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<p>This study explored the mechanisms linking parental phubbing to adolescents’ cyberbullying perpetration and victimization, focusing on the mediating roles of self-control, self-esteem, and social networking sites (SNSs) use. A sample of 410 Turkish adolescents (<i>M</i><sub>age</sub> = 15.74) completed measures assessing perceived maternal and paternal phubbing, self-control, state self-esteem, SNSs use, and cyberbullying involvement. Using a hybrid analytical approach combining Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) analysis, the study identified both linear and non-linear associations. SEM results indicated that maternal, but not paternal, phubbing negatively predicted adolescents’ self-control, which in turn mediated cyberbullying behaviors. Self-control also influenced self-esteem and SNSs use, with SNSs use further predicting cyberbullying outcomes. ANN analysis supported these findings, highlighting self-control and self-esteem as the strongest predictors, with low root mean square error (RMSE) values demonstrating reliable numerical estimations. These findings underscore the differentiated impact of maternal and paternal behaviors on adolescents’ online risks and highlight the value of integrating machine learning methods in psychological research. Practical implications for intervention programs and directions for future research are discussed.</p>

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Ignored at Home, Acting Out Online: A SEM–ANN Model of Parental Phubbing and Adolescent Cyberbullying

  • 期刊论文

  • 发表日期：

    2025年11月2日

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  • M. Furkan Kurnaz

摘要

This study explored the mechanisms linking parental phubbing to adolescents’ cyberbullying perpetration and victimization, focusing on the mediating roles of self-control, self-esteem, and social networking sites (SNSs) use. A sample of 410 Turkish adolescents (Mage = 15.74) completed measures assessing perceived maternal and paternal phubbing, self-control, state self-esteem, SNSs use, and cyberbullying involvement. Using a hybrid analytical approach combining Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) analysis, the study identified both linear and non-linear associations. SEM results indicated that maternal, but not paternal, phubbing negatively predicted adolescents’ self-control, which in turn mediated cyberbullying behaviors. Self-control also influenced self-esteem and SNSs use, with SNSs use further predicting cyberbullying outcomes. ANN analysis supported these findings, highlighting self-control and self-esteem as the strongest predictors, with low root mean square error (RMSE) values demonstrating reliable numerical estimations. These findings underscore the differentiated impact of maternal and paternal behaviors on adolescents’ online risks and highlight the value of integrating machine learning methods in psychological research. Practical implications for intervention programs and directions for future research are discussed.