Social Interaction Anxiety and Problematic Smartphone Use Among Rural-Urban Adolescents in China: A Moderated Moderated-Mediation Model

Abstract

This study explored the relationship between social interaction anxiety (SIA) and problematic smartphone use (PSU) among Chinese adolescents. Further, the roles of online basic psychological needs satisfaction (online BPNS), nature connectedness (NC), and related differences between urban and rural adolescents were examined. We recruited 840 Chinese adolescents to complete a questionnaire. Results showed that online BPNS mediated the relationship between SIA and PSU after controlling for sex, age, duration of smartphone use, and rural-urban differences. NC moderated the mediating effect of online BPNS as a second-stage moderator, and the mediating effect was stronger for adolescents with lower NC. The indirect effect of SIA on PSU through online BPNS depended on NC. Our findings suggested that reducing SIA and online BPNS levels, enhancing connection with nature, and training adolescents (especially rural adolescents) on methods to seek social support and develop social abilities can effectively deal with PSU.

Keywords

social interaction anxiety problematic smartphone use online basic psychological need satisfaction nature connectedness rural-urban difference

Introduction

The diverse functions and portability of smartphones have made them a necessary element of everyday life and widely used worldwide (Yang et al., 2022). A recent report indicates that there are 1.01 billion internet users in China, including 158 million adolescents aged 6 to 19 years, and 99.6% of them were using smartphones to surf the internet in mid-2021; this number of users is increasing steadily (CNNIC, 2021). With the popularization of smartphones, problematic smartphone use (PSU) has been receiving research attention (P. Wang & Lei, 2021). PSU, also often referred to as smartphone addiction (Montag et al., 2021) or nomophobia (Nahas et al., 2018), is broadly defined as a compulsive pattern of smartphone usage (Busch & McCarthy, 2021). Compulsive use refers to an uncontrollable overuse characterized by maladaptive dependency (increased frequency of use and compulsive checking; Berger et al., 2018) and a tendency to use the smartphone constantly, unwilling or unable to be separated from it (Canale et al., 2021). However, spending significant time on the smartphone does not necessarily mean that the user will experience negative consequences; it varies depending on functions and motivations for usage, across age, sex, culture, etc. (Busch & McCarthy, 2021; Q. Q. Liu et al., 2018). PSU can negatively affect individuals’ physically, psychologically, and behaviorally, leading to changes in the brain structure (Chun et al., 2018), impaired cognitive function (Wilmer et al., 2017), decreased academic performance (Samaha & Hawi, 2016), emotional health problems (Busch & McCarthy, 2021), and sleep disorders (Lemola et al., 2015). Addressing this issue requires an examination of the mechanisms involved to ensure that appropriate intervention methods are formulated.

Among the many influencing factors of adolescent PSU, the role of social interaction anxiety (SIA) has been highlighted (Turgeman et al., 2020) as an important cause of PSU (Elhai et al., 2018). A cognitive-behavioral model of pathological internet use proposes that the formation of internet addiction includes proximal and distal factors (Davis, 2001). According to Davis (2001), in the development of addiction, the initial factors are called distal causes, which include environmental and psychopathological factors. Alternatively, proximal causes, seen at the end of the process, include the individual’s maladaptive cognition concerning self and the environment. Furthermore, although people with high social anxiety often avoid social interactions, they are not necessarily uninterested in social activities (Brown et al., 2007). Such people are more inclined to interact with others online and are more likely to experience PSU (Turgeman et al., 2020). People choose to use mobile media to socialize to avoid the social anxiety present in real-world interactions, which is one of the reasons for the excessive use of social networks (Lee et al., 2014). In addition, people with social anxiety tend to participate in online games and social networks more frequently and have a stronger motivation to indulge in smartphone use (Hallauer et al., 2022).

Although studies have confirmed that SIA and PSU are inseparable, the mechanism by which SIA affects PSU remains unclear. Specifically, research is required to clarify the intermediary factors through which SIA affects PSU (mediation mechanism) and how the influence of SIA on PSU is regulated by other factors (regulation mechanism). Clarifying intermediary and regulation mechanisms can provide a scientific foundation and basis for the targeted development of adolescent social adaptation intervention programs that promote recovery from PSU.

