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A longitudinal study of daily hassles, internet expectancy, self-control, and problematic internet use in Chinese adolescents: A moderated mediation model
Personality and Individual Differences 152 (2020) 109571
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A longitudinal study of daily hassles, internet expectancy, self-control, and problematic internet use in Chinese adolescents: A moderated mediation model Minmin Gu
T
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Department of Social and Behavioral Sciences, City University of Hong Kong, Hong Kong Department of Applied Social Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
A R T I C LE I N FO
A B S T R A C T
Keywords: Problematic internet use Perceived stress Internet expectancy Self-control Moderated mediation Chinese adolescents
Using data collected over a 1-year period on a stratified random sample of 782 secondary schools students (mean age = 14.86 years) in Shanghai, China, this study examined a moderated mediation model to explain how perceived stress lead to problematic internet use. Results showed that perceived stress positively predicted problematic internet use in adolescents and this prospective relation was mediated by adolescents' internet expectancy. Further, the mediation effect was moderated by adolescents' self-control. In addition, the moderated mediation effect of self-control existed only for adolescent girls who reported higher self-control than boys. Overall, the finding suggested that problematic internet use maybe instigated by strong internet expectancy coupled with weak self-control, especially for adolescents living under stress. Therefore, enhancing adolescents' self-control could be the focus of PIU intervention and prevention programmes.
1. Introduction Problematic Internet use (PIU; otherwise known as Internet Addiction) refers to “use of the internet that creates psychological, social, school and/or work difficulties in a person's life” (Beard & Wolf, 2001, p.378). The prevalence of PIU in adolescents is reported to be 2% ~11% around the world (Aboujaoude, 2010). In China, the prevalence of adolescents' PIU was estimated at 7.5% (Wang et al., 2013). A substantial body of risk factors related to PIU have been identified, among which cognitive factors such as internet expectancy have received considerable attention (Anderson, Steen, & Stavropoulos, 2017). 1.1. Internet expectancy and PIU Internet expectancy refers to a person's cognitive belief that internet use will lead to various pleasures and gratifications such as acquiring information, making friends and obtaining social support, disinhibition, tension reduction, and self-realization (Liu, Fang, Wan, & Zhou, 2016). A vast amount of empirical studies have shown that internet expectancy is associated with PIU (Lee, Ko, & Chou, 2015; Lin, Ko, & Wu, 2008; Lin, Wu, Chen, & You, 2018). Furthermore, internet expectancy has been found to mediate the relationship between psychopathological vulnerabilities and PIU (Brand, Laier, & Young, 2014; Wu, Ko, Tung, & Li,
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2016). These findings support the cognitive-behavioral model of PIU (Davis, 2001), which asserted that individual and environmental stressors are distal and necessary causes while cognitive beliefs are proximal and sufficient causes of PIU. In line with the cognitive behavioral model, this study hypothesized that stressed people would develop higher internet expectancy, which in turn would give rise to a higher likelihood of PIU. The relationship between stressful feelings and PIU in adolescents has been well recognized (Akin & Iskender, 2011; Li, Zhang, Li, Zhen, & Wang, 2010), yet few studies explored mediators of the relationship. Moreover, as far as I know, all except two studies (Leung, 2014; Wu et al., 2016) have used cross-sectional data to examine the relationship among stress, internet expectancy, and PIU, which hampers inferences about causality. Hence, this study adopted a two-wave longitudinal design. 1.2. The moderated mediation model To the best of my knowledge, this study was the first to conceptualize and examine a moderated mediation model with internet expectancy as the mediator and self-control as the moderator. The conceptual framework was guided by the dual system theory (DST) of addictive behavior. The DST holds that human behavior is guided by two distinct systems of information processing: the impulsive system
Corresponding author at: Department of Social and Behavioral Sciences, City University of Hong Kong, Hong Kong. E-mail address: [email protected].
https://doi.org/10.1016/j.paid.2019.109571 Received 2 May 2019; Received in revised form 4 August 2019; Accepted 19 August 2019 0191-8869/ © 2019 Elsevier Ltd. All rights reserved.
Personality and Individual Differences 152 (2020) 109571
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Hypothesis 3. Internet expectancy would mediate the relationship between perceived stress and PIU.
and the reflective system (Hofmann, Friese, & Strack, 2009; Liau et al., 2015). From the perspective of the DST, addictive behaviors are elicited by the strong impulsive system combined with the ineffective or impaired reflective system (Hofmann et al., 2009). Internet expectancy could be considered as a component of the impulsive system. Expectancy is the memory network that links the stimulus, the reinforcing effects, and the associated behavioral tendencies (Brown, 1993). Through repeated internet use experiences, a specific associative cluster may be formed that links (a) the concept of internet use, (b) positive and negative reinforcement experienced during stressful situations, and (c) the behavioral schema. Once formed and laid down in the long-term memory (Jones, Corbin, & Fromme, 2001), this associative cluster could be activated very quickly by both internal and environmental cues (Hofmann et al., 2009). In the case of PIU, the cues could be stressed feelings and internet accessibility, which lead to internet expectancy and consequently a great urge to be online. In contrast, self-control is the power engine of the reflective system (Evans, 2008). The stock of control resources enables sophisticated mental functions (e.g. consideration of long-term goals, deliberate evaluations and judgments) to inhibit or override impulses (Strack & Deutsch, 2004). When self-control is high, people are less motivated by the impulsive system and the overall effects of the impulsive system on one's behavioral choices are reduced (Strack & Deutsch, 2004). Therefore, self-control is expected to have a moderating impact such that high levels of self-control weaken the effect of internet expectancy on PIU. Turel and Qahri-Saremi's (2016) work provides initial empirical support to the applicability of DST in explaining PIU. The sample was 341 adults (Mean age = 23.00 years, SD = 4.36) from a North American university. The results showed that cognitive-behavioral control moderated the impact of internet expectancy on PIU. Furthermore, Li and colleagues (Li et al., 2010) found that self-control moderated the mediation effect of maladaptive cognition on the relationship between stressful life events and PIU. The sample was 660 junior high school students (Mean age = 14.14 years, SD = 0.86) in China. Yet it should be noted that maladaptive cognition and internet expectancy are two similar but distinct concepts. Maladaptive cognition is defined as the biased cognitive belief that the online world is better than real life or the extreme thought that internet use is the only way to get needs satisfied (Mai et al., 2012) whereas internet expectancy does not necessarily involve thoughts comparing the benefits of online and offline activities (Lee et al., 2015). To the best of my knowledge, no study was conducted to investigate if self-control moderated the mediating effect of internet expectancy. Moreover, these two pioneer studies were limited in methodology. Both studies were based on convenience sample. Li and colleagues (Li et al., 2010)’s study was cross-sectional while Turel and Qahri-Saremi (2016) collected data at two time points with one week interval. To address these knowledge gaps, this study adopted a representative sampling method and a longitudinal design with an interval of one year between the two waves.
Hypothesis 4. Self-control would be negatively associated with PIU. Hypothesis 5. Self-control would moderate the mediation effect of internet expectancy on the relation between perceived stress and PIU.
