Next Article in Journal
Biosorption Characteristics of Hg(II) from Aqueous Solution by the Biopolymer from Waste Activated Sludge
Next Article in Special Issue
Internet Gaming Disorder Clustering Based on Personality Traits in Adolescents, and Its Relation with Comorbid Psychological Symptoms
Previous Article in Journal
Involuntary Pregnancy Loss and Nursing Care: A Meta-Ethnography
Previous Article in Special Issue
Problematic Smartphone Use and Mental Health in Chinese Adults: A Population-Based Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 17
Issue 5
10.3390/ijerph17051487
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Parental Monitoring and Adolescent Problematic Mobile Phone Use: The Mediating Role of Escape Motivation and the Moderating Role of Shyness

by
Xinchen Fu
1,
Jingxuan Liu
2,
Ru-De Liu
1,*,
Yi Ding
3,
Jia Wang
4,
Rui Zhen
5 and
Fangkai Jin
1
1
Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
2
Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, USA
3
Graduate School of Education, Fordham University, New York, NY 10023, USA
4
Teachers’ College, Beijing Union University, Beijing 100011, China
5
Institute of Psychological Sciences, College of Education, Hangzhou Normal University, No. 2318 Yuhangtang Road, Hangzhou 311121, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(5), 1487; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051487
Submission received: 5 February 2020 / Revised: 19 February 2020 / Accepted: 24 February 2020 / Published: 25 February 2020
(This article belongs to the Special Issue Internet and Smartphone Use-Related Addiction Health Problems: Treatment, Education and Research)
Browse Figures
Review Reports Versions Notes

Abstract

:
In an attempt to reduce the negative consequences of adolescent media use, parents often monitor their children’s online activities. However, research suggests that parental monitoring often does not reduce children’s problematic mobile phone use as expected. Based on the results of a survey of 584 Chinese adolescents, we found that parental monitoring positively predicted children’s problematic mobile phone use (PMPU) within a Chinese cultural context. The results also showed that children’s escape motivation partially mediated this relationship, while their level of shyness moderated both the mediated path and the direct impact of parental monitoring on children’s PMPU. The findings suggested that a higher level of shyness increased the likelihood that parental monitoring would increase the child’s escape motivation and PMPU. The study results provide guidelines for parents and educators regarding interventions for adolescents’ problematic phone use.

1. Introduction

Mobile phones are the most common platform used by adolescents to access the internet. In Europe, adolescents use mobile phones more frequently than they do computers to access online information [1]. In China, 98.6% of internet users, or 817 million people, of whom 18% are adolescents, connect to the internet through mobile phones [2]. Some common internet activities adolescents engage in via mobile phones are socializing [3,4], accessing entertainment [5,6], and obtaining study resources [7].
Because mobile phones offer such a wide range of attractions, some adolescents overuse their mobile phones, which can lead to problematic mobile phone use (PMPU) [8,9]. PMPU, also known as mobile phone dependency [10] and mobile phone addiction [11], can elicit unpleasant withdrawal symptoms when an individual’s mobile phone access is restricted. Studies have found that adolescents are more prone to mobile phone addiction than adults [12,13].
Considering the wide range of negative health and psychological consequences of PMPU for adolescents [14,15,16], parents often mediate their child’s mobile phone use in an attempt to reduce PMPU. One type of mediation strategy used frequently by parents, parental monitoring, has drawn wide social attention [17]. Parental monitoring refers to parents’ tracking of children’s online activities [18] and is often thought by parents to be an effective way to prevent their child’s PMPU. However, research has shown that parental monitoring may not reduce adolescent PMPU behaviors as expected [8,19,20]. Therefore, the current study aimed to further explore the impact of parental monitoring on children’s problematic mobile phone use and to investigate the mechanism underlying such an impact in the Chinese cultural context.

1.1. Parental Monitoring and Problematic Mobile Phone Use (PMPU)

PMPU is a type of behavioral addiction [21,22]. It can produce a series of negative health and psychological consequences, such as sleep problems and physical harm [16,23]; decreased level of physical activity [24,25]; social problems [16]; decreased life satisfaction [26]; academic problems [15,27,28,29]; anxiety when separated from mobile phones [30,31]; negative emotions including stress, anxiety, and depression [14,32]; alexithymia [33,34]; and decreased empathy [26].
In an attempt to reduce the negative effects of children’s media use, parents often adopt different mediation strategies to influence their child’s media-use behaviors [35]. Parental mediation is a multi-dimensional concept. It encompasses all types of parental strategies, including mediation, controlling, and providing instruction and interpretation regarding media content, to reduce the negative effect of media use on children [36]. Parental mediation was originally divided into three dimensions: restrictive mediation, active mediation, and co-use [37]. These three dimensions were found to be present in parental mediation of children’s TV viewing [38,39], gaming [40], and internet use behaviors [35]. However, as internet use became more prevalent among adolescents, researchers suggested that the traditional three dimensions needed to be expanded to further address parental strategies regarding children’s internet use behaviors [17,41,42]. Nikken and Jansz [18] proposed a five-dimensional model, in which the restrictive mediation dimension was divided into time restriction and special content restriction, and supervision (i.e., monitoring) was added as a new dimension. Parental monitoring refers to parents’ tracking of their child’s online activities and history, such as email interactions and website access [17,18,20].
Parental monitoring is the most common mediation strategy for adolescent internet use [43] and can predict adolescent internet-use behaviors [44]. Yet surprisingly, many studies have found that parental monitoring is ineffective in reducing those problematic behaviors. Livingstone and Helsper [17] found that parental monitoring failed to reduce risky online behaviors (such as porn, violence, and privacy viewing) in children aged 9–16 years; a follow-up study showed that parental monitoring was positively correlated with increased internet-related risk [45]. Studies also found that parental monitoring positively predicted children’s internet-use behaviors [44] and adolescent internet addiction [46]. In addition, cross-cultural research conducted by Bayraktar [47] found that whereas parental monitoring was negatively correlated with adolescent risky internet-use behaviors (such as porn, viewing violence and excessive internet use) in Europe, it positively predicted adolescent involvement in such risky behaviors in Turkey. With increased adolescent access to the internet via mobile phones, parental monitoring of internet use has evolved to monitoring of internet-related mobile phone-use behaviors [48]. A study conducted in Germany [19] found the correlation between parental monitoring and adolescent mobile phone dependency to be insignificant: Parental monitoring was inefficient in reducing the negative effects of children’s PMPU. Similar results were obtained in Taiwan [8]. These study results suggested that parental monitoring may not reduce children’s PMPU. The first goal of our study was to examine whether parental monitoring was related to or positively predicted adolescent PMPU.