Mediating Role of Online Basic Psychological Need Satisfaction

Online basic psychological need satisfaction (online BPNS) concerns an individual’s motivational level, an important internal driving force of individual behavior (R. M. Ryan & Deci, 2000). Compensatory satisfaction theory explains the role of online BPNS in PSU: When individuals discover the advantage of the internet over real life in satisfying their needs, they tend to seek satisfaction online instead of in real life (Q. X. Liu et al., 2016). Smartphones are the most widely used communication device, and their portability and availability make it easier for adolescents to seek psychological needs satisfaction. Online BPNS positively predicts adolescent PSU (Q. Liu et al., 2020).

In turn, SIA positively predicts adolescent online BPNS (Novak et al., 2021; Shen et al., 2013). The social needs of adolescents with high SIA are more difficult to meet, and they tend to avoid social situations (Asendorpf, 1990). Social interaction is one of the basic psychological needs of human beings. When this need is not met, an individual adopts measures to compensate (Weidman et al., 2012). The anonymity offered by the internet (Young & Lo, 2012) eliminates the adolescent anxiety and concerns endured during face-to-face communication (Valkenburg & Peter, 2009). Therefore, smartphone use is more likely to become a medium for satisfying psychological needs (Young & Lo, 2012). However, this compensatory approach may lead to smartphone overuse (Kardefelt-Winther, 2014). Therefore, we proposed the following:

Hypothesis 1: Online BPNS mediates the relationship between SIA and PSU.

Moderating Role of Nature Connectedness

Nature connectedness (NC) refers to an individual’s experience of a sense of connection with the natural environment (Zylstra et al., 2014). Individuals with a higher level of NC have greater contact with their natural environment (Hinds & Sparks, 2008), with many positive cognitive and affective effects. For example, NC may enhance happiness by satisfying individuals’ basic psychological needs concerning competence, relatedness, and autonomy (Passmore & Howell, 2014), addressing anxieties (Martyn & Brymer, 2016), improving physical health (Richardson et al., 2016), contributing to holistic well-being (Martin et al., 2020), and finding a sense of meaning in life (Lumber et al., 2017).

With the acceleration of the economy and urbanization, young people tend to experience more social anxiety (Buttazzoni et al., 2022; van der Wal et al., 2021) and are increasingly inclined to seek comfort and relieve anxiety via electronic products and the internet (Zhan et al., 2021). Simultaneously, their time in contact with the natural environment decreases, which may be further exacerbated by PSU. Nature has therapeutic effects, helps alleviate anxiety’s distressing symptoms, and demonstrates a decompression effect (Pritchard et al., 2020). The decompression effect of NC is mainly due to the restoration of self-control resources manifested at the social level, where it can enhance prosocial/moral behavior (Poon et al., 2016), promote cooperation, and environmentally sustainable behavior (Zelenski et al., 2015). Furthermore, at the individual level, it can reduce impulsive behavior (Taylor et al., 2002) and effectively suppress the need for instant gratification (Berry et al., 2014).

Therefore, NC is beneficial for alleviating online psychological needs (Richardson et al., 2018), enhancing self-control resources (Orkibi & Ronen, 2017), and reducing PSU (C. Wang et al., 2021). High online BPNS reduces the availability of self-control resources, but NC can alleviate psychological network needs and supplement self-control resources. According to natural decompression theory (Naor & Mayseless, 2021; Pritchard et al., 2020), NC may compensate for the negative effects of online psychological needs. Specifically, individuals with high NC are more likely to recover from high online psychological needs, whereas individuals with lower NC may turn to smartphones for compensation after failing to meet their psychological needs in real life. Therefore, we hypothesized the following:

Hypothesis 2: NC moderates the relationship between online BPNS and PSU.

