# A Positive View of Excessive Smartphone Utilization and Its Relationship With Other Academic-Related Variables Within the Online Course Setting

Kevin Kertechian, Hussein Ismail

► To cite this version:

Kevin Kertechian, Hussein Ismail. A Positive View of Excessive Smartphone Utilization and Its Relationship With Other Academic-Related Variables Within the Online Course Setting. Psychological Reports, 2023, 10.1177/00332941231183338. hal-04271548

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Submitted on 6 Nov 2023

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A positive view of excessive smartphone utilization and its relationship with other
academic-related variables within the online course setting

Abstract

The continual use of smartphones is a global problem that requires scholars’ attention.
This study investigates the impact of excessive smartphone use, self-regulation, and
procrastination on students’ online academic performance. A total of n = 238 university students
participated in the study. Mean comparisons unveiled clear discrepancy scores on
procrastination, self-regulation, and daily hours spent on smartphones between the smartphone-
addicted and non-addicted students. Structural Equation Modeling helps us to answer our
hypothesis. Unusually, smartphone use had a significant and positive effect on online students’
academic performance. Also, the study provides a better understanding of the procrastination
factor that significantly impacts students’ smartphone use and online academic performance.
Results are discussed considering possible interventions at the academic level.

**Keywords:** Smartphone use, self-regulation, procrastination, Grade Point Average, UAE

Word count: 7528 words
# 1. Introduction

The dawn of the digital age is characterized by two important moments: the demise of mobile phones in favor of smartphones and the use of smartphones as personal computers. Companion to humans, smartphones are multifaceted tools that occupy a unique place in our everyday life. The United Arab Emirates (UAE) accounts for 90% of smartphone owners, and the remaining respondents, 70% of them, claim that they will buy a smartphone (YouGov, 2018). Also, UAE residents aged between 18 and 29 spend 7 hours daily using their smartphones (YouGov, 2018). A similar result is found in a more recent survey conducted by Statista (2023); specifically, around 97.6% of people in the UAE are using smartphones in 2021. Also, a recent study has confirmed the increased number of smartphone users and its problematic nature within the UAE (Ameen, Willis and Shah, 2018). More than a simple distraction when doing different activities such as watching TV, working, or dining in a restaurant with friends, its use is now a synonym of addiction. Smartphones are now considered inseparable companions (Lepp et al. 2015). In the YouGov (2018) study for the UAE, 76% of the respondents admitted to looking at their phone frequently, while 72% claimed to reach for their device first thing in the morning.

The use of a smartphone can be a double-edged sword. As mentioned, its use may become rather addictive very quickly. Nevertheless, it is a technological advance that has made everything reachable through touch, such as ordering groceries, organizing video calls with your loved ones, entertaining while having free time, reading books and journals, and having access to many applications that enable those activities during the pandemic. But the border between the benefits and limitations of smartphone use is relatively blurry where the use of smartphones can become dangerously addictive. It is well known that constant smartphone usage impacts sleeping habits, fosters stress and anxiety, and dampens academic performance and physical activity
(Thomée et al., 2011; Yang et al., 2019). Smartphone addiction is not country-specific; a recent study has shown a cross-cultural effect (Panek et al., 2018).

Studies have considered the idea of excessive use of smartphones as a “problematic use” leading to dependence or addiction (e.g., Hussain et al., 2017; Yang et al., 2019). The COVID-19 pandemic has led people to use their phone more extensively than normal (Sebir, 2020). The excessive use of smartphones is considered an addiction and, more precisely, a non-substance addiction as psychiatrically diagnosed by professionals. Nonetheless, we believe “smartphone use” is forceful enough to fulfill the study’s objective; for example, we used “hours spent on smartphones” as an indicator as suggested by scholars (Domoff, Foley, and Ferkel, 2020).

To strengthen our knowledge of excessive smartphone use in an online academic setting, this study examines the relationship between smartphone use, self-regulation, academic performance, and procrastination. Although the use of smartphones in academic performance has been discussed in previous studies (e.g., Amez and Baert, 2020), much less is known about the impact that addictive smartphone use may have on academic performance (Domoff, Foley, & Ferkel, 2020). Apart from a few exceptions (Felisoni & Godoi, 2018; Giunchiglia, Zeni, Gobbi, Bignotti, & Bison, 2018), this study contributes further to this limited body of knowledge in at least two ways.

First, we investigate this relationship in the Middle East region which critically lacks such studies (Alotaibi et al., 2022), especially when smartphone use is known to have increased remarkably in the Middle East region over the past decade based on a recent report by YouGov (2018). Second, in line with recommendations to further examine the impact of smartphone addiction in the academic field (Domoff, Foley, & Ferkel, 2020), this study explored age and gender as essential covariation factors between self-regulation and procrastination. Moreover,
this research will indirectly explore the direct impact of self-regulation and procrastination on
academic performance through smartphone use. The role of procrastination relates to the act of
delaying academic work (Gustavson & Miyake, 2017; Kertechian, 2018) while self-regulation
involve the ability of students to manage their own behavior and emotions (Bidjerano & Dai,
2007; Dörrenbächer & Perels, 2016; Eilam et al., 2009) – both concepts are well documented in
academic settings.

Besides, one can note that the UAE is the most digital-friendly country in the Middle East
(Euler Hermes, 2018). Coupled with the fact that research on smartphone use within the
academic context is yet to be explored in that area, we provide a solid ground for the present
study's narrative (see figure 1). This study has several implications for providing scholars with
detailed predictions on how some variables might foster or negatively impact academic
performance, and ultimately aims to assist university counselors in building personalized
interventions strategies for students such as training or seminars/webinars.

This paper focuses on the context of “academic online settings” regardless of other
environmental factors. Although the data for this study may have been collected during the
COVID-19 pandemic, it is worth noting that the authors are in no way inferring that the COVID-
19 pandemic has stimulated online learning. The online learning environment has been active
long before the pandemic with many universities embracing online learning more than a decade
ago. This is the case of University of the People, for example, which is a fully online institution
recognized by the American Distance Education Accrediting Commission (DEAC). While this
example represents an entirely online institution, major universities in the world offer fully
online certificates, diplomas, and degrees as well. The choice to embrace an online setting is
strategic- it existed before the pandemic and early discussions of this concept date back to the
2000s' (Harasim, 2000).

In this paper, we aim at analyzing the importance of smartphones, its uses, and regulations within the UAE academic ecosystem as a unique contribution of this study. Therefore, classical relationships (e.g. smartphone use and GPA) have been investigated in the context of the UAE and in an online learning setting.

## 2. Literature Review

### 2.1. *Smartphone Use and Academic Performance*

A recent systematic review demonstrated a negative association between students' frequency of smartphone use and their academic performance (Amez & Baert, 2020), and this inference holds true during the COVID-19 pandemic (Albursan et al., 2022) as well as in post-COVID situations in Asia (Yao & Wang, 2022; Iqbal et al., 2021) and the Middle-east (Alotaibi et al., 2022). While academic performances are negatively impacted by the extensive use of smartphones, the whole learning process is also weakened (Sunday, Adesope, & Maarhuis, 2021). Specifically, “possessing a smartphone” is a strong and positive conjecture among people aged 18–29, potentially representing many students. A more fine-tuned perspective led us to add that individuals engaging in study-related excessive use of their smartphones tend to achieve higher academic results, whereas game-related usage would negatively affect academic outcomes (Abbasi et al., 2021).

The COVID-19 pandemic irremediably accelerated the establishment of online teaching to become the new teaching standard among universities. While in-class instruction would have been preventing smartphone use, being at home reinforces its use. Research has found a negative impact of smartphone use on aspects of health such as sleep quality (Christensen et al., 2016;
Demirci et al., 2015; Rosen et al., 2016), mental health, and physical fitness (Lepp et al., 2015). Concomitantly, academic performance is, in turn impacted by the weakening of these health indicators (Baert et al., 2015; Galambos et al., 2013).

Beyond the impact of smartphone use on students’ health conditions, it impacts students’ ability to concentrate during class. While most of the studies have been conducted in an actual class session, we expect to find a similar and even stronger negative relationship between smartphone use and online academic performance (Junco & Cotten, 2012; Levine et al., 2012). The literature has identified four main reasons explaining such a relationship. First, multitasking in smartphones is a common feature that negatively impacts academic performance (Karpinski et al., 2013; Rosen et al., 2013). Second, smartphone features such as notifications may distract students during class or coursework (Junco & Cotten, 2012), which is another reason why students get distracted by their smartphones relating to the fear of missing out (i.e., “FOMO”); in other words, students feel the need to know about the latest news as soon as it is sent out, thus impacting students’ academic performance (Chen & Yan, 2016). Third, students’ online presence on social networks interferes with studying (Vitak et al., 2011). Fourth, students may feel unmotivated towards studying, and smartphones then represent a way to escape academic boredom (Hawi & Samaha, 2017). The Middle East and particularly the UAE are no exception, a priori, to this framework, thus:

**H1.** The over usage of smartphones among students in the UAE would exacerbate its relationship with academic performance, so that this relationship is negative.

