examining the Relationship Between M-Learning Readiness, Cyberloafing, Nomophobia and Addiction Variables. International Journal of Progressive Education , 16 (6), 94-120. https://doi.org/10.29329/ijpe.2020.280.6 Grant, J. E., Lust, K., & Chamberlain, S. R. (2019). Problematic smartphone use associated with greater alcohol consumption, mental health issues, poorer academic performance, and impulsivity. Journal of Behavioral Addictions, 8 (2), 335–342. https://doi.org/10.1556/2006.8.2019.32 Greengard, S. (2000, December 1). The high cost of cyberslacking. Workforce . https://workforce.com/news/the-high-cost-of-cyberslacking Gugushvili, N., Täht, K., Rozgonjuk, D., Raudlam, M., Ruiter, R., & Verduyn, P. (2020). Two dimensions of problematic smartphone use mediate the relationship between fear of missing out and emotional well-being. Cyberpsychology: Journal of Psychosocial Research on Cyberspace , 14 (2), Article 3. https://doi.org/10.5817/CP2020-2-3 Gullu, B. F., & Serin, H. (2020). The Relationship Between Fear of Missing Out (FoMO) Levels and Cyberloafing Behaviour of Teachers. Journal of Education and Learning , 9 (5), 205-214. https://doi.org/10.5539/jel.v9n5p205 Gupta, M., & Sharma, A. (2021). Fear of missing out: A brief overview of origin, theoretical underpinnings and relationship with mental health. World Journal of Clinical Cases , 9(19), 4881–4889. https://doi.org/10.12998/wjcc.v9.i19.4881 Hair, J. J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) . Sage. Hato, B. (2013). (Compulsive) Mobile Phone Checking Behavior Out of a Fear of Missing Out: Development, Psychometric Properties and Test-Retest Reliability of a C-FoMO-Scale . [Master’s thesis, Tilburg University, Netherlands]. https://arno.uvt.nl/show.cgi?fid=130541 Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. The Electronic Journal of Business Research Methods , 6 (1), 53-60. https://academic-publishing.org/index.php/ejbrm/article/view/1224 Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal , 6 (1), 1–55. https://doi.org/10.1080/10705519909540118 Hussain, Z., Griffiths, M. D., & Sheffield, D. (2017). An investigation into problematic smartphone use: The role of narcissism, anxiety, and personality factors. Journal of Behavioral Addictions , 6 (3), 378-386. https://doi.org/10.1556/2006.6.2017.052 Ikeda, K., & Nakamura, K. (2014). Association between mobile phone use and depressed mood in Japanese adolescents: A cross-sectional study. Environmental Health and Preventive Medicine , 19 (3), 187-193. https://doi.org/10.1007/s12199-013-0373-3 Jenaro, C., Flores, N., Gómez-Vela, M., González-Gil, F., & Caballo, C. (2007). Problematic internet and cell-phone use: Psychological, behavioral, and health correlates. Addiction Research and Theory , 15 (3), 309-320. https://doi.org/10.1080/16066350701350247 Jiang, Z., & Zhao, X. (2016). Self-control and problematic mobile phone use in Chinese college students: the mediating role of mobile phone use patterns. BMC Psychiatry , 16 (1), Article 416. https://doi.org/10.1186/s12888-016-1131-z Kalaycı, E. (2010). The investigation of relationship between cyberloafing and self-regulated learning strategies among undergraduate students [Master’s thesis, Hacettepe University, Turkey]. https://tez.yok.gov.tr/UlusalTezMerkezi/tezDetay.jsp?id=up27hIAneb7E6plPO9vxIw&no=XB0tB25Z5baqKEuCPTvcSg Kasahara, G. M., Houlihan, D., & Estrada, C. (2019). Gender Differences in Social Media Use and Cyberbullying in Belize: A Preliminary Report. International Journal of Psychological Studies , 11 (2), 32-41. https://doi.org/10.5539/ijps.v11n2p32 Kaspersky Lab. (2016). I don’t want to miss a thing Why we cannot keep our fingers off our smartphones A psychological study by the Universities of Würzburg and Nottingham Trent for Kaspersky Lab . https://media.kaspersky.com/pdf/I%20dont%20want%20to%20miss%20a%20thing.pdf Kim, S. J., & Byrne, S. (2011). Conceptualizing personal web usage in work contexts: A preliminary framework. Computers in Human Behavior , 27 (6), 2271-2283. https://doi.org/10.1016/j.chb.2011.07.006 Kim, Y., Jeong, J.-E., Cho, H., Jung, D.-J., Kwak, M., Rho, M. J., Yu, H., Kim, D.-J., & Choi, I. Y. (2016). Personality factors predicting smartphone addiction predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control. PLoS ONE , 11 (8), Article e0159788. https://doi.org/10.1371/journal.pone.0159788 Kimbrough, A. M., Guadagno, R. E., Muscanell, N. L., & Dill, J. (2013). Gender differences in mediated communication: Women connect more than do men. Computers in Human Behavior , 29 (3), 896-900. https://doi.org/10.1016/j.chb.2012.12.005 Kline, R. B. (1998). Principles and Practice of Structural Equation Modeling . Guilford. Koay, K. Y., Soh, P. C.-H., & Chew, K. W. (2017). Antecedents and consequences of cyberloafing: Evidence from the Malaysian ICT industry. First Monday , 22 (3). https://doi.org/10.5210/fm.v22i3.7302 Kwon, M., Kim, D.-J., Cho, H., & Yang, S. (2013). The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS ONE , 8 (12), Article e83558. https://doi.org/10.1371/journal.pone.0083558 Kwon, M., Lee, J.-Y., Won, W.-Y., Park, J.-W., Min, J.-A., Hahn, C., Gu, X., Choi, J.-H., & Kim, D.-J. (2013). Development and Validation of a Smartphone Addiction Scale (SAS). PLoS ONE , 8 (2), Article e56936. https://doi.org/10.1371/journal.pone.0056936 Lee, S.-Y., Lee, D., Nam, C. R., Kim, D. Y., Park, S., Kwon, J.-G., Kweon, Y.-S., Lee, Y., Kim, D. J., & Choi, J.-S. (2018). Distinct patterns of Internet and smartphone-related problems among adolescents by gender: Latent class analysis. Journal of Behavioral Addictions , 7 (2), 454-465. https://doi.org/10.1556/2006.7.2018.28 Lee, Y. K., Chang, C. T., Lin, Y., & Cheng, Z. H. (2014). The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress. Computers in Human Behavior , 31 (1), 373-383. https://doi.org/10.1016/j.chb.2013.10.047 Leung, L. (2008). Linking Psychological Attributes to Addiction and Improper Use of the Mobile Phone Among Adolescents in Hong Kong. Journal of Children and Media , 2 (2), 93-113. https://doi.org/10.1080/17482790802078565 Li, L., Griffiths, M. D., Mei, S., & Niu, Z. (2020). Fear of Missing Out and Smartphone Addiction Mediates the Relationship Between Positive and Negative Affect and Sleep Quality Among Chinese University Students. Frontiers in Psychiatry , 11 , Article 877. https://doi.org/10.3389/fpsyt.2020.00877 Lim, V. K. G., & Chen, D. J. Q. (2012). Cyberloafing at the workplace: Gain or drain on work? Behaviour and Information Technology , 31 (4), 343-353. https://doi.org/10.1080/01449290903353054 Lim, V. K. G., Teo, T. S. H., & Loo, G. L. (2002). How do I loaf here? Let me count the ways. Communications of the ACM , 45 (1), 66-70. https://doi.org/10.1145/502269.502300 Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., & Primack, B. A. (2016). Association between social media use and depression among US young adults. Depression and Anxiety , 33 (4), 323-331. https://doi.org/10.1002/da.22466 Liu, D., Baumeister, R. F., Yang, C., & Hu, B. (2019). Retracted: Digital Communication Media Use and Psychological Well-Being: A Meta-Analysis. Journal of Computer-Mediated Communication, 24 (5), 259–273. https://doi.org/10.1093/jcmc/zmz013 Lo Coco, G., Salerno, L., Franchina, V., La Tona, A., Di Blasi, M., & Giordano, C. (2020). Examining bi-directionality between Fear of Missing Out and problematic smartphone use. A two-wave panel study among adolescents. Addictive Behaviors , 106 , Article 106360. https://doi.org/10.1016/j.addbeh.2020.106360 Long, J., Liu, T.-Q., Liao, Y.-H., Qi, C., He, H.-Y., Chen, S.-B., & Billieux, J. (2016). Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates. BMC Psychiatry , 16 (1), Article 408. https://doi.org/10.1186/s12888-016-1083-3 Lopez-Fernandez, O. (2017). Short version of the Smartphone Addiction Scale adapted to Spanish and French: Towards a cross-cultural research in problematic mobile phone use. Addictive Behaviors , 64 , 275-280. https://doi.org/10.1016/j.addbeh.2015.11.013 Lopez-Fernandez, O., Honrubia-Serrano, L., Freixa-Blanxart, M., & Gibson, W. (2014). Prevalence of Problematic Mobile Phone Use in British Adolescents. Cyberpsychology, Behavior, and Social Networking , 17 (2), 91-98. https://doi.org/10.1089/cyber.2012.0260 Lopez-Fernandez, O., Kuss, D. J., Romo, L., Morvan, Y., Kern, L., Graziani, P., Rousseau, A., Rumpf, H. J., Bischof, A., Gässler, A. K., Schimmenti, A., Passanisi, A., Männikkö, N., Kääriänen, M., Demetrovics, Z., Király, O., Chóliz, M., Zacarés, J. J., Serra, E.,…Billieux, J. (2017). Self-reported dependence on mobile phones in young adults: A European cross-cultural empirical survey. Journal of Behavioral Addictions , 6 (2), 168-177. https://doi.org/10.1556/2006.6.2017.020 Lopez-Fernandez, O., Losada-Lopez, J. L., & Honrubia-Serrano, M a . L. (2015). Predictors of Problematic Internet and mobile phone usage in adolescents. Aloma, 33 (2), 49-58. http://revistaaloma.net/index.php/aloma/article/view/261/200200303 Lowe-Calverley, E., & Grieve, R. (2017). Web of deceit: Relationships between the dark triad, perceived ability to deceive and cyberloafing. Cyberpsychology: Journal of Psychosocial Research on Cyberspace , 11(2). https://doi.org/10.5817/CP2017-2-5 Lu, X., Watanabe, J., Liu, Q., Uji, M., Shono, M., & Kitamura, T. (2011). Internet and mobile phone text-messaging dependency: Factor structure and correlation with dysphoric mood among Japanese adults. Computers in Human Behavior , 27 (5), 1702-1709. https://doi.org/10.1016/j.chb.2011.02.009 MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods , 1 (2), 130-149. https://doi.org/10.1037/1082-989X.1.2.130 Merlo, L. J., Stone, A. M., Bibbey, A., Merlo, L. J., Stone, A. M., & Bibbey, A. (2013). Measuring Problematic Mobile Phone Use: Development and Preliminary Psychometric Properties of the PUMP Scale, Measuring Problematic Mobile Phone Use: Development and Preliminary Psychometric Properties of the PUMP Scale. Journal of Addiction, Journal of Addiction , 2013, Article e912807. https://doi.org/10.1155/2013/912807 Metin-Orta, I., & Demirutku, K. (2020). Cyberloafing behaviors among university students and its relation to Hedonistic-Stimulation value orientation, cyberloafing attitudes, and time spent on the Internet. Current Psychology , 41 , 4271-4282. https://doi.org/10.1007/s12144-020-00932-9 Miles, J., & Shevlin, M. (2007). A time and a place for incremental fit indices. Personality and Individual Differences , 42 (5), 869-874. https://doi.org/10.1016/j.paid.2006.09.022 Misra, N., Dangi, S., & Patel, S. (2015). Gender Differences in Usage of Social Networking Sites and Perceived Online Social Support on Psychological Well Being of Youth. The International Journal of Indian Psychology , 3 (1), 7751-7754. https://doi.org/10.25215/0301.008 Mok, J.-Y., Choi, S.-W., Kim, D.-J., & Choi, J.-S. (2014). Neuropsychiatric Disease and Treatment Dovepress latent class analysis on internet and smartphone addiction in college students. Journal of Neuropsychiatric Disease and Treatment , 10 , 817-828. https://doi.org/10.2147%2FNDT.S59293 Muscanell, N. L., & Guadagno, R. E. (2012). Make new friends or keep the old: Gender and personality differences in social networking use. Computers in Human Behavior , 28 (1), 107-112. https://doi.org/10.1016/j.chb.2011.08.016 Nahas, M., Hlais, S., Saberian, C., & Antoun, J. (2018). Problematic smartphone use among Lebanese adults aged 18–65 years using MPPUS-10. Computers in Human Behavior , 87 , 348-353. https://doi.org/10.1016/j.chb.2018.06.009 Nunnally, J. C. (1967). Psychometric Theory . McGraw-Hill. Oberst, U., Wegmann, E., Stodt, B., Brand, M., & Chamarro, A. (2017). Negative consequences from heavy social networking in adolescents: The mediating role of fear of missing out. Journal of Adolescence , 55 , 51-60. https://doi.org/10.1016/j.adolescence.2016.12.008 Ozler, D. E., & Polat, G. (2012). Cyberloafing Phenomenon in Organizations: Determinants and Impacts. International Journal of EBusiness and EGovernment Studies , 4 (2), 1-15. https://dergipark.org.tr/en/pub/ijebeg/issue/26199/275855 Öztunç, M. (2013). Analysis of Problematic Mobile Phone Use, Feelings of Shyness and Loneliness in Accordance with Several Variables. Procedia - Social and Behavioral Sciences , 106 , 456-466. https://doi.org/10.1016/j.sbspro.2013.12.051 Pamuk, M., & Atli, A. (2016). Development of a Problematic Mobile Phone Use Scale for University Students: Validity and Reliability Study. Düşünen Adam, 29 (1), 49-59. https://dusunenadamdergisi.org/article/244 Park, N., & Lee, H. (2014). Nature of Youth Smartphone Addiction in Korea: Diverse Dimensions of Smartphone Use and Individual Traits. Journal of Communication Research , 51 (1), 100-132. https://doi.org/10.22174/jcr.2014.51.1.100 Pivetta, E., Harkin, L., Billieux, J., Kanjo, E., & Kuss, D. J. (2019). Problematic smartphone use: An empirically validated model. Computers in Human Behavior , 100, 105–117. https://doi.org/10.1016/j.chb.2019.06.013 Polito, A. (1997). Cyberloafing can be curbed. Workforce , 76 (18). Poushter, J., Bishop, C., & Chwe, H. (2018, June 19). Social media use continues to rise in developing countries but plateaus across developed ones. Pew Research Center. https://www.pewresearch.org/global/2018/06/19/social-media-use-continues-to-rise-in-developing-countries-but-plateaus-across-developed-ones/ Przybylski, A. K., Murayama, K., Dehaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior , 29 (4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Qutishat, M. G. (2020). Gender Differences in Fear of Missing out Experiences among Undergraduate Students in Oman. New Emirates Medical Journal , 1 (2), 36-40. https://doi.org/10.2174/0250688202002022003 Rosen, L. D., Mark Carrier, L., Pedroza, J. A., Elias, S., O’Brien, K. M., Lozano, J., Kim, K., Cheever, N. A., Bentley, J., & Ruiz, A. (2018). The role of executive functioning and technological anxiety (FOMO) in college course performance as mediated by technology usage and multitasking habits. Psicologia Educativa , 24 (1), 14-25. https://doi.org/10.5093/psed2018a3 Roser, K., Schoeni, A., Foerster, M., & Röösli, M. (2016). Problematic mobile phone use of Swiss adolescents: is it linked with mental health or behaviour? International Journal of Public Health , 61 (3), 307-315. https://doi.org/10.1007/s00038-015-0751-2 Salehan, M., & Negahban, A. (2013). Social networking on smartphones: When mobile phones become addictive. Computers in Human Behavior , 29 (6), 2632-2639. https://doi.org/10.1016/j.chb.2013.07.003 Sánchez-Martínez, M., & Otero, A. (2009). Factors associated with cell phone use in adolescents in the community of Madrid (Spain). Cyberpsychology and Behavior , 12 (2), 131-137. https://doi.org/10.1089/cpb.2008.0164 Santana-Vega, L.-E., Gómez-Muñoz, A.-M., & Feliciano-García, L. (2019). Adolescents problematic mobile phone use, Fear of Missing Out and family communication. Comunicar , 27 (59), 39-47. https://doi.org/10.3916/C59-2019-04 Saritepeci, M. (2020). Predictors of cyberloafing among high school students: unauthorized access to school network, metacognitive awareness and smartphone addiction. Education and Information Technologies , 25(3), 2201–2219. https://doi.org/10.1007/s10639-019-10042-0 Savci, A., Damar, H. T., Comez, S., & Bilik, O. (2021). Effects of Smartphone Addiction and Cyberloafing Behaviour of Nursing Students on their Clinical Decision-Making during Clinical Practice. International Journal of Caring Sciences , 14 (1), 534-542. http://www.internationaljournalofcaringsciences.org/docs/56_savsi_original_14_1.pdf Shannon, H., Bush, K., Villeneuve, P. J., Hellemans, K. G., & Guimond, S. (2022). Problematic Social Media Use in Adolescents and Young Adults: Systematic Review and Meta-analysis. JMIR Mental Health, 9 (4), e33450. https://doi.org/10.2196/33450 Shin, W., & Kim, H.-W. (2023). Problematic mobile phone use and cyberbullying perpetration in adolescents. Behaviour & Information Technology, 42 (4), 424–443. https://doi.org/10.1080/0144929X.2022.2104756 Smetaniuk, P. (2014). A preliminary investigation into the prevalence and prediction of problematic cell phone use. Journal of Behavioral Addictions , 3 (1), 41-53. https://doi.org/10.1556/JBA.3.2014.004 Stead, H., & Bibby, P. A. (2017). Personality, fear of missing out and problematic internet use and their relationship to subjective well-being. Computers in Human Behavior , 76 , 534-540. https://doi.org/10.1016/j.chb.2017.08.016 Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual Differences , 42 (5), 893-898. https://doi.org/10.1016/j.paid.2006.09.017 Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.). Allyn & Bacon/Pearson Education. Takao, M. (2014). Problematic mobile phone use and big-five personality domains. Indian Journal of Community Medicine , 39 (2), 111-113. https://doi.org/10.4103/0970-0218.132736 Takao, M., Takahashi, S., & Kitamura, M. (2009). Addictive Personality and Problematic Mobile Phone Use. CyberPsychology & Behavior , 12 (5), 501-507. https://doi.org/10.1089/cpb.2009.0022 Tandon, A., Dhir, A., Talwar, S., Kaur, P., & Mäntymäki, M. (2022). Social media induced fear of missing out (FoMO) and phubbing: Behavioural, relational and psychological outcomes. Technological Forecasting and Social Change, 174 , 121149. https://doi.org/10.1016/j.techfore.2021.121149 Taywade, A., & Khubalkar, R. (2019). Gender differences in smartphone usage patterns of adolescents. The International Journal of Indian Psychology , 7 (4), 509-515. https://doi.org/10.25215/0704.060 Toda, M., Nishio, N., Ezoe, S., & Takeshita, T. (2015). Chronotype and smartphone use among Japanese medical students. International Journal of Cyber Behavior, Psychology and Learning , 5 (2), 75-80. https://doi.org/10.4018/IJCBPL.2015040106 Twenge, J. M. (2019). More Time on Technology, Less Happiness? Associations Between Digital-Media Use and Psychological Well-Being. Current Directions in Psychological Science, 28 (4), 372–379. https://doi.org/10.1177/0963721419838244 Vally, Z., & El Hichami, F. (2019). An examination of problematic mobile phone use in the United Arab Emirates: Prevalence, correlates, and predictors in a college-aged sample of young adults. Addictive Behaviors Reports , 9 , Article 100185. https://doi.org/10.1016/j.abrep.2019.100185 Van Deursen, A. J. A. M., Bolle, C. L., Hegner, S. M., & Kommers, P. A. M. (2015). Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Computers in Human Behavior , 45 , 411-420. https://doi.org/10.1016/j.chb.2014.12.039 Vannucci, A., Flannery, K. M., & Ohannessian, C. M. (2017). Social media use and anxiety in emerging adults. Journal of Affective Disorders , 207 , 163-166. https://doi.org/10.1016/j.jad.2016.08.040 Villanti, A. C., Johnson, A. L., Ilakkuvan, V., Jacobs, M. A., Graham, A. L., & Rath, J. M. (2017). Social media use and access to digital technology in US Young Adults in 2016. Journal of Medical Internet Research , 19 (6), Article e196. https://doi.org/10.2196/jmir.7303 Vitak, J., Crouse, J., & Larose, R. (2011). Personal Internet use at work: Understanding cyberslacking. Computers in Human Behavior , 27 (5), 1751-1759. https://doi.org/10.1016/j.chb.2011.03.002 Wallen, N. E., & Fraenkel, J. R. (2001). Educational research: A guide to the process (2nd ed.). Erlbaum. Walsh, S. P., White, K. M., Cox, S., & Young, R. M. D. (2011). Keeping in constant touch: The predictors of young Australians’ mobile phone involvement. Computers in Human Behavior , 27 (1), 333-342. https://doi.org/10.1016/j.chb.2010.08.011 Walsh, S. P., White, K. M., Young, R. M., & Walsh, S. (2007). Young and connected: Psychological influences of mobile phone use amongst Australian youth. In G. Goggin & L. Hjorth (Eds.), Proceedings: International Conference on Social and Cultural Aspects of Mobile Phones, Convergent Media and Wireless Technologies (pp. 125-134). University of Sydney, Australia. Wang, J.-L., Wang, H.-Z., Gaskin, J., & Wang, L.-H. (2015). The role of stress and motivation in problematic smartphone use among college students. Computers in Human Behavior , 53 , 181-188. https://doi.org/10.1016/j.chb.2015.07.005 West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyl (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56-75). Sage. Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing Reliability and Stability in Panel Models. Sociological Methodology , 8 , 84-136. https://doi.org/10.2307/270754 Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety. Educational and Psychological Measurement , 73 (6), 913-934. https://doi.org/10.1177/0013164413495237 Wolniewicz, C. A., Tiamiyu, M. F., Weeks, J. W., & Elhai, J. D. (2018). Problematic smartphone use and relations with negative affect, fear of missing out, and fear of negative and positive evaluation. Psychiatry Research , 262 , 618-623. https://doi.org/10.1016/j.psychres.2017.09.058 Yasar, S. (2013). Üniversite öğrencilerinin denetim odağı ve bilgisayar laboratuvarına yönelik tutumlarının siberaylaklık davranışlarına etkisi [The effects of students’ locus of control and attitudes towards computer laboratory on their cyberloafing behaviour]. [Master’s thesis, Hacettepe University, Ankara Turkey]. http://www.openaccess.hacettepe.edu.tr:8080/xmlui/handle/11655/1727 Yen, C.-F., Tang, T.-C., Yen, J.-Y., Lin, H.-C., Huang, C.-F., Liu, S.-C., & Ko, C.-H. (2009). Symptoms of problematic cellular phone use, functional impairment and its association with depression among adolescents in Southern Taiwan. Journal of Adolescence , 32 (4), 863-873. https://doi.org/10.1016/j.adolescence.2008.10.006 Zhitomirsky-Geffet, M., & Blau, M. (2016). Cross-generational analysis of predictive factors of addictive behavior in smartphone usage. Computers in Human Behavior , 64 , 682-693. https://doi.org/10.1016/j.chb.2016.07.061 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6871454","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590223831,"identity":"2a43b1ac-6e90-4451-b8bd-a17494dc1a36","order_by":0,"name":"Zafer Kadirhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACHgYGiQSGAwwM7A0MjA0QMQMitfAcQNaSQEALA0gLUCNxWuR7Dj+88XDPHXlzycfPHs6ouSfPwN68TYLxxz2cWhh724wtEp49M9w5O83ccMOxYsMGnmNlEgwJxTi1MPMzmEkkHDjMuOF2gpnkA7YExgaJHDOgFtwuY+Nn/wbSYr/h5vFvkg/+Jdg3yL/Br4WHtwdsS+KGGzxmkhvbEhIbJHjwa5HgOVNsAdSSvOFMTpnkzL6E5DaeNKBIGm4t8j3pG2/+OHDYdsPx49ske74l2PazH95444MNbi1YfAciSNEwCkbBKBgFowATAADNP1eKbe+cIAAAAABJRU5ErkJggg==","orcid":"","institution":"Ankara University","correspondingAuthor":true,"prefix":"","firstName":"Zafer","middleName":"","lastName":"Kadirhan","suffix":""},{"id":590223832,"identity":"914ece2f-718d-4d1c-8b25-1b24b65f9cfc","order_by":1,"name":"Yunus Alkis","email":"","orcid":"","institution":"Middle East Technical University","correspondingAuthor":false,"prefix":"","firstName":"Yunus","middleName":"","lastName":"Alkis","suffix":""},{"id":590223835,"identity":"1009ec87-aa04-4486-801f-15b65a647518","order_by":2,"name":"Berkan Celik","email":"","orcid":"","institution":"Van Yüzüncü Yıl University","correspondingAuthor":false,"prefix":"","firstName":"Berkan","middleName":"","lastName":"Celik","suffix":""},{"id":590223836,"identity":"fd76bcc8-fec6-4c24-a428-70d7fcb70504","order_by":3,"name":"Sacip Toker","email":"","orcid":"","institution":"Atilim University","correspondingAuthor":false,"prefix":"","firstName":"Sacip","middleName":"","lastName":"Toker","suffix":""},{"id":590223838,"identity":"53b8859b-d4bc-47e0-a395-1329130e662f","order_by":4,"name":"Soner Yildirim","email":"","orcid":"","institution":"Middle East Technical University","correspondingAuthor":false,"prefix":"","firstName":"Soner","middleName":"","lastName":"Yildirim","suffix":""}],"badges":[],"createdAt":"2025-06-11 11:38:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6871454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6871454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102593664,"identity":"d25ca9b0-9892-4031-99df-10316407a503","added_by":"auto","created_at":"2026-02-13 11:51:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120739,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6871454/v1/d12d7da1b94772396cf853b5.png"},{"id":102593663,"identity":"3d201476-d3fc-437a-9dee-d8108cd46879","added_by":"auto","created_at":"2026-02-13 11:51:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149007,"visible":true,"origin":"","legend":"\u003cp\u003eModel estimation\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6871454/v1/c119cfb1cbe898172555e401.png"},{"id":104808137,"identity":"5e23f54a-db23-4d4c-9244-9fb9405d3343","added_by":"auto","created_at":"2026-03-17 12:18:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1530046,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6871454/v1/918ee0b9-d5b0-47ac-b354-d94b155023b3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUnveiling Contributing Pathways to Problematic Mobile Phone Use: Mediating Effects of FOMO, Cyberloafing, Mobile Phone and Social Media Use\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn today\u0026apos;s digitally interconnected world, the widespread use of mobile phones and social media platforms has revolutionized how individuals engage with information, communication, and social interaction. The integration of these technologies into daily life has resulted in significant changes in human behavior, providing researchers with a unique opportunity to investigate the intricate relationships between various psychological constructs and technology-mediated activities. The present study examines the complex relationship between four key constructs: Fear of Missing Out (FoMO), Cyberloafing, Problematic Mobile Phone Use (PMPU), and Social Media Use Hours (SMUH).\u003c/p\u003e\n\u003cp\u003eAccording to Przybylski et al. (2013), FoMO is the concern that one is missing out on rewarding experiences that others may be having as a result of their use of social media and digital communication. As mobile phones and social media platforms continue to proliferate, researchers, psychologists, and social scientists have directed increasing attention to the concept of FoMO. Numerous studies have linked FoMO to a range of psychological constructs, including increased anxiety, reduced well-being, low self-esteem, and excessive digital engagement (Tandon et al., 2022; Gupta and Sharma, 2021; Barry and Wong, 2020; Przybylski et al., 2013). Additionally, some studies have investigated the underlying factors behind this phenomenon (Dogan, 2019). This paper aims to investigate the impact of FoMO on individuals\u0026apos; use of mobile phones and social media, as well as the possible effects of this concern on their digital behaviors.\u003c/p\u003e\n\u003cp\u003eCyberloafing, on the other hand, is a phenomenon characterized by the diversion of work-related internet use toward non-work-related activities during working hours (Batabyal \u0026amp; Bhal, 2020; Askew et al., 2019; Lowe-Calverley \u0026amp; Grieve, 2017; Greengard, 2000; Lim et al., 2002; Polito, 1997). Similarly, school-related cyberloafing is defined as students\u0026rsquo; use of the Internet during school hours for non-school-related activities (Kalaycı, 2010). Even though cyberloafing has primarily been studied in work-based settings, it has recently begun to attract attention in the field of education. A growing number of studies have explored this phenomenon within educational settings (Alyahya \u0026amp; Alqahtani, 2022; Demirtepe-Saygılı \u0026amp; Metin-Orta, 2020; Saritepeci, 2019). This shift is attributed to the increased prevalence of technology integration and students\u0026rsquo; increased access to digital technologies (Akbulut et al., 2016). Given the ubiquity of smartphones and the accessibility of social media platforms in these settings, cyberloafing has become a concern for organizations, researchers, and educators. While previous research has focused on the factors that lead to cyberloafing and its consequences (e.g., Toker \u0026amp; Baturay, 2021), this study aims to investigate whether FoMO plays a significant role in motivating individuals to engage in cyberloafing behaviors, ultimately affecting their productivity and work-related outcomes.\u003c/p\u003e\n\u003cp\u003eIn parallel, PMPU has emerged as an essential concern related to the excessive and compulsive utilization of mobile phones, which disrupts everyday activities (Bianchi \u0026amp; Phillips, 2005; Billieux et al., 2015; Shin \u0026amp; Kim, 2022; Tako et al., 2009). As individuals increasingly utilize mobile devices for social media interaction and information consumption, it is crucial to comprehend the role of FOMO in the evolution of PMPU. Previous studies have investigated several factors associated with PMPU, such as psychological distress, desire for social connectedness, and poor academic performance (Grant et al., 2019; Pivetta et al., 2019). The present study expands upon the existing body of literature by examining the potential role of FoMO as a stimulant for PMPU, thereby providing insights into the underlying processes associated with these two variables.\u003c/p\u003e\n\u003cp\u003eLastly, the amount of time individuals spends on social media platforms, often referred to as SMUH, has become an essential measure in contemporary research on technology use and well-being (Liu et al., 2019; Primack et al., 2017; Twenge, 2019). As excessive social media use has been associated with negative mental health outcomes (Shannon et al., 2021), it is essential to comprehend the relationship between FoMO and SMUH. This study investigates the relationship between FoMO and SMUH, while also exploring the potential mediating and moderating factors that may impact this correlation.\u003c/p\u003e\n\u003cp\u003eIn sum, this study examines the complex relationships between FoMO, Cyberloafing, PMPU, and SMUH. By investigating these connections, this study attempts to contribute to a deeper comprehension of the way psychological reactions and technological behaviors interact in the digital age. Such insights hold significant implications for both academic discourse and practical interventions aimed at promoting healthy technology use and well-being in an increasingly digitalized society. With mobile phones and social media platforms now ubiquitous, their impact on student behavior, including cyberloafing during school hours, remains a significant concern for educators and institutions. Understanding how individuals engage with information and communication is particularly relevant to education, where technology integration has become a focal point worldwide.\u003c/p\u003e"},{"header":"2.\tLiterature Review and Hypothesis Development","content":"\u003cp\u003e\u003cstrong\u003e2.1 \u0026nbsp;Age, Cyberloafing, FoMO, MPUH, and SMUH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch studies revealing the relationship between age and cyberloafing have, to date, provided some differing results. To this end, Vitak et al. (2011) found that being younger is associated with cyberloafing variety and frequency. Aybas and Gungor (2020) revealed a significant negative association between age and the prevalence of cyberloafing. However, according to Ozler and Polat (2012), no significant difference was found in relation to cyberloafing behavior based on age. Similarly, Ahmad and Omar (2017) and Koay et al. (2017) indicated that age had no significant influence on cyberloafing. Regarding the relationship between age and FoMO, (Przybylski et al., 2013) revealed a negative correlation, and other research studies have also exposed a significant negative link between age and FoMO (Dogan, 2019; Elhai, McKay, et al., 2021; Giagkou et al., 2018). However, Busch et al. (2021) revealed that the prevalence of FoMO was considered very rare among older adults. Other evidence has shown that FoMO is more related to younger age (Elhai, Yang, \u0026amp; Montag, 2021), with younger people expressing higher levels of FoMO (Abel et al., 2016). According to Bianchi and Phillips (2005), self-reported time spent using mobile phones was shown to be negatively predicted by age. In a study by Andone et al. (2016), the mobile phone use of participants was tracked for a period of 28 days. The results indicated that younger people used their mobile phones for longer durations than older people. Moreover, the daily average smartphone use duration was shown to be negatively related to age by both Erdem et al. (2017) and Hussain et al. (2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWorldwide, younger adults have been shown to use and engage in social media more than their older counterparts\u0026nbsp;(Blackwell et al., 2017; Poushter et al., 2018), and they are regular social media users\u0026nbsp;(Villanti et al., 2017). A younger age was shown to predict social networking site participation and therefore, younger individuals reported more frequent use (Chou et al., 2009) and more engagement in social media (Blackwell et al., 2017). Similarly, a younger age was associated with spending a greater amount of time using social media per day and more social media site visits per week\u0026nbsp;(Lin et al., 2016). In other words, age and social media use were revealed to be negatively associated\u0026nbsp;(Correa et al., 2010).\u0026nbsp;Although several research studies have indicated a reverse relationship between age and social media use, only a very limited number have\u0026nbsp;found no association between age and social media use\u0026nbsp;(Vannucci et al., 2017).\u0026nbsp;The following hypothesis has been suggested considering the abovementioned studies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 1: Participants\u0026rsquo; age impacts (a) cyberloafing, (b) FoMO, (c) MPUH, and (d) SMUH.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;Gender, Cyberloafing, FoMO, MPUH, and SMUH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccordingly, it was found that gender has a positive impact on cyberloafing, with males cyberloafing more than females (Akbulut et al., 2017; Andreassen et al., 2014; Baturay \u0026amp; Toker, 2015; Dursun \u0026amp; Donmez, 2018; Lim \u0026amp; Chen, 2012; Vitak et al., 2011). Contrary to previous studies, Arabaci (2017) revealed cyberloafing behaviors to be more in favor of female participants than males in their research. As such, the literature still lacks any consensus in this regard. While some studies have found no significant gender differences associated with FoMO (Casale \u0026amp; Fioravanti, 2020), Lo Coco et al. (2020) reported females as having higher levels of FoMO behaviors. The fact that females engage with social media more than males may negatively affect their FoMO behaviors (Oberst et al., 2017), and females have been reported to use social media more often, more actively, and spend much more time using it than males (Burke et al., 2010; Kasahara et al., 2019; Misra et al., 2015). Similarly, other studies have indicated that females prefer, connect, and use social media more frequently than males (Kimbrough et al., 2013; Muscanell \u0026amp; Guadagno, 2012). Excessive use of mobile phones is also associated with female users (Jenaro et al., 2007; Lopez-Fernandez et al., 2017), and this association is probably related to females tending to depend more on their mobile phones than males (Leung, 2008; Lopez-Fernandez et al., 2015, 2017). In light of these previous studies, the following hypothesis has been formed:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 2: Participants\u0026rsquo; gender impacts (a)\u0026nbsp;cyberloafing, (b)\u0026nbsp;FoMO, (c)\u0026nbsp;MPUH, and (d)\u0026nbsp;SMUH.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eCyberloafing and PMPU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCyberloafing is associated with PMPU and is considered one of the predictors of PMPU in the literature. PMPU is also known as smartphone/mobile phone addiction (Kim \u0026amp; Byrne, 2011). According to Walsh et al. (2007), dangerous usage (e.g., whilst driving), inappropriate usage (e.g., in the cinema or in the classroom), and overuse are three indicators of PMPU and which are also known causes of smartphone addiction (Ch\u0026oacute;liz, 2012). In addition, according to Baturay and Toker (2015), males exhibit more cyberloafing behaviors and are also more likely to engage in such behaviors than females. Moreover, Garrett and Danziger (2008a, 2008b) found that the male gender was positively associated with cyberloafing behavior. The results of G\u0026ouml;k\u0026ccedil;earslan et al. (2016) indicated that cyberloafing positively affected mobile phone addiction / PMPU. Accordingly, based on their results, it may be inferred that students\u0026rsquo; cyberloafing behavior within the school-based environment increases their tendency towards PMPU, hence the following hypothesis has been stated:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 3: Cyberloafing impacts PMPU.