Secondary Moderating Role of Rural-Urban Differences

China’s socio-economic development is characterized by regional differences, especially urban-rural differences (Yan et al., 2021). Urban areas have undergone tremendous social and economic changes, whereas rural areas remain relatively underdeveloped (B. B. Chen & Santo, 2016). This disparity has driven many rural laborers to seek opportunities in cities. Consequently, the social support of rural families has weakened, and the family structure has become unstable (Lv & Liu, 2011). Over time, this development is likely to be harmful to rural adolescents’ emotional health and social functioning (Fan & Lu, 2020; L. Wang & Mesman, 2015), particularly those left behind by parents working in cities, as it makes them more likely to develop problematic behaviors such as internet addiction (Guo et al., 2020). Particularly for children left behind in rural areas, the long-term weakening of parent-child relationships and communication may bring further adverse effects. A rural survey in China reported that PSU is more common in a significantly high proportion of rural adolescents (Xia, 2021). Although adolescents in rural areas are closely connected with nature, the absence of parental guidance pushes them to compensate through smartphone use. Compared to urban adolescents, left-behind children in rural areas, estimated at over 68 million (Chang et al., 2021), have weaker family and school guardianship (Jiang, 2019) and fewer recreational activities (Ren et al., 2017). These deficits make it difficult for them to escape the appeal of smartphones. Even in the absence of mobile games, the availability of many short videos and live broadcasts, which lack necessary viewing guidance, occupy their lives (H. Chen, 2021). As noted, high NC can weaken PSU among adolescents with high online BPNS. Therefore, we further proposed that compared to living in urban areas, living in rural areas would weaken the moderating effect of high NC between online BPNS and PSU.

Hypothesis 3: The indirect effect of SIA on PSU through online BPNS depends on NC and rural-urban differences.

Methods

Participants

After approval by the research ethics committee of the first author’s institution, we obtained informed consent from the teachers and students who participated in our study. Using convenience sampling and after excluding 38 students who do not use smartphones daily, the sample comprised 887 students (95.89% of the original sample) with experience using smartphones daily drawn from two public junior high schools in urban and rural areas in Shaanxi Province, China. Well-trained psychology postgraduate students explained the study overview and gave instructions to the participants, who were then invited to complete paper-and-pencil questionnaires anonymously. All 887 students completed the questionnaires in class. After excluding the questionnaires with regular answers and over 2% of data missing, 840 valid questionnaires were included (effective response rate, 94.70%).

Measures

Social interaction anxiety

Fergus et al. (2012) revised the short forms of the Social Interaction Anxiety Scale (SIAS) compiled by Mattick and Clarke (1998). The scale contains six items (e.g., “Feeling tense when talking about self or self-feelings”), each rated on a five-point Likert scale ranging from (1) “does not describe me well” to (5) “describes me very well.” High means of these items indicate high levels of SIA. The SIAS has good reliability and validity (Le Blanc et al., 2014). The Cronbach’s alpha in our study was .70.

Problematic smartphone use

PSU was measured using the smartphone addiction scale (Su et al., 2014). This 22-item scale has six dimensions: withdrawal behavior (e.g., “If I don’t have my phone handy for a while, I often worry about missing a call”), highlighting behavior (e.g., “My classmates and friends often say that I spend too much time on my smartphone”), social comfort (e.g., “I prefer chatting on my smartphone to communicating face-to-face”), negative impact (e.g., “My academic performance has dropped because of playing on my smartphone”), app use (e.g., “I often open mobile apps unconsciously”), and app update (e.g., “I always care about the updates of the existing apps in my smartphone and download the latest version”). Each item is rated on a five-point Likert scale ranging from (1) “does not describe me well” to (5) “describes me very well.” High means of these items indicate high levels of PSU. The scale has good reliability and validity and has been widely used to measure PSU among Chinese adolescents (Q. Liu et al., 2020). The Cronbach’s alpha in our study was .89.

Online basic psychological need satisfaction

The Online BPNS Scale (Shen et al., 2013) was adapted from previous studies (McAuley et al., 1989; Richer & Vallerand, 1998; Standage et al., 2005). The scale includes 12 items describing three types of needs: autonomy (e.g., “When I surf online, I can freely choose what I want to do”), competence (e.g., “I believe my performance on the internet is great”), and relatedness (e.g., “When I surf online, I feel supported by netizens”). Each item is rated on a seven-point Likert scale ranging from (1) “strongly disagree” to (7) “strongly agree.” High means of these items indicate high levels of adolescent online BPNS. The Cronbach’s alpha in our study was .73.