## 2.2. Self-Regulation

Moving from a physical in-class setting to an online environment has undeniably affected university students. This transition, which can be seen as a process, needs to be fully embraced
by students to maintain a certain academic level. Parallelism is found with self-regulation, described as “those processes, internal and transactional, that enable an individual to guide his/her goal-directed activities over time and across changing circumstances (contexts)” (Karoly, 1993, p. 25). Thus, students who can engage successfully in self-regulation tend to be less smartphone-addicted, while students who fail to express self-regulation will be more likely smartphone-addicted and are more likely to undergo well-being issues (Soror et al., 2012). If such association has been established in the European context (Gökçearslan et al., 2016; van Deursen et al., 2015), it remains crucial to clarify this association among UAE university students. Thus, we envision the following hypothesis:

H2. UAE students who are incapable of focusing on academic activities through self-regulations processes would have a higher tendency to use their smartphones.

Failure in educational self-regulation has been associated with procrastination (Hosseini & Khayyer, 2009; Özer et al., 2009). Naturally, high self-regulators tend to finalize their course work on time (Darling-Hammond et al., 2020). Rahmani Javanmard and Mohammadi (2017) suggested that fear of failure was a positive predictor of academic procrastination. Mohsenzadeh et al. (2016) established that high-school students' personality characteristics associated with fear of failure were significantly related to academic procrastination. One study found that conscientiousness was negatively associated with procrastination, confirming its highly recognized positive effects on academic-related outcomes (Kertechian 2018). To the best of our knowledge, educational self-regulation's association with procrastination is not well understood, while other factors, such as fear of failure, have been widely studied (Mohsenzadeh et al. 2016; Rahmani & Mohammadi 2017). Then, the analysis of the association between self-regulation and procrastination requires more attention. Thus, we hypothesize the following:
**H3.** If students cannot show a high level of self-regulation that incorporates the ability to adapt to new settings, then their procrastination level will increase.

Scholars have persistently associated self-regulation as a strong predictor of academic performance (Bidjerano and Dai 2007; Dörrenbächer and Perels 2016; Eilam, Zeidner, and Aharon 2009). In Pakistan, for example, self-regulation has been identified as an antecedent of academic performance and this relationship is even stronger with the presence of a positive use of academic social networking sites (Iqbal et al., 2021). Within self-regulation, “effort regulation” in learning seems to be a robust predictor for academic performance (Richardson et al., 2012; Sitzmann & Ely, 2011) and academic achievement (Estévez et al., 2021). In other studies, self-regulation appeared to be a mediator fostering academic performance, between contextual characteristics and academic performance (Pintrich, 2004) and between self-efficacy and academic performance (Komarraju & Nadler, 2013). In a more recent study conducted during the COVID-19 pandemic, self-directed learning was a predictor of academic achievement (Yavuzalp & Bahcivan, 2021). The online learning setting forced universities to adapt to changes quickly, thus we hypothesize the following:

**H4.** Students who can self-regulate in time of change have less difficulties in obtaining a higher GPA.

## 2.3. Academic Procrastination

Over the last decade, procrastination has been defined several times. Klingsieck (2013) suggested a unified definition of procrastination as “the voluntary delay of an intended and necessary and [personally] important activity, despite expecting potential negative consequences that outweigh the positive consequences of the delay.” (Klingsieck, 2013, p. 26). Moreover, procrastination is mostly present among students (Eerde, 2003; Schouwenburg et al., 2004) and
needs to be overcome to ensure a certain level of success and happiness in life (Steel, 2007).

Correspondingly, academic procrastination would be defined as postponing academic-related activities such as coursework (Kandemir, 2014; Schouwenburg et al., 2004). This phenomenon is quite common as half of the students engage in procrastination (Özer et al., 2009), and this estimation appears to increase (Steel, 2007). Thus, procrastination remains an exciting topic of investigation among students, especially when analyzing its association with academic performance.

A recent meta-analysis from Kim & Seo (2015) has identified inconsistent results between
procrastination and academic performance. For example, procrastination hurts learning and
achievement (Aremu et al., 2011; Balkis, 2013). Within the procrastination framework, time is
seen as a pressure that participates in lowering accuracy and punctuality. Based on this, it is
argued that procrastination will negatively impact academic performance (Eerde, 2003). Other
studies have found an absence of a relationship between procrastination and academic
performance (Odaci, 2011; Solomon & Rothblum, 1984) and sometimes a positive relationship
(Brinthaupt & Shin, 2001; Schraw et al., 2007). Ferrari et al. (1995) stated that students with
higher ability tend to be more procrastinators than those with lower ability. Nevertheless, two
recent studies confirmed the negative relationship (Gustavson & Miyake, 2017; Kertechian,
2018). Furthermore, studies have also established that procrastination is a mediator between
Instagram addiction and academic performance, whereby the relationship became negative in the
presence of procrastination (Pekpazar et al., 2021). As confirmed in previous studies, we
similarly hypothesize the following among UAE students:

**H5.** A high level of procrastination among UAE students leads to lower GPA.
Procrastination is an older concept than the use of the smartphone. Moreover, we believe that procrastination emerged as a predictor of smartphone use. Indeed, students might be distracted by their smartphones when they engage in academic procrastination (e.g., postponing an assignment). While studies have been interested in predicting general procrastination and have found a positive relationship with internet addiction (Geng et al., 2018; Rozgonjuk et al., 2018; Ryan et al., 2016), we aim to identify academic procrastination as a predictor of smartphone use. To the best of our knowledge, there is a lack of empirical evidence on the relationship between academic procrastination and smartphone use. Thus, we propose the following:

**H6.** UAE students who postpone their academic work have a higher tendency to use their smartphone than their counterpart with lower level of procrastination.

# 3. Methodology

## 3.1. Participants

Data were collected from 238 students following a convenience sampling method. At the time of the survey, between January 10th, 2021, and February 10th, 2021, students were registered in a wide range of academic majors in a university located in Downtown Dubai, United Arab Emirates.

## 3.2. Procedure

Data were collected by sending emails to students regularly registered at the University. The email template was composed of an explanation text and a link to redirect students to the questionnaire. Students were reminded of a few important pieces of information such as the opportunity to leave the questionnaire at any time, the anonymity of their responses (i.e., emails and names were not requested), the shortness of the present questionnaire (i.e., not more than 10
minutes), and that only the researcher in charge of the present study could access the data and
analyze them.

### 3.3. *Instruments*

The survey package includes items directly taken from already available questionnaires, developed by scholars and used several times in similar topic research. This applies to the self-regulation questionnaire (Gökçearslan et al., 2016; Yang et al., 2019), the smartphone addiction questionnaire (Rozgonjuk et al., 2018; Yang et al., 2019), and the procrastination questionnaire (Brando-Garrido et al., 2020; Yockey, 2016). We established a five-point Likert scale for each instrument to standardize the questionnaire package.

The first part included questions related to demographic characteristics such as age, gender, and nationality. The second part of the questionnaire included self-regulation, smartphone use, and procrastination questions. A single item was used to measure academic performance. Students who participated in the study provided their cumulative GPAs. The question was configured as an open response format (Paul et al., 2012). The supplementary question has been added to capture information about the actual cumulative GPA as follows: “*What is your current cumulative grade point average - GPA?*”. Additionally, we asked participants to estimate their daily smartphone use from 0 hour to 24 hours (i.e., ratio variable) and if they considered themselves as addicted or not addicted (i.e., dummy variable).

**Self-regulation.** The scale has been first operationalized in Germany by Schwarzer, Diehl and Schmitz (1999). This 10-item scale measures an individual goal-pursuit propension and the difficulties associated with it. To achieve such purpose, individuals must focus on attention on the task (e.g., learning in an online class setting) and keep a favorable emotional balance (e.g.,
transition to the online class setting). Both German (Schwarzer et al., 1999) and English (Diehl et al., 2006) versions presented high internal consistency, Cronbach's α were respectively .76 and .80. Item example is #1 "I can concentrate on one activity for a long time, if necessary." In our sample, Cronbach's α was .82. Interitem correlations were comprised between .03 (between #item 5 and #item 8) and .73 (between #item 3 and #item 4). The 10 items had a moderate to highly satisfactory inter-item correlations as suggested by Cohen and Swerdlik (2005).

