The Internet has become an important learning tool, communication bridge, and entertainment platform for Chinese adolescents, and its impact on their studies and life has increased with more widespread Internet use (Zheng, 2010). Rather than focusing solely on the negative impacts, such as pathological Internet use, some researchers have started to investigate positive outcomes as well, such as Internet altruistic behavior (IAB; Zheng et al., 2016), that is, helping behavior offered by individuals with no expectation of reward. IAB in adolescents has received substantial scholarly attention in the last decade (Guéguen, 2008; Wright & Li, 2011; Zheng & Zhao, 2015). Zheng (2013) found that Chinese teenagers, in particular, often engage in IAB compared to other Chinese age groups. Further, IAB has been found to be positively correlated with subjective well-being in adolescents (Zheng et al., 2016).

Subjective well-being (SWB) is a three-dimensional psychological concept reflecting people’s life satisfaction and the positive and negative affect they experience (Diener, 1994). SWB has been a focus of research for several decades (Frederick et al., 2016; Newman et al., 2014). Previous studies have indicated that adolescents’ SWB is positively influenced by factors such as IAB (Zheng & Wang, 2017; Zheng et al., 2016) and trait self-control (Briki, 2016, 2018; Hofmann et al., 2014; Ouyang et al., 2016).

Internet altruistic behavior (IAB) is online helping behavior offered by individuals with no expectation of any reward (Zheng & Zhao, 2015). Altruistic behavior in this context is a multidimensional construct comprising Internet support, Internet guidance, Internet sharing, and Internet reminding (Zheng, 2010). Internet support refers to the behavior of affirming, encouraging, and supporting others online. Internet guidance relates to helping others navigate the network environment. Internet sharing refers to sharing resources with others online. Internet reminding refers to alerting others to avoid scams or to be aware of Internet security. As Internet use has become increasingly widespread over the years, offering help online has become a common behavior; thus, IAB is the expansion of altruistic behavior from the real world to the Internet (Zheng, 2010). Altruistic behaviors online and in real life are similar in that they both entail conscious and voluntary actions aimed at helping others. Further, real-world altruistic behavior has been found to be positively correlated with and to predict IAB (Zheng, 2013). Wallace (1999) suggested that people may tend to offer more helping behaviors online than in real-world situations. Adolescents who engage in more IAB have been found to have a higher level of SWB (Zheng et al., 2017; Zheng & Wang, 2017).

Trait self-control refers to the stable and consistent capacity of the self to make changes to adapt to the environment (Tangney et al., 2004). Trait self-control motivates facilitative strategies and restrains goal-disruptive impulses (Hagger, 2014; Tangney et al., 2004). Research indicates that higher trait self-control is linked with higher life satisfaction, more positive affect, and less negative affect (Grund et al., 2015; Hofmann et al., 2014; Wiese et al., 2018). Further, compared with those who have lower trait self-control, individuals with higher trait self-control report better life satisfaction, higher academic achievement, more harmonious interpersonal relationships, better health, and less maladaptive adjustment (Tangney et al., 2004). Scholars have also revealed a significant positive correlation between trait self-control and SWB (Cheung et al., 2014; Yan et al., 2011). As a key determinant of SWB, trait self-control provides a basis when selecting intervention strategies that increase the level of SWB (Briki, 2019; Tu & Yang, 2016). De Ridder and Gillebaart (2017) proposed that trait self-control can enhance desired behaviors or decrease undesired behaviors; thus, trait self-control increases the level of SWB by promoting goal attainment and positive emotions.

However, the association between IAB and trait self-control in adolescents is unclear. It has been found that IAB has a significant positive predictive effect on self-efficacy, that self-efficacy is positively correlated with self-control (Zheng & Zhao, 2015), and that trait self-control is positively correlated with self-esteem (Tangney et al., 2004; Unger et al., 2016). Therefore, we predicted that IAB would be positively correlated with trait self-control in adolescents.