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u0026nbsp; FoMO and PMPU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch studies have attempted to reveal the links between PMPU and FoMO in addition to mobile phone use and FoMO. Having more FoMO might promote the overuse of mobile phones (Kaspersky Lab, 2016). In their study, Rosen et al. (2018) recorded objective smartphone usage information of 216 college students for a period of at least 21 days. Their results showed that FoMO predicted smartphone usage measured by self-report and real-time application data. Hato (2013) reported a positive link between C-FoMO and smartphone engagement and mobile phone checking frequency. Elhai et al. (2016) investigated a few variables that are conceptually connected with problematic smartphone use and smartphone use frequency in a study with 308 participants. The results showed a significant association between FoMO and problematic smartphone use on the bivariate and multivariate levels. Gokler, Aydin, Unal, and Metintas\u0026rsquo;s (2016) study with 200 university students revealed a significant positive strong correlation between FoMO and PMPU. Moreover, there was also a significant association between FoMO and the number of social media accounts, and Facebook and Twitter checking frequency. In another study, Wolniewicz et al. (2018) found a strong association between FoMO and problematic smartphone use in a study conducted with 296 college students. In brief, when people examine or interact with their phones more frequently, they tend to elicit increased levels of FoMO due to worrying about potentially missing something they deem important (Kaspersky Lab, 2016). On this, the following hypothesis has been suggested:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 4: FoMO impacts PMPU.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;MPUH and PMPU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are a considerable number of studies in the literature in which the duration of MPUH triggers PMPU or smartphone addiction. For example, a study by G\u0026ouml;k\u0026ccedil;earslan et al. (2016) investigated the role of several variables on smartphone addiction and revealed that the duration of mobile phone usage was positively related to smartphone addiction. In another study, Merlo et al. (2013) found that those who use their mobile phones more have higher rates of PMPU. Likewise, in a largescale study involving nearly 5,000 participants, Kim et al. (2016) endeavored to identify personality-based factors that may be indicators of smartphone addiction. The study examined the variables with both smartphone-addicted and non-addicted sample groups and revealed that students from the smartphone-addicted group had more MPUH than those from the non-addicted group. Moreover, in a similar study, Van Deursen et al. (2015) investigated the role of different variables affecting addictive smartphone behavior and concluded that the habitual use of smartphones significantly contributed to smartphone addiction. Furthermore, there have also been research studies published that have indicated the duration of mobile phone usage as an important factor in smartphone addiction (Kwon, Kim, et al., 2013; Kwon, Lee, et al., 2013; Lin et al., 2016). As a result of these various studies, the following hypothesis has been stated:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 5: MPUH impacts PMPU.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.6\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eSMUH and PMPU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough one of the most significant indicators of smartphone addiction is social networking (Salehan \u0026amp; Negahban, 2013), an insufficient number of studies in the literature have examined the association between social media use and PMPU. Among the limited studies available, the survey study by Salehan and Negahban (2013) aimed to model several social networking variables together with mobile addiction and reported that social intensity or social media usage as a significant predictor of mobile addiction. In another study, Van Deursen et al. (2015) revealed that social usage of smartphones, such as for the purposes of interacting with others, maintaining relationships, and contacting people through social media, increased the risk of smartphone addiction. Similarly, Zhitomirsky-Geffet and Blau (2016) found that the use of WhatsApp, a social application for mobile phones, has a strong influence on smartphone addiction. With regards to this area, the following hypothesis was included in the model:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 6: SMUH impacts PMPU.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.7\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;Age and PMPU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between age and PMPU has been inconclusive since differing findings have been reported in the literature. In a study published by Long et al. (2016) on the prevalence and correlation of problematic smartphone usage in a large random sample of undergraduate students, it was reported that no significant impact was established for age on problematic smartphone use. In line with this result, Demirhan et al. (2016) reported that age was not a significant predictor of PMPU. The results of Zhitomirsky-Geffet and Blau\u0026rsquo;s (2016) study found that younger individuals elicit higher emotional dependence on smartphones than older smartphone users. However, the influence of age on addictive behavior can be considered non-linear; that is, the degree of addictive behavior may differ among those from different generations/age groups. In contrast to studies that reported finding no relationships between age and PMPU, Bianchi and Phillips (2005) found age to have a negative influence on PMPU, with young people in their study having higher PMPU scores than other participants. In parallel, Kwon, Lee, et al. (2013) reported that students tend to be more addicted to smartphone use. In a study on modeling habitual and addictive smartphone behavior, Van Deursen et al. (2015) reported that age had a negative effect on both habitual smartphone use and addictive smartphone behavior. Another study focused on the prevalence and prediction of problematic cell phone use, with Smetaniuk (2014) having reported a negative association revealed between age and the degree of PMPU. Similarly, PMPU scores were found to be higher in younger age groups than in other age groups. As a result, younger mobile phone users were found to have more problems related to mobile phone use, whilst older users reported fewer problems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, the literature has revealed that age has an influence over the variables of cyberloafing (Aybas \u0026amp; G\u0026uuml;ng\u0026ouml;r, 2020; Vitak et al., 2011), FoMO (Dogan, 2019; Elhai, McKay, et al., 2021; Giagkou et al., 2018; Przybylski et al., 2013), MPUH\u0026nbsp;(Bianchi \u0026amp; Phillips, 2005), and SMUH (Blackwell et al., 2017; Poushter et al., 2018). Moreover, many previous studies have concluded that a relationship exists between PMPU and the variables of cyberloafing (G\u0026ouml;k\u0026ccedil;earslan et al., 2016), FoMO (Elhai et al., 2016; Kaspersky Lab, 2016; Rosen et al., 2018), MPUH (Kim et al., 2016; Merlo et al., 2013), and SMUH (Salehan \u0026amp; Negahban, 2013; Van Deursen et al., 2015). When these findings are evaluated together as a whole, it can be considered that an indirect relationship may exist between age and PMPU mediated by cyberloafing, FoMO, MPUH, and SMUH variables. Based on the findings suggested to date, the following hypothesis has been identified:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 7: Age indirectly impacts PMPU via Cyberloafing, FoMO, MPUH, and SMUH.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8\u0026nbsp; Gender and PMPU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGender differences in PMPU have been addressed in many studies, but there has been no definitive consensus formed on the subject. Previous studies have found that gender has a tendency to affect PMPU (Lee et al., 2014; Park \u0026amp; Lee, 2014; Van Deursen et al., 2015). For example, it was found that females are more dependent on using their phones (Billieux et al., 2008; Walsh et al., 2011). Likewise, females use text messaging more concentratedly than males (Geser, 2006; S\u0026aacute;nchez-Mart\u0026iacute;nez \u0026amp; Otero, 2009). In contrast, another study showed that males are more likely to become involved in problematic mobile phone behaviors more females. Similarly, another study showed that males have a greater tendency than females to use their mobile phone whilst driving (Billieux et al., 2008). It has been demonstrated that male students have more PMPU than female students (\u0026Ouml;ztun\u0026ccedil;, 2013). Another previous research indicated that intensive mobile phone use and mobile phone dependence were associated with the female gender (S\u0026aacute;nchez-Mart\u0026iacute;nez \u0026amp; Otero, 2009). Other studies have also indicated that females are more likely to be addicted to and experience more PMPU than males (Kim et al., 2016; Mok et al., 2014; Roser et al., 2016; Takao et al., 2009). On the other hand, it has conclusively been shown that although males experience more problematic use of technology than females, gender does not predict PMPU (Bianchi \u0026amp; Phillips, 2005; Zhitomirsky-Geffet \u0026amp; Blau, 2016). Wolniewicz et al.\u0026rsquo;s (2018) study also indicated the mediating impact of FoMO between gender and the fear of positive or negative evaluation and problematic smartphone use.\u003c/p\u003e\n\u003cp\u003eIt has been reported that males have higher engagement in cyberloafing behaviors than females (Metin-Orta \u0026amp; Demirutku, 2020), so that it may be said that gender has an impact on cyberloafing, while cyberloafing positively impacts upon PMPU (Gozum et al., 2020; Savci et al., 2021). Thus, an indirect path seems to exist between gender and PMPU via cyberloafing. While males exhibit higher FoMO behaviors than females (Gullu \u0026amp; Serin, 2020; Qutishat, 2020), it has been noted that gender has an impact upon FoMO. On the other hand, higher levels of FoMO have been linked with higher PMPU (Li et al., 2020; Santana-Vega et al., 2019). Hence, gender has been indirectly associated with PMPU via FoMO. According to Taywade and Khubalkar (2019), females are more prone to PMPU due to spending greater amounts of time using smartphones than males; revealing that their findings support the indirect effect of gender on PMPU via MPUH. It has also been expressed that females are more active on social media than males, and therefore have a greater inclination towards PMPU/smartphone addiction (Chen et al., 2017; Lee et al., 2018). Gender may be indirectly associated with PMPU behaviors through the mediation of cyberloafing, FoMO, MPUH, and SMUH.\u003c/p\u003e\n\u003cp\u003eAlthough gender differences and comparisons regarding to PMPU have been addressed in some of the previous studies, mediators and their indirect effect between gender and PMPU have yet to be investigated, hence the following hypothesis was constructed:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 8: Gender indirectly impacts PMPU via Cyberloafing, FoMO, MPUH, and SMUH.\u003c/em\u003e\u003c/p\u003e"},{"header":"3.\tMethod","content":"\u003cp\u003e\u003cstrong\u003e3.1 Research Method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlational research method was used in this study. Correlational research, as one of the quantitative research approaches, is used to explore the association between two or more variables (Creswell, 2012). Correlational research, according to Fraenkel et al. (2012), analyzes the possibility of correlations between two or more variables without attempting to affect or alter them. In the current study, the correlational research method was used to determine the degree of relationship between cyberloafing, FoMO, MPUH, SMUH, and PMPU.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1,272 students from universities in Ankara, Turkey, took part in the study. Convenience sampling method was used, and participation was voluntary. Of the participants, 521 (41%) were male, with the rest (\u003cem\u003en =\u0026nbsp;\u003c/em\u003e751, 59%) being female. The study\u0026rsquo;s participants consisted of 1,171 (92.1%) undergraduate students and 101 (7.9%) graduate students. Split by study level, the participants consisted of 108 (8.5%) preparatory class students, 243 (19.1%) freshmen, 277 (21.8%) sophomores, 229 (18.0%) juniors, and 314 (24.7%) senior students. Additionally, 65 (5.1%) of the participants were studying for a master\u0026rsquo;s degree and 36 (2.8%) were PhD students. The participants were studying in 36 different departments, and their ages ranged from 18 to 40 years old (\u003cem\u003eM\u003c/em\u003e = 21.60; \u003cem\u003eSD\u003c/em\u003e = 2.74). The daily Internet usage of the participants ranged from between 1 to 16 hours (\u003cem\u003eM\u003c/em\u003e = 5.28; \u003cem\u003eSD\u003c/em\u003e = 3.11). Details on the demographics of the participants are presented in Table 1 below.\u003c/p\u003e\n\u003cp\u003eTable 1. Gender, age, and study year distribution of the participants\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e751\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAge Range (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e18-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e37.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e21-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e24 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eYear of Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ePreparatory Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e108\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFreshman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e243\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSophomore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e277\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e229\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e314\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1,272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Data Collection Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were gathered through using a paper-pencil questionnaire that consisted of seven questions related to demographic information and 59 items related to three separate scales. The data questionnaire used to collect data was divided into four sections. The first section included seven questions about the participants\u0026apos; demographics (gender, age, department, grade level, daily average Internet usage, daily average mobile phone usage, and daily average social media usage). The second part consisted of the 26-item PMPU\u003cem\u003e\u0026nbsp;\u003c/em\u003escale, whilst the third part consisted of the 10-item FoMO scale\u003cem\u003e.\u0026nbsp;\u003c/em\u003eLastly, the fourth part consisted of the 23-item cyberloafing scale.\u003c/p\u003e\n\u003cp\u003eBianchi and Phillips were the first to establish the PMPU scale (2005). Pamuk and Atli (2016) later produced a Turkish-adapted version of the scale. The items were formed in a 5-point, Likert-type scale (1 = \u003cem\u003enot suitable at all\u003c/em\u003e, 2 = \u003cem\u003erarely suitable\u003c/em\u003e, 3 = \u003cem\u003esomewhat suitable\u003c/em\u003e, 4 = \u003cem\u003equite suitable\u003c/em\u003e, 5 = \u003cem\u003ecompletely suitable\u003c/em\u003e). The scale had a four-factor structure. Internal consistency of the scale was acceptable (Cronbach\u0026apos;s alpha coefficient \u0026gt;.70). Internal consistency of the scale was acceptable (Cronbach\u0026apos;s alpha coefficient \u0026gt;.70).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrzybylski et al. (2013) established the FoMO scale, which was then modified to the Turkish context by G\u0026ouml;kler, Aydin, \u0026Uuml;nal, Metintaş, alşmada, et al (2016). The items were organized into a five-point, Likert-type, single-dimension scale (1 = not at all true of me, 2 = slightly true of me, 3 = moderately true of me, 4 = very true of me, 5 = extremely true of me). Internal consistency of the scale was acceptable (Cronbach\u0026apos;s alpha coefficient \u0026gt;.70).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBlanchard and Henle created the first cyberloafing scale (2008). Kalaycı (2010) altered it for the Turkish context, and Yasar later updated it (2013). Within a four-factor structure, the scale\u0026apos;s items were developed as a 5-point, Likert-type scale (1 = severely disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, 5 = strongly agree). Internal consistency of the scale was acceptable (Cronbach\u0026apos;s alpha coefficient \u0026gt;.70).\u003c/p\u003e\n\u003cp\u003eTable 2 summarizes the three measurement scales employed in the current study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Summary of measurement scales\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" summary=\"Contact List\" width=\"85%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeveloped by\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(in English)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdapted by\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(into Turkish)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFormat for Measurement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReliability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ePMPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"center\"\u003e\u003cspan lang=\"EN-US\"\u003eBianchi \u0026amp; Phillips (2005)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003ePamuk \u0026amp; Atli (2016)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5-Point\u003cbr\u003e\u0026nbsp;Likert-type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eFoMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"center\"\u003e\u003cspan lang=\"EN-US\"\u003ePrzybylski et\u0026nbsp;al. (2013)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eG\u0026ouml;kler, Aydin, \u0026Uuml;nal, Metintaş, et al. (2016)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5-Point\u003cbr\u003e\u0026nbsp;Likert-type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCyberloafing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"center\"\u003e\u003cspan lang=\"EN-US\"\u003eBlanchard \u0026amp; Henle (2008)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eKalaycı (2010)\u003c/span\u003e\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eYasar (2013)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5-Point\u003cbr\u003e\u0026nbsp;Likert-type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Data Collection Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected from university students using paper-pencil surveys. Before the study, an ethical approval was taken from the Human Subjects Committee. The participants were advised that their participation in the study was entirely voluntary, and their consent was obtained to that end. Moreover, informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003eThe information from the paper-and-pencil questionnaires was entered into a Microsoft Excel spreadsheet. Following a preliminary inspection, the data was exported into SPSS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Data Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate normality tests were performed in order to check the distribution of data related to normality prior to starting the statistical analysis. Then, descriptive statistics were conducted to explore the sample characteristics. Path analysis was performed on possible associations between variables using structural equation modeling. IBM\u0026apos;s SPSS 26 and AMOS 24 tools were used to examine the data.