The problem of social media addiction troubles the minds of numerous researchers (Balakrishnan & Griffiths, 2017; Hou et al., 2019; Turel et al., 2018; Tutgun-Ünal & Deniz, 2015). Some researchers focus on different types kinds of platforms, including YouTube (Balakrishnan & Griffiths, 2017), Facebook (Hou et al., 2019), and Instagram (Martinez-Pecino & Garcia-Gavilán, 2019). While some researchers state that increased use of social media has a negative impact on user self-esteem (Hawi & Samaha, 2017), others found a positive correlation between increased social media use and self-esteem (Ellison et al., 2007). Thus, the relationship between self-esteem and social media use remains a matter of controversy.
Factors affecting the likelihood of developing addiction can be subdivided into four different groups, including social, technological, behavioral, and mental (Al-Samarraie et al., 2021). Excessive social media use is one of the central factors affecting the likelihood of developing an addiction to social media (Al-Samarraie et al., 2021). Other factors include well-being, anxiety, depression, loneliness, fear of missing out, increased use of smartphones, passive co-use of technology, gaming, and lack of self-control (Al-Samarraie et al., 2021). It is crucial to notice that some researchers see low self-esteem as a consequence of social media addiction (Hawi & Samaha, 2017), while others see increased use of social media as a cause of developing addiction (Al-Samarraie et al., 2021).
The present paper aims at analyzing the relationship between Instagram addiction and self-esteem in undergraduate students to address the identified uncertainty about the effect of social media addiction on self-esteem. This study assumes that increased active Instagram use is closely correlated with the intensity of addiction in undergraduate students based on the study by Al-Samarraie et al. (2021). Thus, the study utilizes the active use of social media as an equivalent measure of social media addiction. The research was guided by the following research question:
RQ1: What is the relationship between social media Instagram addiction and self-esteem in undergraduate students?
Literature Review
The effect of social media use is reported to have a mixed effect on the user. On the one hand, some studies demonstrated that active social media use has a positive impact on social capital (Ellison et al., 2007) and connectedness with friends and relatives (Verduyn et al., 2017). On the other hand, extensive social media use is believed to be associated with negative outcomes, such as depression (Lin et al., 2016), inadequate smartphone use (Al-Samarraie et al., 2021), and dissatisfaction with one’s life and body (Hinojo-Lucena et al., 2020). At the same time, some researchers argued that social media use has no real effect on users, as the effect sizes of the majority of studies are very small, which implies that even though the results were statistically significant, they had no real implications to practice (Orben et al., 2019). Thus, there is a significant amount of controversy around social media use and its consequences.
While the research results are uncertain about the user outcomes, social media addiction is reported to have a mostly negative impact. Hou et al. (2019) stated that social media addiction had a negative effect on the mental health of college students, as it was associated with depression and anxiety. Additionally, Hou et al. (2019) reported that social media addiction negatively affected academic achievement and engagement in studies of undergraduates. Hawi and Samaha (2017) reported that social media addiction had a negative impact on satisfaction with life. The relationship was partially mediated by the self-esteem of the participants (Hawi & Samaha, 2017). At the same time, social media addiction had a negative impact on employee productivity (Priyadarshini et al., 2020).
It is crucial to notice, however, that social media addiction is reported to have a mixed effect on subjective well-being. Priyadarshini et al. (2020) collected qualitative data from employees in different spheres that reported a negative impact of social media addiction on their sleeping patterns, backache, and eye strain. In other words, Priyadarshini et al. (2020) revealed a negative correlation between social media addiction and subjective well-being. Zhao (2021), however, reported a more complicated relationship between subjective well-being and social media addiction. Gaming use of social media was associated with a negative impact on subjective well-being, while social use of social media was associated with a positive impact on subjective well-being (Zhao, 2021). Thus, it may be concluded that social media addiction may have a mixed effect on subjective well-being depending on the type of use.
Social media use is reported to have a mixed effect on self-esteem as well. On the one hand, excessive social media use is associated with negative self-esteem, the dependence of approval on social media through “likes,” and feeling of envy (Hawi & Samaha, 2017; Priyadarshini et al., 2020). At the same time, Al-Samarraie et al. (2021) reported low self-esteem to be one of the risk factors of social media addiction. On the other hand, Verduyn et al. (2017) report that self-esteem has a more complicated relationship with social media use. Active social media use is reported to have a positive impact on self-esteem, while passive use is associated with a negative impact on social media us (Verduyn et al., 2017).