2. Methods 2.1. Participants and procedures A representative sample of Chinese secondary school students in Shanghai was recruited through stratified random sampling. The 15 districts of Shanghai were divided into three regions based on geographic location and household income: urban, suburban, and rural. In each region, one school was randomly selected; in each selected school, two classes were randomly selected from each grade. This yielded a total sample of 929 adolescents. The selected students and their families received the invitation letter and the consent form, which explained the purposes of the study and emphasized that the students' participation was voluntary and confidential. Only the students with consent forms signed by both themselves and their parents or guardians were given the questionnaires. The research was approved by the Research Ethics Review Committee of the Chinese university of Hong Kong. In Time 1 (T1), 892 adolescents participated in the survey (refusal rate: 3.98%). One year later at Time 2 (T2), 782 participants were matched to the T1 participants via student code. The attrition rate is 12.33%, with major reasons of being unidentified since some participants did not write down their student code (59.2%), dropping out or school transfer (22.7%), being absent at the day of data collection (7.2%), and other undisclosed personal reasons (10.9%). This study reported data on the 782 adolescents who completed surveys at T1 and T2. The sex distribution (50.4% males and 49.6% females) matched the actual sex distribution of adolescents in Shanghai (male: female = 102.1:100) (Shanghai Municipal Statistics Bureau, 2011). Participants' age at Time 1 ranged from 12 to 19 years, with a mean age of 14.86 years old (SD = 2.95). > 80% (84.5%) of the respondents lived with both parents. Forty-one percent of the sample reported the family monthly income of CNY 8000 (USD 1264) or below. 2.2. Measures PIU was assessed using the Revised Chen Internet Addiction Scale (CIAS-R). The CIAS-R is written in Chinese (Chen, Weng, Su, Wu, & Yang, 2003) and comprised by 26 items on symptoms and problems of PIU. An example item is “I always spend much more time online than that I intend to”. The items were rated on a 4-point scale from 1 = “none” to 4 = “often”. The scores are summed, and higher scores indicate more severe PIU. High reliability and factorial validity have been reported for the CIAS-R (Mak et al., 2014). The Cronbach's alpha of the CIAS-R in this study was 0.90. Perceived stress was assessed using a modified, Chinese version of the Daily Hassles1 Scale (DHS) (Sim, 2000). Only three subscales of parents (8 items), friends (7 items), and teachers and school (7 items) of the original DHS were adopted. An example item is “I am unhappy about not getting along with my friends very well”. Adolescents were
1.3. The present study As reviewed above, existing studies have demonstrated the direct effects of perceived stress and internet expectancy on PIU. In addition, self-control has been found to be negatively related to PIU in adolescents (Koo & Kwon, 2014; Özdemir, Kuzucu, & Ak, 2014). Furthermore, this study proposed the mediation effect of internet expectancy and moderation effect of self-control, based on the cognitive-behavioral model of PIU and the DST. The following hypotheses were made.
1 There are two modes of stress measurement, life event or daily hassles. This study chose to measure perceived stress in terms of daily hassles for two reasons. First, daily hassles are reported as the major source of stress for adolescents (McIntosh, Gillanders, & Rodgers, 2010). Second, a substantial body of studies have found that stressful life events is associated with PIU (Lam, Peng, Mai, & Jing, 2009; Whang, Lee, & Chang, 2003), yet few studies investigated the relationship between adolescents' daily hassles and problematic internet use.
Hypothesis 1. Perceived stress would be positively associated with PIU. Hypothesis 2. Internet expectancy would be positively associated with PIU. 2
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control variables, only adolescents' gender was significantly associated with self-control and PIU at both baseline and follow up. Adolescent males consistently reported significantly higher scores of PIU and lower scores of self-control as compared to adolescent females. Hence, gender was included in the follow-up analyses.
instructed to rate their stress levels regarding each type of daily hassles in the past six months, using a 3-point scale from 0 (none or not at all) to 3 (very much). Respondents' ratings for all the items were summed to provide a composite measure of perceived stress, with higher scores indicating higher perceived stress. The Chinese version of DHS was translated and validated by the author (Gu, 2012). The Cronbach's alpha of the Chinese DHS was 0.85 in this study. Internet expectancy was assessed using the Chinese version of the Internet expectancy Questionnaire-Revised (POEIUQ-R) (Lin et al., 2008). It is a 22-item scale consisting of four subscales: disinhibition, tension reduction, making new friends, and acquiring information. An example item is “I forget what bothers me when I am online”. With the time frame during the past month, all items were answered on a 5-point scale (1 = completely disagree, 5 = completely degree). Scores were summed with higher scores indicating higher internet expectancy. Evidence on high reliability and validity of POEIUQ-R has been amply demonstrated (Lin et al., 2008). In the present study, the Cronbach's alpha of this scale was 0.82. Self-control was assessed using the 13-item Brief Self-Control Scale (BSCS, Tangney, Baumeister, & Boone, 2004). Adolescents indicated how true each item was on a 5-point scale from 1 (not at all) to 5 (very much). An example item is “I am good at resisting temptation”. Scores were summed with higher scores indicating higher self-control. The BSCS was translated and validated in Chinese (Unger, Bi, Xiao, & Ybarra, 2016). In the present study, this scale reported the Cronbach's alpha of 0.80.
3.3. Tests of the moderated mediation model The mediation model with internet expectancy T1 as mediator was examined firstly. Perceived stress T1 was significantly associated with internet expectancy T1 (β = 0.25, p < .001). Moreover, both perceived stress T1 and internet expectancy T1 positively predicted PIU T2 (β = 0.35 and β = 0.24, p's < .001). In addition, the direct effect of perceived stress T1 on PIU T2 was reduced (β = 0.41 and 0.35 respectively, p's < .001) when perceived stress and internet expectancy were both included in the regression equation. Hence, adolescents' internet expectancy was found to partially mediate the relation between perceived stress and PIU. Then, the moderated mediation model which hypothesizes internet expectancy as the mediator and self-control as the moderator was tested. Gender and PIU T1 were included as covariates. The overall model (Table 2) accounted for 38% variance of PIU, F (6, 776) =46.16, p < .001. Perceived stress T1 and internet expectancy T1 positively predicted PIU T2 (β = 0.38 and β = 0.23, p's < .001). Self-control T1 negatively predicted PIU T2 (β = −0.19, p < .001). Moreover, the interaction term of the mediator (internet expectancy T1) and moderator (self-control T1) was significant (β = −0.08, p < .01). The indirect effects through internet expectancy were conditional at different levels of the moderator self-control. All the conditional indirect effects were significant, as the 95% bias-corrected bootstrap CIs did not contain zero. The conditional indirect effect of perceived stress on PIU decreased from 0.10 for -1SD self-control over 0.07 for Mean self-control to 0.04 for +1SD self-control. In other words, the indirect effect of perceived stress on PIU via internet expectancy weakened when the level of self-control increased. Fig. 1 displays the interaction of internet expectancy and self-control in predicting PIU and shows that internet expectancy predicted PIU better with lower self-control.
2.3. Data analyses The mediation model was tested using a series of regression equations, following procedures outlined by Baron and Kenny (1986). The moderated mediation model was examined using model 14 of the Process Macro for SPSS (Preacher & Hayes, 2008). The moderated mediation effect is demonstrated when the interaction of the mediator and the moderator reaches statistical significance and the bootstrapped confidence interval of the conditional indirect effects does not contain zero. A bootstrapped confidence interval was created with 5000 resamples. All analyses were performed using SPSS version 24.0.
3.4. Gender-specific analyses 3. Results As this study found that adolescent males reported significantly higher scores of PIU and lower scores of self-control as compared to adolescent females, the moderated mediation model was further tested in the male and female subsample. In the male and female subsample, internet expectancy T1 partially mediated the link between perceived stress T1 and PIU T2.The association between internet expectancy T1 and PIU T2 was stronger for adolescent males (β = 0.43, p < .001) than for adolescent females (β = 0.21, p < .001). However, the moderated mediation effect of self-control was significant only for the female subsample only. For adolescent females, the interaction of selfcontrol T1 and internet expectancy T1 was significant in predicting PIU T2 (β = −0.13, p < .001). The 95% bias-corrected bootstrap CIs for the conditional indirect effect at -1SD self-control, Mean self-control, and + 1SD self-control did not contain zero (CI: 0.05 to 0.20, 0.03 to 0.10, and 0.02 to 0.07). By contrast, the moderating effect of selfcontrol T1 on the link between internet expectancy T1 and PIU T2 was not significant in the male subsample (β = −0.03, n.s.) (Figs. 2 and 3).
