A Conceptual Model of Adolescents' Social Self-Efficacy, Seen from the Perspective of Resistance to Cyberspace Addiction

The article describes the conceptual model of adolescents’ social self-efficacy from the perspective of cyberspace addiction resistance. The theoretical foundations of social self-efficacy are built on the self-efficacy and social learning theory developed by Al. Bandura, complemented by B. Weiner’s theories of attribution, H. Gardner’s theories of multiple intelligences, and J. Bowlby’s attachment theory. These theories provide a complex understanding of how social self-efficacy develops and manifests itself, emphasizing the role of successful experiences, social role models, encouragement, and early relationships in forming and reinforcing self-confidence in social contexts.

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Publication Details

Published
2024-12-23
DOI
https://doi.org/10.5281/zenodo.14545339
Primary Topic
Bioinformatics and Genomic Networks
Type
article
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article

A Conceptual Model of Adolescents' Social Self-Efficacy, Seen from the Perspective of Resistance to Cyberspace Addiction

Bioinformatics and Genomic Networks
article

A Conceptual Model of Adolescents' Social Self-Efficacy, Seen from the Perspective of Resistance to Cyberspace Addiction

article en

Abstract

The article describes the conceptual model of adolescents’ social self-efficacy from the perspective of cyberspace addiction resistance. The theoretical foundations of social self-efficacy are built on the self-efficacy and social learning theory developed by Al. Bandura, complemented by B. Weiner’s theories of attribution, H. Gardner’s theories of multiple intelligences, and J. Bowlby’s attachment theory. These theories provide a complex understanding of how social self-efficacy develops and manifests itself, emphasizing the role of successful experiences, social role models, encouragement, and early relationships in forming and reinforcing self-confidence in social contexts.

Ion Creangă Pedagogical State University (MD)
Openalex Percentile: Top 62%
Bioinformatics and Genomic Networks
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