Nature connectedness

The closer the relationship between people, the more there are overlapping components within an individual’s cognitive representations concerning the relationship between the self and others (Mackay & Schmitt, 2019; Pritchard et al., 2020). Schultz (2002) used this concept to describe and explain the relationship between the self and the natural environment to assess the connection between an individual and the natural environment. The scale has only one item, is easy to measure and has a wide range of uses. Participants are presented with seven pairs of circles that represent the gradually increasing degree of overlap between “self” and “nature.” A high score indicates high connectedness between the self and nature (the natural environment).

Data Analysis

All data in the study were obtained from self-reported questionnaires. Missing data amounted to less than 1% of the total and were processed using mean imputation (Little & Rubin, 2002). We also applied Harman’s one-factor test to examine common method biases (Zhou & Long, 2004). The results of the unrotated factor analysis showed that 11 factors were generated, and the first principal factor explained only 19.26% of the variance, less than 40% of the threshold, indicating the absence of serious common method bias. We used IBM SPSS Statistics for Windows, Version 20.0, and PROCESS macro 3.5 software as applied by Hayes (2013) to perform descriptive statistics, correlation analysis, and moderated moderated-mediation analyses.

Results

Descriptive and Correlation Analyses

Table 1 presents the means, standard deviations, and Pearson correlations of all variables. The results showed that SIA, online BPNS, and PSU were positively correlated; NC was significantly negatively correlated with SIA, online BPNS, duration of smartphone use, and PSU; and residence in urban and rural areas was significantly positively correlated with duration of smartphone use and PSU. Therefore, the tendency for PSU in adolescents increased with an increase in SIA and online BPNS. Moreover, as their SIA levels increased, the adolescents tended to use smartphones to satisfy their online psychological needs. Consistent with previous studies (T. Ryan et al., 2014; Zhang & Wu, 2020), correlation analysis showed that sex, age, and duration of smartphone use were significantly correlated with personal smartphone use.

Table 1.

Descriptive Statistics and Correlations Among the Variables.

Variables	1	2	3	4	5	6	7	8
1. Sex	1
2. Age	−.06	1
3. Duration of smartphone use	−.06	.21**	1
4. SIA	.12**	.03	.06	1
5. Online BPNS	−.05	.17**	.12**	.12**	1
6. NC	.02	−.09*	−.12**	−.16**	−.10**	1
7. Rural-urban differences	−.02	−.02	.38**	.03	−.04	0.01	1
8. PSU	−.08*	.16**	.40**	.37**	.40**	−.25**	.21**	1
Mean	1.48	13.29	2.43	2.83	3.93	4.86	1.44	2.44
SD	.50	.74	2.41	.78	1.06	1.29	.50	.66

Note. N = 840. Sex and rural-urban differences were dummy coded such that 1 = male and 2 = female, 1 = urban area and 2 = rural area. SIA = social interaction anxiety; BPNS = basic psychological needs satisfaction; NC = nature connectedness; PSU = problematic smartphone use.

p < .05. **p < .01.

The results of the independent-samples t-test concerning sex and rural-urban differences showed that boys scored significantly higher than girls for PSU and girls scored significantly higher than boys for SIA. Furthermore, rural adolescents scored significantly higher for smartphone use duration and PSU than urban adolescents. Regarding online BPNS and NC variables, sex and urban-rural differences were not significant.

Testing for Mediation

The mediation analysis results using Model 4 of PROCESS macro (Hayes, 2013) are as follows: After controlling for covariates (sex and age), SIA positively predicted PSU (b = .38, p < .001), SIA positively predicted online BPNS (b = .12, p < .001), and online BPNS positively predicted PSU (b = .34, p < .001). The bias-corrected bootstrap mediation test (with a sampling frequency of 5,000 times) showed that the indirect effect was 0.04 and the 95% confidence interval (CI) was [0.01, 0.07]. The mediation effect accounted for 10.74% of the total effect (0.38). Consequently, online BPNS partially mediated the relationship between SIA and PSU. Thus, H1 was supported.

Testing for Moderated-Mediation Model

The results of the moderated-mediation analysis using Model 14 of the PROCESS macro (Hayes, 2013) are presented in Table 2. After controlling for covariates (sex, age, duration of smartphone use, and rural-urban differences), we found that online BPNS negatively and significantly interacted with NC on PSU, indicating that NC moderated the association of online BPNS with PSU.