3.1. Missing data analyses A logistic regression was conducted by examining whether the 110 participants who did not fill in the questionnaire at T2 differ from those who completed the survey at both time points. A logistic regression was conducted to regress participation (1 = yes, 0 = no) on all study variables (gender, age, family type, perceived stress T1, internet expectancy T1, self-control T1, and PIU T2). No significant predictors of attrition were identified (all ps > .05), indicating that the longitudinal sample is representative of the total sample. The final sample was composed of 782 participants. There was a small amount of data missing at the item level (< 1%) for the study variables. The MCAR test indicated that missing values were randomly distributed (p > .5). The missing values were handled with mean imputation. 3.2. Preliminary analyses
4. Discussion Means, SD, and inter-correlations between the study variables are presented in Table 1. As anticipated, PIU was positively correlated with perceived stress (r's range from 0.31 to 0.54, p's < .01) and internet expectancy (r's range from 0.22 to 0.35, p's < .01), and negatively related to self-control (r's range from −0.20 to −0.24, p's < .01). Moderate correlations were observed for the study variables at the two time points (r's range from 0.42 to 0.52, p's < .01). Regarding the
Corroborating previous empirical research (Brand et al., 2014; Wu et al., 2016), this study found that internet expectancy mediated the association between perceived stress and PIU. Internet expectancy are expected internet use outcomes including both positive reinforcement (e.g. pleasure seeking) and negative reinforcement (e.g. tension reduction). When stressed adolescents repeatedly experience these 3
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Table 1 Means, standard deviations, and bivariate correlations of study variables (N = 782).
1.Perceived stress T1 2. Perceived stress T2 3. Internet expectancy T1 4. Internet expectancy T2 5. Self-control T1 6. Self-control T2 7. PIU T1 8. PIU T2 Range Mean SD
1
2
3
4
5
6
7
8
– 0.50⁎⁎ 0.45⁎⁎ 0.35⁎⁎ −0.37⁎⁎ −0.26⁎⁎ 0.54⁎⁎ 0.31⁎⁎ 22–66 29.96 16.40
– 0.25⁎⁎ 0.44⁎⁎ −0.20⁎⁎ −0.31⁎⁎ 0.34⁎⁎ 0.54⁎⁎ 22–66 28.10 15.39
– 0.49⁎⁎ −0.42⁎⁎ −0.31⁎⁎ 0.34⁎⁎ 0.22⁎⁎ 22–110 51.88 20.04
– −0.34⁎⁎ −0.41⁎⁎ 0.26⁎⁎ 0.35⁎⁎ 22–110 49.90 21.27
– 0.42⁎⁎ −0.21⁎⁎ −0.20⁎⁎ 1–5 2.83 1.12
– −0.23⁎⁎ −0.24⁎⁎ 1–5 3.00 1.15
– 0.52⁎⁎ 26–104 40.82 28.50
– 26–104 41.43 28.66
Note. PIU = problematic internet use. ⁎⁎ p < .01. Table 2 Regression analyses for testing moderated mediation effects (N = 782). Variables
β
t (Sig.)
DV = PIU T2 Perceived stress T1 (predictor) Internet expectancy T1 (mediator) Self-control (moderator) Internet expectancy T1 ∗ Self-control T1 PIU T1 (covariate) Gender (covariate) R2
0.32 0.23 −0.19 −0.08 0.40 −0.06 0.38
10.74⁎⁎ 4.44⁎⁎ −6.38⁎⁎ −2.96⁎ 0.13.39⁎⁎ −4.38⁎⁎
Direct effect Conditional indirect effect at different values of self-control
−1 SD below mean Mean +1 SD above mean
Value
SE
Confidence interval
0.13 0.10 0.07 0.04
0.03 0.02 0.01 0.01
[0.08, [0.03, [0.03, [0.02,
Note. Gender: 1 = male, 2 = female. PIU = problematic internet use. ⁎ p < .05. ⁎⁎ p < .01.
3 2.8 2.6 2.4
PIU
2.2 LowSC 2
MediumSC
1.8
HighSC
1.6 1.4 1.2 1 LowIE
MediumIE
HighIE
Fig. 1. Interaction between Internet expectancy and self-control in predicting PIU (N = 782). PIU = problematic internet use, IE = internet expectancy, SC = self-control. All of the three slopes are significant at the 0.05 level.
4
0.19] 0.17] 0.08] 0.07]
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Fig. 2. Moderated mediation model predicting PIU in adolescent females (n = 388). PIU = problematic internet use. R2 = 0.3681. ***p < .001, **p < .01.
Fig. 3. Moderated mediation model predicting PIU in adolescent males (n = 394). PIU = problematic internet use. R2 = 0.3592 ***p < .001, **p < .01.
study demonstrates the applicability of DST to PIU and implies that investigating the impulsive, the reflective system, as well as their interactions should lead to a richer and deeper understanding of PIU. As one single study cannot address all the factors, future research could take into account other components of the impulsive system (e.g. trait impulsivity) and the reflective system (e.g. emotional regulation) (Liau
reinforcing effects, the expectancy memory network is established (Jones et al., 2001), which provokes strong urges towards internet use in future stressful situations. Moreover, as hypothesized, the mediation effect was found to be moderated by self-control. The identified moderating role of self-control provides at least three important theoretical implications. First, this
5
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et al., 2015; Turel & Qahri-Saremi, 2016). Second, the present study showed that adolescents with higher self-control are more likely to manage their internet use behavior despite the strong impulses. A large number of studies have suggested that self-control is negatively and directly related to PIU (Koo & Kwon, 2014; Özdemir et al., 2014) or its subtypes, e.g. problematic mobile phone use (Jiang & Zhao, 2017). This study demonstrated that self-control could moderate the effects of risk factors (perceived stress and internet expectancy in this study) and act as a protective factor against PIU. Third, this study echoed previous findings on gender difference of self-control in predicting PIU. The finding that girls displayed a stronger ability to inhibit impulses and regulate behaviors is in agreement with previous studies (Duckworth & Seligman, 2006). Moreover, in line with Li et al. (2010), this study found that adolescent female's self-control moderated the relationship between internet expectancy and PIU, yet such moderating effect was not observed for males. With regard to practical implications, this study implied that enhancing adolescents' self-control could be an important component of PIU intervention and prevention programmes. One study showed that teaching adolescents self-regulation skills through group games and discussions was effective in reducing risk of PIU (Du, Jiang, & Vance, 2010). Future practice may consider incorporating other strategies that have proven effective in strengthening self-control, e.g. regular practice of small acts of inhibiting urges and impulses (Muraven, 2010) and mindfulness meditation such as relaxing, visualizing, and attending to and accepting ongoing thoughts, feelings, or sensations (Friese, Messner, & Schaffner, 2012). Findings of the current study need to be weighed against the backdrop of several limitations. First, because the study variables were assessed at two time points, the current research tends to underestimate the indirect associations when compared with three-wave data (Little, 2013). Nevertheless, the longitudinal design adopted in this study facilitated us to establish the temporal precedence between the mediator and the outcome. Second, the moderated mediation model tested in this study accounted for about one-third of the variance in PIU, which suggest that there might be other risk and protective factors not included in this study. Previous research has identified multiple risk factors such as psychopathology, personality traits, parenting, and school bullying (Anderson et al., 2017). There are also a growing number of studies on protective factors such as resilience, emotional competence and coping skills (Robertson, Yan, & Rapoza, 2018). Third, the data were based on adolescent self-reported measures. Although previous research has shown that teenage self-report of problematic behavior would not be strongly biased under conditions of confidentiality (Tourangeau & Yan, 2007), future studies should consider collecting data from multiple informants and multiple methods. Despite these limitations, the current study delineated how the mediation effect of internet expectancy on the relation between stress and PIU was moderated by self-control, based on a sound theoretical foundation and rather strong empirical evidence.