**Smartphone addiction.** We assessed smartphone use using the 10 items of the smartphone addiction short scale developed by Kwon, Kim, Cho, and Yang (2013). In their South Korean sample, researchers have obtained an overall Cronbach's α of .91, thus confirming its internal validity. For example, item #3 is "Feeling pain in the wrists or at the back of the neck while using a smartphone." In the present study, Cronbach's α was .87. Interitem correlations were comprised between .04 (between #item 1 and #item3) and .65 (between #item2 and #item3). The 10 items had a moderate to highly satisfactory inter-item correlations as suggested by Cohen and Swerdlik (2005). While psychometric properties of the original scale have been investigated among adolescents (Kwon et al., 2013), validity and reliability of the smartphone addiction scale have been assessed using a university sample in Turkey (Demirci et al., 2014), as well as in Malaysia (Ching et al., 2015). Validity and reliability of the shortened version of the smartphone addiction scale have been assessed using a university sample in Belgium and Spain (Lopez-Fernandez, 2017).

**Procrastination.** To evaluate students' procrastination, we used the 5-item questionnaire first developed by McCloskey (2011), who obtained an internal consistency of .93. The following instruction introduced a series of 5-items: "How much do you, yourself agree to the
following statements?” (McCloskey, 2011, p. 57). Further validation in different samples has been found and has shown strong internal consistencies. Both Yockey (2016) and Brando-Garrido et al., (2020) obtained a Cronbach’s α of .87. An item example is #2 “I know I should work on course work, but I just don’t do it.” In the present study, Cronbach’s α was .91. Inter-item correlations were comprised between .69 (between #item 1 and #item2) and .95 (between #item3 and #item4). Given the theoretical background of the scale, the five items had a satisfactory inter-item correlations as suggested by Cohen and Swerdlik (2005).

To ensure the readability and the overall quality of the present questionnaire, a team composed of one undergraduate student and one faculty member evaluated the questionnaire package with the help of a rating grid, including wording quality, items pertinency, and clarity of instruction (Fowler, 2013). The questionnaire was modified accordingly before sending it out to students. It has to be noted that none of the items has been deleted or significantly modified.

### 3.4. Descriptive statistics and mean comparisons

The sample was composed of 113 (47.5%) male participants and 125 (52.5%) female participants. We asked participants to indicate their nationality, and they described themselves as Emiratis (n = 233; 97.9%). The sample was also composed of other Middle Eastern students (n = 4; 1.7%) and one American (i.e., 0.4%). Most of the respondents completed a bachelor’s degree (n = 146; 61.3%); the number of master’s degree students among the respondents was 91 (38.2%). The mean age of the students was 23.55. The cumulative GPA was obtained through an open question, and the data showed an average of 3.28. Finally, in our sample, students indicated spending an average of 7.20 hours per day on their smartphones and slightly more than the majority considered themselves non-addicted (n = 128; 53.8%) against addicted (n = 110;
46.2%). We conducted a sensitivity power analysis using G*Power version 3.1.9.6 (Faul et al., 2007). For the given sample size *N* = 238 at a significant criterion of α = .05 and achieving a power of 0.95, we detected a medium effect size of ρ = 0.21 (Cohen, 1992).

Not surprisingly, our data do not follow a normal distribution. The Shapiro-Wilk test indicated significant results for our data (p < .001). A recent meta-analysis showed that research in health, education, and social science uses, for a large majority, non-normal data distribution (Bono, Blanca, Arnau, and Gomez-Benito, 2017). In addition, kurtosis and skewness indicators were not satisfactory; data is deemed “normal” if the skewness is between -2 and +2 and the kurtosis is between -7 and +7 (Hair et al., 2010; Byrne, 2010).

A first analysis of mean comparison through the Kruskal-Wallis H test indicated significant
differences. There was a statistically significant difference between number of hours spent on
smartphone (H(1)= 28.677, p < .001) indicating that students who perceived themselves as
addicted are spending more hours per day on their smartphone (M = 8.5, SD = .34) than students
who perceived themselves as non-addicted (M = 6.07, SD = .23). In the same vein, we found
significant differences on self-regulation scores (H(1)= 13.701, p < .001), smartphone addiction
(H(1)= 29.458, p < .001), and procrastination (H(1)= 6.616, p = .010), but no significance was
found for GPA scores (H(1)= 1.473, p = .225).

The same pattern has been found when running the one-way ANOVA analysis, a
parametrical test that, in some situations showed robustness toward non-normal data distribution
(Box and Watson, 1962; Lanz, 2013; Blanca, Alarcón, Arnau, Bono, and Bendayan, 2017).
Significant differences have been found between students who perceived themselves as addicted
and students who perceived themselves as non-addicted on self-regulation scores (F = 15.762, p
< .001, $\eta^2$ = .06), procrastination ($F$ = 5.910, $p$ = .016, $\eta^2$ = .03), and on the total hours spent per day on a smartphone ($F$ = 37.245, $p$ < .001, $\eta^2$ = .14), but no significance was found for GPA scores ($F$ = 1.083, $p$ = ns, $\eta^2$ = ns).

### 3.5. Analytical strategy

A few strategies can be followed to achieve a normal distribution when the data is not normally distributed, such as removing and handling outliers. However, we believe that outliers are very informative in our case and are considered a normal part of the process in our specific field of study.

Mean comparisons have been conducted using the Kruskal-Wallis H test because our data violated the assumptions of the ANOVA test and specifically related to the normal distribution assumption. One of the advantages of the Kruskal-Wallis test is that variations within groups are established based on the relationship between the sample size for each group in contrast to a simple combined variance estimate, thus tackling the heteroscedasticity issue (Lantz, 2013). The Kruskal-Wallis H test determines whether the medians vary between two groups or more (Lantz, 2013).

The same applies to correlation analysis where most scholars employ Pearson's correlation in their studies. In the context of non-normal distribution, Newson (2002) recommends using Kendall's Tau (i.e., Kendall's $\tau_a$) over Pearson's and Spearman's approaches. Indeed, scholars identified that “confidence intervals for Spearman's $r_S$ are less reliable and interpretable than for Kendall's $\tau$-parameters” (Kendall and Gibson, 1990, p. 47). A recent confirmation has been found in Croux and Dehon (2010, p. 12), where authors indicated that “the Kendall correlation measure is more robust and slightly more efficient than Spearman's rank correlation, making it the preferable estimator.”
Therefore, we decided to select another strategy that orients the analysis toward specific
statistical approaches, such as non-parametric analysis and the use of partial least square.

Finally, we opted for the Partial Least Square - Structural Equation Modeling (i.e., PLS-SEM) regressions to estimate relationships and predictive characteristics of our model. Our analytical approach is considered the most appropriate for the predictive nature of the study and strong enough to test our ideas based on the minimum sample size recommendations among scholars. For a few researchers, the SEM procedure requires a minimum sample size of n = 100-150 (Tinsley and Tinsley, 1987; Anderson and Gerbing, 1988; Ding, Velicer, and Harlow, 1995; Tabachnick and Fidell, 2001); for others, a sample size of n = 200 is the minimum (Boomsma and Hoogland, 2001). A more rigorous view of the minimum sample size is proposed by Kline (2022), where the ratio of observation per parameter should be 10:1. We did not find any missing data in our sample, and multicollinearity has been fixed to propose a better model and thus avoid redundancy (Tabachnick and Fidell, 2007). Our sample's statistical attributes led us to retain the PLS method over the common bootstrapping method found in structural equation modelling (SEM). Among these attributes, we have identified the number of constructs and indicators present in our model, the overall objective of the present study (i.e., to clarify the utilization of smartphone within the online learning setting), the relatively small sample size, and the lack of normality in our sample (Hair et al., 2010).

3.5.1. *A PLS-SEM approach*

A visual specification of our model and hypothesis is provided (see figure 1). They represent
the expected relationships among our endogenous variables (i.e., Procrastination, Smartphone
addiction, and GPA) and exogenous variable (i.e., self-regulation). The model has been
established on the idea that self-regulation is theoretically a variable that foster academic
activities and outcomes. Within our model, self-regulation does not depend on another variable.

This is in contrast to GPA that is influenced by several other indicators as identified in the
literature review.

In other to proceed with the identification our model we made sure that the data collection
would result in a fruitful analysis. Therefore, we specified the use of PLS-SEM and made sure
that required thresholds have been followed such as minimum sample size. PLS-SEM
demonstrates an excellent performance in analyzing small sample sizes along with non-normal
distribution - in other words PLS-SEM approach is a robust method to deviations from normality
(Kock, 2016). SmartPLS software offers a wide range of model-fit measures and respectively
SRMR, NFI (i.e., Normed Fit Index), Chi². The Standardized Root Mean Square Residual (i.e.,
SRMR) assesses the average discrepancy between observed and expected correlations. A good
fit is established when SRMR is less than .08 (Hu and Bentler, 1999). The Normal Fit Index (i.e.,
NFI) compares real Chi² value with a supposed Chi² value. The closer the NFI to 1, the better is
the fit (Lohmöller, 1989). Other fit indices are GFI (Goodness of Fit Index), PGFI (Parsimony
Goodness of Fit) and CFI (Comparative Fit Index). They are deemed acceptable when they are >
.8 and excellent when > .9 (Baumgartner and Homburg, 1995; Doll, Xia, and Torkzadeh, 1994).
Other scholars such as Mulaik et al. (1989) indicated that indices such as PGFI are not concerned
with a threshold and are more useful for model comparison.