\u003c/p\u003e\n\u003cp\u003eSeveral mediation analyses were also carried out to investigate the potential influence of cyberloafing, FoMO, MPUH, and SMUH on the relationship between age and gender and PMPU. In some cases, a mediation variable stands for full mediation, meaning that the causation variable cannot directly impact the outcome variable when the mediator was present. To examine mediation, The following four steps were proposed by Baron and Kenny (1986):\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eThe causation variable must be significantly related to the outcome variable.\u003c/li\u003e\n \u003cli\u003eThe mediator variable must be significantly related to the result variable.\u003c/li\u003e\n \u003cli\u003eThe mediator variable must be significantly related to the causation variable.\u003c/li\u003e\n \u003cli\u003eThe mediator variable requires that the causation variable not be significantly related with the outcome variable.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWhen all four of these steps are true, the outcome may be inferred as evidence for mediation. Based on the information provided in the literature review; the following direct-effect hypotheses were developed, which are also illustrated in Figure 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1770981467.png\" width=\"746\" height=\"446\"\u003e\u003c/p\u003e\n\u003cp\u003eThe study sample was compromised of participants selected via convenience sampling. Many social science researchers are not able to use random sampling given the lack of resources such as money and time (Wallen \u0026amp; Fraenkel, 2001). In the current investigation, we used the power analysis particular to structural equation modeling (SEM) (MacCallum et al., 1996), which produced 825 individuals for.99 power, df = 5, RMSEA-H0 =.10 (mis-fit), and RMSEA-H1 =.035 (near to good-fit), p =.05. We had 1,272 participants at the end of the data collection. Furthermore, according to Boomsma (1985) and Kline (1985), the minimal or moderate size of the sample for SEM might range between 100 and 200. (1998). More specifically, Bentler and Chou (1987) and Bollen (1989) indicated that five to 10 observations per estimated parameter should be reached, whilst Nunnally (1967) stated 10 cases per variable would be sufficient for SEM. According to other experimentally validated evidence, the minimum required sample size can range from 30 to 80. (Wolf et al., 2013). Additionally, Wolf et al. (2013) noted how determining sample size may be affected by the model\u0026apos;s complexity, and that a larger sample size does not always lead to improved results. The current study exceeded the sample size more than the recommended as a result of the power analysis, hence the results were deemed interpretable. However, because of the sampling strategy used, the study\u0026apos;s generalizability should be approached with caution.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003ePrior to proceeding with the data analysis step, we assessed univariate and multivariate normality, a crucial assumption of SEM (Arbuckle, 2007). Kurtosis values are thought to be more important to analyze than skewness for variance- and covariance-based analysis (DeCarlo, 1997); thus, while skewness influences means-based tests, kurtosis has a significant impact on variance- and covariance-based tests. Because SEM is based on covariance matrices, we focused on the kurtosis value. Table 3 shows the current study\u0026apos;s univariate and multivariate normal distribution assessments.\u003c/p\u003e\n\u003cp\u003eTable 3. Assessment of Normality\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkewness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCritical Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKurtosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCritical Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eSMUH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e26.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e4.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e30.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eCyberloafing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-3.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-2.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eMPUH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e21.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e19.472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eFoMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e7.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003ePMPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e10.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eMultivariate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e20.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e31.930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings show a violation of normality because the critical ratio values for kurtosis were greater than 7.0 (West et al., 1995) and the critical ratio value for multivariate distribution was greater than 5.0. (Byrne, 2013). In these situations, Byrne (2013) recommended bootstrapping and calculating confidence intervals, and then comparing the model\u0026rsquo;s significance values with the bootstrapped confidence intervals. We applied this recommendation in the current study in order to ensure the significance values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 shows bivariate correlations for all variables in the study. As can be seen, PMPU was shown to be positively related with gender, cyberloafing, FoMO, MPUH, and SMUH, and negatively related with age. The magnitudes of relationship with cyberloafing, FoMO, MPUH, and SMUH were close to medium, whilst, in contrast, they were low for the association with age and gender. Cyberloafing was shown to be positively related with all variables, and their magnitudes were low. FoMO was negatively related to age; however, it was positively correlated with gender, MPUH, and SMUH.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Bivariate Correlations of All Variables\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003col\u003e\n \u003cli\u003eAge\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003eGender\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-.078**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003eCyberloafing\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.063*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003eFoMO\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-.158**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.074**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.211**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003eMPUH\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-.087**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.169**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.107**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.161**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003col start=\"6\"\u003e\n \u003cli\u003eSMUH\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-.064*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.143**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.151**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.250**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.622**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003col start=\"7\"\u003e\n \u003cli\u003ePMPU\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-.101**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.118**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.321**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.464**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.329**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e.347**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNote. \u003cem\u003eN\u003c/em\u003e = 1,272; Dashes indicate that the value of the cells is 1.00; * \u003cem\u003ep\u003c/em\u003e \u0026lt; .05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn the first set of hypotheses, the results confirmed that the participants\u0026rsquo; age negatively impacted their FoMO and MPUH. Where the participants were older, their FoMO and MPUH were shown to be less than that of the younger participants. Age was unable to significantly explain cyberloafing and SMUH.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe second set of hypotheses also confirmed that gender positively impacted upon cyberloafing, FoMO, MPUH, and SMUH. Females were shown to have been cyberloafing more than males and had greater FoMO than males. Moreover, they used mobile phones and social media longer than males. For the third, fourth, fifth, and sixth hypotheses, cyberloafing, FoMO, MPUH, and SMUH positively predicted PMPU. For the seventh and eighth hypotheses, there was an indirect effect of age on PMPU via FoMO and MPUH; while gender had an indirect effect on PMPU via cyberloafing, FoMO, MPUH, and SMUH. Table 5 presents the unstandardized, standardized, and critical values for the significance testing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Unstandardized and Standardized Regression Weights\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypotheses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized Estimates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized Estimates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatus\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eParticipants\u0026rsquo; age impacts\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eCyberloafing,\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eNot approved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eFoMO,\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-5.527**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eMPUH,\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-2.675**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eSMUH.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-1.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eNot approved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eParticipants\u0026rsquo; gender impacts\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eCyberloafing,\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.334*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eFoMO,\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.253*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eMPUH,\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.886**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eSMUH.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.983**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eCyberloafing impacts PMPU.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8.842**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eFoMO impacts PMPU.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e15.102**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eMPUH impacts PMPU\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.030**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eSMUH impacts PMPU\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.864**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eAge indirectly impacts PMPU use via Cyberloafing, FoMO, MPUH, and SMUH.\u003csup\u003ea\u003c/sup\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e[-.029. -.013]**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003ePartially approved\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003col\u003e\n \u003cli\u003eGender indirectly impacts PMPU use Cyberloafing, FoMO, MPUH, and SMUH.\u003csup\u003ea\u003c/sup\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e[.093. .185]\u003csup\u003e\u0026nbsp;\u003c/sup\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 625px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e The estimates were also bootstrapped (N = 1,000) to ensure consistency and confidence intervals, and that there no differences between them. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values of predicted values were: 0.5%, cyberloafing; 2.9%, FoMO; 3.4%, MPUH; 2.3%, SMUH; and, 33.4%, PMPU.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eFor these hypotheses, indirect standardized estimates were computed using bootstrapping in AMOS 24. Because bootstrapping findings in AMOS 24 do not provide exact t-values, confidence intervals are given for indirect effects.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eMediation analysis Step 1 for H7 was provided by H1a and H1d as not significant; hence, cyberloafing and SMUH were unable to mediate the relationship between age and PMPU.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e** \u003cem\u003ep\u003c/em\u003e \u0026lt; .01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; .05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe model fit values indicated that a good fit was obtained for the model under consideration, as shown in Table 6.\u003c/p\u003e\n\u003cp\u003eTable 6. The Model Fit Evaluation\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFit Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCriteria for Good Fit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e (\u003cem\u003edf\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3.172, \u003cem\u003ep\u003c/em\u003e = .205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eLow \u0026chi;\u003csup\u003e2\u003c/sup\u003e value and\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026gt; .05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp class=\"MsoNoSpacing\"\u003e\u003cspan lang=\"EN-US\"\u003eHooper et al. (2008)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e / \u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e / \u003cem\u003edf\u0026nbsp;\u003c/em\u003e\u0026lt;\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eWheaton et al. (1977)\u003c/p\u003e\n \u003cp class=\"MsoNoSpacing\"\u003e\u003cspan lang=\"EN-US\"\u003eTabachnick and Fidell (2007)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eRMSEA\u0026nbsp;\u0026lt;\u0026nbsp;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eHu and Bentler (1999)\u003c/p\u003e\n \u003cp class=\"MsoNoSpacing\"\u003e\u003cspan lang=\"EN-US\"\u003eSteiger (2007)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.0078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eSRMR\u0026nbsp;\u0026le;\u0026nbsp;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eByrne (2013)\u003c/p\u003e\n \u003cp class=\"MsoNoSpacing\"\u003e\u003cspan lang=\"EN-US\"\u003eDiamantopoulos and Siguaw (2006)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e.95\u0026nbsp;\u0026le;\u0026nbsp;GFI\u0026nbsp;\u0026le;\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eTabachnick and Fidell (2007)\u003c/p\u003e\n \u003cp class=\"MsoNoSpacing\"\u003e\u003cspan lang=\"EN-US\"\u003eMiles and Shevlin (2007)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eAGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e.85\u0026nbsp;\u0026le;\u0026nbsp;AGFI\u0026nbsp;\u0026le;\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp class=\"MsoNoSpacing\"\u003e\u003cspan lang=\"EN-US\"\u003eTabachnick and Fidell (2007)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e.97\u0026nbsp;\u0026le;\u0026nbsp;CFI\u0026nbsp;\u0026le;\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp class=\"MsoNoSpacing\"\u003e\u003cspan lang=\"EN-US\"\u003eHu and Bentler (1999)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e.95\u0026nbsp;\u0026le;\u0026nbsp;IFI\u0026nbsp;\u0026le;\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp class=\"MsoNoSpacing\"\u003e\u003cspan lang=\"EN-US\"\u003eMiles and Shevlin (2007)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e.97\u0026nbsp;\u0026le;\u0026nbsp;NNFI\u0026nbsp;\u0026le;\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"DE\"\u003eHu and Bentler (1999)\u003c/span\u003e\u003c/p\u003e\n \u003cp class=\"MsoNoSpacing\"\u003e\u003cspan lang=\"DE\"\u003eFan et al.\u0026nbsp;\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e(1999)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 605px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Bootstrapping was employed to check the consistency of the model fit values (N = 1,000), and no difference was discovered.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the seventh hypothesis, we applied Baron and Kenny\u0026rsquo;s (1986) mediation steps within the hypothesized model, which is explained as follows:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eThe causation variable should have a strong relationship with the outcome variable. The causation variable in the current study was age, while the outcome variable was PMPU. Age had a significant and negative impact on PMPU, with a regression weight of -.028, = -.093, p.01; Step 1 was confirmed.\u003c/li\u003e\n \u003cli\u003eThe mediator variables, cyberloafing, FoMO, MPUH, and SMUH, should be significantly associated with the causation variable (age). Age was found to be significantly connected with FoMO and MPUH in Hypotheses 1a, 1b, 1c, and 1d (see Table 5), but not with cyberloafing and SMUH. Because Step 2 was approved for FoMO and MPUH, Steps 3 and 4 take these variables into account.\u003c/li\u003e\n \u003cli\u003eThe mediators (FoMO and MPUH) should have a significant influence on the outcome variable (PMPU). Significant correlations were found, as indicated in Hypotheses 4 and 5 (see Table 5). Step 3 for FoMO and MPUH was approved.\u003c/li\u003e\n \u003cli\u003eThe impact of the causation variable (age) on the outcome variable (PMPU) ought not\u0026nbsp;to be significant when the mediators (FoMO and MPUH) are present. To test this step, we introduced a direct relationship from the causal to the outcome variable in the hypothesized model shown in Figure 1 and re-ran the analysis. When FoMO and MPUH were present, we discovered that the direct impact of age on PMPU was not significant; regression weight was -.008, = -.026, p =.273.