Chen, S. H., Weng, L. J., Su, Y. J., Wu, H. M., & Yang, P. F. (2003). Zhong wen wang lu cheng yin liang biao zhi bian zhi yu xin li ji liang te xing yan jiu. [Development of a Chinese Internet addiction scale and its psychometric study]. Chinese Journal of Psychology, 45, 279–294. Davis, R. (2001). A cognitive-behavioral model of pathological internet use. Computers in Human Behavior, 17, 187–195. Du, Y. S., Jiang, W., & Vance, A. (2010). Longer term effect of randomized, controlled group cognitive behavioural therapy for Internet addiction in adolescent students in Shanghai. Australian and New Zealand Journal of Psychiatry, 44, 129–134. Duckworth, A. L., & Seligman, M. E. (2006). Self-discipline gives girls the edge: Gender in self-discipline, grades, and achievement test scores. Journal of Educational Psychology, 98, 198–208. Evans, J. S. B. T. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278. Friese, M., Messner, C., & Schaffner, Y. (2012). Mindfulness meditation counteracts selfcontrol depletion. Consciousness and Cognition, 21, 1016–1022. Gu, M. (2012). Study of adolescents' Internet use and Internet addiction in Shanghai, China: Implications for social work practice (Doctoral dissertation)Hong Kong: The Chinese University of Hong Kong. Hofmann, W., Friese, M., & Strack, F. (2009). Impulse and self-control from a dual-systems perspective. Perspectives on Psychological Science, 4, 162–176. Jiang, Z., & Zhao, X. (2017). Brain behavioral systems, self-control and problematic mobile phone use: The moderating role of gender and history of use. Personality and Individual Differences, 106, 111–116. Jones, B. T., Corbin, W., & Fromme, K. (2001). A review of expectancy theory and alcohol consumption. Addiction, 96, 57–72. Koo, H. J., & Kwon, J. H. (2014). Risk and protective factors of Internet addiction: A metaanalysis of empirical studies in Korea. Yonsei Medical Journal, 55, 1691–1711. Lam, L. T., Peng, Z. W., Mai, J. C., & Jing, J. (2009). Factors associated with Internet addiction among adolescents. Cyberpsychology & Behavior, 12, 551–555. Lee, Y. H., Ko, C. H., & Chou, C. (2015). Re-visiting Internet addiction among Taiwanese students: A cross-sectional comparison of students' expectations, online gaming, and online social interaction. Journal of Abnormal Child Psychology, 43, 589–599. Leung, L. (2014). Predicting Internet risks: A longitudinal panel study of gratificationssought, Internet addiction symptoms, and social media use among children and adolescents. Health Psychology and Behavioral Medicine, 2, 424–439. Li, D., Zhang, W., Li, X., Zhen, S., & Wang, Y. (2010). Stressful life events and problematic Internet use by adolescent females and males: A mediated moderation model. Computers in Human Behavior, 26, 1199–1207. Liau, A. K., Neo, E. C., Gentile, D. A., Choo, H., Sim, T., Li, D., & Khoo, A. (2015). Impulsivity, self-regulation, and pathological video gaming among youth: Testing a mediation model. Asia-Pacific Journal of Public Health, 27, 2188–2196. Lin, M. P., Ko, H. C., & Wu, J. Y. W. (2008). The role of positive/negative outcome expectancy and refusal self-efficacy of Internet use on Internet addiction among college students in Taiwan. Cyberpsychology & Behavior, 11, 451–457. Lin, M. P., Wu, J. Y. W., Chen, C. J., & You, J. (2018). Positive outcome expectancy mediates the relationship between social influence and Internet addiction among senior high-school students. Journal of Behavioral Addictions, 7, 292–300. Little, T. D. (2013). Longitudinal structural equation modeling. New York: The Guilford Press. Liu, Q. X., Fang, X. Y., Wan, J. J., & Zhou, Z. K. (2016). Need satisfaction and adolescent pathological Internet use: Comparison of satisfaction perceived online and offline. Computers in Human Behavior, 55, 695–700. Mai, Y., Hu, J., Yan, Z., Zhen, S., Wang, S., & Zhang, W. (2012). Structure and function of maladaptive cognitions in Pathological Internet Use among Chinese adolescents. Computers in Human Behavior, 28, 2376–2386. Mak, K. K., Lai, C. M., Ko, C. H., Chou, C., Kim, D. I., Watanabe, H., & Ho, R. C. (2014). Psychometric properties of the revised Chen internet addiction scale (CIAS-R) in Chinese adolescents. Journal of Abnormal Child Psychology, 42, 1237–1245. McIntosh, E., Gillanders, D., & Rodgers, S. (2010). Rumination, goal linking, daily hassles and life events in major depression. Clinical Psychology & Psychotherapy, 17, 33–43. Muraven, M. (2010). Building self-control strength: Practicing self-control leads to improved self-control performance. Journal of Experimental Social Psychology, 46, 465–468. Özdemir, Y., Kuzucu, Y., & Ak, Ş. (2014). Depression, loneliness and Internet addiction: How important is low self-control? Computers in Human Behavior, 34, 284–290. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891. Robertson, T. W., Yan, Z., & Rapoza, K. A. (2018). Is resilience a protective factor of internet addiction? Computers in Human Behavior, 78, 255–260. Shanghai Municipal Statistics Bureau (2011). Shanghai qing shao nian ren kou xian zhuang yu te zheng fen xi [Analysis on conditions and characteristics of adolescents in Shanghai]. Retreived from: www.statssh.gov.cn/html/fxbg/201111/236190. html. Sim, H. O. (2000). Relationship of daily hassles and social support to depression and antisocial behavior among early adolescents. Journal of Youth and Adolescence, 29, 647–659. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72, 271–324. Tourangeau, R., & Yan, T. (2007). Sensitive questions in surveys. Psychological Bulletin, 133, 859–883. Turel, O., & Qahri-Saremi, H. (2016). Problematic use of social networking sites:
References Aboujaoude, E. (2010). Problematic internet use: An overview. World Psychiatry, 9, 85–90. Akin, A., & Iskender, M. (2011). Internet addiction and depression, anxiety and stress. International Online Journal of Educational Sciences, 3, 138–148. Anderson, E. L., Steen, E., & Stavropoulos, V. (2017). Internet use and Problematic Internet Use: A systematic review of longitudinal research trends in adolescence and emergent adulthood. International Journal of Adolescence and Youth, 22, 430–454. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Beard, K. W., & Wolf, E. M. (2001). Modification in the proposed diagnostic criteria for internet addiction. Cyberpsychology & Behavior, 4, 377–383. Brand, M., Laier, C., & Young, K. S. (2014). Internet addiction: Coping styles, expectancies, and treatment implications. Frontiers in Psychology, 5, 1256. Brown, S. A. (1993). Drug effect expectancies and addictive behavior change. Experimental and Clinical Psychopharmacology, 1, 55–67.
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Whang, L. S. M., Lee, S., & Chang, G. (2003). Internet over-users' psychological profiles: A behavior sampling analysis on internet addiction. Cyberpsychology & Behavior, 6, 143–150. Wu, J. Y. W., Ko, H. C., Tung, Y. Y., & Li, C. C. (2016). Internet use expectancy for tension reduction and disinhibition mediates the relationship between borderline personality disorder features and Internet addiction among college students–One-year follow-up. Computers in Human Behavior, 55, 851–855.
Antecedents and consequence from a dual-system theory perspective. Journal of Management Information Systems, 33, 1087–1116. Unger, A., Bi, C., Xiao, Y. Y., & Ybarra, O. (2016). The revising of the Tangney Self-control Scale for Chinese students. PsyCh Journal, 5, 101–116. Wang, L., Luo, J., Bai, Y., Kong, J., Luo, J., Gao, W., & Sun, X. (2013). Internet addiction of adolescents in China: Prevalence, predictors, and association with well-being. Addiction Research & Theory, 21, 62–69.