Using social media is also often associated with positive outcomes, as it supports the generation of social capital. Verduyn et al. (2017) explained social capital as benefits earned from a social network, such as a feeling of belonging. Ellison et al. (2007) stated that active use of Facebook is associated with increased levels of social capital. The higher the user intensity is, the more likely a user is to generate social capital (Ellison et al., 2007). Additionally, it should be reported that self-esteem is strongly correlated with subjective well-being (Yang et al., 2019).
The analysis of the literature review led to the following conclusions. First, social media addiction may have a varying effect on self-esteem depending on the type of use (Hawi & Samaha, 2016; Priyadarshini et al., 2020; Verduyn et al., 2017; Zhao, 2021). Second, active Instagram use is expected to have a positive impact on the self-esteem of undergraduate students as Instagram does not offer gaming (Verduyn et al., 2017; Zhao, 2021). Active use of social media for social needs is associated with a positive effect on self-esteem. Finally, intensity, which is defined as the strength of emotional connection to social media, is expected to mediate the relationship between social media addiction and self-esteem (Ellison et al., 2007; Verduyn et al., 2017).
The conclusions of the literature review can be translated into the following hypotheses:
- H1: There is a positive correlation between Instagram addiction and self-esteem in undergraduate students.
- H2: Intensity of use mediates the relationship between self-esteem and Instagram addiction in undergraduate students.
The present study is expected to close a significant gap in the literature by contributing to the understanding of the complicated relationship between social media addiction and self-esteem. In particular, it will add to the discussion of how excessive use of Instagram impacts self-esteem of undergraduate students. At present, the findings concerning the relationship between these concepts were contradictory (Hawi & Samaha, 2017; Priyadarshini et al., 2020; Verduyn et al., 2017; Zhao, 2021). The findings of the present study will add to the current body of literature by testing a statistical model to understand if the intensity of use has a significant mediating effect between Instagram addiction and self-esteem.
Methods
Method Selection
A qualitative approach was used to answer the research question. According to Saunders et al. (2019), a quantitative approach is appropriate when a researcher needs to test a hypothesis or theory. The quantitative approach aims at deriving information from numerical data by applying mathematical and statistical tools (Saunders et al., 2019). Thus, quantitative is appropriate for the purpose of the present study, which is to analyze the relationship between two identified variables using hypothesis testing.
Data Availability
The present research used secondary data to answer the research questions. In particular, a dataset created by Trifiro and Prena (2021) and published by Boston University was used. The dataset includes 44 variables and 359 responses from undergraduate students from different universities in the US. The dataset does not have any copyright restrictions and can be used by anyone.
Sampling
The data was acquired using the snowball sampling method. According to Parker et al. (2019), snowball sampling is a purposive sampling method usually used for qualitative research. Since it is not a probability sampling method, not all members of the population have a similar chance to become a participant in the study. The data collection procedure started in January of 2018 and lasted for six months. A total of 411 questionnaires were acquired, among which 358 were usable. The participants were recruited by posting advertisements in Facebook pages and Instagram channels of Universities. Some participants were also offered to participate in the study for extra credit. The only inclusion criterion was being an undergraduate student in one of the US higher education institutions. The study was approved by Institutional Review Board. The gender and age distribution of the participants is provided in Figures 1 and 2, respectively.
Survey Questions
The survey used by Trifiro and Prena (2021) included 36 questions subdivided into six sections. The first section included three demographic questions (sex, undergraduate status, and age). The second section was included eight questions measuring the intensity of Instagram use. The third section had five questions measuring subjective well-being. The fourth and the fifth sections measured active and passive Instagram use, respectively. The final section measured self-esteem.
Variables
The present research studied the relationship between three variables, including Instagram addiction, the intensity of use, and self-esteem. The description of methods for measuring the variables is provided below.
Instagram Addiction
As it was mentioned in the introduction to the present paper, the present paper assumes a close correlation between social media addiction and excessive Instagram use (Al-Samarraie et al., 2021). Therefore, the level of Instagram addiction was seen as a near equivalent of active social media use. Active social media use was represented as a sum of five questions of the instrument developed by Gerson et al. (2017). Instagram addiction scores varied between 5 and 25, with a mean of 15.03 and a standard deviation of 3.89.
Intensity of Use
The intensity of use was measured using an adapted scale from the study by Ellison et al. (2007) by replacing the Facebook-related questions with Instagram-related questions. The measure included a self-reported evaluation of Instagram behavior using a five-point Likert scale. A sum of eight questions was used as a measure of intensity. The scores differed between 8 and 44, with a mean value of 32.74 and a standard deviation of 7.58.
Self-Esteem
Self-esteem was measured using Rosenberg Self-Esteem Scale (Diener et al., 1985). The variable was quantified as a sum of ten five-point Likert scale questions. The scores differed between 11 and 44, with a mean of 31.01 and a standard deviation of 5.71.
Data Analysis Procedures
The data was analyzed using Statistical Package for Social Studies (SPSS) 26. Descriptive statistics, Pearson’s correlation analysis, and regression analysis were used. Haye’s (2013) macro was used to perform mediation analysis in SPSS. This model assumed that, first, self-esteem should be regressed using Instagram addiction. Then, the intensity of use should be regressed against Instagram addiction. After that, self-esteem is to be regressed against Instagram addiction and Intensity of use. If the p-value of Instagram addiction is decreased, the mediation exists. The significance of mediation was measured using bootstrapping. The significance level for all the tests was α = 0.05.
Results
Descriptive Statistics
Means and standard deviations of all the variables were provided in the methods section. This section provides a summary table for all the variables (see Table 1 below).
Table 1. Descriptive statistics
Hypothesis 1
The hypothesis was tested using Pearson’s correlation analysis and linear regression analysis. Pearson’s correlation coefficient for two variables was 0.126 with a significance level of p = 0.017, which indicates a positive linear correlation. Regression analysis also demonstrated a positive linear relationship between the two variables with p = 0.017. However, the effect size was modest, with R2 = 0.16. This implies that 1.6% of changes in self-esteem can be explained by Instagram addiction. In summary, the data analysis found significant support for Hypothesis 1 with a limited effect size.
Hypothesis 2
Hypothesis 2 was tested using the mediation PROCESS model (version 4) created by Hayes (2013). The analysis revealed that there was a significant mediation effect of Intensity in the relationship between Instagram addiction and Self-Esteem. The summary of results is provided in Table 2 below. The summary demonstrates that the effect of Instagram addiction became insignificant after applying the PROCESS mediation model, which supports Hypothesis 2. However, the effect size of R2 = 0.3 is also limited, as only 3% of the changes in self-esteem can be explained by the model.
Table 2. Mediation model results summary
Discussion
The results of the analysis revealed that both hypotheses were supported. This implies that Instagram addiction is positively associated with the self-esteem of undergraduate students. However, the effect of Instagram addiction on self-esteem is indirect, as demonstrated in the mediation analysis. The relationship is mediated by the intensity of use. This implies that Instagram addiction may have a varying effect on self-esteem depending on the nature of the engagement. In particular, the higher the emotional connection to Instagram, the higher the effect on self-esteem. In other words, if an undergraduate student is addicted to Instagram and does not feel any emotional attachment to social media, the effect of self-esteem may be negative. However, as the analysis suggests, the higher the Instagram addiction (active use level), the higher the emotional attachment (intensity of use) of these undergraduate students.
The research finding provides a possible explanation for the controversy in the literature about the relationship between self-esteem and social media addiction. Social media addiction may grow to the level when a person does no longer feel any emotional attachment to social media but continues using it regardless. This can lead to a negative correlation between social media addiction and self-esteem, as mentioned in previous research (Al-Samarraie et al. 2021; Hawi & Samaha, 2017; Priyadarshini et al., 2020). At the same time, if a person is addicted to social media at preserves high emotional attachment to it, the effect of social media on self-esteem is positive (Ellison, 2007; Verduyn et al., 2017).
There is another possible explanation to the results of the present study supported by Zhao (2021). As mentioned in the literature review, Zhao (2021) reported that social use of social media had a positive impact on subjective well-being, while gaming had a negative impact on subjective well-being. Since self-esteem is strongly correlated with subjective well-being (Yang et al., 2019), it may be concluded that social use of social media has a positive impact on self-esteem as well. Since Instagram does not offer any gaming options, its use is associated with positive associated with self-esteem in undergraduate students.
While the results of the analysis provide an explanation for the controversy in the current body of literature, there are some limitations of the study that must be acknowledged. The primary limitation of the present study is the use of secondary data. The researcher did not have the chance to collect primary data to measure social media addiction using appropriate scales. For instance, Hou et al. (2019) utilized Bergen Social Media Addiction Scale and Bergen Facebook Addiction Scale for measuring social media addiction. The present paper assumed that the correlation between Instagram addiction and active Instagram use is high enough to treat these concepts as near-equivalents. However, it may not be an accurate measure of the Instagram addiction, which in turn led to biased results. Such inconsistency may serve as another explanation of inconsistency between the results of Hou et al. (2019), who stated that there is a negative correlation between self-esteem and social media addiction, and the present study.
Another limitation of the present research is the effect size. The coefficients of determination for all models were very low, demonstrating that the models had a low predictive ability. This implies that self-esteem is a very complicated matter affected by a wide variety of factors. Thus, discussing the implications of the present study for practice is challenging.
References
Al-Samarraie, H., Bello, K. A., Alzahrani, A. I., Smith, A. P., & Emele, C. (2021). Young users’ social media addiction: causes, consequences and preventions.Information Technology & People.
Balakrishnan, J., & Griffiths, M. D. (2017). Social media addiction: What is the role of content in YouTube?Journal of Behavioral Addictions, 6(3), 364-377.
Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale.Journal of Personality Assessment, 49(1), 71–75.
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and college students use of online social network sites. Journal of Computer-Mediated Communication, 12, 1143–1168.
Gerson, J., Plagnol, A., & Corr, P. J. (2017). Passive and active Facebook use measure (PAUM): Validation and relationship to the Reinforcement Sensitivity Theory.Personality and Individual Differences, 117, 81–90.
Hawi, N. S., & Samaha, M. (2017). The relations among social media addiction, self-esteem, and life satisfaction in university students. Social Science Computer Review, 35(5), 576-586.
Hayes, A. F. (2013). Methodology in the social sciences. Introduction to mediation, moderation, and conditional process analysis: A regression based approach. Guilford Press.
Hinojo-Lucena, F. J., Aznar-Diaz, I., Caceres-Reche, M. P., Trujillo-Torres, J. M., & Romero-Rodriguez, J. M. (2020). Instagram use as a multimedia platform for sharing images and videos: Links to smartphone addiction and self-esteem. IEEE MultiMedia, 28(1), 48–55. Web.
Hou, Y., Xiong, D., Jiang, T., Song, L., & Wang, Q. (2019). Social media addiction: Its impact, mediation, and intervention.Cyberpsychology: Journal of psychosocial research on cyberspace, 13(1), Article 4.
Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., & Primack, B. A. (2016). Association between social media use and depression among US young adults. Depression and Anxiety, 33(4), 323–331.
Martinez-Pecino, R., & Garcia-Gavilán, M. (2019). Likes and problematic Instagram use: the moderating role of self-esteem. Cyberpsychology, Behavior, and Social Networking, 22(6), 412-416.
Orben, A., Dienlin, T., & Przybylski, A. K. (2019). Social media’s enduring effect on adolescent life satisfaction. Proceedings of the National Academy of Sciences of the United States of America, 116(21), 10226–10228.
Parker, C., Scott, S., & Geddes, A. (2019). Snowball sampling. SAGE Research Methods Foundations.
Priyadarshini, C., Dubey, R. K., Kumar, Y. L. N., & Jha, R. R. (2020). Impact of a Social Media Addiction on Employees’ Wellbeing and Work Productivity. The Qualitative Report, 25(1), 181-196.
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2019) Research methods for business students. Pearson.
Trifiro, B., & Prena, K. (2021). Active Instagram Use and its Association with Self-Esteem and Well-Being [Dataset]. Boston University.
Turel, O., Brevers, D., & Bechara, A. (2018). Time distortion when users at-risk for social media addiction engage in non-social media tasks. Journal of psychiatric research, 97, 84-88.
Tutgun-Ăśnal, A., & Deniz, L. (2015). Development of the social media addiction scale. AJIT-e: BiliĹźim Teknolojileri Online Dergisi, 6(21), 51-70. Web.
Verduyn, P., Ybarra, O., Résibois, M., Jonides, J., & Kross, E. (2017). Do social network sites enhance or undermine subjective well-being? A critical review. Social Issues and Policy Review, 11(1), 274–302.
Yang, Q., Tian, L., Huebner, E. S., & Zhu, X. (2019). Relations among academic achievement, self-esteem, and subjective well-being in school among elementary school students: A longitudinal mediation model. School Psychology, 34(3), 328-241.
Zhao, L. (2021). The impact of social media use types and social media addiction on subjective well-being of college students: A comparative analysis of addicted and non-addicted students.Computers in Human Behavior Reports, 4, 100122.