1.2. Mediating Role of Escape Motivation

Parental monitoring refers to parental supervision of children’s internet use behaviors with the aim to reduce the negative impacts of media use [48]. Why would parental monitoring perpetuate children’s PMPU behaviors instead of reducing them? The underlying mechanism warrants further investigation. Based on the existing theories and study results, we hypothesized that escape motivation might mediate the path between parental monitoring and children’s PMPU.
Within the context of adolescent PMPU, escape motivation refers to the motivation that drives adolescents to escape negative emotions via mobile phone use, leading to PMPU [49]. Escaping reality is one of two major functions of PMPU [50]. In many cases, the underlying motivation of addictive behaviors is to escape reality in an effort to reduce painful and negative emotions [51]. Among all types of motivation, escape motivation is often thought to be the most important predictor of internet addiction; it serves as a strong predictor for a wide range of internet and mobile phone-related addictions, including internet addiction [52,53,54], video game addiction [55,56], online game addiction [57,58], online video apps overuse (i.e., YouTube) [59], and PMPU [60,61,62].
Parental monitoring can also induce negative emotions in adolescents. According to the self-determination theory, all humans strive for freedom, and therefore their motivation is optimal when they are void of external influence and interference [63]. Thus, when children’s freedom is restricted, they may experience reactance [64]. Some studies have already examined the mediating roles of escape motivation in the relations between negative emotions, addictive behaviors, and PMPU. For example, escape motivation was found to mediate the relation between psychological distress (such as emotional imbalance, depression, and anxiety) and video game addiction [65,66] and the relation between psychiatric disorders (such as somatization and OCD) and video game addiction [67]. Escape motivation was also found to mediate the path from loneliness to PMPU [67]. Accordingly, parental monitoring may result in conflicts between parents and child, and thus cause the child to experience stress and negative emotions. As a result, through escape motivation, the child’s mobile phone-use behaviors might increase [68,69], leading to PMPU [62].

1.3. Moderating Role of Shyness

When choosing mediation strategies to reduce children’s PMPU, parents should take their child’s temperament into consideration [70]. Shyness, one of the most stable temperament types, should be given special attention [71].
Shyness is a common social experience that involves timidity, discomfort, embarrassment, and fear of being evaluated. It is often accompanied by a desire to minimize social interactions [72,73,74]. Research suggests that shyness can induce a series of negative consequences, including loneliness [75], anxiety, and depression [76,77]. To reduce or avoid those negative consequences, shy individuals often view online communication as a means to avoid face-to-face interaction [77]. An individual’s degree of shyness can positively predict online social activities [78] and internet addictive behaviors [79,80]. Such a predictive effect was shown to be consistent over time [81]. Similarly, many studies suggest that shy individuals increase their mobile phone use to avoid face-to-face contact, which leads to PMPU [82,83,84].
Research concerning parenting styles suggests that parents should adopt adaptive parenting that is suitable to their child’s temperament [85]. Studies have found that inhibitive temperament (similar to shyness) mediates the relation between maternal authoritative parenting and girls’ prosocial behaviors [86]. Zarra-Nezhad et al. [87] discovered that parental emotional support positively predicted prosocial behavior only among shy children, while parental control positively predicted prosocial behaviors only among children who were not shy. Because parental mediation strategies are similar to parenting styles to some degree [88], parental monitoring of children’s mobile phone use should be similar to parental control. Thus, it could be inferred that parental monitoring exerts different effects based on the shyness level of a child. Furthermore, a study [89] showed that shyness also predicts an individual’s drinking motivation; shy individuals are more inclined to reduce their negative emotions and navigate social contexts via drinking, which can eventually lead to alcohol addiction. This suggests that shyness is related to behavioral motivation. Therefore, we hypothesized that parental monitoring would positively predict more escape motivation for shy adolescents than for those who are not shy.

1.4. The Present Study

Based on the above, the present study aimed to investigate the mechanism and impact of parental monitoring on adolescent PMPU through a hypothesized moderated mediation model (Figure 1). We anticipated that (a) parental monitoring would positively predict adolescent PMPU (H1) via the mediation of escape motivation (H2), and (b) adolescent level of shyness would moderate the impact of parental monitoring on adolescent escape motivation (H3) and PMPU (H4).

2. Materials and Methods

2.1. Participants

In the present study, we contacted an urban middle school in Beijing, China, and informed them of the purpose of our study. All students recruited in grades 7, 8, 10, and 11 were voluntary to participate. A total of 584 students were recruited. The mean age of these participants was 16.13 years (standard deviation (SD) = 2.80) with a range from 13 to 18; 267 (45.7%) were boys, 281 (49.0%) were girls, and 31 (5.3%) did not report gender. Among these students, 23.3% of fathers and 25.8% of mothers received a high-school education or below and 76.7% of fathers and 74.2% of mothers received an undergraduate level of education or above.

2.2. Procedures

We obtained the approval to conduct the study from the Research Ethics Committee of a major research university in Beijing and the principals of the participating schools. The students were informed of the voluntary nature of this study and their right to opt out at any time during the course of the study. Then, they were asked to complete a paper-pencil questionnaire that included demographic information, Parental Monitoring, Escape Motivation, Shyness and Problematic mobile phone-use measures.

2.3. Measures

2.3.1. Parental Monitoring

Parental monitoring was assessed by the Parental Mediation of Children’s Internet Use Scale [18], which was validated in Chinese and exhibited satisfactory reliability and validity [90]. In this study, the word “internet use” was changed to “mobile phone use” to assess parental monitoring of children’s mobile phone-use behaviors. The scale included four items (e.g., “My parents check my mobile phone use behaviors; My parents check my mobile phone chatting records; My parents keep an eye on me when I use mobile phone”) on a 5-point Likert scale (1 = never, 5 = always). Higher scores reflected higher levels of parent monitoring of children’s mobile phone use. Cronbach’s α for the present study was 0.892.

2.3.2. Escape Motivation

Escape motivation was assessed by the Mobile Phone-Use Motivations Scale [49]. It consisted of six items (e.g., I use/play with my smartphone to feel less lonely; to fill uncomfortable silence; to make myself feel better when I feel down) using a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree); higher scores indicated a higher degree of escape motivation. This measure yielded a Cronbach’s α of 0.865 in the present study.

2.3.3. Shyness

Shyness was assessed by the Shyness Scale [72], which was validated in Chinese and showed satisfactory reliability and validity [91]. This scale included 13 items (e.g., I have trouble looking someone right in the eye; I feel tense when I’m with people I don’t know well; I feel inhibited in social situations.) on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating a higher degree of shyness. Cronbach’s α for the scale was 0.803 in the present study.

2.3.4. Problematic Mobile Phone Use (PMPU)

Problematic mobile phone use (PMPU) was assessed by the Problematic Mobile Phone-Use Scale [92], which was translated into Chinese and proved to be valid [93]. It consisted of 10 items (e.g., I find it difficult to switch off my mobile phone; I feel anxious if I have not checked for messages or switched on my mobile phone for some time; I find myself engaged on the mobile phone for longer periods of time than intended) on a 5-point scale that ranged from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating a higher degree of problematic mobile phone use. Cronbach’s α for the present study was 0.821.

2.3.5. Statistical Analysis

We conducted descriptive analyses and Pearson correlations with SPSS 22.0 (IBM, New York, NY, USA). The pattern of missing data was first evaluated. The results showed that 1.25% of the data was missing, and the missing rates on all variables were less than 10%. Therefore, we used the listwise method to handle the missing data in the following structural equation model [94]. Among the 583 participants, 518 provided complete data on all the variables. Next, the moderated mediation model was tested using the SPSS macro PROCESS 3.0 (model 8) (http://www.afhayes.com) recommended by Hayes [95]. We generated 5000 bootstrapped samples to estimate the confidence interval of the model effect. A 95% confidence interval without zero indicates statistical significance.