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Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
A longitudinal study of daily hassles, internet expectancy, self-control, and problematic internet use in Chinese adolescents: A moderated mediation model Minmin Gu
T
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Department of Social and Behavioral Sciences, City University of Hong Kong, Hong Kong Department of Applied Social Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
A R T I C LE I N FO
A B S T R A C T
Keywords: Problematic internet use Perceived stress Internet expectancy Self-control Moderated mediation Chinese adolescents
Using data collected over a 1-year period on a stratified random sample of 782 secondary schools students (mean age = 14.86 years) in Shanghai, China, this study examined a moderated mediation model to explain how perceived stress lead to problematic internet use. Results showed that perceived stress positively predicted problematic internet use in adolescents and this prospective relation was mediated by adolescents' internet expectancy. Further, the mediation effect was moderated by adolescents' self-control. In addition, the moderated mediation effect of self-control existed only for adolescent girls who reported higher self-control than boys. Overall, the finding suggested that problematic internet use maybe instigated by strong internet expectancy coupled with weak self-control, especially for adolescents living under stress. Therefore, enhancing adolescents' self-control could be the focus of PIU intervention and prevention programmes.
1. Introduction Problematic Internet use (PIU; otherwise known as Internet Addiction) refers to “use of the internet that creates psychological, social, school and/or work difficulties in a person's life” (Beard & Wolf, 2001, p.378). The prevalence of PIU in adolescents is reported to be 2% ~11% around the world (Aboujaoude, 2010). In China, the prevalence of adolescents' PIU was estimated at 7.5% (Wang et al., 2013). A substantial body of risk factors related to PIU have been identified, among which cognitive factors such as internet expectancy have received considerable attention (Anderson, Steen, & Stavropoulos, 2017). 1.1. Internet expectancy and PIU Internet expectancy refers to a person's cognitive belief that internet use will lead to various pleasures and gratifications such as acquiring information, making friends and obtaining social support, disinhibition, tension reduction, and self-realization (Liu, Fang, Wan, & Zhou, 2016). A vast amount of empirical studies have shown that internet expectancy is associated with PIU (Lee, Ko, & Chou, 2015; Lin, Ko, & Wu, 2008; Lin, Wu, Chen, & You, 2018). Furthermore, internet expectancy has been found to mediate the relationship between psychopathological vulnerabilities and PIU (Brand, Laier, & Young, 2014; Wu, Ko, Tung, & Li,
⁎
2016). These findings support the cognitive-behavioral model of PIU (Davis, 2001), which asserted that individual and environmental stressors are distal and necessary causes while cognitive beliefs are proximal and sufficient causes of PIU. In line with the cognitive behavioral model, this study hypothesized that stressed people would develop higher internet expectancy, which in turn would give rise to a higher likelihood of PIU. The relationship between stressful feelings and PIU in adolescents has been well recognized (Akin & Iskender, 2011; Li, Zhang, Li, Zhen, & Wang, 2010), yet few studies explored mediators of the relationship. Moreover, as far as I know, all except two studies (Leung, 2014; Wu et al., 2016) have used cross-sectional data to examine the relationship among stress, internet expectancy, and PIU, which hampers inferences about causality. Hence, this study adopted a two-wave longitudinal design. 1.2. The moderated mediation model To the best of my knowledge, this study was the first to conceptualize and examine a moderated mediation model with internet expectancy as the mediator and self-control as the moderator. The conceptual framework was guided by the dual system theory (DST) of addictive behavior. The DST holds that human behavior is guided by two distinct systems of information processing: the impulsive system
Corresponding author at: Department of Social and Behavioral Sciences, City University of Hong Kong, Hong Kong. E-mail address: [email protected].
https://doi.org/10.1016/j.paid.2019.109571 Received 2 May 2019; Received in revised form 4 August 2019; Accepted 19 August 2019 0191-8869/ © 2019 Elsevier Ltd. All rights reserved.
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Hypothesis 3. Internet expectancy would mediate the relationship between perceived stress and PIU.
and the reflective system (Hofmann, Friese, & Strack, 2009; Liau et al., 2015). From the perspective of the DST, addictive behaviors are elicited by the strong impulsive system combined with the ineffective or impaired reflective system (Hofmann et al., 2009). Internet expectancy could be considered as a component of the impulsive system. Expectancy is the memory network that links the stimulus, the reinforcing effects, and the associated behavioral tendencies (Brown, 1993). Through repeated internet use experiences, a specific associative cluster may be formed that links (a) the concept of internet use, (b) positive and negative reinforcement experienced during stressful situations, and (c) the behavioral schema. Once formed and laid down in the long-term memory (Jones, Corbin, & Fromme, 2001), this associative cluster could be activated very quickly by both internal and environmental cues (Hofmann et al., 2009). In the case of PIU, the cues could be stressed feelings and internet accessibility, which lead to internet expectancy and consequently a great urge to be online. In contrast, self-control is the power engine of the reflective system (Evans, 2008). The stock of control resources enables sophisticated mental functions (e.g. consideration of long-term goals, deliberate evaluations and judgments) to inhibit or override impulses (Strack & Deutsch, 2004). When self-control is high, people are less motivated by the impulsive system and the overall effects of the impulsive system on one's behavioral choices are reduced (Strack & Deutsch, 2004). Therefore, self-control is expected to have a moderating impact such that high levels of self-control weaken the effect of internet expectancy on PIU. Turel and Qahri-Saremi's (2016) work provides initial empirical support to the applicability of DST in explaining PIU. The sample was 341 adults (Mean age = 23.00 years, SD = 4.36) from a North American university. The results showed that cognitive-behavioral control moderated the impact of internet expectancy on PIU. Furthermore, Li and colleagues (Li et al., 2010) found that self-control moderated the mediation effect of maladaptive cognition on the relationship between stressful life events and PIU. The sample was 660 junior high school students (Mean age = 14.14 years, SD = 0.86) in China. Yet it should be noted that maladaptive cognition and internet expectancy are two similar but distinct concepts. Maladaptive cognition is defined as the biased cognitive belief that the online world is better than real life or the extreme thought that internet use is the only way to get needs satisfied (Mai et al., 2012) whereas internet expectancy does not necessarily involve thoughts comparing the benefits of online and offline activities (Lee et al., 2015). To the best of my knowledge, no study was conducted to investigate if self-control moderated the mediating effect of internet expectancy. Moreover, these two pioneer studies were limited in methodology. Both studies were based on convenience sample. Li and colleagues (Li et al., 2010)’s study was cross-sectional while Turel and Qahri-Saremi (2016) collected data at two time points with one week interval. To address these knowledge gaps, this study adopted a representative sampling method and a longitudinal design with an interval of one year between the two waves.
Hypothesis 4. Self-control would be negatively associated with PIU. Hypothesis 5. Self-control would moderate the mediation effect of internet expectancy on the relation between perceived stress and PIU.