Table 2.

The Moderating Role of NC in the Mediation Model.

Predictors	Model 1 (criterion: PSU)			Model 2 (criterion: online BPNS)			Model 3 (criterion: PSU)
Predictors	b	SE	t	b	SE	t	b	SE	t
Sex	−.19	0.06	−3.22***	−.10	0.07	−1.43	−.15	0.05	−2.82**
Age	.09	0.04	2.36*	.19	0.05	4.05***	.04	0.04	1.07
Duration of smartphone use	.13	0.01	9.86***	.05	0.02	2.93**	.12	0.01	9.64***
Rural-urban differences	.16	0.06	2.61**	−.17	0.07	−2.37*	.22	0.06	3.79***
SIA	.34	0.03	11.48***	.11	0.03	3.37***	.30	0.03	11.08***
Online BPNS							.32	0.03	11.73***
NC							−.13	0.03	−4.70***
Online BPNS × NC							−.07	0.02	−2.71**
R ²		.32			.06			.42
F		57.07***			9.93***			76.03***

Note. SIA = social interaction anxiety; BPNS = basic psychological needs satisfaction; NC = nature connectedness; PSU = problematic smartphone use.

p < .05. **p < .01. ***p < .001.

The simple slope analysis results are shown in panel A of Figure 1. The direct effect of online BPNS on PSU was weakened in the high NC group. Specifically, online BPNS was positively correlated with PSU for both adolescents with higher NC (B_simple = .25, p < .001) and those with lower NC (B_simple = .39, p < .001). Conditional indirect effect analysis further revealed that the indirect effect was more significant for adolescents with lower NC (β = .04, SE = 0.02, 95% CI = [0.02, 0.08]) than for those with higher NC (β = .03, SE = 0.01, 95% CI = [0.01, 0.05]). Thus, NC moderated the relationship between the mediator and PSU.

Figure 1.

Simple slope analysis.

Testing for Moderated Moderated-Mediation Model

The moderated moderated-mediation analysis results using Model 18 of PROCESS macro (Hayes, 2018) are presented in Table 3. After controlling for covariates (sex, age, smartphone use duration), we found that online BPNS positively and significantly interacted with NC and rural-urban differences on PSU, negatively and significantly interacted with NC on PSU, and negatively and significantly interacted with rural-urban differences in PSU. We also found that NC positively and significantly interacted with rural-urban differences in PSU. These findings indicated a three-way interaction between online BPNS, NC, and rural-urban differences in the model of PSU, with NC as the primary moderator and rural-urban differences as the secondary moderator.

Table 3.

Testing for the Moderated Moderated-Mediation Model.

Predictors	Model 1 (criterion: online BPNS)			Model 2 (criterion: PSU)
Predictors	b	SE	t	b	SE	t
Sex	−.10	0.07	−1.43	−.15	0.05	−2.88**
Age	.20	0.05	4.32***	.05	0.04	1.39
Duration of smartphone use	.03	0.01	2.18*	.12	0.01	10.04***
SIA	.11	0.03	3.33***	.30	0.03	10.92***
Online BPNS				.47	0.08	5.53***
RUD				.22	0.06	3.82***
NC				−.28	0.09	−3.34***
Online BPNS × NC				−.25	0.08	−2.98**
Online BPNS × RUD				−.10	0.05	−1.93*
NC × RUD				.11	0.05	2.04*
Online BPNS × RUD × NC				.12	0.05	2.32*
R ²		.05			.43
F		10.95***			57.34***

Note. SIA = social interaction anxiety; BPNS = basic psychological needs satisfaction; NC = nature connectedness; PSU = problematic smartphone use; RUD = rural-urban differences.

p < .05. **p < .01. ***p < .001.