Outer loadings, outer weights, standard errors, Joreskog rho, average variance extracted (i.e., VE) composite reliability (i.e., CR), and VIF have also been assessed to attest the overall scales' robustness. Results as shown in Table 1 indicate that the scales show both robustness and a good fit to the data. Factor loading with value less than 0.6 have been deleted (Hair et al., 2010) which
was the case for three items for self-regulation and one item for smartphone addiction (see appendix).

*Insert Table 1 about here*

# 4. Results

## 4.1. *Descriptive and correlation analysis*

We first checked if a gender difference existed in the perceived smartphone use. A between-subject analysis indicated non-significant mean differences between men and women on their propension to spend more or less time on their smartphones. Descriptive statistics are displayed in Table 1 along with correlation's coefficients (i.e., Kendall's tau: rτ).

*Insert Table 2 about here*

As for results, younger students were more likely to have scores that ranked higher on estimated hours spent on smartphone per day ($r_{τ} = -.138, p < .001$), as well as scores that ranked lower on self-regulation ($r_{τ} = .094, p < .001$). Those with lower GPA were more likely to have scores that ranked higher on estimated hours spent on smartphone per day ($r_{τ} = -.147, p < .001$), have scores that ranked higher on procrastination ($r_{τ} = -.137, p < .001$), and lower on self-regulation ($r_{τ} = .167, p < .001$). Students with higher self-regulation scores were more likely to have scores that ranked lower on smartphone addiction ($r_{τ} = -.250, p < .001$), and lower on procrastination ($r_{τ} = -.156, p < .001$), and higher on GPA ($r_{τ} = .167, p < .001$). Students with
higher procrastination level were more likely to have scores that ranked higher on smartphone addiction ($r_{τ} = .372$, p < .001).

## 4.2. Model's estimates

Estimation and reporting of results are the last step in PLS-SEM analysis (Kline, 2022). Using Smart-PLS to estimate goodness-of-fit, we obtained acceptable to very good fit to the data (Hair et al., 2017). Our model depicted in figure 2 obtained the following criteria (refer to path coefficients in parentheses in figure 2): $X^2 = 680.380$, SRMR = .076, NFI = .772; GFI= .807; PGFI= .650; CFI= .831).

Nonetheless, PLS-SEM is also limited as it does not provide a global scalar function. As such, the global goodness of fit (i.e., GoF) does not match the overall PLS-SEM objective. Henseler and Sarstedt (2013) have confirmed that GoF is not a fit measure as indicated by Tenenhaus et al. (2004) rather a useful indicator for a PLS multi-group analysis (PLS-MGA). If the PLS-SEM model is compared to multiple other models then GoF calculation and interpretation is justified (see Hair et al., 2022).

Standardized estimates are presented in figure 2. Hypothesis 2 is partially supported: the relationship between self-regulation and smartphone use indicated a significance at the 95% confidence level ($γ = -.16$). The same conclusion is adopted for Hypothesis 3: we partially confirmed that self-regulation negatively predicted procrastination ($γ = .19$) because of the 95% confidence level. Hypotheses #4, #5, and #6 are significant at the 99% confidence level in the expected direction; thus, we confirmed hypothesis #4, #5, and #6. Respectively, self-regulation predicted GPA ($γ = .23$), procrastination negatively predicted GPA ($γ = -.26$), and positively predicted smartphone use ($γ = .54$).
Hypothesis 1, while being rejected, might be the expression of an interesting relationship among students between the intensive use of their smartphone and their GPA. While we were expecting a negative relationship, we found a positive and significant relationship between smartphone use and GPA (γ = .20).

# 5. Discussion

Smartphones are becoming more pervasive in our everyday life, and while they're omnipresent among the young population, elderly people will not be exempt from its penetration (Deloitte, 2018). The COVID-19 pandemic has forced universities to accelerate their transition to an online teaching method, reenforcing the use of online academic procedures. Our results are useful for an online setting educational system regardless of the forces that have led to such learning choices. Both COVID-19 and post-COVID 19 literatures have been presented. While at home or outside the campus, it is obvious that students are using their smartphones, and our sample indicated an average use of 7 hours a day. Precisely, we found a negative relationship between age and time spent utilizing smartphones, where it was found that younger students spent more time on their smartphones. This first result is relatively similar to the one found in van Deursen, van Dijk, and ten Klooster (2015) among a Dutch sample; slightly less than the half of the respondents perceived themselves as being addicted. On the other hand, since gender did not present any significant results, we decided to exclude this variable from subsequent discussions. Therefore, our study offers a different direction in comparison to others conducted in the Middle East region (Özer, Demir, & Ferrari, 2009; Albursan et al., 2022).

## 5.1. *Teaching and fostering self-regulation*
The results indicated that self-regulation, which is the ability to adapt oneself to new circumstances, or context (Karoly, 1993), had a negative impact on smartphone use (H2) and procrastination (H3). Results were significant at 95% level of confidence and the direction of the relationships were as expected. Considering these results, when students are regulating themselves by setting goals and embracing changes, there are more likely to be fewer procrastinators (Rahmani and Mohammadi, 2017) and smartphone addicts (Soror et al., 2012). In this respect, this study makes an important contribution to the body of literature and allows university counselors and educators to propose workshops or webinars on how to self-regulate in an academic online setting. Indeed, the temptation to procrastinate is even stronger in the comfort of students' own homes and self-regulation appears to be a solution in our study. Consequently, setting up a clear work schedule, avoiding task uncertainty, and planning for interruptions can be considered as solutions to tackle procrastination temptation and foster academic productivity (Nota, Soresi, and Zimmerman, 2004). There is an essential benefit of self-regulation in our study. Hypothesis 4 stipulated that self-regulation was positively related to academic performance (i.e., GPA). Specifically, engaging in the effort of regulating oneself was associated with academic performance (Richardson et al., 2012), and we found similar results in our sample of university students. We enhance the body of literature in this area by providing evidence that self-regulation is also a predictor of academic performance in a new environmental context such as the online academic setting. We therefore offered an extension of the contextual characteristics established by Pintrinch (2004) by signifying that the online academic setting might be one of them. In the same vein, we aimed to close the loop by exploring the impact of procrastination on GPA (H5) and smartphone use (H6). In our sample, procrastination negatively led to lower GPA compared to students who procrastinate less; our results are siding with recent
literature on this topic (Gustavson & Miyake, 2017; Kertechian, 2018). In the same vein, the more a student procrastinates, the more he or she will spend time on his or her smartphone and then become addicted to it (Geng et al., 2018). In addition, the mediating role of procrastination has been established in a study where Instagram addiction predicted academic results in the presence of procrastination only (Pekpazar et al., 2021).

Overall, our findings indicate that self-regulation is a good factor to foster academic outcomes and reduce procrastination and smartphone excessive use.

## 5.2. Addicted to a smartphone but addicted to higher GPAs

Counterintuitive results have been found when analyzing the association between smartphone use and GPA. While studies identified certain findings (e.g., Amez and Baert, 2020) relating to the negative relationship between smartphone use and GPA (Lepp et al. 2015; Hawi and Samaha, 2017; Chen and Yen, 2016; Junco and Cotton, 2012; Levine et al., 2012; Karpinski et al., 2013; Rosen et al., 2013), we found a strong and positive relationship between excessive smartphone use on GPA scores. In other words, the more the student spends times on his or her smartphone, the higher his or her GPA score is. The academic online setting imposed by the COVID-19 regulations might have impacted students to intensively use their smartphone for educational and academic purposes. Students may engage in group activities or share their knowledge on a specific assignment with other students, more easily with the help of a smartphone in the current environment. This result matches a recent study identified in the literature that also provides a basic rationale about this positive relationship; students who have an extensive usage of their smartphone toward more study-related use would obtain higher academic results than those using their phone for non-study-related activities (Abbasi et al., 2021). This result goes against other studies done during the COVID-19 pandemic that have found a negative relationship between
the excessive use of smartphones and academic results (Albursan et al., 2022; Sunday; Adesope,
& Maarhuis, 2021) and specifically in the Middle-East (Alotaibi et al., 2022). In the UAE
context, universities remained closed, but coffee shops, restaurants, malls and other gathering
places remained opened during the pandemic and students had the possibility to meet group
members or other students in these places. Nevertheless, the smartphone use path to GPA
indicated an important magnitude, thus confirming its unambiguous positive effect in our
sample. A single study has found similar results indicating that study-related excessive use of
smartphones would have a positive impact on academic performance (Abassi et al., 2021). To
the best of our knowledge, we did not find other studies indicating that smartphone use strongly
predicts a higher level of GPA. In light of this intriguing result, we provide an added value to
current knowledge and encourage scholars to engage in this direction. Therefore, qualitative
studies might explore the motivation behind the use of smartphones in this unique academic
context. Our study did not support previous studies indicating that smartphone use was
negatively related to academic performance (Albursan et al., 2022; Alotaibi et al., 2022) and we
proposed a renewed approach concerning smartphone use and its impact on academic
performance.