2. Methods 2.1. Participants and procedures A representative sample of Chinese secondary school students in Shanghai was recruited through stratified random sampling. The 15 districts of Shanghai were divided into three regions based on geographic location and household income: urban, suburban, and rural. In each region, one school was randomly selected; in each selected school, two classes were randomly selected from each grade. This yielded a total sample of 929 adolescents. The selected students and their families received the invitation letter and the consent form, which explained the purposes of the study and emphasized that the students' participation was voluntary and confidential. Only the students with consent forms signed by both themselves and their parents or guardians were given the questionnaires. The research was approved by the Research Ethics Review Committee of the Chinese university of Hong Kong. In Time 1 (T1), 892 adolescents participated in the survey (refusal rate: 3.98%). One year later at Time 2 (T2), 782 participants were matched to the T1 participants via student code. The attrition rate is 12.33%, with major reasons of being unidentified since some participants did not write down their student code (59.2%), dropping out or school transfer (22.7%), being absent at the day of data collection (7.2%), and other undisclosed personal reasons (10.9%). This study reported data on the 782 adolescents who completed surveys at T1 and T2. The sex distribution (50.4% males and 49.6% females) matched the actual sex distribution of adolescents in Shanghai (male: female = 102.1:100) (Shanghai Municipal Statistics Bureau, 2011). Participants' age at Time 1 ranged from 12 to 19 years, with a mean age of 14.86 years old (SD = 2.95). > 80% (84.5%) of the respondents lived with both parents. Forty-one percent of the sample reported the family monthly income of CNY 8000 (USD 1264) or below. 2.2. Measures PIU was assessed using the Revised Chen Internet Addiction Scale (CIAS-R). The CIAS-R is written in Chinese (Chen, Weng, Su, Wu, & Yang, 2003) and comprised by 26 items on symptoms and problems of PIU. An example item is “I always spend much more time online than that I intend to”. The items were rated on a 4-point scale from 1 = “none” to 4 = “often”. The scores are summed, and higher scores indicate more severe PIU. High reliability and factorial validity have been reported for the CIAS-R (Mak et al., 2014). The Cronbach's alpha of the CIAS-R in this study was 0.90. Perceived stress was assessed using a modified, Chinese version of the Daily Hassles1 Scale (DHS) (Sim, 2000). Only three subscales of parents (8 items), friends (7 items), and teachers and school (7 items) of the original DHS were adopted. An example item is “I am unhappy about not getting along with my friends very well”. Adolescents were
1.3. The present study As reviewed above, existing studies have demonstrated the direct effects of perceived stress and internet expectancy on PIU. In addition, self-control has been found to be negatively related to PIU in adolescents (Koo & Kwon, 2014; Özdemir, Kuzucu, & Ak, 2014). Furthermore, this study proposed the mediation effect of internet expectancy and moderation effect of self-control, based on the cognitive-behavioral model of PIU and the DST. The following hypotheses were made.
1 There are two modes of stress measurement, life event or daily hassles. This study chose to measure perceived stress in terms of daily hassles for two reasons. First, daily hassles are reported as the major source of stress for adolescents (McIntosh, Gillanders, & Rodgers, 2010). Second, a substantial body of studies have found that stressful life events is associated with PIU (Lam, Peng, Mai, & Jing, 2009; Whang, Lee, & Chang, 2003), yet few studies investigated the relationship between adolescents' daily hassles and problematic internet use.
Hypothesis 1. Perceived stress would be positively associated with PIU. Hypothesis 2. Internet expectancy would be positively associated with PIU. 2
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control variables, only adolescents' gender was significantly associated with self-control and PIU at both baseline and follow up. Adolescent males consistently reported significantly higher scores of PIU and lower scores of self-control as compared to adolescent females. Hence, gender was included in the follow-up analyses.
instructed to rate their stress levels regarding each type of daily hassles in the past six months, using a 3-point scale from 0 (none or not at all) to 3 (very much). Respondents' ratings for all the items were summed to provide a composite measure of perceived stress, with higher scores indicating higher perceived stress. The Chinese version of DHS was translated and validated by the author (Gu, 2012). The Cronbach's alpha of the Chinese DHS was 0.85 in this study. Internet expectancy was assessed using the Chinese version of the Internet expectancy Questionnaire-Revised (POEIUQ-R) (Lin et al., 2008). It is a 22-item scale consisting of four subscales: disinhibition, tension reduction, making new friends, and acquiring information. An example item is “I forget what bothers me when I am online”. With the time frame during the past month, all items were answered on a 5-point scale (1 = completely disagree, 5 = completely degree). Scores were summed with higher scores indicating higher internet expectancy. Evidence on high reliability and validity of POEIUQ-R has been amply demonstrated (Lin et al., 2008). In the present study, the Cronbach's alpha of this scale was 0.82. Self-control was assessed using the 13-item Brief Self-Control Scale (BSCS, Tangney, Baumeister, & Boone, 2004). Adolescents indicated how true each item was on a 5-point scale from 1 (not at all) to 5 (very much). An example item is “I am good at resisting temptation”. Scores were summed with higher scores indicating higher self-control. The BSCS was translated and validated in Chinese (Unger, Bi, Xiao, & Ybarra, 2016). In the present study, this scale reported the Cronbach's alpha of 0.80.
3.3. Tests of the moderated mediation model The mediation model with internet expectancy T1 as mediator was examined firstly. Perceived stress T1 was significantly associated with internet expectancy T1 (β = 0.25, p < .001). Moreover, both perceived stress T1 and internet expectancy T1 positively predicted PIU T2 (β = 0.35 and β = 0.24, p's < .001). In addition, the direct effect of perceived stress T1 on PIU T2 was reduced (β = 0.41 and 0.35 respectively, p's < .001) when perceived stress and internet expectancy were both included in the regression equation. Hence, adolescents' internet expectancy was found to partially mediate the relation between perceived stress and PIU. Then, the moderated mediation model which hypothesizes internet expectancy as the mediator and self-control as the moderator was tested. Gender and PIU T1 were included as covariates. The overall model (Table 2) accounted for 38% variance of PIU, F (6, 776) =46.16, p < .001. Perceived stress T1 and internet expectancy T1 positively predicted PIU T2 (β = 0.38 and β = 0.23, p's < .001). Self-control T1 negatively predicted PIU T2 (β = −0.19, p < .001). Moreover, the interaction term of the mediator (internet expectancy T1) and moderator (self-control T1) was significant (β = −0.08, p < .01). The indirect effects through internet expectancy were conditional at different levels of the moderator self-control. All the conditional indirect effects were significant, as the 95% bias-corrected bootstrap CIs did not contain zero. The conditional indirect effect of perceived stress on PIU decreased from 0.10 for -1SD self-control over 0.07 for Mean self-control to 0.04 for +1SD self-control. In other words, the indirect effect of perceived stress on PIU via internet expectancy weakened when the level of self-control increased. Fig. 1 displays the interaction of internet expectancy and self-control in predicting PIU and shows that internet expectancy predicted PIU better with lower self-control.
2.3. Data analyses The mediation model was tested using a series of regression equations, following procedures outlined by Baron and Kenny (1986). The moderated mediation model was examined using model 14 of the Process Macro for SPSS (Preacher & Hayes, 2008). The moderated mediation effect is demonstrated when the interaction of the mediator and the moderator reaches statistical significance and the bootstrapped confidence interval of the conditional indirect effects does not contain zero. A bootstrapped confidence interval was created with 5000 resamples. All analyses were performed using SPSS version 24.0.
3.4. Gender-specific analyses 3. Results As this study found that adolescent males reported significantly higher scores of PIU and lower scores of self-control as compared to adolescent females, the moderated mediation model was further tested in the male and female subsample. In the male and female subsample, internet expectancy T1 partially mediated the link between perceived stress T1 and PIU T2.The association between internet expectancy T1 and PIU T2 was stronger for adolescent males (β = 0.43, p < .001) than for adolescent females (β = 0.21, p < .001). However, the moderated mediation effect of self-control was significant only for the female subsample only. For adolescent females, the interaction of selfcontrol T1 and internet expectancy T1 was significant in predicting PIU T2 (β = −0.13, p < .001). The 95% bias-corrected bootstrap CIs for the conditional indirect effect at -1SD self-control, Mean self-control, and + 1SD self-control did not contain zero (CI: 0.05 to 0.20, 0.03 to 0.10, and 0.02 to 0.07). By contrast, the moderating effect of selfcontrol T1 on the link between internet expectancy T1 and PIU T2 was not significant in the male subsample (β = −0.03, n.s.) (Figs. 2 and 3).