The simple slope analysis results are shown in Figure 1 panel B. The moderation by NC of the indirect effect of SIA depended on rural-urban differences, and the moderation by rural-urban differences in the indirect effect of SIA depended on NC. Specifically, online BPNS positively predicted PSU for urban adolescents with lower NC (B_simple = .49, p < .001) compared to rural adolescents with lower NC (B_simple = .27, p < .001). Online BPNS positively predicted PSU for rural adolescents with higher NC (B_simple = .25, p < .001) compared with urban adolescents with higher NC (B_simple = .23, p < .001). Conditional indirect effect analysis further revealed that the indirect effect was more significant for urban adolescents with low NC (β = .06, SE = 0.02, 95% CI = [0.02, 0.10]) than for urban adolescents with high NC (β = .03, SE = 0.01, 95% CI = [0.01, 0.05]), rural adolescents with low NC (β = .03, SE = .02, 95% CI = [0.01, 0.07]), and rural adolescents with high NC (β = .03, SE = 0.01, 95% CI = [0.01, 0.06]). Thus, the moderation of the indirect effect of SIA by one moderator (NC) depended on the other moderator (rural-urban differences).

Discussion

The present study explored the relationship between SIA and PSU among adolescents to determine the potential mechanism of how and when SIA predicts PSU. As expected, the results revealed a positive association between SIA and PSU, the mediating effect of online BPNS, and the primary moderator (NC) and secondary moderator (rural-urban differences). The mediating effect of online BPNS was distinguished between different levels of NC and rural-urban differences.

Mediating Role of Online BPNS

As expected, in line with our hypothesis 1, SIA positively predicted PSU through the mediating effect of online BPNS. When individuals feel threatened or distressed owing to SIA, their online psychological needs increase. The anonymity of the internet (including mobile networks) (Young & Lo, 2012) can eliminate anxiety and worries present in face-to-face communication (Valkenburg & Peter, 2009), which tends to make adolescents with SIA seek satisfaction online through smartphones. Social support obtained through the internet may be better than that obtained through face-to-face interaction. Thus, the smartphone has become a tool for people with social anxiety to avoid negative emotions and influences, and escape from real-world problems (Chan & Cheng, 2016). This is similar to the reported “compensatory use” motivation involved in internet addiction (Valkenburg & Peter, 2009). Self-compensation is likely to improve an individual’s subjective feelings. However, it may also increase the duration of mobile phone use, owing to excessive attention to the virtual world, thereby causing PSU. This discovery has encouraged scholars to pay attention to online BPNS and attempt to generate solutions to the problem of PSU in adolescents with SIA. For example, educators can now address online BPNS and replace the excessive use of smartphones by guiding students with SIA to adopt healthier self-compensation methods, such as self-improvement.

Moderating Role of NC

The results illustrated that NC moderated the association between online BPNS and PSU, consistent with hypothesis 2. Specifically, compared to adolescents with higher NC, the indirect effect had a greater effect on adolescents with lower NC. Expressly, a high level of NC acted as an effective buffer in the moderated moderated-mediation model. When individuals feel threatened or distressed due to social anxiety, their online psychological needs will likely increase, leading to further PSU. However, high levels of NC can enhance internal positive experiences, such as positive emotions, high self-concordance (Passmore & Howell, 2014), and happiness (Richardson et al., 2016), and reduce psychological needs for the internet, thereby reducing the risk of PSU (Shen et al., 2013). This conclusion has practical significance. With the popularity of smartphones, SIA drives individuals to seek pleasure by phubbing, which refers to snubbing others in social situations by using a smartphone instead of talking to the person directly (Chotpitayasunondh & Douglas, 2016). However, an increased sense of connectedness between humans and the natural environment can effectively alleviate this phubbing trend. The results have a certain elucidating significance for the large number of urban-based individuals who often live their lives with little engagement with the natural environment.

Secondary Moderating Role of Rural-Urban Differences

We also found that NC played a moderating role in the moderated moderated-mediation model concerning social anxiety affecting PSU through online BPNS. Further, the moderation by NC of the indirect effect of SIA depended on rural-urban differences. Compared to urban adolescents, rural adolescents with lower online BPNS had a higher risk of PSU; compared to urban adolescents with higher online BPNS and high NC, rural adolescents with high online BPNS and high NC were more likely to have PSU. However, compared to rural adolescents with high online BPNS and low NC, urban adolescents were more likely to have PSU. These findings are consistent with the results of previous studies. It is necessary to focus on adolescents with low NC, especially urban adolescents whose risk of PSU is relatively high (Richardson et al., 2018). Conversely, it is necessary to focus on PSU, which is common among rural teenagers who are highly dependent on smartphones (Li & Hao, 2019).