Studies have shown that trait self-control mediates the relationship between some variables and SWB. Trait self-control was found to partially mediate the relationship between exercise motivation and SWB (Briki, 2016), and to fully mediate the relationship between trait malicious jealousy and SWB (Briki, 2019). Numerous researchers have investigated the underlying mechanisms in the link between IAB and SWB (Zheng et al., 2016, 2018). It has been found that self-concordance (Zheng et al., 2018), self-efficacy (Zheng et al., 2016), and online social support all act as partial mediators (Zheng et al., 2017), which suggests that some other variables (e.g., self-esteem and personality traits) might also mediate the effect of IAB on SWB (Zheng et al., 2018). Therefore, this issue needs to be studied further, and we predicted that trait self-control would mediate the relationship between IAB and SWB.

Our hypotheses were as follows:
Hypothesis 1: Internet altruistic behavior will be positively correlated with trait self-control and subjective well-being.
Hypothesis 2: Trait self-control will be positively correlated with subjective well-being.
Hypothesis 3: Internet altruistic behavior and trait self-control will have a joint positive effect on subjective well-being.
Hypothesis 4: Trait self-control will mediate the relationship between Internet altruistic behavior and subjective well-being.

Method

Participants and Procedure

Participants were 415 Chinese adolescents (179 boys and 236 girls) recruited through convenience sampling from three middle schools in Shandong Province, China (M_age = 13.86 years, SD = 1.58, range = 12–17). The Institutional Review Board and Human Subjects Review Committee at the University of Jinan and the respective principals of the participating schools approved the study protocol. Before the formal investigation began, all participants were informed of the research purpose, guaranteed confidentiality of their responses, and advised that taking part in the study would be on a voluntary basis. Written consent forms were delivered to all participants’ parents, signed, and returned to the researchers. Questionnaires were completed anonymously during students’ regular class time using paper and pencil. The whole procedure took about 20 minutes. All participants were given a small gift at the end of the study.

Measures

Internet Altruistic Behavior
We used the Internet Altruistic Behavior Scale to evaluate IAB (Zheng et al., 2011). This scale has 26 items divided across four dimensions: Internet support (nine items), Internet guidance (six items), Internet sharing (six items), and Internet reminding (five items). A sample item is “I report harmful information on the Internet.” Participants rate the items on a 4-point Likert scale ranging from 1 (never or very rarely) to 4 (always or very often). Higher scores reflect higher levels of IAB. The Internet Altruistic Behavior Scale has demonstrated good reliability and validity in studies with Chinese adolescents (Zheng & Zhao, 2015; Zheng et al., 2016). Cronbach’s alpha in this study was .96.

Trait Self-Control
We evaluated trait self-control using the Chinese Self-Control Scale (Tan & Guo, 2008), which was adapted from the English-language version developed by Tangney et al. (2004). This scale has 19 items divided across five dimensions: impulse control (six items), healthy habits (three items), temptation avoidance (four items), work concentration (three items), and entertainment-harming resistance (three items). A sample item is “I have trouble concentrating.” Responses are rated on a 5-point Likert scale ranging from 1 (not at all like me) to 5 (very much like me). Higher scores indicate better trait self-control. Cronbach’s alpha in this study was .83.

Subjective Well-Being
We used the nine-item Index of Well-Being to measure SWB (Campbell et al., 1976) across the dimensions of general affect (eight items) and life satisfaction (one item). Participants rate the items on a 7-point Likert scale ranging from 1 (not a lot) to 7 (a lot). An aggregate score of SWB is formed based on the composite of the two subscales, and higher scores indicate higher levels of SWB. The Chinese version of the scale was revised by Wang et al. (1999) and has been shown to be valid and reliable (Li & Zhao, 2000; Liu et al., 2015; Zhou et al., 2011). Cronbach’s alpha in this study was .96.

Data Analysis

Statistical analyses were performed using SPSS 22.0 and Amos 24.0, via a three-stage process. In the first stage, common method bias was analyzed with Harman’s single-factor test by conducting an exploratory factor analysis. In the second stage, we calculated descriptive statistics and used Pearson correlation analysis to test the relationships between all the variables. In the final stage, all available data were used to test the mediating effect using a two-step modeling approach, as recommended by Anderson and Gerbing (1988): First, the validity of the measurement model was assessed; second, we used structural equation modeling with maximum likelihood estimation to check the fit of the model to the data.

