رابطه ساختاری بین سیستم های مغزی رفتاری با وابستگی به شبکه های اجتماعی با میانجی گری گری نظم جویی شناختی هیجانی در دانش آموزان (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
هدف از انجام این پژوهش، بررسی رابطه ساختاری بین سیستم های مغزی رفتاری و نظم جویی شناختی هیجانی با وابستگی به شبکه های اجتماعی در دانش آموزان بود. روش پژوهش توصیفی، از نوع همبستگی مبتنی بر روش مدل یابی معادلات ساختاری می باشد. جامعه آماری شامل دانش آموزان مقطع دوم متوسطه می باشند. برای تعیین حجم نمونه با توجه به تعداد متغیرهای مشاهده شده (17 متغیر) و تخصیص ضریب 15 برای هر متغیر مشاهده شده تعداد 261 نفر با روش نمونه گیری در دسترس به عنوان نمونه انتخاب شدند و به پرسشنامه های سیستم های مغزی رفتاری کارور و وایت (1994)، پرسشنامه نظم جویی شناختی هیجان گرانفسکی و همکاران (۲۰۰۱) و پرسشنامه وابستگی به شبکه های اجتماعی خواجه احمدی و همکاران (۱۳۹۵) پاسخ دادند. تجزیه وتحلیل داده ها با روش معادلات ساختاری و با استفاده از نرم افزار SPSS 18 و Amos 24 انجام شد. یافته ها نشان داد سیستم فعال ساز رفتاری با وابستگی به شبکه های اجتماعی رابطه مثبت و معنادار و سیستم بازداری رفتاری با وابستگی به شبکه های اجتماعی رابطه منفی و معنادار و نظم جویی شناختی هیجانی منفی با وابستگی به شبکه های اجتماعی رابطه مثبت و معنا دار و نظم جویی شناختی هیجانی مثبت با وابستگی به شبکه های اجتماعی رابطه منفی و معنا دار دارد. همچنین نظم جویی شناختی هیجانی میانجی گر رابطه سیستم های مغزی رفتاری و وابستگی به شبکه های اجتماعی است. مدل پژوهش نیز تأیید شد و به طورکلی 75 درصد از واریانس وابستگی به شبکه های اجتماعی توسط سیستم های مغزی رفتاری و نظم جویی شناختی هیجانی قابل تبیین می باشد.Structural Relationship Between Behavioral Brain Systems and Social Network Dependence Mediated by Emotional Cognitive Regulation in Students
Introduction
The era of information and networked society has brought forth the rise of web-based social media platforms, which have been successful in drawing users and influencing various trends and lifestyles (Shamshadi et al, 2019). Social networks have created new possibilities for human interactions, but these novel frontiers come with challenges as new factors emerge with each new window. Studying the effects of media in the context of social networks is complicated, given their status as a global technology (Mahmood et al., 2018). Gray (1981) first introduced the concept of two distinct brain systems related to social networks: the behavioral activation system and the behavioral inhibition system. These systems are believed to be sensitive to reward and punishment cues (Gray, 1981). Behavioral systems play a crucial role in controlling and regulating behaviors and emotions and provide the neurological basis for mood. The behavioral activation system is responsible for regulating behavior in the presence of rewards, and it is sensitive to both reward cues and the absence of punishment. The behavioral inhibition system, on the other hand, is responsible for regulating behavior in the presence of punishment. This system is associated with negative mood and increases avoidance behavior as well as feelings of anxiety (Albrecht & Stork, 2017). Research has indicated that the behavioral activation system positively correlates with social network dependence, whereas the behavioral inhibition system negatively correlates with social network dependence (Niko, 2016). Cognitive regulation of emotions plays a crucial role in initiating, evaluating, and organizing adaptive behaviors while preventing negative emotions and maladaptive behaviors (Heweh et al., 2017). This type of regulation is considered an important factor in influencing one's emotional state and decision-making process. The research conducted by Trumello et al. (2018) discovered a negative correlation between positive emotional regulation and dependence on social networks, as well as a positive correlation between negative emotional regulation and social network dependence. Additionally, Gholami et al.'s study (2019) found a negative relationship between the behavioral activation system and psychological vulnerability, while a positive correlation was observed between emotional dysregulation and psychological vulnerability. The current research aims to explore the role of behavioral brain systems (i.e., behavioral activation and inhibition systems) in conjunction with emotional cognitive regulation as potential factors influencing students' dependence on social networks.
Methodology
The study utilized a correlation-based design with structural equation modeling. The statistical population consisted of all students enrolled in high school. Considering the number of observed variables and a coefficient of 15 for each variable, a sample of 261 students was randomly selected through a cluster sampling method. Participating students completed the Carver & White Behavioral Inhibition/Activation Systems questionnaires (1994) and Garnefski et al.'s emotional regulation questionnaire. The data collected through questionnaires were analyzed using Pearson's correlation coefficient test and structural equation modeling techniques. The analyses were conducted using SPSS and AMOS software versions 24 and 23, respectively.
Results
The results revealed a significant positive correlation between the behavioral activation system and the use of negative emotional cognitive regulation strategies in relation to social network dependence. Meanwhile, a significant negative correlation was found between the behavioral inhibition system and positive emotional cognitive regulation strategies vis-à-vis social network dependence. The findings also confirmed that emotional-cognitive regulation strategies served as mediating factors in the relationship between behavioral brain systems and social network dependence.
Conclusion
The results of this research study indicated that the behavioral inhibition system, in conjunction with emotional cognitive regulation, influences one's dependence on social networks among students. This finding provides valuable insights into the factors impacting social media usage habits and behavior. The findings suggest that the behavioral activation system plays a crucial role in regulating pleasant motivations and experiences positive emotional states. Individuals who exhibit social network dependence may indeed display a stronger behavioral activation system, rendering them more susceptible to becoming dependent on these platforms. The results indicate that higher levels of behavioral activation are associated with increased tendencies to become socially network dependent. When the behavioral activation system is more sensitive, it likely intensifies individuals' enthusiasm for social network engagement. Individuals with high behavioral inhibition system activity tend to prefer less risky behaviors, such as internet usage, in order to obtain rewards. Due to the advantages of virtual and non-face-to-face interactions, such as freedom to enter and leave without limitations and the absence of direct physical harm, people may feel relieved from immediate anxiety caused by their behavior. However, this freedom can lead to problems, such as conflicts in chatrooms. Online gaming allows players to compensate for failures by leaving the chat room and resuming the game, reducing the immediate consequences. This risk-free virtual environment of the internet appeals to individuals who experience anxiety about the direct outcomes of their actions. On the other hand, negative cognitive regulatory strategies show a significant positive correlation with depression, addiction in various aspects of life, including internet and social network addiction, social anxiety, and stress. In contrast, positive cognitive regulatory strategies have a positive impact on emotional well-being and can reduce the risk of experiencing negative emotional states. Therefore, it can be concluded that negative cognitive and emotional regulation strategies contribute to an increase in dependence on social networks. Non-adaptive strategies and maladaptive emotional regulation are the foundation for the development and persistence of various forms of psychopathology. On the other hand, adaptive and healthy strategies in emotional regulation function as protective factors.
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