关键词: 青少年, 网络适应, 网络成瘾, 拓扑结构, 网络分析

Abstract:

As the saying goes, “Survival of the fittest”. Nowadays, the Internet has become a critical channel for information acquisition, social interaction, and educational learning. Adolescents’ internet adaptation capabilities must be continuously improved to adapt to this rapidly developing information age. Internet adaptation is inherently a “multidimensional system” encompassing various stages and dimensions. However, there remains a gap in the research exploring the internal topological characteristics and functional mechanisms of internet adaptation. Consequently, this study aims to employ network analysis techniques to elucidate the core characteristics, internal structure, dynamic evolution, and relationships with external variables of adolescents’ internet adaptation through network analysis. This approach will offer a comprehensive framework for understanding adolescents’ successful adaptation in the digital age and provide scientific insights for preventing and intervening in adolescent internet addiction.

This study collected all data through paper-and-pencil questionnaires. At Time 1, valid data were obtained from 5783 participants (Males for 37.4%, M_age = 17.20 years, SD = 2.62). Five months later, data from 1235 of these participants were tracked (Males for 38%, M_age = 14.98 years, SD = 1.66). Based on the research objectives, we conducted cross-sectional network analysis, network comparison, and cross-lagged network analysis. All cross-sectional and cross-lagged network analyses were primarily conducted using R (V.4.3.2). Network visualizations were created with the qgraph package (version 1.9.5). The accuracy of edge estimates was assessed by performing 1000 bootstrap iterations to construct 95% non-parametric bootstrap confidence intervals for each edge.

In the cross-sectional network of internet adaptation, “Internet curiosity” is the node with the highest strength (1.18). Network comparison results indicate no significant difference in the overall strength between the T1 (3.52) and the T2 network (3.79) (p = 0.120), although the network invariance test result is significant (p < 0.001). The cross-lagged network analysis shows that “Internet self-efficacy” has the strongest out-expected influence (0.60), “Internet learning ability” and “Internet information searching” has the strongest in-expected influence (0.31 & 0.30). Additionally, the cross-lagged network analysis of internet adaptation and internet addiction reveals that “Internet information protection capability” exhibits the strongest outgoing predictive ability.

The main conclusions are as follows: (1) Adolescent internet adaptation is characterized by its dynamic and staged nature; (2) Adolescents’ internet curiosity plays a multifaceted role in their internet adaptation process: insufficient curiosity can lead to low internet self-efficacy, while excessive curiosity can result in poor internet self-control; (3) Internet self-efficacy has the most significant impact on the overall development of internet adaptation, serving as the “primary driving force”. (4) Internet learning ability and internet information search receive the most internal influence, constituting the main “landing point” of adolescents’ internet adaptation. (5) Internet information protection is the strongest predictor of cross-cluster outgrowth of internet addiction networks, acting as a “guardian” of adolescents’ internet adaptation.

Key words: adolescents, internet adaptation, internet addiction, topological structure, network analysis