3.1. Missing data analyses A logistic regression was conducted by examining whether the 110 participants who did not fill in the questionnaire at T2 differ from those who completed the survey at both time points. A logistic regression was conducted to regress participation (1 = yes, 0 = no) on all study variables (gender, age, family type, perceived stress T1, internet expectancy T1, self-control T1, and PIU T2). No significant predictors of attrition were identified (all ps > .05), indicating that the longitudinal sample is representative of the total sample. The final sample was composed of 782 participants. There was a small amount of data missing at the item level (< 1%) for the study variables. The MCAR test indicated that missing values were randomly distributed (p > .5). The missing values were handled with mean imputation. 3.2. Preliminary analyses
4. Discussion Means, SD, and inter-correlations between the study variables are presented in Table 1. As anticipated, PIU was positively correlated with perceived stress (r's range from 0.31 to 0.54, p's < .01) and internet expectancy (r's range from 0.22 to 0.35, p's < .01), and negatively related to self-control (r's range from −0.20 to −0.24, p's < .01). Moderate correlations were observed for the study variables at the two time points (r's range from 0.42 to 0.52, p's < .01). Regarding the
Corroborating previous empirical research (Brand et al., 2014; Wu et al., 2016), this study found that internet expectancy mediated the association between perceived stress and PIU. Internet expectancy are expected internet use outcomes including both positive reinforcement (e.g. pleasure seeking) and negative reinforcement (e.g. tension reduction). When stressed adolescents repeatedly experience these 3
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Table 1 Means, standard deviations, and bivariate correlations of study variables (N = 782).
1.Perceived stress T1 2. Perceived stress T2 3. Internet expectancy T1 4. Internet expectancy T2 5. Self-control T1 6. Self-control T2 7. PIU T1 8. PIU T2 Range Mean SD
1
2
3
4
5
6
7
8
– 0.50⁎⁎ 0.45⁎⁎ 0.35⁎⁎ −0.37⁎⁎ −0.26⁎⁎ 0.54⁎⁎ 0.31⁎⁎ 22–66 29.96 16.40
– 0.25⁎⁎ 0.44⁎⁎ −0.20⁎⁎ −0.31⁎⁎ 0.34⁎⁎ 0.54⁎⁎ 22–66 28.10 15.39
– 0.49⁎⁎ −0.42⁎⁎ −0.31⁎⁎ 0.34⁎⁎ 0.22⁎⁎ 22–110 51.88 20.04
– −0.34⁎⁎ −0.41⁎⁎ 0.26⁎⁎ 0.35⁎⁎ 22–110 49.90 21.27
– 0.42⁎⁎ −0.21⁎⁎ −0.20⁎⁎ 1–5 2.83 1.12
– −0.23⁎⁎ −0.24⁎⁎ 1–5 3.00 1.15
– 0.52⁎⁎ 26–104 40.82 28.50
– 26–104 41.43 28.66
Note. PIU = problematic internet use. ⁎⁎ p < .01. Table 2 Regression analyses for testing moderated mediation effects (N = 782). Variables
β
t (Sig.)
DV = PIU T2 Perceived stress T1 (predictor) Internet expectancy T1 (mediator) Self-control (moderator) Internet expectancy T1 ∗ Self-control T1 PIU T1 (covariate) Gender (covariate) R2
0.32 0.23 −0.19 −0.08 0.40 −0.06 0.38
10.74⁎⁎ 4.44⁎⁎ −6.38⁎⁎ −2.96⁎ 0.13.39⁎⁎ −4.38⁎⁎
Direct effect Conditional indirect effect at different values of self-control
−1 SD below mean Mean +1 SD above mean
Value
SE
Confidence interval
0.13 0.10 0.07 0.04
0.03 0.02 0.01 0.01
[0.08, [0.03, [0.03, [0.02,
Note. Gender: 1 = male, 2 = female. PIU = problematic internet use. ⁎ p < .05. ⁎⁎ p < .01.
3 2.8 2.6 2.4
PIU
2.2 LowSC 2
MediumSC
1.8
HighSC
1.6 1.4 1.2 1 LowIE
MediumIE
HighIE
Fig. 1. Interaction between Internet expectancy and self-control in predicting PIU (N = 782). PIU = problematic internet use, IE = internet expectancy, SC = self-control. All of the three slopes are significant at the 0.05 level.
4
0.19] 0.17] 0.08] 0.07]
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Fig. 2. Moderated mediation model predicting PIU in adolescent females (n = 388). PIU = problematic internet use. R2 = 0.3681. ***p < .001, **p < .01.
Fig. 3. Moderated mediation model predicting PIU in adolescent males (n = 394). PIU = problematic internet use. R2 = 0.3592 ***p < .001, **p < .01.
study demonstrates the applicability of DST to PIU and implies that investigating the impulsive, the reflective system, as well as their interactions should lead to a richer and deeper understanding of PIU. As one single study cannot address all the factors, future research could take into account other components of the impulsive system (e.g. trait impulsivity) and the reflective system (e.g. emotional regulation) (Liau
reinforcing effects, the expectancy memory network is established (Jones et al., 2001), which provokes strong urges towards internet use in future stressful situations. Moreover, as hypothesized, the mediation effect was found to be moderated by self-control. The identified moderating role of self-control provides at least three important theoretical implications. First, this
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et al., 2015; Turel & Qahri-Saremi, 2016). Second, the present study showed that adolescents with higher self-control are more likely to manage their internet use behavior despite the strong impulses. A large number of studies have suggested that self-control is negatively and directly related to PIU (Koo & Kwon, 2014; Özdemir et al., 2014) or its subtypes, e.g. problematic mobile phone use (Jiang & Zhao, 2017). This study demonstrated that self-control could moderate the effects of risk factors (perceived stress and internet expectancy in this study) and act as a protective factor against PIU. Third, this study echoed previous findings on gender difference of self-control in predicting PIU. The finding that girls displayed a stronger ability to inhibit impulses and regulate behaviors is in agreement with previous studies (Duckworth & Seligman, 2006). Moreover, in line with Li et al. (2010), this study found that adolescent female's self-control moderated the relationship between internet expectancy and PIU, yet such moderating effect was not observed for males. With regard to practical implications, this study implied that enhancing adolescents' self-control could be an important component of PIU intervention and prevention programmes. One study showed that teaching adolescents self-regulation skills through group games and discussions was effective in reducing risk of PIU (Du, Jiang, & Vance, 2010). Future practice may consider incorporating other strategies that have proven effective in strengthening self-control, e.g. regular practice of small acts of inhibiting urges and impulses (Muraven, 2010) and mindfulness meditation such as relaxing, visualizing, and attending to and accepting ongoing thoughts, feelings, or sensations (Friese, Messner, & Schaffner, 2012). Findings of the current study need to be weighed against the backdrop of several limitations. First, because the study variables were assessed at two time points, the current research tends to underestimate the indirect associations when compared with three-wave data (Little, 2013). Nevertheless, the longitudinal design adopted in this study facilitated us to establish the temporal precedence between the mediator and the outcome. Second, the moderated mediation model tested in this study accounted for about one-third of the variance in PIU, which suggest that there might be other risk and protective factors not included in this study. Previous research has identified multiple risk factors such as psychopathology, personality traits, parenting, and school bullying (Anderson et al., 2017). There are also a growing number of studies on protective factors such as resilience, emotional competence and coping skills (Robertson, Yan, & Rapoza, 2018). Third, the data were based on adolescent self-reported measures. Although previous research has shown that teenage self-report of problematic behavior would not be strongly biased under conditions of confidentiality (Tourangeau & Yan, 2007), future studies should consider collecting data from multiple informants and multiple methods. Despite these limitations, the current study delineated how the mediation effect of internet expectancy on the relation between stress and PIU was moderated by self-control, based on a sound theoretical foundation and rather strong empirical evidence.