Presently, preventing and resolving the problem of PSU has become a primary task of education institutions in China (King et al., 2018; Ministry of Education of China, 2021). Our study offers clues for reducing PSU and the negative effects of SIA among adolescents. First, educators and parents should focus more on adolescents with high SIA, who may have high psychological needs when experiencing negative emotions, potentially increasing their risk for PSU. First, reducing SIA levels can avoid the occurrence of multiple related potential problem behaviors. Second, adolescents with high SIA should be given active guidance to change their levels and patterns of online BPNS and thereby prevent the emergence of PSU. Third, attention should be paid to young people with high SIA and low NC. They may have a high level of online BPNS because their social communication needs cannot be satisfied, potentially increasing their PSU risk. Finally, there needs to be a greater awareness in society of the increased risk faced by rural adolescents for PSU.

Currently, little is known about the prevalence, possible risks, and preventive factors concerning PSU among adolescents in rural China. Our study contributes novel insights in highlighting specific issues young people face in rural areas. Therefore, educators and families of these adolescents should be especially attentive to them, care for them, strengthen their emotional interaction with them, and establish appropriate levels of intimacy to address the adverse effects of PSU. Promoting more extracurricular activities for teenagers in rural schools and encouraging communities to make full use of public places to provide entertainment spaces for teenagers would also be effective means to help prevent PSU among rural teenagers.

Limitations and Directions for Future Research

The results of this study provide strong evidence to support previous studies. However, this study is not without some limitations. First, although the cross-sectional design had a theoretical basis, it could not fully examine causality or directionality. Future research could apply experimental or longitudinal designs to verify the directionality of these relationships. Second, PSU includes different forms, such as generalized problematic use and specific problematic use (e.g., shopping, gaming, gambling; I. H. Chen et al., 2020), which may vary between individuals’ motivations for usage. These also require examination in future investigations. Third, it is important to note that the direct effect of SIA on PSU remained significant even after accounting for the mediating role of online BPNS. Future research could consider other factors that may explain this association, such as maladaptive emotion regulation strategies (Zsido et al., 2021). Finally, all data in this study were self-reported and may have been affected by social desirability and other factors. Future studies should consider multiple sources of data collection to improve the objectivity of the measurements.

Conclusion

Despite these limitations, the findings of this study are significant for the development of PSU research. Specifically, the results reveal that SIA had a significantly positive link to PSU, and online BPNS mediated the relationship between SIA and PSU. In addition, NC moderated the mediating effect of online BPNS as a second-stage moderator, and the mediating effect was stronger for adolescents with lower NC. We also found rural-urban differences. Thus, moderation of the indirect effect of SIA by one moderator (NC) depended on other moderators (rural-urban differences). In conclusion, this study highlighted the importance of uncovering the link between SIA and adolescent PSU and emphasized the underlying mechanisms, which are of great significance for the prevention and intervention of rural and urban adolescents’ PSU.

Footnotes

Acknowledgements

We wish to express our gratitude to Mrs. Mingyu Ma, Mr. Ankang Zhao, and Mr. Xinhong Chen for their helpful support in collecting data.

Author Contributions

Yuanyuan Chen: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Visualization, Writing-Original draft preparation, Writing-Reviewing and Editing. Yongquan Huo: Formal analysis, Methodology, Resources, Supervision, Validation.

Data Availability

Additional data and details from this study can be obtained by contacting the corresponding author.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Fundamental Research Funds for the Central Universities (2020TS018) through Shaanxi Normal University in mainland China.

ORCID iD

Yongquan Huo

Author Biographies

Yuanyuan Chen is a doctoral student in the Department of Psychology at Shaanxi Normal University. Her research interests are cyberpsychology and personality of adolescents.

Yongquan Huo is a professor of psychology at Shaanxi Normal University. His work focuses on the history of psychology and Chinese psychology. He has published six books and more than 100 articles, and is now the vice director of the Committee for the Theoretical and History of Psychology in the Chinese Psychological Society.

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