3. Results

3.1. Preliminary Analyses

The descriptive statistics and correlation matrix are presented in Table 1. Parental monitoring was positively correlated with children’s escape motivation, shyness, and PMPU. Children’s escape motivation was positively correlated with their shyness level and PMPU. Children’s shyness level was positively correlated with PMPU.

3.2. Testing for the Proposed Model

The analysis results of SPSS macro PROCESS are presented in Table 2, which consists of four parts: Model 1, Model 2, conditional indirect effect analysis of Model 1, and conditional direct effect analysis of Model 2. Model 1 was used to test the effects of parental monitoring on children’s escape motivation (part of H2), and the interaction between parental monitoring and children’s shyness on children’s escape motivation (H4), after controlling for age and gender. Model 2 examined the effects of parental monitoring on children’s PMPU (H1), children’s escape motivation on children’s PMPU (part of H2), and the interaction between parental monitoring and children’s shyness on children’s PMPU (H3).
The conditional indirect effect analysis of Model 1 tested the effects of parental monitoring at its mean, plus one, and minus one standard deviation on children’s escape motivation at the mean of shyness. The conditional direct effect analysis of Model 2 tested the effects of parental monitoring at its mean, plus one standard deviation, and minus one standard deviation on children’s PMPU at the mean of the shyness. According to Model 1 (F = 5.96, R2 = 0.05, p < 0.001) and Model 2 (F = 65.74.45, R2 = 0.41, p < 0.001), after controlling for gender and age, parental monitoring positively predicted children’s PMPU (β = 0.078, p < 0.05), supporting H1 (see Figure 2). Parental monitoring positively predicted children’s escape motivation (β = 0.101, p < 0.05), and children’s escape motivation positively predicted children’s PMPU (β = 0.612, p < 0.001), supporting H2.
The interaction of parental monitoring and shyness showed significant effects on children’s escape motivation (β = 0.102, p < 0.05). Thus, H4 was supported. This finding suggests that the relation between parental monitoring and children’s escape motivation was moderated by children’s level of shyness (see Figure 3). In addition, two of the three conditional indirect effects (based on the moderator values at the mean and at plus and minus one standard deviation) were positive and significantly different from zero (see conditional indirect effect analysis of Model 1). That is, according to the interaction of parental monitoring and children’s shyness, the indirect effects of parental monitoring on children’s escape motivation were stronger when children’s shyness level was moderate to high, but lower when children’s shyness level was low.
Furthermore, the interaction of parental monitoring and shyness showed marginally significant effects on children’s PMPU (β = 0.063, p < 0.10). Therefore, H3 was supported. Two of the three conditional direct effects (based on the moderator values at the mean and at plus and minus one standard deviation) were positive and significantly different from zero (see conditional direct effect analysis of Model 2). These findings suggest that the relation between parental monitoring and children’s PMPU was moderated by children’s level of shyness (see Figure 4). The direct effects of parental monitoring on children’s PMPU were higher when children’s shyness was moderate to high, but lower when children’s shyness was low. In conclusion, the above results indicated that parental monitoring affects children’s PMPU through a moderated mediation path, with children’s escape motivation as the mediator and children’s shyness level as the moderator.

4. Discussion

To reduce the negative impact of children’s mobile phone use, parental monitoring has not achieved consistent results [45,48]; its underlying mechanism remains unclear. The present study examined a moderated mediation model and found that children’s escape motivation partially mediated the association of parental monitoring and their PMPU. In addition, children’s degree of shyness moderated the path from parental monitoring to their escape motivation and to PMPU.

4.1. Escape Motivation Partially Mediates the Relation between Parental Monitoring and Adolescent PMPU

Parental monitoring positively correlated with adolescent PMPU, which supports H1. This is consistent with the findings of previous studies regarding internet addiction [46]. Research has shown that adolescents are especially vulnerable to the negative impacts of increased mobile phone use, and parents hope to reduce these negative influences via mediation. Considering that teenage years are a critical period of learning and social development, most parents employ monitoring strategies to mediate their children’s mobile phone use. However, research findings suggest that parental monitoring often leads to unexpected results. Because of adolescents’ desire for freedom and psychological reactance induced by restriction, they may be subjected to the “Pandora effect” [63,68,96]; as the strength of parental monitoring of mobile phone use increases, adolescents’ mobile phone-use behaviors also increase, which can eventually lead to PMPU. Similar results were found in other areas of addiction, including adolescent internet addiction [97], internet dependency [98], and sexual behaviors [99]. From the perspective of the sociology of emotions, emotions serve as an important precursor to effective parental mediation. However, parental monitoring often results in the conflicts between parents and child, causing negative emotions. The model of compensatory internet use suggests that internet use is viewed as a compensatory means to escape the reality. An individual with relatively low overall happiness tends to relieve negative emotions and escape from real-life problems via mobile phone use [100,101,102]. Moreover, in the family environment, whether the parents’ supervision is effective or not partially depends on their own behaviors. According to Bandura’s social cognitive theory, children observe the behaviors of others around them and are especially prone to observing, and imitating their parents’ behaviors [103]. It has been found that parents’ looking down at their own mobile phone (parent phubbing) in the process of communication with their children will not only aggravate children’s addiction [104], but also affect children’s attitude towards self-control mobile phone use [105]. Therefore, if parents want to achieve efficient monitoring results, they should control their own mobile phone-use behaviors. In general, although parental monitoring aims to improve children’s mobile phone-use behaviors, it is often ineffective or leads to undesirable outcomes.
In addition, as we hypothesized, escape motivation mediated the relation between parental monitoring and adolescent PMPU, which supports H2. This result is consistent with similar studies. One study found that escape motivation mediated the predictive effect of negative emotions on PMPU [67]. When individuals face real-life struggles, escape motivation can prompt them to compensate through video games and/or the internet, which can eventually lead to video game or internet addiction [54,106,107]. Furthermore, Erikson [108] proposed that there is a developmental issue in every psychosocial stage of human development; adolescence is a crucial period for identity development, as well as a time when psychological reactance peaks. To adolescents, parental monitoring acts as a restriction of freedom, which could induce their escape motivation. In addition, parental monitoring often creates parent-child conflict, which could cause adolescents to develop negative emotions, thus leading to escape motivation.

4.2. Shyness Moderates Both the Relations of Parental Monitoring with Adolescent Escape Motivation and Adolescent PMPU

The impact of parental monitoring on adolescent PMPU differs across individuals. The present study found that adolescent shyness level moderated the relation between parental monitoring and adolescent escape motivation, and between parental monitoring and adolescent PMPU, which supports H4 and H3. As children’s shyness level increased, increases in parental monitoring strengthened their escape motivation and PMPU. This could be explained by the fact that shy individuals rely more on mobile phones to socialize, and parental monitoring can cause them to generate more negative reactions, such as psychological reactance and feelings of insecurity; this in turn perpetuates their escape motivation and exacerbates PMPU. There are three possible reasons why shy individuals rely more on mobile-phone socialization. First, shyness can be related to social anxiety; to avoid the embarrassment and discomfort elicited by face-to-face interactions, shy individuals tend to socialize via the internet, thus increasing internet use behaviors that can lead to internet and mobile-phone addiction [83,109]. Second, shy individuals often avoid occasions that expose them to evaluation [110]. Internet and mobile phone interactions can disguise their identity and thus protect them from others’ evaluations. Third, shyness that originates from social anxiety can expose individuals to psychological challenges [111]. To mitigate and overcome these challenges, shy individuals tend to avoid face-to-face interactions, devote themselves to internet use, and achieve satisfaction through online interactions. Overall, when their parents employ monitoring strategies, shy adolescents might feel insecure and avoid being evaluated, which prompts them to escape reality via mobile phone use.

