Self-Regulation and Social Network Sites Use as a Factors of Academic Motivation

Online social network intensity and intrusion

The term Intensity refers to the studies of N. Ellison and colleagues. They create a Facebook intensity scale for a testing hypothesis about the links between social capital and Facebook use ( Ellison et al., 2007). Generally speaking, Intensity refers to the Facebook activity; it describes the amount and frequency of contacts, the emotion, and the feel of a community. Facebook usage was found to interact with measures of psychological well-being, suggesting that it might provide more significant benefits for users experiencing low self-esteem and low life satisfaction. The frequency of Facebook uses also was found to be a positive predictor of Facebook addiction ( Busalim et al., 2019).

The other term – Intrusion, appears about Facebook also. Facebook Intrusion, referred to as problematic Facebook use, or Facebook addiction, is described as an excessive attachment to Facebook that negatively impacts on everyday life. Online social network sites addiction may be defined as being overly concerned about social media, driven by an uncontrollable motivation to login to or use social media, devoting so much time and effort in social media that it impairs other important life areas ( Griffiths, 2005). Various studies have highlighted the possible detrimental impacts of excessive Facebook use and Facebook addiction on an individual's general well-being, anxiety, depression, and general mood ( Błachnio & Przepiorka, 2016). Personality traits are also discussed in relation to Facebook addiction. It was found that personality traits that are associated with Facebook use were found to be associated with Facebook addiction also ( Błachnio et al., 2016; Rajesh & Rangaiah, 2020; Zafar et al., 2018). Summarizing, we may assume that Intrusion is characterized by an excessive attachment to Online social network, which interferes with everyday activities, and includes such symptoms as distress, relapse and reinstatement, and euphoria.

Self-regulation

Self-regulation is the ability to control actions, cognitions and emotions are critical to success across the different domains ( Carver & Scheier, 2001). At a broad level, self-regulation is multi-faceted and refers to a broad range of psychological processes that involve the integration of thoughts, emotions, and behavior ( Hofmann et al., 2012). More specifically, it refers to a cognitive system of information processing (including goal planning, modeling of significant conditions, programming of actions, and results evaluation), and, to instrumental personality-regulatory properties: flexibility, independence, reliability, Responsibility, etc. ( Morosanova & Fomina, 2017).

Concerning Morosanova’s approach, self-regulation is a construct integrating both cognitive and personal resources to solve various problems of vital activity ( Morosanova, & Bondarenko, 2015). Their studies demonstrated the positive impact of the self-regulation on academic success and performance reliability in educational situations ( Morosanova et al., 2018; Morosanova & Fomina, 2017).

The concept of self-regulation is interpreted through the various theoretical points of view and studies of the self-regulation role in the academic sphere. However, there is a small number of works concerning self-regulation and Internet behavior. As an example, due to the Social Online Self-regulation Theory, people use Facebook in order to self-regulate ( Ozimek & Förster, 2017).

Emotional regulation strategies

Many different terms are used to refer to emotion and emotion regulation-related processes, as well as many theoretical approaches toward emotion and emotion regulation exist. One commonly used framework for studying emotion regulation strategies is the process model of emotion regulation ( Gross, 2015). The process model of emotion regulation makes the prediction that different emotion regulation strategies – and the specific tactics by which these strategies are implemented in any given situation – should have different consequences for how a person feels, thinks, and acts, both immediately and over the longer term. Gross proposed two main emotional regulation strategies: cognitive reappraisal and expressive suppression. Cognitive reappraisal is an antecedent-focused strategy, a sort of cognitive change that requires construing a potentially emotion-eliciting situation in a way that changes its emotional impact. Expressive suppression is a response-focused strategy, is a form of response modulation that involves inhibiting ongoing emotion-expressive behavior ( Gross, 2015). Many studies revealed the individual differences among the persons who prefer one of the emotional regulation strategies, the role of interpersonal functioning and well-being, self-esteem, aggression, and implication to the clinical samples ( Chen et al., 2020; Zhang et al., 2020). The studies of Internet behavior including Online social network sites usage underlying the importance of emotion and their regulation in addiction formation ( Foroughi et al., 2019; Moretta & Buodo, 2018; Rajesh & Rangaiah, 2020).

Academic motivation

This study examines motivation on the ground of the self-determination theory (SDT) and its application to the academic motivation made by Vallerand et al. ( 1992) and colleagues. At the core of SDT is the theory that individual motivation can take many forms, differing from one another based on their degree of relative autonomy or self-determination. In the academic context, results from numerous studies supported the SDT assumption that more autonomous forms of motivations will be associated with more positive educational outcomes ( Litalien et al., 2017; Liu, 2015). The academic motivation includes internal and external motivation features, and amotivation as a lack of motivation or avoidance of activity. The primary outcome from the previous investigation between Facebook and achievements showed significant adverse effects of OSN use on students’ academic performance ( Busalim et al., 2019; Doleck & Lajoie, 2018).

Participants

The sample consists of 197 undergraduate students aged 18-24 (M = 19.59; SD = 2.21), 58 % female recruited to participate in the study in exchange for psychology course credits. Participants were informed of the nature of the study and were asked to give consent if they wished to participate; only those who gave consent have been included. Participants were completed an online survey as part of the testing session in their psychology course. The front page of the survey provided information on the nature of the study, as well as relevant ethical issues. At the end of the study, participants were debriefed and thanked. All participants are using the most popular Russian SNS – VKontakte as the main online social network site.

Measures

Social Network Site addiction was measured using the SNS Intrusion Questionnaire. This method was developed on the basis of the Facebook Intrusion Questionnaire ( Błachnio & Przepiorka, 2016; Elphinston & Noller, 2011). The only change in questions was the replacement of Facebook to VKontakte — the most popular Russian social network site. This measure contains eight items, scored on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Item responses are summed, with higher numbers indicating higher levels of SNS (VKontakte) addiction. This measure had good reliability, Cronbach’s alpha (α) = 0.79.

To assess the intensity of SNS use, we used the Online Social Network Site Intensity Scale. This measure was constructed on the basis of the Facebook Intensity Scale ( Ellison et al., 2007) with the change of the word “Facebook” to “VKontakte”. This scale measures emotional connection to Social Network sites and its integration into everyday life. The scale consists of six items rated on a 5-point Likert scale (1 = disagree strongly, 5= agree strongly). The internal reliability of the Online Social Network Site Intensity Scale of α = 0.83.

Two emotion regulation strategies were measured: cognitive reappraisal and suppression subscales with the Emotion Regulation Questionnaire (ERQ) ( Gross, 2015). ERQ is a 10-item self-report measure where each item is rated on a 7-point Likert scale (1 = strongly disagree; 7 = strongly agree). This questionnaire demonstrated an adequate Cronbach's alpha of cognitive reappraisal 0.79 and emotional suppression subscales 0.73 ( Pankratova & Korniyenko, 2017).

The 28 items from the Russian adaptation of Academic motivation scale AMS ( Gordeeva et al., 2014; Vallerand et al., 1992) were used to assess students' motivation toward educational process: internal motivation (intrinsic motivation to know; intrinsic motivation to experience stimulation; intrinsic motivation to accomplish; identified regulation); enteral motivation (introjected regulation; external regulation); amotivation. Participants rated each item using a 7-point Likert scale (1 = does not correspond at all, 7 = corresponds exactly). All scales demonstrated an adequate Cronbach's alpha value ranged from 0.76-0.91 ( Gordeeva et al., 2014).

Regulatory features were evaluated with the Self-Regulation Style Questionnaire ( Morosanova & Bondarenko, 2015). This questionnaire includes 46 statements and six scales: planning, modeling, programming, results evaluation, flexibility, independence. The general level of self-regulation as activity and personality features of self-regulation estimated by summing up the scores of initial scales. Cronbach's alpha value ranged from 0. 67-0.78.

Data analyses

All analyses were performed using SPSS version 20. First, zero-order correlations were calculated between SNS Intrusion, SNS Intensity and cognitive reappraisal, emotional suppression, activity self-regulation, and personality self-regulation. Further, the shared influence of emotional self-regulation, activity self-regulation, and personality self-regulation controlled for age and sex, were examined via hierarchical regression.

In a second step, in order to get a more detailed insight, hierarchical regression analyses were conducted to test the predictive value of regulation features on online social network sites addiction and intensity.

SNS Intensity and SNS Intrusion and self-regulation measures

We began by correlating the SNS Intensity and SNS Intrusion and self-regulation measures. At the zero-order level SNS Intensity (r = -0.24, p < 0.001), SNS Intrusion were correlated with self-regulation as activity (r = -0.26, p < 0.001). Next we correlated SNS Intensity and SNS Intrusion with the aspects of the emotional regulation. The SNS Intensity correlated only with emotional suppression (r = -0.18, p < 0.001). The SNS Intrusion was correlated with emotional suppression (r = -0.20, p < 0.001) and also with cognitive reappraisal (r = -0.17, p < 0.01). See table 1 for means and standard deviations. Next we examined the associations between the SNS Intensity and SNS Intrusion and academic motivation measures. The SNS Intensity correlated only with external motivation (r = 0.29, p < 0.001). The SNS Intrusion was associated with internal motivation (r = -0.23, p < 0.001), external motivation (r = 0.32, p < 0.001) and amotivation (r = 0.24, p < 0.001).

Hierarchical multiple regression analyses explored associations self-regulation measures, emotional regulation strategies and SNS Intensity and SNS Intrusion. Age and sex were controlled for in the first step of model, and the emotional suppression and cognitive reappraisal were entered in the second step, self-regulation as activity and personality self-regulation were entered in the final step to assess their contribution to the variance. At the final step the analysis was found to be statistically significant F (197) = 7.983, p < 0.001. This regression accounted for 20 % of the variability for SNS Intensity, with three significant predictors age (β = -0.15, p < 0.01), sex (β = 0.29, p < 0.001) and self-regulation as activity (β = -0.23, p < 0.001). The regression model for the SNS Intrusion on the final step explains 15 % of variability with two significant predictors sex (β = 0.21, p < 0.01) and self-regulation as activity (β = -0.25, p < 0.001).

SNS Intensity and SNS Intrusion and academic motivation

Next we explored the relationship between SNS Intensity and SNS Intrusion and academic motivation. Correlation analysis was found to be statistically significant, indicating a strong positive relationship between SNS Intensity (r = 0.30, p < 0.01), SNS Intrusion (r = 0.32, p < 0.01) and external motivation. SNS Intrusion also negatively correlated with internal motivation (r = -0.23, p < 0.01) and positively with amotivation (r = 0.24, p < 0.01).

The regression model for the internal motivation as dependent variable and age, sex added on the step 1, SNS Intensity and SNS Intrusion variables added on the step 2 showed statistically significance (F (197) = 5.931, p < 0.001). The final model explains 11 % of variability with SNS Intensity (β = 0.24, p < 0.005), and SNS Intrusion (β = -0.42, p < 0.001) as significant predictors. The final model for the external motivation accounted 11 % of variability (F (197) = 6.09, p < 0.001) with SNS Intrusion (β = 0.22, p < 0.05) as the only one predictor. The same result is for the amotivation, the model accounted 10 % of variance (F (197) = 5.49, p < 0.001) with SNS Intrusion (β = 0.36, p < 0.001) as the predictor.