Results

Assessing Common Method Bias

Because the research data were collected using self-report questionnaires, common method bias may have occurred in the measures (Podsakoff et al., 2003). For statistical control, we used Harman’s single-factor test and an unrotated principal components factor analysis to determine the number of factors needed to explain the variance in the key variables. To control for common method bias we followed the recommendation that at least two eigenvalues should be greater than 1.0 and that the largest variance explained should be less than the threshold of 40% (Podsakoff et al., 2003). There were eight eigenvalues above 1.0 and the variance explained by the first factor was less than the recommended threshold of 40%. No single factor explained most of the variance, which indicates that common method bias did not significantly affect the results.

Relationships Among Variables

Table 1 presents the descriptive statistics for all observed variables. The correlation analysis results indicate that IAB was positively correlated with both trait self-control and SWB, such that adolescents with higher levels of IAB also had higher levels of trait self-control and SWB (see Table 1). Similarly, trait self-control was positively correlated with SWB, suggesting that the higher the trait self-control, the greater the SWB. Thus, Hypotheses 1 and 2 were supported.

Mediating Effect of Trait Self-Control in the Relationship Between Internet Altruistic Behavior and Subjective Well-Being

The Measurement Model
To examine the psychometric properties of the measures in terms of reliability and convergent validity, we used Amos 24.0 to calculate factor loadings and average variance extracted (AVE) values. All factor loadings should be significant to indicate acceptable reliability (Anderson & Gerbing, 1988). AVE is applied to evaluate the degree to which the items of a particular factor converge or share a high proportion of variance (Hair et al., 2010), and values greater than .50 are acceptable. There were three latent factors (IAB, trait self-control, and SWB) and 11 observed variables in the measurement model.

The results reveal that the AVE values were acceptable for all variables (see Table 1). Further, all factor loadings for the indicators of the latent factors were statistically significant (p < .001), indicating satisfactory convergent validity. Overall, the reliability and validity of the measures were acceptable, and the respective indicators represented all latent variables well. Therefore, the structural model could be tested.

Structural Equation Modeling
We conducted structural equation modeling to test the significance of both direct and indirect paths by calculating standardized and unstandardized estimates (Preacher & Hayes, 2008). We also used a bootstrapping analysis with 5,000 resamples to generate 95% bias-corrected bootstrapped confidence intervals (CIs; Arbuckle, 2016).

First, we examined the direct and indirect effects and the comparisons of indirect effects between IAB and SWB. The results are presented in Table 2.

Next, we used structural equation modeling to investigate whether trait self-control mediated the relationship between IAB and SWB. In the model, trait self-control was the external latent variable as well as the mediating variable, and it was measured by five observed indicators: impulse control, healthy habits, temptation avoidance, work concentration, and entertainment-harming resistance. IAB was the internal latent variable and the dependent variable in the model, and it was measured by four observed indicators: Internet support, Internet guidance, Internet sharing, and Internet reminding. SWB was the internal latent variable and the independent variable in the model, and it was measured by general affect and life satisfaction. The mediation model is presented in Figure 1.

The structural model (see Figure 1) with trait self-control as a mediator revealed a good fit to the data, according to the criteria of Hu and Bentler (1999), chi square (degrees of freedom = 41) = 78.02, p < .001; 90% CI [.03, .06], root mean square error of approximation = .05, Tucker–Lewis index = .98, comparative fit index = .99, standardized root mean square residual = .03. IAB exerted significant and positive effects on trait self-control, β = .53, 95% CI [0.46, 0.60], p < .01, and on SWB, β = .43, 95% CI [0.34, 0.50], p < .05. Trait self-control positively influenced SWB, β = .22, 95% CI [0.11, 0.32], p < .05, and also played a partial mediating role in the relationship between IAB and SWB, β = .12, 95% CI [0.07, 0.18], p < .01. Therefore, Hypotheses 3 and 4 were supported.

Discussion

We examined the mediating role of trait self-control in the relationship between IAB and SWB. The findings offer insight into the underlying mechanisms of how IAB may promote SWB. Consistent with previous studies, our results indicate that IAB was positively correlated with SWB. In other words, adolescents who offered help more often to others online were more likely than were their peers to have a higher level of SWB. This supports the conclusions of previous researchers (Grant & Gino, 2010; Pareek & Jain, 2012; Zheng et al., 2016, 2017), who observed that IAB was a positive predictor of SWB. There are two possible reasons why IAB affects SWB: First, when adolescents perform IAB, they engage in online communication with someone who needs help. Online communication has been shown to increase adolescents’ well-being (Morgan & Cotten, 2003; Valkenburg & Peter, 2007). Second, the use of websites increases people’s perception of the availability of social support online (DeAndrea et al., 2012). In turn, social support has been shown to have a positive effect on SWB (Burns & Machin, 2010).