4.3. Limitation and Implication

The present study has a few limitations. First, our data are cross-sectional, and thus cannot infer strong causational relationships. Experimental or longitudinal designs could be used to further prove the relations between these variables. Second, all of our data came from adolescents’ subjective responses. Although our measures have relatively high reliability/validity, the addition of responses from other sources (such as parents) would make our results more persuasive. Data of parental monitoring were obtained from children’s self-report, but not from their parents’ reports due to the limitation of our research conditions. In future research, obtaining data of parental monitoring from parents will help to reduce potential bias. Third, the findings in this study are in the context of Chinese culture, and its generalization to other culture should be made with caution. In order to avoid the negative effects of mobile phones, many parents in China prohibit children from bringing mobile phones to school. Using mobile phones at home is also often restricted. In comparison to children in other cultural contexts, Chinese children might have less freedom to use mobile phones. Moreover, studies on parental monitoring and children’s PMPU have yielded different results under different cultural and national contexts (such as Turkey and Germany) [19,47]. Therefore, future studies can explore the effects of cultural differences and cultural contexts on the effect of parental monitoring on children’s PMPU. Finally, future research should involve more schools and students to explore the differences between children in different developmental stages, such as early adolescence (12–13 years) and late adolescence (16–18).
Despite the above limitations, the contributions of the present study are relevant to educators, parents, and adolescents. First, our study results found that parental monitoring positively predicts adolescents’ PMPU. However, as the forms of online activities increase and the access to the internet becomes easier, parents are more likely to use monitoring strategies [20]. Moreover, parental monitoring is the most direct mediation method and, therefore, the most convenient for parents [18]. One study points out parents’ greater tendency to monitor their child’s mobile phone-use behaviors when their use increases [46]. When parental monitoring increases children’s mobile phone use, parental monitoring in turn increases, leading to a maladaptive cycle. Parents and educators should be cautious and optimize the use of monitoring strategies to control adolescent mobile phone use. Second, the present study found that parental monitoring positively predicted adolescents’ escape motivation, which predicted their PMPU. Third, the effect of parental monitoring on adolescents differed across temperaments. When parents strengthen their monitoring, shy adolescents are more likely to display escape motivation and develop problematic mobile phone-use behaviors. Parents should adopt appropriate mediation strategies according to their child’s temperament.

5. Conclusions

Parental monitoring positively predicts children’s PMPU, and this predictive effect is partially mediated by children’s escape motivation. Children’s level of shyness moderates the relation between parental monitoring and children’s escape motivation, and the relation between parental monitoring and children’s PMPU, Thus, increasing the strength of parental monitoring can lead to an increase in escape motivation and PMPU among shy adolescents.