In an online academic setting, it seems that excessive use goes hand-in-hand with academic
results. Additionally, it appears that self-regulation is an important factor on how students can
use their smartphone excessively but positively. The mean for self-regulation in our sample was
higher than the mean of excessive smartphone. Therefore, students can engage in controlling
their behavior or emotions to foster their academic performance. Thus, if the intensive use of
smartphones is governed and organized through educational goals, then students can benefit
from this excessive use for more positive academic outcomes (Abbasi et al., 2021). Therefore,
this type of orientation takes the opposing view of a deteriorated learning process through the
extensive use of smartphones (Sunday, Adesope, & Maarhuis, 2021). To go further, we
explained earlier that self-regulation was an essential characteristic among students to enable
educational productivity, thus, we propose that excessive smartphone use went through a
conceptual shift and became a positive psychological state for students with a higher level of
self-regulation. This latest contribution offers a new theoretical perspective for the association
between smartphone excessive use and academic performance through the presence of self-
regulation. Our literature review provided some indications about this shift because of the
positive relationship between self-regulation and academic achievement (e.g., Estévez et al.,
2021; Iqbal et al., 2021). Therefore, excessive usage of smartphones is possibly positive if
students are engaged in study-related utilizations of their device and demonstrate strong
regulation of their learning process.

Overall, our study suggests that a smartphone might be considered an educative tool for
university students while establishing self-regulation procedures to avoid being in a situation
where smartphone use would have a negative impact on academic performance exclusively.

**6. Limitations and recommendations**

Our study is not without limitations. The sample size was relatively small regarding the
academic population either in UAE or around the world. Consequently, our results could not be
generalized to other cultural contexts or different populations of students. In addition, the results
would have been more reliable if the sample size was larger. That being said, we reached the
minimum sample size recommended by scholars in the case of SEM analysis. One additional
limit of the present model are the fit indices. While the majority of them are considered
acceptable to excellent, the Parsimony Goodness of Fit Index (PGFI) was equal to .650 which is considered as a relatively weak value, but one can consider that this indices is not really relevant within our framework (Mulaik et al., 1989).

Our study has been conducted in the midst of a rapid change towards an online environment. While this was an objective of the present study (i.e., academic performance in an online academic setting), results have been undoubtedly affected. Therefore, results must be compared either with on-campus settings or with other studies including online academic settings, to verify the relationship between smartphone use and academic performance.

More generally, this study used self-reported measures to test our hypothesis, while other research on smartphone addiction used more direct approaches in measurement such as mobile applications (Lin et al., 2015).

We could not distinguish whether the positive impact of smartphone excessive use was explained by the impact of the pandemic or whether students were really engaged in a positive cycle where smartphones are considered devices to help them achieve better grades. Questions on the specific use of smartphones need to be explored and finding solutions in future studies to identify types of activities that students are engaging when using their smartphone would be crucial.

Finally, there are many ways to improve the present model, such as including moderators with the help of individual differences, since the literature shows that personality affects academic performance (Kertechian, 2018).
# Appendix

## Self-Regulation Questionnaire - Schwarzer, Diehl, Gerdamari, and Schmitz, 1999

1. I can concentrate on one activity for a long time, if necessary.

2. If I am distracted from an activity, I don't have any problem coming back to the topic quickly.

3. If an activity arouses my feelings too much, I can calm myself down so that I can continue with the activity soon.

4. If an activity requires a problem-oriented attitude, I can control my feelings.

5. It is difficult for me to suppress thoughts that interfere with what I need to do.

6. I can control my thoughts from distracting me from the task at hand.

7. When I worry about something, I cannot concentrate on an activity.

8. After an interruption, I don't have any problem resuming my concentrated style of working.

9. I have a whole bunch of thoughts and feelings that interfere with my ability to work in a focused way.

10. I stay focused on my goal and don't allow anything to distract me from my plan of action.

## Smartphone Addiction Scale Short Version - Kwon, Kim and Young (2013)

1. Missing planned work due to smartphone use

2. Having a hard time concentrating in online class, while doing assignments, or while working due to smartphone use.

3. Feeling pain in the wrists or at the back of the neck while using a smartphone.

4. Will not be able to stand not having a smartphone.

5. Feeling impatient and fretful when I am not holding my smartphone.

6. Having my smartphone in my mind even when I am not using it.

7. I will never give up using my smartphone even when my daily life is already greatly affected by it.

8. Constantly checking my smartphone so as not to miss conversations between other people on Twitter or Facebook.

9. Using my smartphone longer than I had intended.

10. The people around me tell me that I use my smartphone too much.

## Procrastination questionnaire Short Version - McCloskey (2011)

1. I put off projects until the last minute.

2. I know I should work on assignment/course work, but I just don't do it.

3. I get distracted by other, more fun, things when I am supposed to work on assignment/course work.

4. When given an assignment/course work, I usually put it away and forget about it until it is due.

5. I frequently find myself putting important deadlines off.

Nb: Items in bold have not been retained in the analysis
<table><caption>**Table 1.** Construct and discriminant validity (i.e., Factor's loading, Joreskog's rho, AVE, CR, VIF and HTMT)</caption><thead><tr><th>Scales Items</th><th>Outer Loadings</th><th>Outer Weights</th><th>Standard Errors</th><th>Joreskog's rho</th><th>Average Variance Extracted</th><th>Composite Reliability</th><th>VIF</th></tr></thead><tbody><tr><td>Self-regulation</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item1</td><td>.64</td><td>.18</td><td>.07</td><td>.83</td><td>.50</td><td>.87</td><td><strong>1.401</strong></td></tr><tr><td>Item2</td><td>.74</td><td>.23</td><td>.06</td><td></td><td></td><td></td><td><strong>1.808</strong></td></tr><tr><td>Item3</td><td>.71</td><td>.17</td><td>.06</td><td></td><td></td><td></td><td><strong>1.827</strong></td></tr><tr><td>Item4</td><td>.67</td><td>.21</td><td>.06</td><td></td><td></td><td></td><td><strong>1.625</strong></td></tr><tr><td>Item5</td><td>.74</td><td>.22</td><td>.06</td><td></td><td></td><td></td><td><strong>1.644</strong></td></tr><tr><td>Item6</td><td>.72</td><td>.18</td><td>.05</td><td></td><td></td><td></td><td><strong>1.635</strong></td></tr><tr><td>Item7</td><td>.69</td><td>.18</td><td>.05</td><td></td><td></td><td></td><td><strong>1.438</strong></td></tr><tr><td>Smartphone addiction</td><td></td><td></td><td></td><td>.89</td><td>.51</td><td>.90</td><td></td></tr><tr><td>Item1</td><td>.72</td><td>.20</td><td>.09</td><td></td><td></td><td></td><td><strong>1.891</strong></td></tr><tr><td>Item2</td><td>.76</td><td>.22</td><td>.09</td><td></td><td></td><td></td><td><strong>2.149</strong></td></tr><tr><td>Item3</td><td>.63</td><td>.10</td><td>.12</td><td></td><td></td><td></td><td><strong>1.571</strong></td></tr><tr><td>Item4</td><td>.57</td><td>.10</td><td>.11</td><td></td><td></td><td></td><td><strong>1.550</strong></td></tr><tr><td>Item5</td><td>.72</td><td>.12</td><td>.09</td><td></td><td></td><td></td><td><strong>2.198</strong></td></tr><tr><td>Item6</td><td>.75</td><td>.16</td><td>.07</td><td></td><td></td><td></td><td><strong>2.225</strong></td></tr><tr><td>Item7</td><td>.67</td><td>.15</td><td>.07</td><td></td><td></td><td></td><td><strong>1.575</strong></td></tr><tr><td>Item8</td><td>.76</td><td>.17</td><td>.08</td><td></td><td></td><td></td><td><strong>1.869</strong></td></tr><tr><td>Item9</td><td>.77</td><td>.15</td><td>.07</td><td></td><td></td><td></td><td><strong>2.006</strong></td></tr><tr><td>Procrastination</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item1</td><td>.83</td><td>.22</td><td>.07</td><td>.91</td><td>.73</td><td>.93</td><td><strong>2.242</strong></td></tr><tr><td>Item2</td><td>.89</td><td>.22</td><td>.05</td><td></td><td></td><td></td><td><strong>3.008</strong></td></tr><tr><td>Item3</td><td>.84</td><td>.25</td><td>.07</td><td></td><td></td><td></td><td><strong>2.187</strong></td></tr><tr><td>Item4</td><td>.85</td><td>.21</td><td>.06</td><td></td><td></td><td></td><td><strong>2.586</strong></td></tr><tr><td>Item5</td><td>.88</td><td>.27</td><td>.06</td><td></td><td></td><td></td><td><strong>2.779</strong></td></tr></tbody><tfoot><tr><td colspan="8">Heterotrait-monotrait ratio (HTMT)</td></tr><tr><td colspan="8">Procrastination – GPA = .20</td></tr><tr><td colspan="8">Smartphone Addiction – GPA = .11</td></tr><tr><td colspan="8">Smartphone Addiction – Procrastination = .60</td></tr><tr><td colspan="8">Self-regulation – GPA = .25</td></tr><tr><td colspan="8">Self-regulation – Procrastination = .22</td></tr><tr><td colspan="8">Self-regulation – Smartphone Addiction = .30</td></tr></tfoot></table>

Note: 1) Standard error for self-regulation is .07; standard error for procrastination is .14; standard error for smartphone addiction is .09. 2) three items from self-regulation and one item
from smartphone addiction have been removed from the final model because of a not reaching factor loading thresholds (Hair et al., 2010).

<table><caption>**Table 2.** Correlation analysis ($r_t$) and descriptive statistics</caption><thead><tr><th>Variables</th><th>Mean (SD)</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>Age (1)</td><td>23.55 (3.95)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GPA (2)</td><td>3.28 (.44)</td><td>.060</td><td></td><td></td><td></td><td></td></tr><tr><td>Hours per day (3)</td><td>7.20 (3.27)</td><td><strong>-.138</strong></td><td><strong>-.147</strong></td><td></td><td></td><td></td></tr><tr><td>Self-regulation (4)</td><td>3.70 (.71)</td><td><strong>.094</strong></td><td><strong>.167</strong></td><td>-.091</td><td></td><td></td></tr><tr><td>Smartphone addiction (5)</td><td>2.87 (.92)</td><td>-.016</td><td>-.008</td><td>.051</td><td><strong>-.250</strong></td><td></td></tr><tr><td>Procrastination (6)</td><td>2.74 (1.11)</td><td>.016</td><td><strong>-.137</strong></td><td>.067</td><td><strong>-.156</strong></td><td><strong>.372</strong></td></tr></tbody></table>

Nb: Items in bold denote a significant at .01 level; $r_t$ = Kendall's Tau; GPA = Grade Point Average; Hours per Day = Number of hours spent on smartphone in a day

<figure><img src="image_2.png" /><figcaption>**Figure 2.** Path analysis</figcaption></figure>
<figure><img src="image_3.png" /></figure>

## References

Abbasi, G. A., Jagaveeran, M., Goh, Y-N., & Tariq, B. (2021). The impact of type of content use on smartphone addiction and academic performance: Physical activity as moderator.

*Technology in Society, 64*, 101521.

https://doi.org/10.1016/j.techsoc.2020.101521

Albursan, I. S., Al. Qudah, M. F., Al-Barashdi, H. S., Bakhiet, S. F., Darandari, E., Al-Asqah, S. S., Hammad, H. I., et al. (2022). Smartphone Addiction among University Students in Light of the COVID-19 Pandemic: Prevalence, Relationship to Academic

Procrastination, Quality of Life, Gender and Educational Stage. *International Journal of Environmental Research and Public Health*, 19(16), 10439.

http://dx.doi.org/10.3390/ijerph191610439

Alotaibi, M. S., Fox, M., Coman, R., Ratan, Z. A., & Hosseinzadeh, H. (2022). Smartphone Addiction Prevalence and Its Association on Academic Performance, Physical Health, and Mental Well-Being among University Students in Umm Al-Qura University (UQU), Saudi Arabia. *International Journal of Environmental Research and Public Health*,

19(6), 3710.
http://dx.doi.org/10.3390/ijerph19063710

Ameen, N., Willis, R., & Shah, M. H. (2018). An examination of the gender gap in smartphone adoption and use in Arab countries: A cross-national study. *Computers in Human Behavior, 89*, 148-162.

https://doi.org/10.1016/j.chb.2018.07.045

Amez, S., & Baert, S. (2020). Smartphone use and academic performance: A literature review.
*International Journal of Educational Research, 103*, 101618.

https://doi.org/10.1016/j.ijer.2020.101618

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. *Psychological Bulletin, 103*(3), 411-423.

Aremu, A. O., Williams, T. M., & Adesina, F. T. (2011). Influence of academic procrastination and personality types on academic achievement and efficacy of in-school adolescents in Ibadan. *IFE PsychologIA, 19*(1), Article 1. https://doi.org/10.4314/ifep.v19i1.64591

Baert, S., Omey, E., Verhaest, D., & Vermeir, A. (2015). Mister Sandman, bring me good marks!

On the relationship between sleep quality and academic achievement. *Social Science & Medicine (1982), 130*, 91–98. https://doi.org/10.1016/j.socscimed.2015.02.011

Balkis, M. (2013). Academic procrastination, academic life satisfaction and academic achievement: The mediation role of rational beliefs about studying. *Journal of Cognitive and Behavioral Psychotherapies, 13*(1), 57-74.

Baumgartner, H., & Homburg, C. (1996). Applications of Structural Equation Modeling in Marketing and Consumer Research: a review. *International Journal of Research in Marketing, 13*(2), 139-161.

Bidjerano, T., & Dai, D. Y. (2007). The relationship between the big-five model of personality
and self-regulated learning strategies. *Learning and Individual Differences*, 17(1), 69–81.
https://doi.org/10.1016/j.lindif.2007.02.001

Bono, R., Blanca, M. J., Arnau, J., & Gómez-Benito, J. (2017). Non-normal distributions commonly used in health, education, and social sciences: A systematic review. *Frontiers in Psychology, 8*, Article 1602. https://doi.org/10.3389/fpsyg.2017.01602

Boomsma, A., & Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. *Structural Equation Modeling: A Multidisciplinary Journal*, 8(4), 535-569.

Box, G. E., & Watson, G. S. (1962). Robustness to non-normality of regression tests. *Biometrika*, 49(1-2), 93-106.

Brando-Garrido, C., Montes-Hidalgo, J., Limonero, J. T., Gómez-Romero, M. J., & Tomás-Sábado, J. (2020). Academic procrastination in nursing students. Spanish adaptation of the Academic Procrastination Scale-Short Form (APS-SF). *Enfermería Clínica (English Edition)*, 30(6), 371-376. https://doi.org/10.1016/j.enfcle.2020.02.003

Brinthaupt, T. M., & Shin, C. M. (2001). The relationship of academic cramming to flow experience. *College Student Journal*, 35(3), 457-471.

Byrne, B.M. (2010). *Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming*. Taylor and Francis Group Publication

Chen, Q., & Yan, Z. (2016). Does multitasking with mobile phones affect learning? A review. *Computers in Human Behavior*, 54, 34–42. https://doi.org/10.1016/j.chb.2015.07.047

Ching, S.M., Yee, A., Ramachandran, V., Sazlly Lim, S.M., Wan Sulaiman, W.A., Foo, Y.L., & Foo, W.A. (2015) Validation of a Malay Version of the Smartphone Addiction Scale among Medical Students in Malaysia. *PLoS ONE* 10(10): e0139337.
https://doi.org/10.1371/journal.pone.0139337
Christensen, M. A., Bettencourt, L., Kaye, L., Moturu, S. T., Nguyen, K. T., Olgin, J. E.,
Pletcher, M. J., & Marcus, G. M. (2016). Direct Measurements of Smartphone Screen-
Time: Relationships with Demographics and Sleep. *PLOS ONE*, 11(11), e0165331.
https://doi.org/10.1371/journal.pone.0165331

Cohen, J. (1992). A power primer. *Psychological Bulletin*, 112(1), 155-159.
https://doi.org/10.1037/0033-2909.112.1.155

Cohen, R. J., & Swerdlik, M. E. (2005). *Psychological Testing and Assessment: An Introduction to Tests and Measurement* (6th ed.). New York: McGraw-Hill.