Chen, S. H., Weng, L. J., Su, Y. J., Wu, H. M., & Yang, P. F. (2003). Zhong wen wang lu cheng yin liang biao zhi bian zhi yu xin li ji liang te xing yan jiu. [Development of a Chinese Internet addiction scale and its psychometric study]. Chinese Journal of Psychology, 45, 279–294. Davis, R. (2001). A cognitive-behavioral model of pathological internet use. Computers in Human Behavior, 17, 187–195. Du, Y. S., Jiang, W., & Vance, A. (2010). Longer term effect of randomized, controlled group cognitive behavioural therapy for Internet addiction in adolescent students in Shanghai. Australian and New Zealand Journal of Psychiatry, 44, 129–134. Duckworth, A. L., & Seligman, M. E. (2006). Self-discipline gives girls the edge: Gender in self-discipline, grades, and achievement test scores. Journal of Educational Psychology, 98, 198–208. Evans, J. S. B. T. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278. Friese, M., Messner, C., & Schaffner, Y. (2012). Mindfulness meditation counteracts selfcontrol depletion. Consciousness and Cognition, 21, 1016–1022. Gu, M. (2012). Study of adolescents' Internet use and Internet addiction in Shanghai, China: Implications for social work practice (Doctoral dissertation)Hong Kong: The Chinese University of Hong Kong. Hofmann, W., Friese, M., & Strack, F. (2009). Impulse and self-control from a dual-systems perspective. Perspectives on Psychological Science, 4, 162–176. Jiang, Z., & Zhao, X. (2017). Brain behavioral systems, self-control and problematic mobile phone use: The moderating role of gender and history of use. Personality and Individual Differences, 106, 111–116. Jones, B. T., Corbin, W., & Fromme, K. (2001). A review of expectancy theory and alcohol consumption. Addiction, 96, 57–72. Koo, H. J., & Kwon, J. H. (2014). Risk and protective factors of Internet addiction: A metaanalysis of empirical studies in Korea. Yonsei Medical Journal, 55, 1691–1711. Lam, L. T., Peng, Z. W., Mai, J. C., & Jing, J. (2009). Factors associated with Internet addiction among adolescents. Cyberpsychology & Behavior, 12, 551–555. Lee, Y. H., Ko, C. H., & Chou, C. (2015). Re-visiting Internet addiction among Taiwanese students: A cross-sectional comparison of students' expectations, online gaming, and online social interaction. Journal of Abnormal Child Psychology, 43, 589–599. Leung, L. (2014). Predicting Internet risks: A longitudinal panel study of gratificationssought, Internet addiction symptoms, and social media use among children and adolescents. Health Psychology and Behavioral Medicine, 2, 424–439. Li, D., Zhang, W., Li, X., Zhen, S., & Wang, Y. (2010). Stressful life events and problematic Internet use by adolescent females and males: A mediated moderation model. Computers in Human Behavior, 26, 1199–1207. Liau, A. K., Neo, E. C., Gentile, D. A., Choo, H., Sim, T., Li, D., & Khoo, A. (2015). Impulsivity, self-regulation, and pathological video gaming among youth: Testing a mediation model. Asia-Pacific Journal of Public Health, 27, 2188–2196. Lin, M. P., Ko, H. C., & Wu, J. Y. W. (2008). The role of positive/negative outcome expectancy and refusal self-efficacy of Internet use on Internet addiction among college students in Taiwan. Cyberpsychology & Behavior, 11, 451–457. Lin, M. P., Wu, J. Y. W., Chen, C. J., & You, J. (2018). Positive outcome expectancy mediates the relationship between social influence and Internet addiction among senior high-school students. Journal of Behavioral Addictions, 7, 292–300. Little, T. D. (2013). Longitudinal structural equation modeling. New York: The Guilford Press. Liu, Q. X., Fang, X. Y., Wan, J. J., & Zhou, Z. K. (2016). Need satisfaction and adolescent pathological Internet use: Comparison of satisfaction perceived online and offline. Computers in Human Behavior, 55, 695–700. Mai, Y., Hu, J., Yan, Z., Zhen, S., Wang, S., & Zhang, W. (2012). Structure and function of maladaptive cognitions in Pathological Internet Use among Chinese adolescents. Computers in Human Behavior, 28, 2376–2386. Mak, K. K., Lai, C. M., Ko, C. H., Chou, C., Kim, D. I., Watanabe, H., & Ho, R. C. (2014). Psychometric properties of the revised Chen internet addiction scale (CIAS-R) in Chinese adolescents. Journal of Abnormal Child Psychology, 42, 1237–1245. McIntosh, E., Gillanders, D., & Rodgers, S. (2010). Rumination, goal linking, daily hassles and life events in major depression. Clinical Psychology & Psychotherapy, 17, 33–43. Muraven, M. (2010). Building self-control strength: Practicing self-control leads to improved self-control performance. Journal of Experimental Social Psychology, 46, 465–468. Özdemir, Y., Kuzucu, Y., & Ak, Ş. (2014). Depression, loneliness and Internet addiction: How important is low self-control? Computers in Human Behavior, 34, 284–290. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891. Robertson, T. W., Yan, Z., & Rapoza, K. A. (2018). Is resilience a protective factor of internet addiction? Computers in Human Behavior, 78, 255–260. Shanghai Municipal Statistics Bureau (2011). Shanghai qing shao nian ren kou xian zhuang yu te zheng fen xi [Analysis on conditions and characteristics of adolescents in Shanghai]. Retreived from: www.statssh.gov.cn/html/fxbg/201111/236190. html. Sim, H. O. (2000). Relationship of daily hassles and social support to depression and antisocial behavior among early adolescents. Journal of Youth and Adolescence, 29, 647–659. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72, 271–324. Tourangeau, R., & Yan, T. (2007). Sensitive questions in surveys. Psychological Bulletin, 133, 859–883. Turel, O., & Qahri-Saremi, H. (2016). Problematic use of social networking sites:
References Aboujaoude, E. (2010). Problematic internet use: An overview. World Psychiatry, 9, 85–90. Akin, A., & Iskender, M. (2011). Internet addiction and depression, anxiety and stress. International Online Journal of Educational Sciences, 3, 138–148. Anderson, E. L., Steen, E., & Stavropoulos, V. (2017). Internet use and Problematic Internet Use: A systematic review of longitudinal research trends in adolescence and emergent adulthood. International Journal of Adolescence and Youth, 22, 430–454. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Beard, K. W., & Wolf, E. M. (2001). Modification in the proposed diagnostic criteria for internet addiction. Cyberpsychology & Behavior, 4, 377–383. Brand, M., Laier, C., & Young, K. S. (2014). Internet addiction: Coping styles, expectancies, and treatment implications. Frontiers in Psychology, 5, 1256. Brown, S. A. (1993). Drug effect expectancies and addictive behavior change. Experimental and Clinical Psychopharmacology, 1, 55–67.
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M. Gu
Whang, L. S. M., Lee, S., & Chang, G. (2003). Internet over-users' psychological profiles: A behavior sampling analysis on internet addiction. Cyberpsychology & Behavior, 6, 143–150. Wu, J. Y. W., Ko, H. C., Tung, Y. Y., & Li, C. C. (2016). Internet use expectancy for tension reduction and disinhibition mediates the relationship between borderline personality disorder features and Internet addiction among college students–One-year follow-up. Computers in Human Behavior, 55, 851–855.
Antecedents and consequence from a dual-system theory perspective. Journal of Management Information Systems, 33, 1087–1116. Unger, A., Bi, C., Xiao, Y. Y., & Ybarra, O. (2016). The revising of the Tangney Self-control Scale for Chinese students. PsyCh Journal, 5, 101–116. Wang, L., Luo, J., Bai, Y., Kong, J., Luo, J., Gao, W., & Sun, X. (2013). Internet addiction of adolescents in China: Prevalence, predictors, and association with well-being. Addiction Research & Theory, 21, 62–69.
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