Author Contributions

Conceptualization, X.F. and J.L.; Formal analysis, X.F. and F.J.; Funding acquisition, R.-D.L.; Investigation, R.Z., F.J. and J.W.; Project administration, R.-D.L.; Supervision, R.-D.L.; Validation, R.-D.L.; Writing—review and editing, X.F., J.L., R.-D.L. and Y.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Social Science Fund of China: 17BSH102.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Mascheroni, G.; Ólafsson, K. Net Children Go Mobile: Risks and Opportunities, 2nd ed.; Educatt: Milan, Italy, 2014. [Google Scholar]
  2. CNNIC. The 43st China Statistical Report on Internet Development. 2018. Available online: http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201902/P020190318523029756345.pdf (accessed on 20 December 2018).
  3. Bolin, G.; Westlund, O. Mobile generations: The role of mobile technology in the shaping of Swedish media generations. Int. J. Commun. 2009, 3, 108–124. [Google Scholar]
  4. Villanti, A.C.; Johnson, A.L.; Ilakkuvan, V.; Jacobs, M.A.; Graham, A.L.; Rath, J.M. Social media use and access to digital technology in US young adults in 2016. J. Med. Internet Res. 2017, 19, e196. [Google Scholar] [CrossRef] [PubMed]
  5. Chóliz, M. Mobile-phone addiction in adolescence: The test of mobile phone dependence (TMD). Prog. Health Sci. 2012, 2, 33–44. [Google Scholar]
  6. Westlund, O. New(s) functions for the mobile: A cross-cultural study. New Media Soc. 2010, 12, 91–108. [Google Scholar] [CrossRef]
  7. Ott, T.; Magnusson, A.G.; Weilenmann, A.; af Segerstad, Y.H. It must not disturb, it’s as simple as that: Students’ voices on mobile phones in the infrastructure for learning in Swedish upper secondary school. Educ. Inf. Technol. 2018, 23, 517–536. [Google Scholar] [CrossRef] [Green Version]
  8. Chang, F.C.; Chiu, C.H.; Chen, P.H.; Chiang, J.T.; Miao, N.F.; Chuang, H.Y.; Liu, S. Children’s use of mobile devices, smartphone addiction and parental mediation in Taiwan. Comput. Hum. Behav. 2019, 93, 25–32. [Google Scholar] [CrossRef]
  9. Nikhita, C.S.; Jadhav, P.R.; Ajinkya, S.A. Prevalence of mobile phone dependence in secondary school adolescents. J. Clin. Diagn. Res. JCDR 2015, 9, 6–9. [Google Scholar] [CrossRef]
  10. Seo, D.G.; Park, Y.; Kim, M.K.; Park, J. Mobile phone dependency and its impacts on adolescents’ social and academic behaviors. Comput. Hum. Behav. 2016, 63, 282–292. [Google Scholar] [CrossRef]
  11. Kwon, M.; Kim, D.J.; Cho, H.; Yang, S. The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS ONE 2013, 8, e83558. [Google Scholar] [CrossRef] [Green Version]
  12. Bhise, A.T.; Ghatule, A.A.; Ghatule, A.P. Study of mobile addiction among students wRT Gender and education. Indian J. Res. Manag. Bus. Soc. Sci. 2014, 2, 17–21. [Google Scholar]
  13. Bianchi, A.; Phillips, J.G. Psychological predictors of problem mobile phone use. Cyberpsychol. Behav. 2005, 8, 39–51. [Google Scholar] [CrossRef] [PubMed]
  14. Hawi, N.S.; Samaha, M. Relationships among smartphone addiction, anxiety, and family relations. Behav. Inf. Technol. 2017, 36, 1046–1052. [Google Scholar] [CrossRef]
  15. Kates, A.W.; Wu, H.; Coryn, C.L. The effects of mobile phone use on academic performance: A meta-analysis. Comput. Educ. 2018, 127, 107–112. [Google Scholar] [CrossRef]
  16. Thomée, S.; Härenstam, A.; Hagberg, M. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC Public Health 2011, 11, 66. [Google Scholar] [CrossRef] [Green Version]
  17. Livingstone, S.; Helsper, E.J. Parental mediation of children’s internet use. J. Broadcast. Electron. Media 2008, 52, 581–599. [Google Scholar] [CrossRef] [Green Version]
  18. Nikken, P.; Jansz, J. Developing scales to measure parental mediation of young children’s internet use. Learn. Media Technol. 2014, 39, 250–266. [Google Scholar] [CrossRef]
  19. Hefner, D.; Knop, K.; Schmitt, S.; Vorderer, P. Rules? Role model? Relationship? The impact of parents on their children’s problematic mobile phone involvement. Media Psychol. 2019, 22, 82–108. [Google Scholar] [CrossRef]
  20. Sonck, N.; Nikken, P.; de Haan, J. Determinants of Internet mediation: A comparison of the reports by Dutch parents and children. J. Child. Media 2013, 7, 96–113. [Google Scholar] [CrossRef]
  21. Panova, T.; Carbonell, X. Is smartphone addiction really an addiction? J. Behav. Addict. 2018, 7, 252–259. [Google Scholar] [CrossRef]
  22. Sunthlia, A.; Ahmad, S.; Singh, S.P. Menace of mobile phone overuse: An emerging public health concern. Int. J. Commun. Med. Public Health 2017, 3, 153–156. [Google Scholar] [CrossRef]
  23. Randler, C.; Wolfgang, L.; Matt, K.; Demirhan, E.; Horzum, M.B.; Beşoluk, Ş. Smartphone addiction proneness in relation to sleep and morningness–eveningness in German adolescents. J. Behav. Addict. 2016, 5, 465–473. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Haug, S.; Castro, R.P.; Kwon, M.; Filler, A.; Kowatsch, T.; Schaub, M.P. Smartphone use and smartphone addiction among young people in Switzerland. J. Behav. Addict. 2015, 4, 299–307. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Rebold, M.J.; Sheehan, T.; Dirlam, M.; Maldonado, T.; O’Donnell, D. The impact of cell phone texting on the amount of time spent exercising at different intensities. Comput. Hum. Behav. 2016, 55, 167–171. [Google Scholar] [CrossRef] [Green Version]
  26. Lachmann, B.; Sindermann, C.; Sariyska, R.Y.; Luo, R.; Melchers, M.C.; Becker, B.; Montag, C. The role of empathy and life satisfaction in internet and smartphone use disorder. Front. Psychol. 2018, 9, 398. [Google Scholar] [CrossRef] [PubMed]
  27. Alosaimi, F.D.; Alyahya, H.; Alshahwan, H.; Al Mahyijari, N.; Shaik, S.A. Smartphone addiction among university students in Riyadh, Saudi Arabia. Saudi Med. J. 2016, 37, 675–683. [Google Scholar] [CrossRef]
  28. Lepp, A.; Barkley, J.E.; Karpinski, A.C. The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Comput. Hum. Behav. 2014, 31, 343–350. [Google Scholar] [CrossRef]
  29. Samaha, M.; Hawi, N.S. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Comput. Hum. Behav. 2016, 57, 321–325. [Google Scholar] [CrossRef]
  30. Cheever, N.A.; Rosen, L.D.; Carrier, L.M.; Chavez, A. Out of sight is not out of mind: The impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users. Comput. Hum. Behav. 2014, 37, 290–297. [Google Scholar] [CrossRef]
  31. Tams, S.; Legoux, R.; Leger, P.M. Smartphone withdrawal creates stress: A moderated mediation model of nomophobia, social threat, and phone withdrawal context. Comput. Hum. Behav. 2018, 81, 1–9. [Google Scholar] [CrossRef]
  32. Elhai, J.D.; Dvorak, R.D.; Levine, J.C.; Hall, B.J. Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. J. Affect. Disord. 2017, 207, 251–259. [Google Scholar] [CrossRef]
  33. Hao, Z.; Jin, L.; Li, Y.; Akram, H.R.; Saeed, M.F.; Ma, J.; Huang, J. Alexithymia and mobile phone addiction in Chinese undergraduate students: The roles of mobile phone use patterns. Comput. Hum. Behav. 2019, 97, 51–59. [Google Scholar] [CrossRef]
