روانشناسی بالینی و شخصیت

روانشناسی بالینی و شخصیت

پیشبینی اعتیاد به شبکه های اجتماعی مجازی بر اساس صفات شخصیت: نقش میانجی گر عزت‌نفس

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری روانشناسی عمومی، گروه روانشناسی، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران
2 استادیار، گروه روانشناسی تربیتی و روانشناسی شخصیت، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران
چکیده
مقدمه: افزایش استفاده از شبکه‌های اجتماعی، به‌ویژه در میان دانشجویان، نگرانی‌هایی در مورد وابستگی به این فضاها ایجاد کرده است. پژوهش‌ها نشان می‌دهند که ویژگی‌های شخصیتی و سطح عزت نفس می‌توانند در میزان گرایش به شبکه‌های اجتماعی نقش داشته باشند. هدف پژوهش حاضر، بررسی نقش صفات شخصیتی در پیش‌بینی اعتیاد به شبکه‌های اجتماعی با میانجی‌عزت نفس در دانشجویان بود.
روش: جامعه آماری شامل دانشجویان دانشگاه آزاد اسلامی یکی از واحدهای استان تهران بود. نمونه‌ای به حجم ۴۳۹ نفر به روش خوشه‌ای تصادفی چند مرحله‌ای انتخاب شد. داده‌ها از طریق پرسش‌نامه اعتیاد به شبکه های اجتماعی مبتنی بر موبایل خواجه احمدی و همکاران (۱۳۹۵)، فرم کوتاه پرسش‌نامه سنجش صفات پنج گانه شخصیتی (کاستا و مک کری، 1989 م) و پرسش‌نامه عزت نفس روزنبرگ (۱۹۶۵م) گرداوری شد. برای تجزیه و تحلیل داده‌ها از روش تحلیل مسیر استفاده گردید.
نتایج: یافته‌ها نشان داد که صفات شخصیتی می‌توانند هم به صورت مستقیم و هم از طریق میانجی‌گری عزت نفس با اعتیاد به شبکه‌های اجتماعی مرتبط باشند. عزت نفس رابطه بین صفات برون‌گرایی، سازگاری، تجربه‌پذیری و وظیفه‌شناسی با اعتیاد به شبکه‌های اجتماعی را به صورت منفی و رابطه بین عامل روان رنجوری و اعتیاد به شبکه‌های اجتماعی را به صورت مثبت میانجی‌گری کرد. همچنین، عزت نفس توانست اعتیاد به شبکه‌های اجتماعی را به صورت مستقیم و منفی پیشبینی کند.
بحث و نتیجه‏ گیری: با توجه به نقش صفات شخصیتی و عزت نفس در پیش بینی اعتیاد به شبکه‌های اجتماعی، میتوان چنین نتیجه‌ گرفت که تقویت عزت نفس و آموزش مدیریت بهینه استفاده از شبکه‌های اجتماعی می­ توانند در کاهش اعتیاد به این شبکه‌ها مؤثر باشند.
کلیدواژه‌ها

عنوان مقاله English

Predicting Social Network Addiction Based on Personality Traits: The Mediating Role of Self-Esteem

نویسندگان English

Mahshad Tasnimi 1
Solmaz Dabiri 2
Seyed Ali Hosseini Almadani 2
1 PhD Candidate of Psychology, Department of Psychology, NT.C., Islamic Azad University, Tehran, Iran.
2 Assistant Professor, Department of Educational Psychology, NT.C., Islamic Azad University, Tehran, Iran.
چکیده English

Introduction: Social network addiction is increasingly prevalent among university students, affecting their academic performance and mental well-being. The aim of this study was to predict social network addiction based on personality traits with the mediating role of self-esteem among university students.

Method: This study employed a descriptive correlational research design, emphasizing path Analysis. The statistical population consisted of students from the Islamic Azad University. A multistage cluster random sampling method was used, resulting in a sample size of 439 participants. Data were collected using the Mobile-Based Social Network Addiction Questionnaire (Khajeh Ahmadi et al., 2016), the NEO Five-Factor Inventory-Short Form (Costa & McCrae, 1989), and the Rosenberg Self-Esteem Questionnaire (1965). Path analysis was employed for data analysis, and data were processed using SPSS and AMOS software.

Results: The findings revealed that personality traits were associated with social network addiction both directly and indirectly through the mediating role of self-esteem. Self-esteem negatively mediated the relationship between extraversion, agreeableness, openness to experience, and conscientiousness and social media addiction, while positively mediating the relationship between neuroticism and social media addiction. Furthermore, self-esteem directly and negatively predicted social media addiction.

Discussion and conclusion: The findings highlight the importance of self-esteem in reducing social media overuse. Strengthening self-esteem and promoting responsible digital habits such as social network management strategy may help mitigate social network addiction among students.

کلیدواژه‌ها English

Social networks
addiction
personality traits
self-esteem
path analysis
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