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo differentiate and compare the impact of the mediators, FoMO and MPUH, we estimated specific indirect effects using the approach suggested by Gaskin and Lim (2018). The results are illustrated in Table 7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 7. Specific indirect effects for mediation relationships for Age and PMPU via FoMO and MPUH\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect Path\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized Estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eAge --\u0026gt; FoMO --\u0026gt; PMPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eAge --\u0026gt; MPUH --\u0026gt; PMPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLB: Lower Bound, UB: Upper Bound\u003c/p\u003e\n\u003cp\u003eTable 7 demonstrates that FoMO (\u0026beta; = -.054) had a stronger mediation role compared to MPUH (\u0026beta; = -.007). The four-step technique of Baron and Kenny (1986) demonstrated that FoMO and MPUH mediated the association between age and PMPU. On the other hand, cyberloafing and SMUH were unable to mediate this relationship. The indirect effect of age on PMPU via FoMO was -.021, \u0026beta; = -.068, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01. In other words, due to the indirect (mediated) effect of age on PMPU, when age increases by 1 year, PMPU decreases by -.021 points. The proportion variance explained by mediation was 42.23% of the total effect variance, which indicates partial mediation (Hair et al., 2016). More specifically, 37.38% of variance was accounted for by FoMO, and 4.85% was explained by MPUH.\u003c/p\u003e\n\u003cp\u003eFor the eighth hypothesis, we repeated Baron and Kenny\u0026apos;s (1986) mediation procedures within the theorized model, as follows:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eThe causation variable should have a significant relation with the outcome variable. Gender was the causation variable in this study, and PMPU was the outcome variable. Gender had a significant and significant impact on PMPU, with a regression weight of.191, =.111, p.01; Step 1 was accepted.\u003c/li\u003e\n \u003cli\u003eThe mediator variables (cyberloafing, FoMO, MPUH, and SMUH) should be significantly associated with the causation variable (gender). Gender had a significant impact on all variables, as hypotheses 2a, 2b, 2c, and 2d suggested. Step 2 has been confirmed.\u003c/li\u003e\n \u003cli\u003eThe mediators (cyberloafing, FOMO, MPUH, and SMUH) should have a significant impact on the outcome variable (PMPU). All variables had a significant impact on PMPU, as demonstrated by Hypotheses 3, 4, 5, and 6. Step 3 has been confirmed.\u003c/li\u003e\n \u003cli\u003eThe impact of the causal variable (gender) on the outcome variable (PMPU) ought not to be significant when the mediators are present. To test this step, we introduced a direct relationship from the causation to the outcome variable in the hypothetical model shown in Figure 1 and re-ran the analysis. We discovered that when the mediators were present, the direct impact of gender on PMPU was not significant; regression weight was.054, =.031, p =.182.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo differentiate and compare the impact of the mediators MPUH, cyberloafing, FoMO, and SMUH, we estimated specific indirect effects using the approach suggested by Gaskin and Lim (2018). The results are illustrated in Table 8.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 8. Specific indirect effects for mediation relationships for gender and PMPU via MPUH, cyberloafing, FoMO, and SMUH\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect Path\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized Estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eGender --\u0026gt; MPUH --\u0026gt; PMPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eGender --\u0026gt; Cyberloafing --\u0026gt; PMPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eGender --\u0026gt; FoMO --\u0026gt; PMPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eGender --\u0026gt; SMUH --\u0026gt; PMPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLB: Lower Bound, UB: Upper Bound\u003c/p\u003e\n\u003cp\u003eTable 8 demonstrates that the MPUH (\u0026beta; = .029) was the strongest mediator, whilst cyberloafing was the least impactful (\u0026beta; = .013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe four-step technique of Baron and Kenny (1986) demonstrated that there were mediations of cyberloafing, FoMO, MPUH, and SMUH on the association between gender and PMPU. Gender\u0026apos;s indirect influence on PMPU via these mediators was.140, =.081, p.01. In other words, because of the indirect (mediated) influence of gender on PMPU, females had.140 points greater PMPU than males.The proportional variance explained by mediation was 42.19% of the total effect variance, which indicates partial mediation (Hair et al., 2016). MPUH accounted for 15.11% of mediated variance, while FoMO accounted for 11.98%, SMUH accounted for 8.34%, and cyberloafing accounted for 6.77% of mediated variance.\u003c/p\u003e"},{"header":"5.\tDiscussion ","content":"\u003cp\u003eThis study aimed to examine the predictors of problematic mobile phone use (PMPU) with respect to cyberloafing, fear of missing out (FoMO), mobile phone use hours (MPUH), and social media use hours (SMUH) with the effects of gender and age on these variables among university students. The study aimed to bring new insights into the associations of age and gender on PMPU, providing the opportunity to better understand what types of behaviors might result in PMPU via observing any increase in cyberloafing, FoMO, MPUH, and SMUH.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1 Age \u0026amp; Gender \u0026gt; FoMO \u0026gt; PMPU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study\u0026rsquo;s results have shown that while FoMO mediated relations between age and PMPU, it was negatively correlated with age, but positively correlated with PMPU. In addition, whilst FoMO mediated relations between gender and PMPU, it was positively correlated with both gender and PMPU. Likewise, FoMO has previously been shown to be associated positively with PMPU (Alt, 2015; Elhai, Gallinari, et al., 2020; Lo Coco et al., 2020; Przybylski et al., 2013; Wolniewicz et al., 2018), which is similar and in line with the current study\u0026rsquo;s findings. Similarly, a positive and significant relationship was observed between FoMO and PMPU variables, which also support previous work (Fuster et al., 2017; Gil et al., 2016; Santana-Vega et al., 2019). As females attach much more importance to interpersonal relationships than males (Lopez-Fernandez, 2017), this may result in females experiencing feelings of FoMO more, and thus exhibit more PMPU behavior.\u003c/p\u003e\n\u003cp\u003eConsistent with the current study\u0026rsquo;s results, it may also be said that FoMO can trigger and result in PMPU, by drawing a path from FoMO to PMPU related to the direction of this association (Chotpitayasunondh \u0026amp; Douglas, 2016). The current study\u0026rsquo;s findings fit with\u0026nbsp;Elhai, Yang, et al.\u0026rsquo;s (2020)\u0026nbsp;findings in that FoMO is related to problematic smartphone use severity. The findings of the current study suggest that age and gender causal variables have an indirect association with PMPU outcome variable through FoMO as a mediator variable. According to Gugushvili et al. (2020), FoMO is a key predictor and indicator of PMPU. In this respect, the current study addressed this association by directly testing the mediating role of FoMO by indicating pathways.\u003c/p\u003e\n\u003cp\u003eFurthermore, consistent with previous studies in the literature, PMPU differed according to gender, with PMPU being found to be higher among females than males. In relation, some studies in the literature also found that age and gender were related to PMPU, and that both female gender (Wang et al., 2015) and a lower age (Lu et al., 2011; Van Deursen et al., 2015) were associated with PMPU. FoMO has also been previously associated with certain demographic characteristics such as age and gender (Elhai et al., 2018). The current study\u0026rsquo;s findings may therefore be said to be in line with recent studies, which found FoMO to be related more to those of a younger age (Błachnio \u0026amp; Przepi\u0026oacute;rka, 2018; Blackwell et al., 2017), and again, for the female gender rather than male (Beyens et al., 2016; Stead \u0026amp; Bibby, 2017). As such, FoMO can be considered a consequential variable that accounts for mediated relations among age, gender, and PMPU. Accordingly, the current study\u0026rsquo;s findings indicate that positive and negative associations between causal and mediator variables can lead to PMPU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Age \u0026amp; Gender \u0026gt; Cyberloafing \u0026gt; PMPU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere exists a huge body of research regarding the relationship between cyberloafing and PMPU. Almost all of these studies have found a positive relationship between these two variables under investigation (G\u0026ouml;k\u0026ccedil;earslan et al., 2016; Rehman et al., 2019; Saritepeci, 2019). Consistent with these studies, the current study revealed a positive impact of cyberloafing on PMPU, indicating that the more cyberloafing behaviors students have, the more they tend to exhibit PMPU behaviors. In other words, students\u0026rsquo; cyberloafing behaviors within a school-based environment increase their tendency towards PMPU.\u003c/p\u003e\n\u003cp\u003eContrary to many studies in the literature that showed males exhibiting more cyberloafing behaviors than females (Baturay \u0026amp; Toker, 2015; Garrett \u0026amp; Danziger, 2008b), the current study found that it was the female participants rather than the males who displayed cyberloafing behaviors. This result may be related to the sample group determined within the scope of the current study, as well as to recently noted changes in the smartphone usage habits of users, whereby females have been shown to use smartphones more intensely than males (Taywade \u0026amp; Khubalkar, 2019). The current study\u0026rsquo;s results may therefore be due to this change in the profile of smartphone users. This change could also be the reason behind why females were found to exhibit greater levels of PMPU behaviors than their male counterparts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3\u0026nbsp; Age \u0026amp; Gender \u0026gt; MPUH \u0026amp; SMUH \u0026gt; PMPU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study has shown that while age was negatively associated with MPUH and PMPU, it was not found to be associated with SMUH. In addition, MPUH was found to have an indirect association between age and PMPU. There are numerous studies in the literature that have examined these variables. Although Demirhan et al. (2016) could not find any relationship between age and PMPU, many studies have revealed a negative relationship between these two variables, which parallels the results obtained from the current study (Augner \u0026amp; Hacker, 2012; Bianchi \u0026amp; Phillips, 2005; Smetaniuk, 2014).\u003c/p\u003e\n\u003cp\u003eOn the other hand, we found in the current study that gender had a positive association with MPUH, SMUH, and PMPU. Accordingly, females tend to exhibit greater PMPU behaviors than males. Further, both MPUH and SMUH were shown to have indirect associations between gender and PMPU. Even though some studies (Ahmed \u0026amp; Fiaz Qazi, 2011; Bianchi \u0026amp; Phillips, 2005; Demirhan et al., 2016; Dixit et al., 2010; Yen et al., 2009) failed to establish a significant relationship between gender and PMPU, a huge body of research in the literature supports the results obtained in the current study, in that females exhibit PMPU behaviors more than males (Augner \u0026amp; Hacker, 2012; Demirci et al., 2015; Jenaro et al., 2007; Jiang \u0026amp; Zhao, 2016; Lee et al., 2014; Lopez-Fernandez et al., 2014; Nahas et al., 2018; Takao, 2014; Toda et al., 2015). The reason for there being no relationship identified between these two variables in some studies may be due to mobile phone technologies being accepted equally by females and males, as stated by Bianchi and Phillips (2005). In contrast, females exhibiting more PMPU behaviors than males may be because females attach more importance to interpersonal communication (Lopez-Fernandez, 2017).\u003c/p\u003e\n\u003cp\u003eMoreover, the current study\u0026rsquo;s results revealed that PMPU was positively associated with both MPUH and SMUH. There are many studies in the literature supporting the current study\u0026rsquo;s results (Lee et al., 2014; Lopez-Fernandez, 2017; Vally \u0026amp; El Hichami, 2019). Furthermore, based on some previous studies, increased time spent using mobile phone and social media has an association with depression (Brunborg \u0026amp; Burdzovic, 2019; Ikeda \u0026amp; Nakamura, 2014), and that people diagnosed with depression are more likely to exhibit PMPU behaviors (Yen et al., 2009). Although these results are not directly related to the current study\u0026rsquo;s findings, they help provide a meaningful basis to explain the current study\u0026rsquo;s results.\u003c/p\u003e"},{"header":"6.\tConclusion","content":"\u003cp\u003eThe study aimed to investigate the relationships between age, gender, cyberloafing, FoMO, MPUH, SMUH, and their impacts on PMPU. It can be inferred from the study\u0026rsquo;s results that demographic variables such as gender and age can play a critical role in PMPU, with the mediation of cyberloafing, FoMO, MPUH, and SMUH. The findings also revealed that cyberloafing, FoMO, MPUH, and SMUH all played a substantial role in predicting PMPU. Additionally, while age has a direct impact upon FoMO and MPUH, it indirectly impacts on PMPU through the mediations of FoMO and MPUH. Moreover, gender directly impacts upon cyberloafing, FoMO, MPUH, and SMUH, and indirectly impacts upon PMPU through the mediations of FoMO, cyberloafing, MPUH, and SMUH.\u003c/p\u003e\n\u003cp\u003eThere are, however, several limitations to the current study. The first was using the total scores obtained from the instruments. Future studies could consider using the sub-dimensions of each measure and to assess the associations among the variables in more sophisticated detail. Second, the study used participants\u0026rsquo; self-reported data for MPUH, SMUH, and cyberloafing. Future research could apply an objective measure of these variables for a better reflection of the associations. Based on the findings of the current study, additional demographic factors should be addressed in order to better explain the variance of mediating factors. Based on the study\u0026rsquo;s findings, other factors also need to be addressed in order to predict cyberloafing activities and behaviors to more effectively explain the variance.\u003c/p\u003e"},{"header":"7.\tImplications","content":"\u003cp\u003eThe study\u0026apos;s findings hold several significant pedagogical implications for educational institutions and educators. Given the omnipresence of mobile phones and the influence of social media platforms among student populations, it is paramount for educational institutions to incorporate digital literacy education proactively into their curricula. Such programs should encompass responsible mobile phone and social media usage, strategies to address PMPU concerns, and promote positive online behaviors. Moreover, educators should consider designing awareness programs specifically tailored to tackle issues like FoMO and cyberloafing practices, equipping students with the knowledge necessary to understand the potential adverse consequences of these behaviors on their academic performance and overall well-being. Additionally, educators can foster a balanced perspective on technology use by encouraging the establishment of guidelines for mobile phone and social media use during study or instructional time and providing instruction on effective time management and self-regulation. Educational institutions can also harness the power of technology for educational purposes, leveraging mobile applications, social media platforms, and internet resources to enhance the learning process while promoting responsible and purposeful usage. Furthermore, they should offer support services, such as counseling, workshops, and peer support groups, to assist students in managing problematic mobile phone use. Faculty and teachers can benefit from professional development and training programs emphasizing effective technology integration in the classroom and strategies to address and mitigate PMPU issues. Collaborative efforts with parents can also be fruitful in regulating technology usage among children, both at school and in domestic environments, by providing parents with information and awareness about the potential hazards associated with excessive mobile phone and social media usage. By addressing these pedagogical implications, educational institutions can proactively tackle the challenges related to problematic mobile phone use, foster responsible and well-balanced technology usage among students, and ultimately enhance their overall educational experience.\u003c/p\u003e\n\u003cp\u003eThe implications drawn from this study also have significant relevance for both academic theory and practical applications. Firstly, the path model employed in this research successfully identifies a range of significant factors associated with PMPU, laying the groundwork for a deeper understanding of this phenomenon. These findings encourage scholars to delve further into the intricate network of influences contributing to PMPU, ultimately enriching the academic discourse surrounding this topic. Secondly, the insights derived from this study have practical implications for developing interventions to address PMPU. By targeting the specific characteristics highlighted in the path model, practitioners can effectively tailor their strategies to address the root causes and mediators of PMPU. This targeted approach can potentially improve the design and implementation of treatments and interventions, ultimately assisting individuals in managing and mitigating problematic mobile phone use behaviors. In conclusion, this study\u0026apos;s path model and findings offer a valuable foundation for advancing academic understanding and practical solutions to problematic mobile phone use. The complexity and multidimensionality of PMPU underscore the need for comprehensive research and tailored interventions. Future research endeavors can build upon these insights to construct more holistic frameworks and develop pragmatic approaches to tackle the growing issue of PMPU in our digitally connected world.