  34. Volkmer, S.A.; Lermer, E. Unhappy and addicted to your phone? Higher mobile phone use is associated with lower well-being. Comput. Hum. Behav. 2019, 93, 210–218. [Google Scholar] [CrossRef] [Green Version]
  35. Warren, R.; Bluma, A. Parental mediation of children’s Internet use: The influence of established media. Commun. Res. Rep. 2002, 19, 8–17. [Google Scholar] [CrossRef]
  36. Nathanson, A.I. Parent and child perspectives on the presence and meaning of parental television mediation. J. Broadcast. Electron. Media 2001, 45, 201–220. [Google Scholar] [CrossRef]
  37. Bybee, C.R.; Robinson, D.; Turow, J. Determinants of parental guidance of children’s television viewing for a special subgroup: Mass media scholars. J. Broadcast. Electron. Media 1982, 26, 697–710. [Google Scholar] [CrossRef] [Green Version]
  38. Valkenburg, P.M.; Krcmar, M.; Peeters, A.L.; Marseille, N.M. Developing a scale to assess three styles of television mediation: “Instructive mediation,” “restrictive mediation,” and “social coviewing”. J. Broadcast. Electron. Media 1999, 43, 52–66. [Google Scholar] [CrossRef]
  39. Warren, R.; Gerke, P.; Kelly, M.A. Is there enough time on the clock? Parental involvement and mediation of children’s television viewing. J. Broadcast. Electron. Media 2002, 46, 87–111. [Google Scholar] [CrossRef]
  40. Nikken, P.; Jansz, J. Parental mediation of children’s videogame playing: A comparison of the reports by parents and children. Learn. Media Technol. 2006, 31, 181–202. [Google Scholar] [CrossRef]
  41. Livingstone, S.; Haddon, L.; Görzig, A.; Ólafsson, K. Risks and Safety on the Internet: The Perspective of European Children: Full Findings and Policy Implications from the EU Kids Online Survey of 9–16 Year Olds and Their Parents in 25 Countries; EU Kids Online: London, UK, 2011. [Google Scholar]
  42. Mendoza, K. Surveying parental mediation: Connections, challenges and questions for media literacy. J. Media Lit. Educ. 2009, 1, 28–41. [Google Scholar]
  43. Shin, W.; Li, B. Parental mediation of children’s digital technology use in Singapore. J. Child. Media 2017, 11, 1–19. [Google Scholar] [CrossRef]
  44. Vaala, S.E.; Bleakley, A. Monitoring, mediating, and modeling: Parental influence on adolescent computer and Internet use in the United States. J. Child. Media 2015, 9, 40–57. [Google Scholar] [CrossRef]
  45. Duerager, A.; Livingstone, S. How Can Parents Support Children’s Internet Safety; LSE, EU Kids Online: London, UK, 2012. [Google Scholar]
  46. Kalmus, V.; Blinka, L.; Olafsson, K. Does it matter what mama says: Evaluating the role of parental mediation in European adolescents’ excessive Internet use. Child. Soc. 2015, 29, 122–133. [Google Scholar] [CrossRef]
  47. Bayraktar, F. Online Risks and Parental Mediation Strategies Comparison of Turkish Children/Adolescents Who Live in Turkey and Europe. Educ. Sci. Egit. Ve Bilim 2017, 42, 25–37. [Google Scholar] [CrossRef]
  48. Livingstone, S.; Haddon, L.; Vincent, J.; Mascheroni, G.; Ólafsson, K. Net Children Go Mobile: The UK Report; London School of Economics and Political Science: London, UK, 2014. [Google Scholar]
  49. Kim, J.H. Smartphone-mediated communication vs. face-to-face interaction: Two routes to social support and problematic use of smartphone. Comput. Hum. Behav. 2017, 67, 282–291. [Google Scholar] [CrossRef]
  50. Lowry, P.B.; Gaskin, J.; Moody, G.D. Proposing the multi-motive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions. J. Assoc. Inf. Syst. 2015, 16, 515–579. [Google Scholar]
  51. Peele, S. The Meaning of Addiction: Compulsive Experience and Its Interpretation; Lexington Books DC Heath and Com: Lexington, MA, USA, 1985. [Google Scholar]
  52. Parker, B.J.; Plank, R.E. A uses and gratifications perspective on the Internet as a new information source. Am. Bus. Rev. 2000, 18, 43–49. [Google Scholar]
  53. Song, I.; Larose, R.; Eastin, M.S.; Lin, C.A. Internet gratifications and Internet addiction: On the uses and abuses of new media. Cyberpsychol. Behav. 2004, 7, 384–394. [Google Scholar] [CrossRef]
  54. Yee, N. Motivations for play in online games. Cyberpsychol. Behav. 2006, 9, 772–775. [Google Scholar] [CrossRef]
  55. Billieux, J.; Chanal, J.; Khazaal, Y.; Rochat, L.; Gay, P.; Zullino, D.; Van der Linden, M. Psychological predictors of problematic involvement in massively multiplayer online role-playing games: Illustration in a sample of male cybercafé players. Psychopathology 2011, 44, 165–171. [Google Scholar] [CrossRef]
  56. Dauriat, F.Z.; Zermatten, A.; Billieux, J.; Thorens, G.; Bondolfi, G.; Zullino, D.; Khazaal, Y. Motivations to play specifically predict excessive involvement in massively multiplayer online role-playing games: Evidence from an online survey. Eur. Addict. Res. 2011, 17, 185–189. [Google Scholar] [CrossRef]
  57. Király, O.; Urbán, R.; Griffiths, M.D.; Ágoston, C.; Nagygyörgy, K.; Kökönyei, G.; Demetrovics, Z. The mediating effect of gaming motivation between psychiatric symptoms and problematic online gaming: An online survey. J. Med. Internet Res. 2015, 17, e88. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Kuss, D.J.; Griffiths, M.D.; Pontes, H.M. Chaos and confusion in DSM-5 diagnosis of Internet Gaming Disorder: Issues, concerns, and recommendations for clarity in the field. J. Behav. Addict. 2017, 6, 103–109. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Klobas, J.E.; McGill, T.J.; Moghavvemi, S.; Paramanathan, T. Compulsive YouTube usage: A comparison of use motivation and personality effects. Comput. Hum. Behav. 2018, 87, 129–139. [Google Scholar] [CrossRef]
  60. Bae, S.M. The relationship between the type of smartphone use and smartphone dependence of Korean adolescents: National survey study. Child. Youth Serv. Rev. 2017, 81, 207–211. [Google Scholar] [CrossRef]
  61. Khang, H.; Kim, J.K.; Kim, Y. Self-traits and motivations as antecedents of digital media flow and addiction: The Internet, mobile phones, and video games. Comput. Hum. Behav. 2013, 29, 2416–2424. [Google Scholar] [CrossRef]
  62. Wang, J.L.; Wang, H.Z.; Gaskin, J.; Wang, L.H. The role of stress and motivation in problematic smartphone use among college students. Comput. Hum. Behav. 2015, 53, 181–188. [Google Scholar] [CrossRef]
  63. Grolnick, W.S.; Deci, E.L.; Ryan, R.M. Internalization within the family: The self-determination theory perspective. In Parenting and Children’s Internalization of Values: A Handbook of Contemporary Theory; Grusec, J.E., Kuczynski, L., Eds.; John Wiley & Sons Inc.: Hoboken, NJ, USA, 1997; pp. 135–161. [Google Scholar]
  64. White, S.R.; Rasmussen, E.E.; King, A.J. Restrictive mediation and unintended effects: Serial multiple mediation analysis explaining the role of reactance in US adolescents. J. Child. Media 2015, 9, 510–527. [Google Scholar] [CrossRef]
  65. Maroney, N.; Williams, B.J.; Thomas, A.; Skues, J.; Moulding, R. A stress-coping model of problem online video game use. Int. J. Ment. Health Addict. 2019, 17, 845–858. [Google Scholar] [CrossRef]