We also found that IAB was positively related to trait self-control: Adolescents who demonstrated more IAB were more likely than were their peers to have greater trait self-control. It is likely that, when engaging in IAB, adolescents need to mobilize their knowledge and skills to assist others, which requires them to commit time and effort when offering help, thus leading to them being more in control of their own behaviors. Therefore, adolescents with more IAB will have higher trait self-control.

Moreover, our data suggest that trait self-control was positively correlated with and predicted SWB. This finding supports previous conclusions regarding the positive association between trait self-control and SWB (Briki, 2019; Cheung et al., 2014; Hofmann et al., 2014; Wiese et al., 2018). It is likely that people with higher trait self-control are better at seeking benefits and avoiding disadvantages, and that individuals with a higher level of trait self-control have greater SWB (Hofmann et al., 2014). Our findings confirm past conclusions that trait self-control is a critical determinant of SWB (Briki, 2019; Tangney et al., 2004; Tu & Yang, 2016).

As hypothesized, our findings demonstrate that trait self-control partially mediated the relationship between IAB and SWB. Adolescents who perform more IAB tend to report greater SWB and to have higher trait self-control; as a result, adolescents with higher trait self-control benefit from increased SWB. In our study IAB had not only a direct effect on the SWB of adolescents, but also an indirect effect via the mediator of trait self-control. The partial mediating effect we observed indicates that there are other factors (e.g., personality traits, self-esteem, self-concordance, social support) that also mediate the effect of IAB on SWB (Zheng et al., 2016). In two separate studies, Zheng et al. (2016, 2018) found that self-concordance and self-efficacy acted as partial mediators of the link between IAB and SWB. Our findings indicate that trait self-control is another factor mediating this relationship.

This study has several practical implications for enhancing adolescent mental health. We found a direct effect of IAB on SWB, which indicates that educators could improve adolescents’ SWB by guiding them in how to use the Internet for the purpose of helping others. Additionally, our findings highlight the importance of improving trait self-control, because this variable significantly mediated the relationship between IAB and SWB in Chinese adolescents. Considering the possible mechanism we have identified, we suggest that educators develop psychological education programs to promote trait self-control in adolescents.

There are some limitations in this study. First, our data are correlational and, therefore, do not allow for interpretation of causality: It is impossible to conclude from our findings that trait self-control leads to SWB, IAB leads to trait self-control, or IAB leads to SWB through the mediator of trait self-control. Future studies could adopt a longitudinal design to investigate the causal relationships among these variables. Second, all the participants were Chinese adolescents who were a relatively homogeneous group; thus, our findings cannot be generalized to other age and cultural groups. Hence, it is necessary to replicate the results with different populations to increase the generalizability of our findings. Third, we investigated the mediating role of only one variable: trait self-control. Future studies could explore whether there are other factors moderating or mediating the link between IAB and SWB through trait self-control in adolescent populations. Finally, we recommend that further studies be conducted from an integrated viewpoint to explore the behavioral mechanisms underlying the relationship between IAB and SWB.

Despite these limitations, this is the first study to have investigated trait self-control as a mechanism underlying the influence of IAB on SWB in a sample of Chinese adolescents. First, our findings indicate that IAB was positively correlated with SWB, and that trait self-control was significantly related to SWB in Chinese adolescents. Thus, educators could seek to improve SWB by developing more IAB and trait self-control in adolescents. Second, our findings reveal that IAB had not only a direct impact on SWB, but also an indirect impact on SWB via the mediator of trait self-control, which further explains the inner mechanism of the connection between IAB and SWB. These findings provide a useful framework upon which to build potential interventions aimed at improving SWB in adolescents.

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Chao Peng and Weiyi Song contributed equally to this work and share first authorship.

Yuehua Tong, School of Education and Psychology, University of Jinan, No. 336 West Road of Nanxinzhuang, Jinan, Shandong 250022, People’s Republic of China. Email: [email protected], [email protected]