Croux, C., & Dehon, C. (2010). Influence functions of the Spearman and Kendall correlation measures. *Statistical Methods & Applications*, 19(4), 497-515.

Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020).
Implications for educational practice of the science of learning and development. *Applied
Developmental Science*, 24(2), 97-140. https://doi.org/10.1080/10888691.2018.1537791

Deloitte. (2018). *Global outsourcing survey: The rise of the digital core*. Retrieved from
https://www2.deloitte.com/content/dam/Deloitte/sg/Documents/consulting/sea-cons-
global-outsourcing-survey-2018-noexp.pdf

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

Demirci, K., Orhan, H., Demirdas, A., Akpinar, A., & Sert, H. (2014) Validity and Reliability of the Turkish Version of the Smartphone Addiction Scale in a Younger Population. *Klinik Psikofarmakoloji Bülteni-Bulletin of Clinical Psychopharmacology*, 24(3), 226-234, https://doi.org/10.5455/bcp.20140710040824
Diehl, M., Semegon, A. B., & Schwarzer, R. (2006). Assessing Attention Control in Goal Pursuit: A Component of Dispositional Self-Regulation. *Journal of Personality Assessment, 86*(3), 306–317. https://doi.org/10.1207/s15327752jpa8603_06

Ding, L., Velicer, W. F., & Harlow, L. L. (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. *Structural Equation Modeling: A Multidisciplinary Journal, 2*(2), 119-143.

Doll, W.J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument, *MIS Quarterly, 18*(4), 357–369.

Domoff, S. E., Foley, R. P., & Ferkel, R. (2020). Addictive phone use and academic performance in adolescents. *Human Behavior & Emerging Technologies, 2*(1), 33-38.
https://doi.org/10.1002/hbe2.171

Dörrenbächer, L., & Perels, F. (2016). Entwicklung einer digitalen Lernwerkstatt zum selbstregulierten Lernen. 1.

Eerde, W. V. (2003). Procrastination at Work and Time Management Training. *The Journal of Psychology, 137*(5), 421–434. https://doi.org/10.1080/00223980309600625

Eilam, B., Zeidner, M., & Aharon, I. (2009). Student conscientiousness, self-regulated learning, and science achievement: An explorative field study. *Psychology in the Schools, 46*(5), 420–432. https://doi.org/10.1002/pits.20387

Estévez, I., Rodríguez-Llorente, C., Piñeiro, I., González-Suárez, R., & Valle, A. (2021). School Engagement, Academic Achievement, and Self-Regulated Learning. *Sustainability, 13*(6), 3011.
http://dx.doi.org/10.3390/su13063011

Euler Hermes (2018). UAE is the most digital-friendly country in the Middle-East, available at
https://www.khaleejtimes.com/business/uae-is-the-most-digital-friendly-country-in-the-middle-east, accessed in July 2022.

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. *Behavior Research Methods, 41*, 1149-1160

http://doi.org/10.3758/BRM.41.4.1149

Ferrari, J., Johnson, J., & McCown, W. (1995). *Procrastination and Task Avoidance—Theory, Research and Treatment*. https://doi.org/10.1007/978-1-4899-0227-6

Fowler, F. J. (2013). *Survey Research Methods*. Fifth Edition. Sage, Boston, USA.

Galambos, N. L., Vargas Lascano, D. I., Howard, A. L., & Maggs, J. L. (2013). Who sleeps best? Longitudinal patterns and covariates of change in sleep quantity, quality, and timing across four university years. *Behavioral Sleep Medicine, 11*(1), 8-22.

https://doi.org/10.1080/15402002.2011.596234

Geng, J., Han, L., Gao, F., Jou, M., & Huang, C.-C. (2018). Internet addiction and procrastination among Chinese young adults: A moderated mediation model. *Computers in Human Behavior, 84*, 320-333. https://doi.org/10.1016/j.chb.2018.03.013

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

Gustavson, D. E., & Miyake, A. (2017). Academic procrastination and goal accomplishment: A combined experimental and individual differences investigation. *Learning and Individual Differences, 54*, 160-172. https://doi.org/10.1016/j.lindif.2017.01.010
Hair, J., Black, W.C., Babin, B. J., & Anderson, R.E. (2010). Multivariate Data Analysis (7th Edition). NJ: Prentice-Hall Publication.

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2022). *A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)*, 3rd Ed., Sage: Thousand Oaks.

Harasim, L. (2000). Shift happens online education as a new paradigm in learning. *The Internet and Higher Education*, 3(1-2), 41-61. https://doi.org/10.1016/S1096-7516(00)00032-4

Hawi, N. S., & Samaha, M. (2017). The Relations Among Social Media Addiction, Self-Esteem, and Life Satisfaction in University Students. *Social Science Computer Review*, 35(5), 576-586. https://doi.org/10.1177/0894439316660340

Henseler, J., & Sarstedt, M. (2013). Goodness-of-Fit Indices for Partial Least Squares Path Modeling. *Computational Statistics*, 28(2), 565-580.

Hosseini, F., & Khayyer, M. (2009). Prediction of Behavioral and Decisional Procrastination Considering Meta-Cognition Beliefs in University Students. *Iranian Journal of Psychiatry and Clinical Psychology*, 15(3), 265-273.

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.

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

Iqbal, J., Qureshi, N., Ashraf, M. A., Rasool, S. F., & Asghar, M. Z. (2021). The Effect of Emotional Intelligence and Academic Social Networking Sites on Academic Performance During the COVID-19 Pandemic. *Psychology research and behavior*
management, 14, 905–920.

https://doi.org/10.2147/PRBM.S316664

Junco, R., & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. *Computers & Education, 59*(2), 505–514.

https://doi.org/10.1016/j.compedu.2011.12.023

Kandemir, M. (2014). Reasons of Academic Procrastination: Self-regulation, Academic Self-efficacy, Life Satisfaction and Demographics Variables. *Procedia - Social and Behavioral Sciences, 152*, 188–193. https://doi.org/10.1016/j.sbspro.2014.09.179

Karoly, P. (1993). Mechanisms of Self-Regulation: A Systems View. *Annual Review of Psychology, 44*(1), 23–52. https://doi.org/10.1146/annurev.ps.44.020193.000323

Karpinski, A. C., Kirschner, P. A., Ozer, I., Mellott, J. A., & Ochwo, P. (2013). An exploration of social networking site use, multitasking, and academic performance among United States and European university students. *Computers in Human Behavior, 29*(3), 1182–1192. https://doi.org/10.1016/j.chb.2012.10.011

Kendall, M. G., & Gibson, D. G. (1990). *Rank correlation methods* (5th ed.). London, England: Edward Arnold.

Kertechian, S. K. (2018). Conscientiousness as a key to success for academic achievement among French university students enrolled in management studies. *The International Journal of Management Education, 16*(2), 154–165.

https://doi.org/10.1016/j.ijme.2018.02.003

Kim, K. R., & Seo, E. H. (2015). The relationship between procrastination and academic performance: A meta-analysis. *Personality and Individual Differences, 82*, 26–33.

https://doi.org/10.1016/j.paid.2015.02.038
Kline, R.B. (2022). *Principles and practice of Structural Equation Modeling*, 4th Edition, The Guilford Press, New York-London.

Klingsieck, K. B. (2013). *Procrastination: When Good Things Don't Come to Those Who Wait*. *European Psychologist*, 18(1), 24–34. https://doi.org/10.1027/1016-9040/a000138

Kock, N. (2016). *WarpPLS 6.0 User Manual*. ScriptWarp Systems.

Komarraju, M., & Nadler, D. (2013). *Self-efficacy and academic achievement: Why do implicit beliefs, goals, and effort regulation matter?* *Learning and Individual Differences*, 25, 67–72. https://doi.org/10.1016/j.lindif.2013.01.005

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), e83558. https://doi.org/10.1371/journal.pone.0083558

Lantz B. (2013). *The impact of sample non-normality on ANOVA and alternative methods*. *The British journal of mathematical and statistical psychology*, 66(2), 224–244.
https://doi.org/10.1111/j.2044-8317.2012.02047.x

Lepp, A., Li, J., Barkley, J. E., & Salehi-Esfahani, S. (2015). Exploring the relationships between college students' cell phone use, personality and leisure. *Computers in Human Behavior*, 43, 210–219. https://doi.org/10.1016/j.chb.2014.11.006

Levine, L., Waite, B., & Bowman, L. L. (2012). *Mobile Media Use, Multitasking and Distractibility*. *Int. J. Cyber Behav. Psychol. Learn.*
https://doi.org/10.4018/ijcbpl.2012070102

Lin, W.-C., Huang, Y.-C., Chen, Y.-J., & Lin, T.-S. (2015). Examining the impact of system quality, information quality, and service quality on user satisfaction and continuance intention to use mobile applications. *Social Behavior & Personality: An International
*Journal*, 43(1), 17-26.