  66. Ballabio, M.; Griffiths, M.D.; Urbán, R.; Quartiroli, A.; Demetrovics, Z.; Király, O. Do gaming motives mediate between psychiatric symptoms and problematic gaming? An empirical survey study. Addict. Res. Theory 2017, 25, 397–408. [Google Scholar] [CrossRef]
  67. Shen, X.; Wang, J.L. Loneliness and excessive smartphone use among Chinese college students: Moderated mediation effect of perceived stressed and motivation. Comput. Hum. Behav. 2019, 95, 31–36. [Google Scholar] [CrossRef]
  68. Fikkers, K.M.; Piotrowski, J.T.; Valkenburg, P.M. A matter of style? Exploring the effects of parental mediation styles on early adolescents’ media violence exposure and aggression. Comput. Hum. Behav. 2017, 70, 407–415. [Google Scholar] [CrossRef]
  69. Valkenburg, P.M.; Piotrowski, J.T.; Hermanns, J.; De Leeuw, R. Developing and validating the perceived parental media mediation scale: A self-determination perspective. Hum. Commun. Res. 2013, 39, 445–469. [Google Scholar] [CrossRef]
  70. Demirhan, E.; Randler, C.; Horzum, M.B. Is problematic mobile phone use explained by chronotype and personality? Chronobiol. Int. 2016, 33, 821–831. [Google Scholar] [CrossRef]
  71. Rapee, R.M.; Coplan, R.J. Conceptual relations between anxiety disorder and fearful temperament. New Dir. Child Adolesc. Dev. 2010, 127, 17–31. [Google Scholar] [CrossRef]
  72. Cheek, J.M.; Buss, A.H. Shyness and sociability. J. Personal. Soc. Psychol. 1981, 41, 330–339. [Google Scholar] [CrossRef]
  73. Henderson, L.; Zimbardo, P.G. Shyness as a clinical condition: The Stanford model. In International Handbook of Social Anxiety: Concepts, Research and Interventions Relating to the Self and Shyness; Crozier, W.R., Alden, L.E., Eds.; John Wiley & Sons Ltd.: New York, NY, USA, 2001; pp. 431–447. [Google Scholar]
  74. Rubin, K.H.; Coplan, R.J.; Bowker, J.C. Social withdrawal in childhood. Annu. Rev. Psychol. 2009, 60, 141–171. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Tan, J.; Ai, Y.; Wen, X.; Wu, Y.; Wang, W. Relationship between shyness and loneliness among Chinese adolescents: Social support as mediator. Soc. Behav. Personal. Int. J. 2016, 44, 201–208. [Google Scholar] [CrossRef]
  76. Barstead, M.G.; Smith, K.A.; Laursen, B.; Booth-LaForce, C.; King, S.; Rubin, K.H. Shyness, preference for solitude, and adolescent internalizing: The roles of maternal, paternal, and best-friend support. J. Res. Adolesc. 2018, 28, 488–504. [Google Scholar] [CrossRef] [PubMed]
  77. Laghi, F.; Schneider, B.H.; Vitoroulis, I.; Coplan, R.J.; Baiocco, R.; Amichai-Hamburger, Y.; Flament, M. Knowing when not to use the Internet: Shyness and adolescents’ on-line and off-line interactions with friends. Comput. Hum. Behav. 2013, 29, 51–57. [Google Scholar] [CrossRef]
  78. Appel, M.; Gnambs, T. Shyness and social media use: A meta-analytic summary of moderating and mediating effects. Comput. Hum. Behav. 2019, 98, 294–301. [Google Scholar] [CrossRef]
  79. Ang, C.S.; Chan, N.N.; Lee, C.S. Shyness, Loneliness Avoidance, and Internet Addiction: What are the Relationships? J. Psychol. 2018, 152, 25–35. [Google Scholar] [CrossRef] [PubMed]
  80. Chak, K.; Leung, L. Shyness and locus of control as predictors of internet addiction and internet use. Cyberpsychol. Behav. 2004, 7, 559–570. [Google Scholar] [CrossRef] [PubMed]
  81. Tian, Y.; Bian, Y.; Han, P.; Gao, F.; Wang, P. Associations between psychosocial factors and generalized pathological internet use in Chinese university students: A longitudinal cross-lagged analysis. Comput. Hum. Behav. 2017, 72, 178–188. [Google Scholar] [CrossRef]
  82. Bian, M.; Leung, L. Linking loneliness, shyness, smartphone addiction symptoms, and patterns of smartphone use to social capital. Soc. Sci. Comput. Rev. 2015, 33, 61–79. [Google Scholar] [CrossRef] [Green Version]
  83. Han, L.; Geng, J.; Jou, M.; Gao, F.; Yang, H. Relationship between shyness and mobile phone addiction in Chinese young adults: Mediating roles of self-control and attachment anxiety. Comput. Hum. Behav. 2017, 76, 363–371. [Google Scholar] [CrossRef]
  84. Hong, W.; Liu, R.D.; Oei, T.P.; Zhen, R.; Jiang, S.; Sheng, X. The mediating and moderating roles of social anxiety and relatedness need satisfaction on the relationship between shyness and problematic mobile phone use among adolescents. Comput. Hum. Behav. 2019, 93, 301–308. [Google Scholar] [CrossRef]
  85. Luke, F.; Chan, C.C.; Au, A.; Lai, S.M. Adaptive parenting for alleviating young children’s shyness: A randomized controlled trial of an early intervention program. Infant Child Dev. 2017, 26, 1–19. [Google Scholar] [CrossRef]
  86. Hastings, P.D.; Rubin, K.H.; DeRose, L. Links among gender, inhibition, and parental socialization in the development of prosocial behavior. Merrill-Palmer Q. 2005, 51, 467–493. [Google Scholar] [CrossRef]
  87. Zarra-Nezhad, M.; Moazami-Goodarzi, A.; Nurmi, J.E.; Eklund, K.; Ahonen, T.; Aunola, K. Children’s Shyness Moderates the Associations between Parenting Behavior and the Development of Children’s Pro-Social Behaviors. J. Child Fam. Stud. 2018, 27, 3008–3018. [Google Scholar] [CrossRef] [Green Version]
  88. Eastin, M.S.; Greenberg, B.S.; Hofschire, L. Parenting the internet. J. Commun. 2006, 56, 486–504. [Google Scholar] [CrossRef]
  89. Young, C.M.; DiBello, A.M.; Traylor, Z.K.; Zvolensky, M.J.; Neighbors, C. A Longitudinal Examination of the Associations between Shyness, Drinking Motives, Alcohol Use, and Alcohol-Related Problems. Alcohol. Clin. Exp. Res. 2015, 39, 1749–1755. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  90. Wu, N. The Relationship between Children Internet use with Academic Performance: The Role of Parental Mediation. Ph.D. Thesis, Central China Normal University, Wuhan, China, 2016. (In Chinese). [Google Scholar]
  91. Li, F.F.; Luo, Q.; Zhou, Z.K.; Sun, X.J.; Wei, H. Mediating effect of interpersonal communication disturbances between college students’ shyness and dependence of online communication. J. Clin. Psychol. 2012, 20, 182–184. (In Chinese) [Google Scholar]
  92. Foerster, M.; Roser, K.; Schoeni, A.; Röösli, M. Problematic mobile phone use in adolescents: Derivation of a short scale MPPUS-10. Int. J. Public Health 2015, 60, 277–286. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Zhuang, H.; Liu, R.-D.; Liu, Y. The impact of peer attachment on problematic mobile phone use among adolescents: Moderated mediation effect of loneliness and self-construa. Psychol. Sci. 2017, 40, 89–95. (In Chinese) [Google Scholar]
  94. Little, R.J.; Rubin, D.B. Statistical Analysis with Missing Data; John Wiley & Sons: Hoboken, NJ, USA, 2019; Volume 793. [Google Scholar]
  95. Hayes, A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach; The Guilford Press: New York, NY, USA, 2013. [Google Scholar]
  96. Brehm, S.S.; Brehm, J.W. Psychological Reactance: A Theory of Freedom and Control; Academic Press: Cambridge, MA, USA, 2013. [Google Scholar]