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZK-YA-BC-Conceptualization, literature review, formulating research questions, writing and editing the manuscript; ST- Data analysis, writing and editing the manuscript; SY-Conceptualization, data collection, critical review of the manuscript, and guidance of the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval was obtained from the Applied Ethics Research Center of the Middle East Technical University (Approval ID: 2016-EGT-165, dated 05 December 2016). All procedures involving human participants were conducted in accordance with the ethical standards of the Declaration of Helsinki and relevant national/institutional guidelines. The approval covered all aspects of the study, including recruitment, data collection, storage, and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained in written form from all participants prior to data collection. The consent process was carried out face-to-face between 10 and 20 December 2016, during which participants were informed about the purpose of the study, the voluntary nature of participation, the right to withdraw at any time without consequences, and how their data would be used and stored. Only individuals who provided written consent were included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of this study are not publicly available. However, the anonymized datasets analyzed during the current study are available from the corresponding author on reasonable request. All authors had full access to all data used in the analysis and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbel, J. P., Buff, C. L., \u0026amp; Burr, S. A. (2016). Social Media and the Fear of Missing Out: Scale Development and Assessment. \u003cem\u003eJournal of Business \u0026amp; Economics Research (JBER)\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 33-44. https://doi.org/10.19030/jber.v14i1.9554 \u003c/li\u003e\n\u003cli\u003eAhmad, A., \u0026amp; Omar, Z. (2017). Understanding who cyberloafs from the self-control perspective: A study in the public service sector. \u003cem\u003eInternational Journal of ADVANCED AND APPLIED SCIENCES\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(8), 123-128. https://doi.org/10.21833/ijaas.2017.08.017 \u003c/li\u003e\n\u003cli\u003eAhmed, I., \u0026amp; Fiaz Qazi, T. (2011). A look out for academic impacts of social networking sites (SNSs): A student-based perspective. \u003cem\u003eAfrican Journal of Business Management\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(12), 5022-5031. https://doi.org/10.5897/AJBM11.595 \u003c/li\u003e\n\u003cli\u003eAkbulut, Y., D\u0026ouml;nmez, O., \u0026amp; Dursun, \u0026Ouml;. \u0026Ouml;. (2017). Cyberloafing and social desirability bias among students and employees. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e, 87-95. https://doi.org/10.1016/j.chb.2017.02.043 \u003c/li\u003e\n\u003cli\u003eAkbulut, Y., Dursun, \u0026Ouml;. \u0026Ouml;., D\u0026ouml;nmez, O., \u0026amp; Şahin, Y. L. (2016). In search of a measure to investigate cyberloafing in educational settings. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e, 616-625. https://doi.org/10.1016/j.chb.2015.11.002 \u003c/li\u003e\n\u003cli\u003eAlt, D. (2015). College students\u0026rsquo; academic motivation, media engagement and fear of missing out. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 111-119. https://doi.org/10.1016/j.chb.2015.02.057 \u003c/li\u003e\n\u003cli\u003eAlyahya, S., \u0026amp; Alqahtani, A. (2022). Cyberloafing in Educational Settings: A Systematic Literature Review. \u003cem\u003eInternational Journal of Interactive Mobile Technologies (IJIM), 16\u003c/em\u003e(16), 113\u0026ndash;141. https://doi.org/10.3991/ijim.v16i16.32285\u003c/li\u003e\n\u003cli\u003eAndone, I., Błaszkiewicz, K., Eibes, M., Trendafilov, B., Montag, C., \u0026amp; Markowetz, A. (2016). How age and gender affect smartphone usage. In \u003cem\u003eProceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct\u003c/em\u003e (pp. 9-12). ACM. https://doi.org/10.1145/2968219.2971451 \u003c/li\u003e\n\u003cli\u003eAndreassen, C. S., Torsheim, T., \u0026amp; Pallesen, S. (2014). Predictors of use of social network sites at work - a specific type of cyberloafing. \u003cem\u003eJournal of Computer-Mediated Communication\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(4), 906-921. https://doi.org/10.1111/jcc4.12085 \u003c/li\u003e\n\u003cli\u003eArabaci, I. B. (2017). Investigation faculty of education students\u0026rsquo; cyberloafing behaviors in terms of various variables. \u003cem\u003eTurkish Online Journal of Educational Technology\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 72-82. http://www.tojet.net/articles/v16i1/1617.pdf \u003c/li\u003e\n\u003cli\u003eArbuckle, J. L. (2007). \u003cem\u003eAmos\u003csup\u003eTM\u003c/sup\u003e 16 user\u0026rsquo;s guide\u003c/em\u003e. SPSS.\u003c/li\u003e\n\u003cli\u003eAskew, K. L., Ilie, A., Bauer, J. A., Simonet, D. V., Buckner, J. E., \u0026amp; Robertson, T. A. (2019). Disentangling How Coworkers and Supervisors Influence Employee Cyberloafing: What Normative Information Are Employees Attending To? \u003cem\u003eJournal of Leadership \u0026amp; Organizational Studies\u003c/em\u003e, 26(4), 526\u0026ndash;544. https://doi.org/10.1177/1548051818813091 \u003c/li\u003e\n\u003cli\u003eAugner, C., \u0026amp; Hacker, G. W. (2012). Associations between problematic mobile phone use and psychological parameters in young adults. \u003cem\u003eInternational Journal of Public Health\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(2), 437-441. https://doi.org/10.1007/s00038-011-0234-z \u003c/li\u003e\n\u003cli\u003eAybas, M., \u0026amp; Gungor, A. Y. (2020). Does Cyberloafing reduce academic performance? A comparative study between Turkey and Poland. \u003cem\u003eRevista Argentina De Clinica Psicologica\u003c/em\u003e, \u003cem\u003eXXIX\u003c/em\u003e(5), 1060-1072. https://doi.org/10.24205/03276716.2020.1101 \u003c/li\u003e\n\u003cli\u003eBaron, R. M., \u0026amp; Kenny, D. A. (1986). The moderator\u0026ndash;mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. \u003cem\u003eJournal of Personality and Social Psychology\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(6), 1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173 \u003c/li\u003e\n\u003cli\u003eBarry, C. T., \u0026amp; Wong, M. Y. (2020). Fear of missing out (FoMO): A generational phenomenon or an individual difference? \u003cem\u003eJournal of Social and Personal Relationships\u003c/em\u003e, 37(12), 2952\u0026ndash;2966. https://doi.org/10.1177/0265407520945394 \u003c/li\u003e\n\u003cli\u003eBatabyal, S. K., \u0026amp; Bhal, K. T. (2020). Traditional cyberloafing, mobile cyberloafing and personal mobile-internet loafing in business organizations. \u003cem\u003eJournal of Information, Communication and Ethics in Society\u003c/em\u003e, 18(4), 631\u0026ndash;647. https://doi.org/10.1108/JICES-07-2019-0081 \u003c/li\u003e\n\u003cli\u003eBaturay, M. H., \u0026amp; Toker, S. (2015). An investigation of the impact of demographics on cyberloafing from an educational setting angle. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e, 358-366. https://doi.org/10.1016/j.chb.2015.03.081 \u003c/li\u003e\n\u003cli\u003eBentler, P. M., \u0026amp; Chou, C.-P. (1987). Practical Issues in Structural Modeling. \u003cem\u003eSociological Methods \u0026amp; Research\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 78-117. https://doi.org/10.1177/0049124187016001004 \u003c/li\u003e\n\u003cli\u003eBeyens, I., Frison, E., \u0026amp; Eggermont, S. (2016). \u0026ldquo;I don\u0026rsquo;t want to miss a thing\u0026rdquo;: Adolescents\u0026rsquo; fear of missing out and its relationship to adolescents\u0026rsquo; social needs, Facebook use, and Facebook related stress. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e, 1-8. https://doi.org/10.1016/j.chb.2016.05.083 \u003c/li\u003e\n\u003cli\u003eBianchi, A., \u0026amp; Phillips, J. G. (2005). Psychological predictors of problem mobile phone use. \u003cem\u003eCyberpsychology and Behavior\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 39-52. https://doi.org/10.1089/cpb.2005.8.39 \u003c/li\u003e\n\u003cli\u003eBillieux, J. (2012). Problematic Use of the Mobile Phone: A Literature Review and a Pathways Model. \u003cem\u003eCurrent Psychiatry Reviews\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4), 299-307. https://doi.org/10.2174/157340012803520522 \u003c/li\u003e\n\u003cli\u003eBillieux, J., Van der Linden, M., \u0026amp; Rochat, L. (2008). The role of impulsivity in actual and problematic use of the mobile phone. \u003cem\u003eApplied Cognitive Psychology\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(9), 1195-1210. https://doi.org/10.1002/acp.1429 \u003c/li\u003e\n\u003cli\u003eBłachnio, A., \u0026amp; Przepi\u0026oacute;rka, A. (2018). Facebook intrusion, fear of missing out, narcissism, and life satisfaction: A cross-sectional study. \u003cem\u003ePsychiatry Research\u003c/em\u003e, \u003cem\u003e259\u003c/em\u003e, 514-519. https://doi.org/10.1016/j.psychres.2017.11.012 \u003c/li\u003e\n\u003cli\u003eBlackwell, D., Leaman, C., Tramposch, R., Osborne, C., \u0026amp; Liss, M. (2017). Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e116\u003c/em\u003e, 69-72. https://doi.org/10.1016/j.paid.2017.04.039 \u003c/li\u003e\n\u003cli\u003eBlanchard, A. L., \u0026amp; Henle, C. A. (2008). Correlates of different forms of cyberloafing: The role of norms and external locus of control. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(3), 1067-1084. https://doi.org/10.1016/j.chb.2007.03.008 \u003c/li\u003e\n\u003cli\u003eBollen, K. A. (1989). \u003cem\u003eStructural Equations with Latent Variables\u003c/em\u003e. Wiley. https://doi.org/10.1002/9781118619179 \u003c/li\u003e\n\u003cli\u003eBoomsma, A. (1985). Nonconvergence, improper solutions, and starting values in lisrel maximum likelihood estimation. \u003cem\u003ePsychometrika\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(2), 229-242. https://doi.org/10.1007/BF02294248 \u003c/li\u003e\n\u003cli\u003eBrunborg, G. S., \u0026amp; Burdzovic Andreas, J. (2019). Increase in time spent on social media is associated with modest increase in depression, conduct problems, and episodic heavy drinking. \u003cem\u003eJournal of Adolescence\u003c/em\u003e, \u003cem\u003e74(1)\u003c/em\u003e, 201-209. https://doi.org/10.1016/j.adolescence.2019.06.013 \u003c/li\u003e\n\u003cli\u003eBurke, M., Marlow, C., \u0026amp; Lento, T. (2010). Social network activity and social well-being. In \u003cem\u003eCHI \u0026rsquo;10, Proceedings of the 28th International Conference on Human Factors in Computing Systems\u003c/em\u003e (pp. 1909-1912). ACM. https://doi.org/10.1145/1753326.1753613 \u003c/li\u003e\n\u003cli\u003eBusch, P. A., Hausvik, G. I., Ropstad, O. K., \u0026amp; Pettersen, D. (2021). Smartphone usage among older adults. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e, Article 106783. https://doi.org/10.1016/j.chb.2021.106783 \u003c/li\u003e\n\u003cli\u003eByrne, B. M. (2013). \u003cem\u003eStructural equation modeling with AMOS: Basic concepts, applications, and programming\u003c/em\u003e. Routledge.\u003c/li\u003e\n\u003cli\u003eCasale, S., \u0026amp; Fioravanti, G. (2020). Factor structure and psychometric properties of the Italian version of the fear of missing out scale in emerging adults and adolescents. \u003cem\u003eAddictive Behaviors\u003c/em\u003e, \u003cem\u003e102\u003c/em\u003e, Article 106179. https://doi.org/10.1016/j.addbeh.2019.106179 \u003c/li\u003e\n\u003cli\u003eChen, B., Liu, F., Ding, S., Ying, X., Wang, L., \u0026amp; Wen, Y. (2017). Gender differences in factors associated with smartphone addiction: a cross-sectional study among medical college students. \u003cem\u003eBMC Psychiatry\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), Article 341. https://doi.org/10.1186/s12888-017-1503-z \u003c/li\u003e\n\u003cli\u003eCh\u0026oacute;liz, M. (2012). No1 Test Mobile Phone Addiction 33. \u003cem\u003eProgress in Health Sciences, \u003c/em\u003e\u003cem\u003e2\u003c/em\u003e(1), 33-44. https://progressinhealthsciences.publisherspanel.com/resources/html/article/details?id=112500 \u003c/li\u003e\n\u003cli\u003eChotpitayasunondh, V., \u0026amp; Douglas, K. M. (2016). How \u0026ldquo;phubbing\u0026rdquo; becomes the norm: The antecedents and consequences of snubbing via smartphone. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e, 9-18. https://doi.org/10.1016/j.chb.2016.05.018 \u003c/li\u003e\n\u003cli\u003eChou, W.-Y. S., Hunt, Y. M., Beckjord, E. B., Moser, R. P., \u0026amp; Hesse, B. W. (2009). Social media use in the United States: Implications for health communication. \u003cem\u003eJournal of Medical Internet Research\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(4), Article e48. https://doi.org/10.2196/jmir.1249 \u003c/li\u003e\n\u003cli\u003eCorrea, T., Hinsley, A. W., \u0026amp; de Z\u0026uacute;\u0026ntilde;iga, H. G. (2010). Who interacts on the Web?: The intersection of users\u0026rsquo; personality and social media use. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(2), 247-253. https://doi.org/10.1016/j.chb.2009.09.003 \u003c/li\u003e\n\u003cli\u003eCreswell, J. W. (2012). \u003cem\u003eEducational research: Planning, conducting, and evaluating quantitative and qualitative research\u003c/em\u003e (4th ed.). Pearson.\u003c/li\u003e\n\u003cli\u003eDeCarlo, L. T. (1997). On the meaning and use of kurtosis. \u003cem\u003ePsychological Methods\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(3), 292-307. https://doi.org/10.1037/1082-989X.2.3.292 \u003c/li\u003e\n\u003cli\u003eDemirci, K., Akg\u0026ouml;n\u0026uuml;l, M., \u0026amp; Akpinar, A. (2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. \u003cem\u003eJournal of Behavioral Addictions\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 85-92. https://doi.org/10.1556/2006.4.2015.010 \u003c/li\u003e\n\u003cli\u003eDemirhan, E., Randler, C., \u0026amp; Horzum, M. B. (2016). Is problematic mobile phone use explained by chronotype and personality? \u003cem\u003eChronobiology International\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(7), 821-831. https://doi.org/10.3109/07420528.2016.1171232 \u003c/li\u003e\n\u003cli\u003eDemirtepe-Saygılı, D., \u0026amp; Metin-Orta, I. (2021). An Investigation of Cyberloafing in Relation to Coping Styles and Psychological Symptoms in an Educational Setting. \u003cem\u003ePsychological Reports\u003c/em\u003e, 124(4), 1559\u0026ndash;1587. https://doi.org/10.1177/0033294120950299 \u003c/li\u003e\n\u003cli\u003eDiamantopoulos, A., \u0026amp; Siguaw, J. A. (2006). Formative Versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration. \u003cem\u003eBritish Journal of Management\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(4), 263-282. https://doi.org/10.1111/j.1467-8551.2006.00500.x \u003c/li\u003e\n\u003cli\u003eDixit, S., Shukla, H., Bhagwat, A., Bindal, A., Goyal, A., Zaidi, A., \u0026amp; Shrivastava, A. (2010). A study to evaluate mobile phone dependence among students of a medical college and associated hospital of central India. \u003cem\u003eIndian Journal of Community Medicine\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(2), 339-341. https://doi.org/10.4103/0970-0218.66878 \u003c/li\u003e\n\u003cli\u003eDogan, V. (2019). Why Do People Experience the Fear of Missing Out (FoMO)? Exposing the Link Between the Self and the FoMO Through Self-Construal. \u003cem\u003eJournal of Cross-Cultural Psychology\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(4), 524-538. https://doi.org/10.1177/0022022119839145 \u003c/li\u003e\n\u003cli\u003eDursun, O. O., \u0026amp; Donmez, O. (2018). Predictors of Cyberloafing among Preservice Information Technology Teachers. \u003cem\u003eContemporary Educational Technology, \u003c/em\u003e\u003cem\u003e9\u003c/em\u003e(1), 22-41. https://dergipark.org.tr/en/pub/cet/issue/34282/378820 \u003c/li\u003e\n\u003cli\u003eElhai, J. D., Gallinari, E. F., Rozgonjuk, D., \u0026amp; Yang, H. (2020). Depression, anxiety and fear of missing out as correlates of social, non-social and problematic smartphone use. \u003cem\u003eAddictive Behaviors\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e, Article 106335. https://doi.org/10.1016/j.addbeh.2020.106335 \u003c/li\u003e\n\u003cli\u003eElhai, J. D., Levine, J. C., Alghraibeh, A. M., Alafnan, A. A., Aldraiweesh, A. A., \u0026amp; Hall, B. J. (2018). Fear of missing out: Testing relationships with negative affectivity, online social engagement, and problematic smartphone use. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e, 289-298. https://doi.org/10.1016/j.chb.2018.08.020 \u003c/li\u003e\n\u003cli\u003eElhai, J. D., Levine, J. C., Dvorak, R. D., \u0026amp; Hall, B. J. (2016). Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e, 509-516. https://doi.org/10.1016/j.chb.2016.05.079 \u003c/li\u003e\n\u003cli\u003eElhai, J. D., McKay, D., Yang, H., Minaya, C., Montag, C., \u0026amp; Asmundson, G. J. G. (2021). Health anxiety related to problematic smartphone use and gaming disorder severity during COVID-19: Fear of missing out as a mediator. \u003cem\u003eHuman Behavior and Emerging Technologies\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 137-146. https://doi.org/10.1002/hbe2.227 \u003c/li\u003e\n\u003cli\u003eElhai, J. D., Yang, H., Fang, J., Bai, X., \u0026amp; Hall, B. J. (2020). Depression and anxiety symptoms are related to problematic smartphone use severity in Chinese young adults: Fear of missing out as a mediator. \u003cem\u003eAddictive Behaviors\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e, Article 105962. https://doi.org/10.1016/j.addbeh.2019.04.020 \u003c/li\u003e\n\u003cli\u003eElhai, J. D., Yang, H., \u0026amp; Montag, C. (2021). Fear of missing out (FOMO): overview, theoretical underpinnings, and literature review on relations with severity of negative affectivity and problematic technology use. \u003cem\u003eRevista Brasileira de Psiquiatria, \u003c/em\u003e\u003cem\u003e43\u003c/em\u003e(2), 203-209). https://doi.org/10.1590/1516-4446-2020-0870 \u003c/li\u003e\n\u003cli\u003eErdem, H., T\u0026uuml;ren, U., \u0026amp; Kalkin, G. (2017). Mobil Telefon Yoksunluğu Korkusu (Nomofobi) Yayılımı: T\u0026uuml;rkiye\u0026rsquo;den \u0026Uuml;niversite \u0026Ouml;ğrencileri ve Kamu \u0026Ccedil;alışanları \u0026Ouml;rneklemi [No Mobile Phone Phobia (Nomophobia) Prevalence: Samples of Undergraduate Students and Public Employees from Turkey]. \u003cem\u003eBilişim Teknolojileri Dergisi\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 1-12. https://dergipark.org.tr/en/pub/gazibtd/issue/27536/289670 \u003c/li\u003e\n\u003cli\u003eFan, X., Thompson, B., \u0026amp; Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. \u003cem\u003eStructural Equation Modeling: A Multidisciplinary Journal\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 56-83. https://doi.org/10.1080/10705519909540119 \u003c/li\u003e\n\u003cli\u003eFraenkel, J. R., Wallen, N. E., \u0026amp; Hyun, H. H. (2012). \u003cem\u003eHow to design and evaluate research in education\u003c/em\u003e. McGraw Hill.\u003c/li\u003e\n\u003cli\u003eFuster, H., Chamarro, A., \u0026amp; Oberst, U. (2017). Fear of Missing Out, online social networking and mobile phone addiction: A latent profile approach. \u003cem\u003eAloma: Revista de Psicologia, Ci\u0026egrave;ncies de l\u0026rsquo;Educaci\u0026oacute; i de l\u0026rsquo;Esport\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(1), 22-30. https://doi.org/10.51698/aloma.2017.35.1.22-30 \u003c/li\u003e\n\u003cli\u003eGarrett, R. K., \u0026amp; Danziger, J. N. (2008a). Disaffection or expected outcomes: Understanding personal Internet use during work. \u003cem\u003eJournal of Computer-Mediated Communication\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(4), 937-958. https://doi.org/10.1111/j.1083-6101.2008.00425.x \u003c/li\u003e\n\u003cli\u003eGarrett, R. K., \u0026amp; Danziger, J. N. (2008b). On cyberslacking: Workplace status and personal Internet use at work. \u003cem\u003eCyberpsychology and Behavior\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(3), 287-292. https://doi.org/10.1089/cpb.2007.0146 \u003c/li\u003e\n\u003cli\u003eGaskin, J., \u0026amp; Lim, J. (2018). \u003cem\u003eMerge SRW Tables, AMOS Plugin\u003c/em\u003e. Gaskination\u0026rsquo;s StatWiki. \u003c/li\u003e\n\u003cli\u003eGeser, H. (2006). Are girls (even) more addicted? Some gender patterns of cell phone usage. In \u003cem\u003eSociology in Switzerland: Sociology of the Mobile Phone\u003c/em\u003e. University of Zurich. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-327002 \u003c/li\u003e\n\u003cli\u003eGiagkou, S., Hussain, Z., \u0026amp; Pontes, H. (2018). Exploring the Interplay Between Passive Following on Facebook, Fear of Missing out, Self-esteem, Social Comparison, Age, and Life Satisfaction in a Community-based Sample. \u003cem\u003eInternational Journal of Psychology \u0026amp; Behavior Analysis\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), Article 149. https://doi.org/10.15344/2455-3867/2018/149 \u003c/li\u003e\n\u003cli\u003eGil, F., Chamarro, A., \u0026amp; Oberst, U. (2016). Addiction to online social networks: A question of \u0026ldquo;Fear of missing out\u0026rdquo;? \u003cem\u003eJournal of Behavioral Addictions\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 51.\u003c/li\u003e\n\u003cli\u003eG\u0026ouml;k\u0026ccedil;earslan, Ş., Mumcu, F. K., Haşlaman, T., \u0026amp; \u0026Ccedil;evik, Y. D. (2016). Modelling smartphone addiction: The role of smartphone usage, self-regulation, general self-efficacy and cyberloafing in university students. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e, 639-649. https://doi.org/10.1016/j.chb.2016.05.091 \u003c/li\u003e\n\u003cli\u003eGokler, M., Aydin, R., Unal, E., \u0026amp; Metintas, S. (2016). \u0026ldquo;Fear of Missing Out\u0026rdquo; in university students in western area of Turkey. \u003cem\u003eEuropean Journal of Public Health\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(suppl_1), 478. https://doi.org/10.1093/eurpub/ckw175.118 \u003c/li\u003e\n\u003cli\u003eG\u0026ouml;kler, M. E., Aydin, R., \u0026Uuml;nal, E., Metintaş, S., \u0026Ccedil;alışmada, A., Ka\u0026ccedil;ırma, G., \u0026Ouml;l\u0026ccedil;eğinin, K., \u0026amp; T\u0026uuml;rk\u0026ccedil;e, G. (2016). Sosyal Ortamlarda Gelişmeleri Ka\u0026ccedil;ırma Korkusu \u0026Ouml;l\u0026ccedil;eğinin T\u0026uuml;rk\u0026ccedil;e s\u0026uuml;r\u0026uuml;m\u0026uuml;n\u0026uuml;n ge\u0026ccedil;erlilik ve g\u0026uuml;venilirliğinin değerlendirilmesi [Determining validity and reliability of Turkish version of Fear of Missing out Scale]. \u003cem\u003eAnadolu Psikiyatri Dergisi, 17\u003c/em\u003e(1), 53-59. https://doi.org/10.5455/apd.195843 \u003c/li\u003e\n\u003cli\u003eGozum, A. İ. C., Erkul, R., \u0026amp; Aksoy, N. (2020). Use of Smartphones in Class: Examining the Relationship Between M-Learning Readiness, Cyberloafing, Nomophobia and Addiction Variables. \u003cem\u003eInternational Journal of Progressive Education\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(6), 94-120. https://doi.org/10.29329/ijpe.2020.280.6 \u003c/li\u003e\n\u003cli\u003eGrant, J. E., Lust, K., \u0026amp; Chamberlain, S. R. (2019). Problematic smartphone use associated with greater alcohol consumption, mental health issues, poorer academic performance, and impulsivity. \u003cem\u003eJournal of Behavioral Addictions, 8\u003c/em\u003e(2), 335\u0026ndash;342. https://doi.org/10.1556/2006.8.2019.32 \u003c/li\u003e\n\u003cli\u003eGreengard, S. (2000, December 1). The high cost of cyberslacking. \u003cem\u003eWorkforce\u003c/em\u003e. https://workforce.com/news/the-high-cost-of-cyberslacking \u003c/li\u003e\n\u003cli\u003eGugushvili, N., T\u0026auml;ht, K., Rozgonjuk, D., Raudlam, M., Ruiter, R., \u0026amp; Verduyn, P. (2020). Two dimensions of problematic smartphone use mediate the relationship between fear of missing out and emotional well-being. \u003cem\u003eCyberpsychology: Journal of Psychosocial Research on Cyberspace\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(2), Article 3. https://doi.org/10.5817/CP2020-2-3 \u003c/li\u003e\n\u003cli\u003eGullu, B. F., \u0026amp; Serin, H. (2020). The Relationship Between Fear of Missing Out (FoMO) Levels and Cyberloafing Behaviour of Teachers. \u003cem\u003eJournal of Education and Learning\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(5), 205-214. https://doi.org/10.5539/jel.v9n5p205 \u003c/li\u003e\n\u003cli\u003eGupta, M., \u0026amp; Sharma, A. (2021). Fear of missing out: A brief overview of origin, theoretical underpinnings and relationship with mental health. \u003cem\u003eWorld Journal of Clinical Cases\u003c/em\u003e, 9(19), 4881\u0026ndash;4889. https://doi.org/10.12998/wjcc.v9.i19.4881 \u003c/li\u003e\n\u003cli\u003eHair, J. J. F., Hult, G. T. M., Ringle, C., \u0026amp; Sarstedt, M. (2016). \u003cem\u003eA primer on partial least squares structural equation modeling (PLS-SEM)\u003c/em\u003e. Sage.\u003c/li\u003e\n\u003cli\u003eHato, B. (2013). \u003cem\u003e(Compulsive) Mobile Phone Checking Behavior Out of a Fear of Missing Out: Development, Psychometric Properties and Test-Retest Reliability of a C-FoMO-Scale\u003c/em\u003e. [Master\u0026rsquo;s thesis, Tilburg University, Netherlands]. https://arno.uvt.nl/show.cgi?fid=130541 \u003c/li\u003e\n\u003cli\u003eHooper, D., Coughlan, J., \u0026amp; Mullen, M. R. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. \u003cem\u003eThe Electronic Journal of Business Research Methods\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 53-60. https://academic-publishing.org/index.php/ejbrm/article/view/1224 \u003c/li\u003e\n\u003cli\u003eHu, L.-T., \u0026amp; Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. \u003cem\u003eStructural Equation Modeling: A Multidisciplinary Journal\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 1\u0026ndash;55. https://doi.org/10.1080/10705519909540118 \u003c/li\u003e\n\u003cli\u003eHussain, Z., Griffiths, M. D., \u0026amp; Sheffield, D. (2017). An investigation into problematic smartphone use: The role of narcissism, anxiety, and personality factors. \u003cem\u003eJournal of Behavioral Addictions\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), 378-386. https://doi.org/10.1556/2006.6.2017.052 \u003c/li\u003e\n\u003cli\u003eIkeda, K., \u0026amp; Nakamura, K. (2014). Association between mobile phone use and depressed mood in Japanese adolescents: A cross-sectional study. \u003cem\u003eEnvironmental Health and Preventive Medicine\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(3), 187-193. https://doi.org/10.1007/s12199-013-0373-3 \u003c/li\u003e\n\u003cli\u003eJenaro, C., Flores, N., G\u0026oacute;mez-Vela, M., Gonz\u0026aacute;lez-Gil, F., \u0026amp; Caballo, C. (2007). Problematic internet and cell-phone use: Psychological, behavioral, and health correlates. \u003cem\u003eAddiction Research and Theory\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), 309-320. https://doi.org/10.1080/16066350701350247 \u003c/li\u003e\n\u003cli\u003eJiang, Z., \u0026amp; Zhao, X. (2016). Self-control and problematic mobile phone use in Chinese college students: the mediating role of mobile phone use patterns. \u003cem\u003eBMC Psychiatry\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), Article 416. https://doi.org/10.1186/s12888-016-1131-z \u003c/li\u003e\n\u003cli\u003eKalaycı, E. (2010). \u003cem\u003eThe investigation of relationship between cyberloafing and self-regulated learning strategies among undergraduate students\u003c/em\u003e [Master\u0026rsquo;s thesis, Hacettepe University, Turkey]. https://tez.yok.gov.tr/UlusalTezMerkezi/tezDetay.jsp?id=up27hIAneb7E6plPO9vxIw\u0026amp;no=XB0tB25Z5baqKEuCPTvcSg \u003c/li\u003e\n\u003cli\u003eKasahara, G. M., Houlihan, D., \u0026amp; Estrada, C. (2019). Gender Differences in Social Media Use and Cyberbullying in Belize: A Preliminary Report. \u003cem\u003eInternational Journal of Psychological Studies\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 32-41. https://doi.org/10.5539/ijps.v11n2p32 \u003c/li\u003e\n\u003cli\u003eKaspersky Lab. (2016). \u003cem\u003eI don\u0026rsquo;t want to miss a thing Why we cannot keep our fingers off our smartphones A psychological study by the Universities of W\u0026uuml;rzburg and Nottingham Trent for Kaspersky Lab\u003c/em\u003e. https://media.kaspersky.com/pdf/I%20dont%20want%20to%20miss%20a%20thing.pdf \u003c/li\u003e\n\u003cli\u003eKim, S. J., \u0026amp; Byrne, S. (2011). Conceptualizing personal web usage in work contexts: A preliminary framework. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(6), 2271-2283. https://doi.org/10.1016/j.chb.2011.07.006 \u003c/li\u003e\n\u003cli\u003eKim, Y., Jeong, J.-E., Cho, H., Jung, D.-J., Kwak, M., Rho, M. J., Yu, H., Kim, D.-J., \u0026amp; Choi, I. Y. (2016). Personality factors predicting smartphone addiction predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(8), Article e0159788. https://doi.org/10.1371/journal.pone.0159788 \u003c/li\u003e\n\u003cli\u003eKimbrough, A. M., Guadagno, R. E., Muscanell, N. L., \u0026amp; Dill, J. (2013). Gender differences in mediated communication: Women connect more than do men. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 896-900. https://doi.org/10.1016/j.chb.2012.12.005 \u003c/li\u003e\n\u003cli\u003eKline, R. B. (1998). \u003cem\u003ePrinciples and Practice of Structural Equation Modeling\u003c/em\u003e. Guilford.\u003c/li\u003e\n\u003cli\u003eKoay, K. Y., Soh, P. C.-H., \u0026amp; Chew, K. W. (2017). Antecedents and consequences of cyberloafing: Evidence from the Malaysian ICT industry. \u003cem\u003eFirst Monday\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(3). https://doi.org/10.5210/fm.v22i3.7302 \u003c/li\u003e\n\u003cli\u003eKwon, M., Kim, D.-J., Cho, H., \u0026amp; Yang, S. (2013). The smartphone addiction scale: Development and validation of a short version for adolescents. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(12), Article e83558. https://doi.org/10.1371/journal.pone.0083558 \u003c/li\u003e\n\u003cli\u003eKwon, M., Lee, J.-Y., Won, W.-Y., Park, J.-W., Min, J.-A., Hahn, C., Gu, X., Choi, J.-H., \u0026amp; Kim, D.-J. (2013). Development and Validation of a Smartphone Addiction Scale (SAS). \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(2), Article e56936. https://doi.org/10.1371/journal.pone.0056936 \u003c/li\u003e\n\u003cli\u003eLee, S.-Y., Lee, D., Nam, C. R., Kim, D. Y., Park, S., Kwon, J.-G., Kweon, Y.-S., Lee, Y., Kim, D. J., \u0026amp; Choi, J.-S. (2018). Distinct patterns of Internet and smartphone-related problems among adolescents by gender: Latent class analysis. \u003cem\u003eJournal of Behavioral Addictions\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 454-465. https://doi.org/10.1556/2006.7.2018.28 \u003c/li\u003e\n\u003cli\u003eLee, Y. K., Chang, C. T., Lin, Y., \u0026amp; Cheng, Z. H. (2014). The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(1), 373-383. https://doi.org/10.1016/j.chb.2013.10.047 \u003c/li\u003e\n\u003cli\u003eLeung, L. (2008). Linking Psychological Attributes to Addiction and Improper Use of the Mobile Phone Among Adolescents in Hong Kong. \u003cem\u003eJournal of Children and Media\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2), 93-113. https://doi.org/10.1080/17482790802078565 \u003c/li\u003e\n\u003cli\u003eLi, L., Griffiths, M. D., Mei, S., \u0026amp; Niu, Z. (2020). Fear of Missing Out and Smartphone Addiction Mediates the Relationship Between Positive and Negative Affect and Sleep Quality Among Chinese University Students. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, Article 877. https://doi.org/10.3389/fpsyt.2020.00877 \u003c/li\u003e\n\u003cli\u003eLim, V. K. G., \u0026amp; Chen, D. J. Q. (2012). Cyberloafing at the workplace: Gain or drain on work? \u003cem\u003eBehaviour and Information Technology\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(4), 343-353. https://doi.org/10.1080/01449290903353054 \u003c/li\u003e\n\u003cli\u003eLim, V. K. G., Teo, T. S. H., \u0026amp; Loo, G. L. (2002). How do I loaf here? Let me count the ways. \u003cem\u003eCommunications of the ACM\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(1), 66-70. https://doi.org/10.1145/502269.502300 \u003c/li\u003e\n\u003cli\u003eLin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., \u0026amp; Primack, B. A. (2016). Association between social media use and depression among US young adults. \u003cem\u003eDepression and Anxiety\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(4), 323-331. https://doi.org/10.1002/da.22466 \u003c/li\u003e\n\u003cli\u003eLiu, D., Baumeister, R. F., Yang, C., \u0026amp; Hu, B. (2019). Retracted: Digital Communication Media Use and Psychological Well-Being: A Meta-Analysis. \u003cem\u003eJournal of Computer-Mediated Communication, 24\u003c/em\u003e(5), 259\u0026ndash;273. https://doi.org/10.1093/jcmc/zmz013 \u003c/li\u003e\n\u003cli\u003eLo Coco, G., Salerno, L., Franchina, V., La Tona, A., Di Blasi, M., \u0026amp; Giordano, C. (2020). Examining bi-directionality between Fear of Missing Out and problematic smartphone use. A two-wave panel study among adolescents. \u003cem\u003eAddictive Behaviors\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e, Article 106360. https://doi.org/10.1016/j.addbeh.2020.106360 \u003c/li\u003e\n\u003cli\u003eLong, J., Liu, T.-Q., Liao, Y.-H., Qi, C., He, H.-Y., Chen, S.-B., \u0026amp; Billieux, J. (2016). Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates. \u003cem\u003eBMC Psychiatry\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), Article 408. https://doi.org/10.1186/s12888-016-1083-3 \u003c/li\u003e\n\u003cli\u003eLopez-Fernandez, O. (2017). Short version of the Smartphone Addiction Scale adapted to Spanish and French: Towards a cross-cultural research in problematic mobile phone use. \u003cem\u003eAddictive Behaviors\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e, 275-280. https://doi.org/10.1016/j.addbeh.2015.11.013 \u003c/li\u003e\n\u003cli\u003eLopez-Fernandez, O., Honrubia-Serrano, L., Freixa-Blanxart, M., \u0026amp; Gibson, W. (2014). Prevalence of Problematic Mobile Phone Use in British Adolescents. \u003cem\u003eCyberpsychology, Behavior, and Social Networking\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(2), 91-98. https://doi.org/10.1089/cyber.2012.0260 \u003c/li\u003e\n\u003cli\u003eLopez-Fernandez, O., Kuss, D. J., Romo, L., Morvan, Y., Kern, L., Graziani, P., Rousseau, A., Rumpf, H. J., Bischof, A., G\u0026auml;ssler, A. K., Schimmenti, A., Passanisi, A., M\u0026auml;nnikk\u0026ouml;, N., K\u0026auml;\u0026auml;ri\u0026auml;nen, M., Demetrovics, Z., Kir\u0026aacute;ly, O., Ch\u0026oacute;liz, M., Zacar\u0026eacute;s, J. J., Serra, E.,\u0026hellip;Billieux, J. (2017). Self-reported dependence on mobile phones in young adults: A European cross-cultural empirical survey. \u003cem\u003eJournal of Behavioral Addictions\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2), 168-177. https://doi.org/10.1556/2006.6.2017.020 \u003c/li\u003e\n\u003cli\u003eLopez-Fernandez, O., Losada-Lopez, J. L., \u0026amp; Honrubia-Serrano, M\u003csup\u003ea\u003c/sup\u003e. L. (2015). Predictors of Problematic Internet and mobile phone usage in adolescents. \u003cem\u003eAloma, 33\u003c/em\u003e(2), 49-58. http://revistaaloma.net/index.php/aloma/article/view/261/200200303 \u003c/li\u003e\n\u003cli\u003eLowe-Calverley, E., \u0026amp; Grieve, R. (2017). Web of deceit: Relationships between the dark triad, perceived ability to deceive and cyberloafing. Cyberpsychology: \u003cem\u003eJournal of Psychosocial Research on Cyberspace\u003c/em\u003e, 11(2). https://doi.org/10.5817/CP2017-2-5 \u003c/li\u003e\n\u003cli\u003eLu, X., Watanabe, J., Liu, Q., Uji, M., Shono, M., \u0026amp; Kitamura, T. (2011). Internet and mobile phone text-messaging dependency: Factor structure and correlation with dysphoric mood among Japanese adults. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(5), 1702-1709. https://doi.org/10.1016/j.chb.2011.02.009 \u003c/li\u003e\n\u003cli\u003eMacCallum, R. C., Browne, M. W., \u0026amp; Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. \u003cem\u003ePsychological Methods\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(2), 130-149. https://doi.org/10.1037/1082-989X.1.2.130 \u003c/li\u003e\n\u003cli\u003eMerlo, L. J., Stone, A. M., Bibbey, A., Merlo, L. J., Stone, A. M., \u0026amp; Bibbey, A. (2013). Measuring Problematic Mobile Phone Use: Development and Preliminary Psychometric Properties of the PUMP Scale, Measuring Problematic Mobile Phone Use: Development and Preliminary Psychometric Properties of the PUMP Scale. \u003cem\u003eJournal of Addiction, Journal of Addiction\u003c/em\u003e, \u003cem\u003e2013, \u003c/em\u003eArticle e912807. https://doi.org/10.1155/2013/912807 \u003c/li\u003e\n\u003cli\u003eMetin-Orta, I., \u0026amp; Demirutku, K. (2020). Cyberloafing behaviors among university students and its relation to Hedonistic-Stimulation value orientation, cyberloafing attitudes, and time spent on the Internet. \u003cem\u003eCurrent Psychology\u003c/em\u003e\u003cem\u003e, 41\u003c/em\u003e, 4271-4282. https://doi.org/10.1007/s12144-020-00932-9 \u003c/li\u003e\n\u003cli\u003eMiles, J., \u0026amp; Shevlin, M. (2007). A time and a place for incremental fit indices. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(5), 869-874. https://doi.org/10.1016/j.paid.2006.09.022 \u003c/li\u003e\n\u003cli\u003eMisra, N., Dangi, S., \u0026amp; Patel, S. (2015). Gender Differences in Usage of Social Networking Sites and Perceived Online Social Support on Psychological Well Being of Youth. \u003cem\u003eThe International Journal of Indian Psychology\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 7751-7754. https://doi.org/10.25215/0301.008 \u003c/li\u003e\n\u003cli\u003eMok, J.-Y., Choi, S.-W., Kim, D.-J., \u0026amp; Choi, J.-S. (2014). Neuropsychiatric Disease and Treatment Dovepress latent class analysis on internet and smartphone addiction in college students. \u003cem\u003eJournal of Neuropsychiatric Disease and Treatment\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 817-828. https://doi.org/10.2147%2FNDT.S59293 \u003c/li\u003e\n\u003cli\u003eMuscanell, N. L., \u0026amp; Guadagno, R. E. (2012). Make new friends or keep the old: Gender and personality differences in social networking use. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 107-112. https://doi.org/10.1016/j.chb.2011.08.016 \u003c/li\u003e\n\u003cli\u003eNahas, M., Hlais, S., Saberian, C., \u0026amp; Antoun, J. (2018). Problematic smartphone use among Lebanese adults aged 18\u0026ndash;65 years using MPPUS-10. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e, 348-353. https://doi.org/10.1016/j.chb.2018.06.009 \u003c/li\u003e\n\u003cli\u003eNunnally, J. C. (1967). \u003cem\u003ePsychometric Theory\u003c/em\u003e. McGraw-Hill.\u003c/li\u003e\n\u003cli\u003eOberst, U., Wegmann, E., Stodt, B., Brand, M., \u0026amp; Chamarro, A. (2017). Negative consequences from heavy social networking in adolescents: The mediating role of fear of missing out. \u003cem\u003eJournal of Adolescence\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e, 51-60. https://doi.org/10.1016/j.adolescence.2016.12.008 \u003c/li\u003e\n\u003cli\u003eOzler, D. E., \u0026amp; Polat, G. (2012). Cyberloafing Phenomenon in Organizations: Determinants and Impacts. \u003cem\u003eInternational Journal of EBusiness and EGovernment Studies\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 1-15. https://dergipark.org.tr/en/pub/ijebeg/issue/26199/275855 \u003c/li\u003e\n\u003cli\u003e\u0026Ouml;ztun\u0026ccedil;, M. (2013). Analysis of Problematic Mobile Phone Use, Feelings of Shyness and Loneliness in Accordance with Several Variables. \u003cem\u003eProcedia - Social and Behavioral Sciences\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e, 456-466. https://doi.org/10.1016/j.sbspro.2013.12.051 \u003c/li\u003e\n\u003cli\u003ePamuk, M., \u0026amp; Atli, A. (2016). Development of a Problematic Mobile Phone Use Scale for University Students: Validity and Reliability Study. \u003cem\u003eD\u0026uuml;ş\u0026uuml;nen Adam, 29\u003c/em\u003e(1), 49-59. https://dusunenadamdergisi.org/article/244 \u003c/li\u003e\n\u003cli\u003ePark, N., \u0026amp; Lee, H. (2014). Nature of Youth Smartphone Addiction in Korea: Diverse Dimensions of Smartphone Use and Individual Traits. \u003cem\u003eJournal of Communication Research\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(1), 100-132. https://doi.org/10.22174/jcr.2014.51.1.100 \u003c/li\u003e\n\u003cli\u003ePivetta, E., Harkin, L., Billieux, J., Kanjo, E., \u0026amp; Kuss, D. J. (2019). Problematic smartphone use: An empirically validated model. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, 100, 105\u0026ndash;117. https://doi.org/10.1016/j.chb.2019.06.013 \u003c/li\u003e\n\u003cli\u003ePolito, A. (1997). Cyberloafing can be curbed. \u003cem\u003eWorkforce\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(18).\u003c/li\u003e\n\u003cli\u003ePoushter, J., Bishop, C., \u0026amp; Chwe, H. (2018, June 19). Social media use continues to rise in developing countries but plateaus across developed ones. \u003cem\u003ePew Research Center.\u003c/em\u003e https://www.pewresearch.org/global/2018/06/19/social-media-use-continues-to-rise-in-developing-countries-but-plateaus-across-developed-ones/ \u003c/li\u003e\n\u003cli\u003ePrzybylski, A. K., Murayama, K., Dehaan, C. R., \u0026amp; Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 \u003c/li\u003e\n\u003cli\u003eQutishat, M. G. (2020). Gender Differences in Fear of Missing out Experiences among Undergraduate Students in Oman. \u003cem\u003eNew Emirates Medical Journal\u003c/em\u003e\u003cem\u003e, 1\u003c/em\u003e(2), 36-40. https://doi.org/10.2174/0250688202002022003 \u003c/li\u003e\n\u003cli\u003eRosen, L. D., Mark Carrier, L., Pedroza, J. A., Elias, S., O\u0026rsquo;Brien, K. M., Lozano, J., Kim, K., Cheever, N. A., Bentley, J., \u0026amp; Ruiz, A. (2018). The role of executive functioning and technological anxiety (FOMO) in college course performance as mediated by technology usage and multitasking habits. \u003cem\u003ePsicologia Educativa\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 14-25. https://doi.org/10.5093/psed2018a3 \u003c/li\u003e\n\u003cli\u003eRoser, K., Schoeni, A., Foerster, M., \u0026amp; R\u0026ouml;\u0026ouml;sli, M. (2016). Problematic mobile phone use of Swiss adolescents: is it linked with mental health or behaviour? \u003cem\u003eInternational Journal of Public Health\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(3), 307-315. https://doi.org/10.1007/s00038-015-0751-2 \u003c/li\u003e\n\u003cli\u003eSalehan, M., \u0026amp; Negahban, A. (2013). Social networking on smartphones: When mobile phones become addictive. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(6), 2632-2639. https://doi.org/10.1016/j.chb.2013.07.003 \u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Mart\u0026iacute;nez, M., \u0026amp; Otero, A. (2009). Factors associated with cell phone use in adolescents in the community of Madrid (Spain). \u003cem\u003eCyberpsychology and Behavior\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(2), 131-137. https://doi.org/10.1089/cpb.2008.0164 \u003c/li\u003e\n\u003cli\u003eSantana-Vega, L.-E., G\u0026oacute;mez-Mu\u0026ntilde;oz, A.-M., \u0026amp; Feliciano-Garc\u0026iacute;a, L. (2019). Adolescents problematic mobile phone use, Fear of Missing Out and family communication. \u003cem\u003eComunicar\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(59), 39-47. https://doi.org/10.3916/C59-2019-04 \u003c/li\u003e\n\u003cli\u003eSaritepeci, M. (2020). Predictors of cyberloafing among high school students: unauthorized access to school network, metacognitive awareness and smartphone addiction. \u003cem\u003eEducation and Information Technologies\u003c/em\u003e, 25(3), 2201\u0026ndash;2219. https://doi.org/10.1007/s10639-019-10042-0 \u003c/li\u003e\n\u003cli\u003eSavci, A., Damar, H. T., Comez, S., \u0026amp; Bilik, O. (2021). Effects of Smartphone Addiction and Cyberloafing Behaviour of Nursing Students on their Clinical Decision-Making during Clinical Practice. \u003cem\u003eInternational Journal of Caring Sciences\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 534-542. http://www.internationaljournalofcaringsciences.org/docs/56_savsi_original_14_1.pdf \u003c/li\u003e\n\u003cli\u003eShannon, H., Bush, K., Villeneuve, P. J., Hellemans, K. G., \u0026amp; Guimond, S. (2022). Problematic Social Media Use in Adolescents and Young Adults: Systematic Review and Meta-analysis. \u003cem\u003eJMIR Mental Health, 9\u003c/em\u003e(4), e33450. https://doi.org/10.2196/33450 \u003c/li\u003e\n\u003cli\u003eShin, W., \u0026amp; Kim, H.-W. (2023). Problematic mobile phone use and cyberbullying perpetration in adolescents. \u003cem\u003eBehaviour \u0026amp; Information Technology, 42\u003c/em\u003e(4), 424\u0026ndash;443. https://doi.org/10.1080/0144929X.2022.2104756 \u003c/li\u003e\n\u003cli\u003eSmetaniuk, P. (2014). A preliminary investigation into the prevalence and prediction of problematic cell phone use. \u003cem\u003eJournal of Behavioral Addictions\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 41-53. https://doi.org/10.1556/JBA.3.2014.004 \u003c/li\u003e\n\u003cli\u003eStead, H., \u0026amp; Bibby, P. A. (2017). Personality, fear of missing out and problematic internet use and their relationship to subjective well-being. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e, 534-540. https://doi.org/10.1016/j.chb.2017.08.016 \u003c/li\u003e\n\u003cli\u003eSteiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(5), 893-898. https://doi.org/10.1016/j.paid.2006.09.017 \u003c/li\u003e\n\u003cli\u003eTabachnick, B. G., \u0026amp; Fidell, L. S. (2007). \u003cem\u003eUsing Multivariate Statistics\u003c/em\u003e (5th ed.). Allyn \u0026amp; Bacon/Pearson Education.\u003c/li\u003e\n\u003cli\u003eTakao, M. (2014). Problematic mobile phone use and big-five personality domains. \u003cem\u003eIndian Journal of Community Medicine\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(2), 111-113. https://doi.org/10.4103/0970-0218.132736 \u003c/li\u003e\n\u003cli\u003eTakao, M., Takahashi, S., \u0026amp; Kitamura, M. (2009). Addictive Personality and Problematic Mobile Phone Use. \u003cem\u003eCyberPsychology \u0026amp; Behavior\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(5), 501-507. https://doi.org/10.1089/cpb.2009.0022 \u003c/li\u003e\n\u003cli\u003eTandon, A., Dhir, A., Talwar, S., Kaur, P., \u0026amp; M\u0026auml;ntym\u0026auml;ki, M. (2022). Social media induced fear of missing out (FoMO) and phubbing: Behavioural, relational and psychological outcomes. \u003cem\u003eTechnological Forecasting and Social Change, 174\u003c/em\u003e, 121149. https://doi.org/10.1016/j.techfore.2021.121149 \u003c/li\u003e\n\u003cli\u003eTaywade, A., \u0026amp; Khubalkar, R. (2019). Gender differences in smartphone usage patterns of adolescents. \u003cem\u003eThe International Journal of Indian Psychology\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(4), 509-515. https://doi.org/10.25215/0704.060 \u003c/li\u003e\n\u003cli\u003eToda, M., Nishio, N., Ezoe, S., \u0026amp; Takeshita, T. (2015). Chronotype and smartphone use among Japanese medical students. \u003cem\u003eInternational Journal of Cyber Behavior, Psychology and Learning\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(2), 75-80. https://doi.org/10.4018/IJCBPL.2015040106 \u003c/li\u003e\n\u003cli\u003eTwenge, J. M. (2019). More Time on Technology, Less Happiness? Associations Between Digital-Media Use and Psychological Well-Being. \u003cem\u003eCurrent Directions in Psychological Science, 28\u003c/em\u003e(4), 372\u0026ndash;379. https://doi.org/10.1177/0963721419838244 \u003c/li\u003e\n\u003cli\u003eVally, Z., \u0026amp; El Hichami, F. (2019). An examination of problematic mobile phone use in the United Arab Emirates: Prevalence, correlates, and predictors in a college-aged sample of young adults. \u003cem\u003eAddictive Behaviors Reports\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, Article 100185. https://doi.org/10.1016/j.abrep.2019.100185 \u003c/li\u003e\n\u003cli\u003eVan Deursen, A. J. A. M., Bolle, C. L., Hegner, S. M., \u0026amp; Kommers, P. A. M. (2015). Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e, 411-420. https://doi.org/10.1016/j.chb.2014.12.039 \u003c/li\u003e\n\u003cli\u003eVannucci, A., Flannery, K. M., \u0026amp; Ohannessian, C. M. (2017). Social media use and anxiety in emerging adults. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e207\u003c/em\u003e, 163-166. https://doi.org/10.1016/j.jad.2016.08.040 \u003c/li\u003e\n\u003cli\u003eVillanti, A. C., Johnson, A. L., Ilakkuvan, V., Jacobs, M. A., Graham, A. L., \u0026amp; Rath, J. M. (2017). Social media use and access to digital technology in US Young Adults in 2016. \u003cem\u003eJournal of Medical Internet Research\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(6), Article e196. https://doi.org/10.2196/jmir.7303 \u003c/li\u003e\n\u003cli\u003eVitak, J., Crouse, J., \u0026amp; Larose, R. (2011). Personal Internet use at work: Understanding cyberslacking. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(5), 1751-1759. https://doi.org/10.1016/j.chb.2011.03.002 \u003c/li\u003e\n\u003cli\u003eWallen, N. E., \u0026amp; Fraenkel, J. R. (2001). \u003cem\u003eEducational research: A guide to the process\u003c/em\u003e (2nd ed.). Erlbaum.\u003c/li\u003e\n\u003cli\u003eWalsh, S. P., White, K. M., Cox, S., \u0026amp; Young, R. M. D. (2011). Keeping in constant touch: The predictors of young Australians\u0026rsquo; mobile phone involvement. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(1), 333-342. https://doi.org/10.1016/j.chb.2010.08.011 \u003c/li\u003e\n\u003cli\u003eWalsh, S. P., White, K. M., Young, R. M., \u0026amp; Walsh, S. (2007). Young and connected: Psychological influences of mobile phone use amongst Australian youth. In G. Goggin \u0026amp; L. Hjorth (Eds.), \u003cem\u003eProceedings: International Conference on Social and Cultural Aspects of Mobile Phones, Convergent Media and Wireless Technologies\u003c/em\u003e (pp. 125-134). University of Sydney, Australia.\u003c/li\u003e\n\u003cli\u003eWang, J.-L., Wang, H.-Z., Gaskin, J., \u0026amp; Wang, L.-H. (2015). The role of stress and motivation in problematic smartphone use among college students. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e, 181-188. https://doi.org/10.1016/j.chb.2015.07.005 \u003c/li\u003e\n\u003cli\u003eWest, S. G., Finch, J. F., \u0026amp; Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyl (Ed.), \u003cem\u003eStructural equation modeling: Concepts, issues, and applications\u003c/em\u003e (pp. 56-75). Sage.\u003c/li\u003e\n\u003cli\u003eWheaton, B., Muthen, B., Alwin, D. F., \u0026amp; Summers, G. F. (1977). Assessing Reliability and Stability in Panel Models. \u003cem\u003eSociological Methodology\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 84-136. https://doi.org/10.2307/270754 \u003c/li\u003e\n\u003cli\u003eWolf, E. J., Harrington, K. M., Clark, S. L., \u0026amp; Miller, M. W. (2013). Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety. \u003cem\u003eEducational and Psychological Measurement\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(6), 913-934. https://doi.org/10.1177/0013164413495237 \u003c/li\u003e\n\u003cli\u003eWolniewicz, C. A., Tiamiyu, M. F., Weeks, J. W., \u0026amp; Elhai, J. D. (2018). Problematic smartphone use and relations with negative affect, fear of missing out, and fear of negative and positive evaluation. \u003cem\u003ePsychiatry Research\u003c/em\u003e, \u003cem\u003e262\u003c/em\u003e, 618-623. https://doi.org/10.1016/j.psychres.2017.09.058 \u003c/li\u003e\n\u003cli\u003eYasar, S. (2013). \u003cem\u003e\u0026Uuml;niversite \u0026ouml;ğrencilerinin denetim odağı ve bilgisayar laboratuvarına y\u0026ouml;nelik tutumlarının siberaylaklık davranışlarına etkisi \u003c/em\u003e[The effects of students\u0026rsquo; locus of control and attitudes towards computer laboratory on their cyberloafing behaviour]. [Master\u0026rsquo;s thesis, Hacettepe University, Ankara Turkey]. http://www.openaccess.hacettepe.edu.tr:8080/xmlui/handle/11655/1727 \u003c/li\u003e\n\u003cli\u003eYen, C.-F., Tang, T.-C., Yen, J.-Y., Lin, H.-C., Huang, C.-F., Liu, S.-C., \u0026amp; Ko, C.-H. (2009). Symptoms of problematic cellular phone use, functional impairment and its association with depression among adolescents in Southern Taiwan. \u003cem\u003eJournal of Adolescence\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(4), 863-873. https://doi.org/10.1016/j.adolescence.2008.10.006 \u003c/li\u003e\n\u003cli\u003eZhitomirsky-Geffet, M., \u0026amp; Blau, M. (2016). Cross-generational analysis of predictive factors of addictive behavior in smartphone usage. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e, 682-693. https://doi.org/10.1016/j.chb.2016.07.061 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Problematic mobile phone use, fear of missing out, FOMO, cyberloafing, mobile phone use, demographics, mediation ","lastPublishedDoi":"10.21203/rs.3.rs-6871454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6871454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Closely aligned with the pervasive presence of mobile phones in today's society surrounded by technology, this study sets out to examine the mediating roles of respect to cyberloafing, fear of missing out (FoMO), mobile phone use hours (MPUH), and social media use hours (SMUH) in the effects of gender and age on problematic mobile phone use (PMPU) among university students. A correlational research method was employed in this study. The participants were 1,272 university students. Data were collected using paper-pencil questionnaires and analyzed through path analysis. The results showed that cyberloafing, FoMO, mobile phone use hours, and social media use hours significantly contributed to the prediction of PMPU. Furthermore, while age indirectly influenced PMPU via FoMO and mobile phone use hours, gender indirectly influenced PMPU via FoMO, cyberloafing, mobile phone use hours, and social media use hours. The study contributes to a deeper understanding of the interactions between psychological reactions and technological behaviors aligns with the broader goal of promoting healthy technology use in educational environments. Insights from this research can inform interventions and policies aimed at fostering responsible and beneficial technology use among students. The implications of this study are multifaceted and can be significant in addressing issues related to PMPU among university students.","manuscriptTitle":"Unveiling Contributing Pathways to Problematic Mobile Phone Use: Mediating Effects of FOMO, Cyberloafing, Mobile Phone and Social Media Use","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 11:51:11","doi":"10.21203/rs.3.rs-6871454/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-23T06:06:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88237215394871234809953092650173461845","date":"2026-02-10T01:48:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-07T23:33:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-26T02:45:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-19T23:45:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-08T18:02:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-08-08T17:56:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"395cd0da-55a6-4d49-8639-818cf2b05c57","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62875451,"name":"Business and commerce/Information systems and information technology"},{"id":62875452,"name":"Social science/Education"}],"tags":[],"updatedAt":"2026-02-13T11:51:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 11:51:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6871454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6871454","identity":"rs-6871454","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}