Lohmöller, J. B. (1989). *Latent variable path modeling with partial least squares*. Berlin, Germany: Springer Science & Business Media.

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

McCloskey, J. D. (2011). *Finally, my thesis on academic procrastination* (Master's thesis).

Retrieved from ProQuest, UMI Dissertations Publishing. (1506326)

Mohsenzadeh, F., Jahanbakhshi, Z., Keshavarzafshar, H., Eftari, S., & Goudarzi, R. (2016). *The role of fear of failure and personality characteristics in anticipating academic procrastination*. 5(2), 92-108.

Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989).

Evaluation of goodness-of-fit indices for structural equation models. *Psychological Bulletin*, 105(3), 430-445. https://doi.org/10.1037/0033-2909.105.3.430

Newson, R. (2002). Parameters behind "Nonparametric" Statistics: Kendall's tau, Somers' D and Median Differences. *The Stata Journal*, 2(1), 45-64

Nota, L., Soresi, S., & Zimmerman, B. J. (2004). Self-regulation and academic achievement and resilience: A longitudinal study. *International Journal of Educational Research*, 41(3), 198-215.

Odaci, H. (2011). Academic self-efficacy and academic procrastination as predictors of problematic internet use in university students. *Computers & Education*, 57(1), 1109-1113. https://doi.org/10.1016/j.compedu.2011.01.005
Özer, B. U., Demir, A., & Ferrari, J. R. (2009). Exploring Academic Procrastination Among

Turkish Students: Possible Gender Differences in Prevalence and Reasons. *The Journal of Social Psychology, 149*(2), 241–257. https://doi.org/10.3200/SOCP.149.2.241-257

Panek, E., Khang, H., Liu, Y., & Chae, Y.-G. (2018). Profiles of problematic smartphone users: A comparison of South Korean and US college students. *Korea Observer, 49*(3), 437–464.

Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. *Computers in Human Behavior, 28*(6), 2117–2127.

https://doi.org/10.1016/j.chb.2012.06.016

Pekpazar, A., Aydin, G. A., Aydin, U., Beyhan, H., & Ari, E. (2021). Role of Instagram

addiction on academic performance among Turkish university students: mediating effect of procrastination. *Computers and Education Open, 2*, 100049.

https://doi.org/10.1016/j.caeo.2021.100049

Pintrinch, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. *Educational Psychology Review, 16*(4), 385-407.

Rahmani, S., & Mohammadi, S. (2017). The Role Positive and Negative Affect, Fear of failure and Goal achievement orientation in Predicting Academic Procrastination amongst Students. *Knowledge & Research in Applied Psychology, 18*(1).

Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university

students' academic performance: A systematic review and meta-analysis. *Psychological Bulletin, 138*(2), 353–387. https://doi.org/10.1037/a0026838

Rosen, L., Carrier, L. M., Miller, A., Rokkum, J., & Ruiz, A. (2016). Sleeping with technology: Cognitive, affective, and technology usage predictors of sleep problems among college
students. *Sleep Health*, 2(1), 49–56. https://doi.org/10.1016/j.sleh.2015.11.003

Rosen, L. D., Mark Carrier, L., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. *Computers in Human Behavior*, 29(3), 948–958. https://doi.org/10.1016/j.chb.2012.12.001

Rozgonjuk, D., Levine, J. C., Hall, B. J., & Elhai, J. D. (2018). The association between problematic smartphone use, depression and anxiety symptom severity, and objectively measured smartphone use over one week. *Computers in Human Behavior*, 87, 10–17. https://doi.org/10.1016/j.chb.2018.05.019

Ryan, T., Reece, J., Chester, A., & Xenos, S. (2016). Who gets hooked on Facebook? An exploratory typology of problematic Facebook users. *Cyberpsychology: Journal of Psychosocial Research on Cyberspace*, 10(3), Article 3. https://doi.org/10.5817/CP2016-3-4

Schouwenburg, H. C., Lay, C. H., Pychyl, T. A., & Ferrari, J. R. (2004). *Counseling the procrastinator in academic settings* (pp. xiii, 250). American Psychological Association. https://doi.org/10.1037/10808-000

Schraw, G., Wadkins, T., & Olafson, L. (2007). Doing the things we do: A grounded theory of academic procrastination. *Journal of Educational Psychology*, 99(1), 12–25. https://doi.org/10.1037/0022-0663.99.1.12

Schwarzer, R., Diehl, M., & Schmitz, G. (1999). *Self-Regulation questionnaire*. http://userpage.fu-berlin.de/~health/selfreg_e.htm

Sebire, K. The coronavirus lockdown is forcing us to view ‘screen time’ differently. That’s a good thing. The Conversation; 2020. Retrieved January 5, 2022 from https://www.theconversationcom/the-coronavirus-lockdown-is-forcing-us-to-view-
screen-time-differentlythats-a-good-thing-135641

Sitzmann, T., & Ely, K. (2011). A meta-analysis of self-regulated learning in work-related training and educational attainment: What we know and where we need to go.

*Psychological Bulletin*, **137**(3), 421–442. https://doi.org/10.1037/a0022777

Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. *Journal of Counseling Psychology, 31*(4), 503–509.

https://doi.org/10.1037/0022-0167.31.4.503

Soror, A. A., Steelman, Z. R., & Limayem, M. (2012). Discipline Yourself Before Life Disciplines You: Deficient Self-Regulation and Mobile Phone Unregulated Use. *2012 45th Hawaii International Conference on System Sciences*, 849–858.

https://doi.org/10.1109/HICSS.2012.219

Statista. (March 6, 2023). Number of smartphone users in the United Arab Emirates from 2013 to 2028 (in millions) [Graph]. In Statista. Retrieved April 11, 2023, from

https://www.statista.com/forecasts/1143757/smartphone-users-in-the-united-arab-emirates?locale=en

Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. *Psychological Bulletin, 133*(1), 65–94.

https://doi.org/10.1037/0033-2909.133.1.65

Sunday, L. J., Adesope, O. O., & Maarhuis, P. L. (2021). The effects of smartphone addiction on learning: A meta-analysis. *Computers in Human Behavior Reports, 4*, 100114.

https://doi.org/10.1016/j.chbr.2021.100114.

Tabachnick, B. G., & Fidell, L. S. (2001). *Using multivariate statistics* (4th ed.). Boston, MA: Allyn & Bacon.
Tenenhaus, M., Amato, S., & Esposito Vinzi, V. (2004). A Global Goodness-of-Fit Index for PLS Structural Equation Modeling, Proceedings of the XLII SIS Scientific Meeting.
Padova: CLEUP, 739-742.

Thomée, S., Härenstam, A., & Hagberg, M. (2011). Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults—A prospective cohort study. *BMC Public Health, 11*, 66. https://doi.org/10.1186/1471-2458-11-66

Tinsley, H. E., & Tinsley, D. J. (1987). Uses of factor analysis in counseling psychology research. *Journal of Counseling Psychology, 34*(4), 414-424.

van Deursen, A. J. A. M., van Dijk, J. A. G. M., & ten Klooster, P. M. (2015). Increasing inequalities in what we do online: A longitudinal cross sectional analysis of Internet activities among the Dutch population (2010 to 2013) over gender, age, education, and income. *Telematics and Informatics, 32*(2), 259-272.
https://doi.org/10.1016/j.tele.2014.09.003

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

Yang, Z., Asbury, K., & Griffiths, M. D. (2019). An Exploration of Problematic Smartphone Use among Chinese University Students: Associations with Academic Anxiety, Academic Procrastination, Self-Regulation and Subjective Wellbeing. *International Journal of Mental Health and Addiction, 17*(3), 596-614. https://doi.org/10.1007/s11469-018-9961-1

Yao, N., Wang, Q. (2022). Technostress from Smartphone Use and Its Impact on University Students' Sleep Quality and Academic Performance. *Asia-Pacific Education Researcher.*
https://doi.org/10.1007/s40299-022-00654-5

Yavuzalp, N., Bahcivan, E. A (2021). Structural equation modeling analysis of relationships among university students' readiness for e-learning, self-regulation skills, satisfaction, and academic achievement. *Research and Practice in Technology Enhanced Learning*, 16.

https://doi.org/10.1186/s41039-021-00162-y

Yockey, R. D. (2016). Validation of the Short Form of the Academic Procrastination Scale.

*Psychological Reports*, **118**(1), 171–179. https://doi.org/10.1177/0033294115626825

YouGov. (2018). *YouGov | More than a fourth of UAE residents are unwilling to travel until a Covid cure is available*. https://mena.yougov.com/en/news/2020/11/12/more-fourth-uae-residents-are-unwilling-travel-unt/