  97. Yen, J.Y.; Yen, C.F.; Chen, C.C.; Chen, S.H.; Ko, C.H. Family factors of internet addiction and substance use experience in Taiwanese adolescents. Cyberpsychol. Behav. 2007, 10, 323–329. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Xiuqin, H.; Huimin, Z.; Mengchen, L.; Jinan, W.; Ying, Z.; Ran, T. Mental health, personality, and parental rearing styles of adolescents with Internet addiction disorder. Cyberpsychol. Behav. Soc. Netw. 2010, 13, 401–406. [Google Scholar] [CrossRef] [Green Version]
  99. Nikken, P.; de Graaf, H. Reciprocal relationships between friends’ and parental mediation of adolescents’ media use and their sexual attitudes and behavior. J. Youth Adolesc. 2013, 42, 1696–1707. [Google Scholar] [CrossRef]
  100. Hasan, M.R.; Jha, A.K.; Liu, Y. Excessive use of online video streaming services: Impact of recommender system use, psychological factors, and motives. Comput. Hum. Behav. 2018, 80, 220–228. [Google Scholar] [CrossRef]
  101. Kardefelt-Winther, D. A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Comput. Hum. Behav. 2014, 31, 351–354. [Google Scholar] [CrossRef] [Green Version]
  102. Kardefelt-Winther, D. Conceptualizing Internet use disorders: Addiction or coping process? Psychiatry Clin. Neurosci. 2017, 71, 459–466. [Google Scholar] [CrossRef]
  103. Bandura, A. Social learning theory of aggression. J. Commun. 1978, 28, 12–29. [Google Scholar] [CrossRef] [PubMed]
  104. Xie, X.; Chen, W.; Zhu, X.; He, D. Parents’ phubbing increases Adolescents’ Mobile phone addiction: Roles of parent-child attachment, deviant peers, and gender. Child. Youth Serv. Rev. 2019, 105, 104426. [Google Scholar] [CrossRef]
  105. Fu, X.; Liu, J.; Liu, R.D.; Ding, Y.; Hong, W.; Jiang, S. The Impact of Parental Active Mediation on Adolescent Mobile Phone Dependency: A Moderated Mediation Model. Comput. Hum. Behav. 2020, 107, 106280. [Google Scholar] [CrossRef]
  106. Caplan, S.; Williams, D.; Yee, N. Problematic Internet use and psychosocial well-being among MMO players. Comput. Hum. Behav. 2009, 25, 1312–1319. [Google Scholar] [CrossRef]
  107. Billieux, J.; Van der Linden, M.; Achab, S.; Khazaal, Y.; Paraskevopoulos, L.; Zullino, D.; Thorens, G. Why do you play World of Warcraft? An in-depth exploration of self-reported motivations to play online and in-game behaviours in the virtual world of Azeroth. Comput. Hum. Behav. 2013, 29, 103–109. [Google Scholar] [CrossRef]
  108. Erikson, E.H. Identity and the Life Cycle: Selected Papers; International Universities Press: Oxford, UK, 1959. [Google Scholar]
  109. Odacı, H.; Çelik, Ç.B. Who are problematic internet users? An investigation of the correlations between problematic internet use and shyness, loneliness, narcissism, aggression and self-perception. Comput. Hum. Behav. 2013, 29, 2382–2387. [Google Scholar] [CrossRef]
  110. Cheek, J.M.; Melchior, L.M. Shyness and anxious self-preoccupation during a social interaction. J. Soc. Behav. Personal. 1990, 5, 117–130. [Google Scholar]
  111. Jackson, T.; Fritch, A.; Nagasaka, T.; Gunderson, J. Towards explaining the association between shyness and loneliness: A path analysis with American college students. Soc. Behav. Personal. Int. J. 2002, 30, 263–270. [Google Scholar] [CrossRef]
Ijerph 17 01487 g001 550
Figure 1. The hypothesized model.
Figure 1. The hypothesized model.
Ijerph 17 01487 g001
Ijerph 17 01487 g002 550
Figure 2. Moderation effects of shyness in the mediation model. All values shown are standardized coefficients. + p < 0.10. * p < 0.05. *** p < 0.001.
Figure 2. Moderation effects of shyness in the mediation model. All values shown are standardized coefficients. + p < 0.10. * p < 0.05. *** p < 0.001.
Ijerph 17 01487 g002
Ijerph 17 01487 g003 550
Figure 3. Moderation effect of shyness in the relation between parental monitoring and children’s escape motivation.
Figure 3. Moderation effect of shyness in the relation between parental monitoring and children’s escape motivation.
Ijerph 17 01487 g003
Ijerph 17 01487 g004 550
Figure 4. Moderation effect of shyness the relation between parental monitoring and children’s PMPU.
Figure 4. Moderation effect of shyness the relation between parental monitoring and children’s PMPU.
Ijerph 17 01487 g004
Table
Table 1. Means, standard deviations, and correlations between variables.
Table 1. Means, standard deviations, and correlations between variables.
VariablesMSD12345
1. Age16.132.80_
2. Parental monitoring2.061.140.114 **_
3. Escape motivation2.861.05−0.0230.122 **_
4. Shyness2.750.70−0.0310.135 **0.147 ***_
5. PMPU2.450.76−0.092 *0.163 ***0.607 ***0.213 ***_
Note. PMPU = Problematic Mobile Phone Use. * p < 0.05. ** p < 0.01. *** p < 0.001.
Table
Table 2. Bootstrap test on moderated mediation effect.
Table 2. Bootstrap test on moderated mediation effect.
Conditional Process AnalysisβSEtpLLCI–ULCI
Model 1
Outcome: Escape motivation
Predictors:
Age−0.0090.014−0.6680.505−0.037–0.018
Gender0.1500.0791.9020.058−0.005–0.305
Parental monitoring0.101 *0.0422.3920.0170.018–0.183
Shyness0.129 **0.0413.1640.0020.050–0.209
Monitoring × Shyness0.102 *0.0412.4740.0140.021–0.183
Model 2
Outcome: PMPU
Predictors:
Age−0.031 **0.012−2.6720.008−0.054–0.008
Gender−0.156 *0.066−2.3520.019−0.287–0.026
Parental monitoring0.078 *0.0352.1980.0280.008–0.148
Escape motivation0.612 ***0.03517.5710.0000.544–0.681
Shyness0.124 ***0.0353.5820.0000.056–0.191
Monitoring × Shyness0.063 +0.0351.8060.071−0.005–0.131
Conditional indirect effect analysis of model 1 βBoot SEBootLLCI-BootULCI
M − 1 SD0.0070.060−0.111–0.125
M0.103 *0.0420.021–0.186
M + 1 SD0.188 ***0.0510.087–0.289
Conditional direct effect analysis of model 2βBoot SEBootLLCI-BootULCI
M − 1 SD0.0200.050−0.079–0.119
M0.080 *0.0350.010–0.149
M + 1 SD0.132 **0.0440.046–0.217
Note. Bootstrap sample size = 5000. SE = standard error, LL = low limit, CI = confidence interval, UL = upper limit. + p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001.

Share and Cite

MDPI and ACS Style

Fu, X.; Liu, J.; Liu, R.-D.; Ding, Y.; Wang, J.; Zhen, R.; Jin, F. Parental Monitoring and Adolescent Problematic Mobile Phone Use: The Mediating Role of Escape Motivation and the Moderating Role of Shyness. Int. J. Environ. Res. Public Health 2020, 17, 1487. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051487

AMA Style

Fu X, Liu J, Liu R-D, Ding Y, Wang J, Zhen R, Jin F. Parental Monitoring and Adolescent Problematic Mobile Phone Use: The Mediating Role of Escape Motivation and the Moderating Role of Shyness. International Journal of Environmental Research and Public Health. 2020; 17(5):1487. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051487

Chicago/Turabian Style

Fu, Xinchen, Jingxuan Liu, Ru-De Liu, Yi Ding, Jia Wang, Rui Zhen, and Fangkai Jin. 2020. "Parental Monitoring and Adolescent Problematic Mobile Phone Use: The Mediating Role of Escape Motivation and the Moderating Role of Shyness" International Journal of Environmental Research and Public Health 17, no. 5: 1487. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051487

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop