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Article

Investigating the Dynamics of Social Media Addiction and Well-Being in Jordan: An Empirical Analysis

by
Islam Habis Mohammad Hatamleh
1,* and
Rahima Aissani
2
1
Department of Media and Communication Technology, Faculty of Arts and Languages, Jadara University, Irbid 21110, Jordan
2
Department of Communication and Media, College of Communication and Media, Al Ain University, Abu Dhabi P.O. Box 64141Al, United Arab Emirates
*
Author to whom correspondence should be addressed.
Soc. Sci. 2024, 13(7), 351; https://doi.org/10.3390/socsci13070351
Submission received: 7 May 2024 / Revised: 18 June 2024 / Accepted: 24 June 2024 / Published: 29 June 2024
(This article belongs to the Special Issue Disinformation in the Public Media in the Internet Society)
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Versions Notes

Abstract

:
This study examines the complex associations among social media usage, engagement, addiction and subjective well-being. Employing a sophisticated framework that integrates both first- and second-order models, this study employs structural equation modeling (SEM) to analyze data from a sample of 510 Jordanian young people. The findings indicate a surprisingly positive correlation between social media usage and engagement and their effects on social media addiction and subjective well-being. Conversely, social media addiction is found to have a negative connection with subjective well-being. These insights are crucial for experts aiming to improve user experiences and increase well-being. This study contributes to the current literature by offering new perspectives on the dynamics between social media interactions and personal well-being.

1. Introduction

The ubiquity of social media in contemporary society offers unprecedented avenues for communication, social interaction, and entertainment (Knupfer et al. 2023). Yet, this pervasive use raises important concerns, such as the potential for social media addiction—a psychological state marked by excessive, compulsive engagement with social media platforms, which adversely affects daily life and well-being (Nazir et al. 2020a; Kuss and Griffiths 2017).
In the setting of Jordan, social media has experienced a meteoric rise, becoming a cornerstone for both communication and entertainment among a significant segment of the population (Al-Sous et al. 2023). Despite this, there remains a paucity of research exploring the dynamics of social media addiction within the Jordanian context. Cultural and societal nuances specific to Jordan may affect the degree to which factors identified in international studies—such as social influence, gratification, and personality traits—contribute to addiction (Al-Dmour et al. 2020; Sun and Zhang 2021).
Social media addiction is not just a digital concern but also a behavioral one, characterized by uncontrollable and excessive usage patterns that lead to detrimental life outcomes (Koc and Gulyagci 2013). Certain features of social media platforms—like commenting, liking, and sharing—that encourage user engagement could exacerbate the addictive nature of these platforms (Al-Samarraie et al. 2021). Social media engagement, understood as the extent to which users interact with others through sharing, commenting, and reacting to posts, is now recognized as an integral component of the social media experience (Robinson et al. 2019).
Scholarly discourse has increasingly examined social media’s impact on subjective well-being, especially among college students. While some studies posit that social media use can enhance social capital, alleviate loneliness, and bolster subjective well-being (Pang 2018; Phu and Gow 2019; Kim et al. 2020; Guo et al. 2014), others warn that excessive usage can spiral into addiction, thereby compromising both mental and physical health (Andreassen et al. 2017; Marino et al. 2018b). Notably, a few studies have argued that the relationship between social media use and subjective well-being is not statistically significant (Utz and Breuer 2017; Phu and Gow 2019).
Given these divergent perspectives, our own study aims to bridge the existing research gap by examining the variables that influence social media addiction prior to exploring its repercussions on subjective well-being. Specifically, this research will scrutinize the impact of social media use, engagement, and addiction on the subjective well-being of individuals, thereby providing a nuanced understanding of these interrelated constructs.

2. Literature Review

2.1. Understanding Social Media Addiction

Social media addiction is recognized as a psychological disorder typified by a compulsive and excessive use of social media platforms, which hampers an individual’s ability to partake in daily life activities effectively. The repercussions of this addictive behavior manifest as reduced productivity, heightened social isolation, and escalating levels of anxiety and depression (Nazir et al. 2020b). Scholars like Koc and Gulyagci (2013) broaden this definition to describe it as a behavioral addiction, impacting multiple facets of an individual’s life, including personal relationships and occupational performance.
Further nuance is added to the understanding of social media addiction by viewing it as a specialized form of internet addiction disorder. This perspective emphasizes an individual’s broader inability to regulate their online activities, which consequently results in various detrimental outcomes (Hou et al. 2019). Kuss and Griffiths (2017) echo similar sentiments, identifying social media addiction as a specific subtype of behavioral addiction characterized by compulsive engagement with social media platforms, even when such engagement results in negative life impacts.

2.2. The Dynamics of Social Media Use

The omnipresence of social media in contemporary society is beyond dispute, with a very large number of users participating in these platforms on a daily basis (Over 62% of the global population actively uses social media). The average daily usage is 2 h and 23 min (Statista 2022). Social media participation involves not merely accessing these platforms but actively engaging in activities like communication, information dissemination, and entertainment (Lin and Utz 2015). However, the ubiquity of social media use is a double-edged sword; excessive use can precipitate a variety of adverse outcomes, ranging from addiction to exposure to cyberbullying and harmful content (Frost and Rickwood 2017). Nonetheless, the overarching utility of social media as a potent tool for communication and social interaction remains uncontested (Tandoc et al. 2015).

2.3. Nuances of Social Media Engagement

Social media engagement is a complex, multi-dimensional construct that defies an easy definition. According to Brodie et al. (2013), it manifests as a psychological state triggered by interactive and co-creative experiences between a user and a given platform or community. Calder et al. (2009) view engagement as voluntary actions, such as liking, sharing, and following, that a user undertakes on social media. Hollebeek (2011), however, proposes that engagement is not unidimensional but rather a construct shaped by cognitive, emotional, and behavioral components.
For social media engagement to be effective, it necessitates a nuanced understanding of the target demographics and their motivations for platform use. This understanding enables the creation of resonant, compelling content that not only holds users’ attention but also encourages interactive participation (Barger et al. 2016).
Through this literature review, this study aims to knit together these diverse strands of research, offering a synthesized overview that facilitates a deeper understanding of social media addiction, its usage patterns, and the complex dynamics of engagement—each of which has distinct implications for personal and societal well-being.
The term “social media” reflects a complex digital environment (Hatamleh et al. 2023). Considering these spaces as individual representations of a broader category of online environments allows for a more flexible, and likely more informative, approach to investigating social media and its effects (Hatamleh 2024). For instance, we can look at how youth perceive the effects of their use of similar types of content feeds across multiple platforms rather than focusing on a single platform (Carter et al. 2023).

3. Hypothesis Development

The digital age has been characterized by an unprecedented expansion in social media platforms, fundamentally altering the ways we communicate and interact. While these platforms offer countless benefits, they also present concerns surrounding excessive use, often leading to what is now termed “social media addiction.” This article aims to delve into various hypotheses that seek to explore the nuanced relationships between social media use, addiction, engagement and subjective well-being.

3.1. The Rising Concern over Social Media Addiction

Traditionally, the term “addiction” has been primarily associated with substances like alcohol, drugs, and behaviors like gambling (Sutanto and Nayak 2018). However, the advent of computers and the internet has given rise to a new class of addiction: technology and internet addiction (Zhao 2021). Among college students, social media has become an integral facet of their online activity, often contributing to symptoms associated with addiction, such as loss of control over usage and conflicts with other activities (Tang et al. 2016; Ryan et al. 2014). Moreover, the multifaceted nature of social media—ranging from content sharing to gaming—makes it a fertile ground for developing addiction disorders (Griffiths et al. 2014). Hatamleh et al. (2023) states that social media engagement is determined by the Individual Involvement level and Personal Meaning level.
H1: 
A positive relationship exists between social media use and social media addiction.
H2: 
A positive relationship exists between social media engagement (Individual Involvement and Personal Meaning) and social media addiction.

3.2. Subjective Well-Being and Social Media

In social discourse, the pursuit of happiness or subjective well-being is often touted as a primary human goal (Hatamleh et al. 2023). Although happiness is a concept universally sought after, research specifically focused on young people and their experiences with happiness is scant (Freire and Ferreira 2020). Hatamleh et al. (2023) did find a positive correlation between the motivations for using social media and reported levels of subjective happiness.
H3: 
A positive relationship exists between social media use and subjective well-being.

3.3. The Role of Social Engagement

Engaging socially, either online or offline, serves as a cornerstone for building social relationships. Active social engagement has been shown to have a positive impact on an individual’s health behavior, largely by providing an outlet for emotional release (Agarwal and Mewafarosh 2021). This active participation in social networks often results in various forms of social support, crucial for coping with stress (Wheatley and Buglass 2019).
H4: 
A positive relationship exists between social media engagement (Individual Involvement and Personal Meaning) and subjective well-being.

3.4. The Empowerment–Enslavement Paradox of Social Media Use

While social media platforms offer connection and engagement, excessive use has been implicated in causing emotional regulation issues among college students (Andreassen and Pallesen 2014; Hormes et al. 2014). McDaniel (2015) describes this phenomenon as the “empowerment–enslavement paradox,” where the very technologies that empower us may also enslave us, leading to decreased subjective well-being (Coyne et al. 2020).
H5: 
A negative relationship exists between social media addiction and subjective well-being.
The evolving digital landscape has embedded social media into the fabric of daily life, bringing along a complex web of benefits and detriments. As we continue to navigate this intricate relationship, the hypotheses presented herein aim to guide future scholarly investigations into understanding the intricate interplay between social media use, addiction, engagement, and subjective well-being (refer to Figure 1).

4. Research Model

4.1. Research Methodology

Using a quantitative research approach, this study examined the relationship between independent variables and a moderating variable. In Jordanian settings, self-administered questionnaires are common (Lazaraton 2005; Al-Okaily 2023). As an alternative to randomness, convenience sampling was used to select participants based on their proximity and availability (Bougie and Sekaran 2019).
Participants in Jordan were asked to self-administer a questionnaire. On a seven-point Likert-type scale, they rated their responses to the questions related to the variables of interest. These variables included aspects such as social media usage, addiction, and subjective well-being. The questionnaire aimed to capture the participants’ attitudes, behaviors, and experiences concerning the study’s focus.
A pilot test was conducted with a small group of participants to ensure the validity and reliability of the data. In addition to improving the questionnaire’s quality (Hair et al. 2011; Abd Rahman et al. 2020; Al-Okaily 2022), the results from the pilot test were used to refine the questionnaire. The participants were sent the final questionnaire after it had been developed.
The research employed a partial least squares (PLS) approach to assess the hypotheses and analyze the proposed model. PLS enables the simultaneous examination of multiple relationships (Al-Okaily 2024a, 2024b). Specifically, PLS is well suited for complex models involving multiple variable associations and can effectively handle small sample sizes (Hair et al. 2014). The PLS structural equation modeling (PLS-SEM) framework incorporates both an outer and an inner model (Hair et al. 2011). In the outer model, the reliability and validity of constructs and indicators are assessed, whereas in the inner model, the significance of the hypotheses is evaluated.

4.2. Sample Size

This investigation determined the sample size using the Krejcie and Morgan sample size guideline (1970). The study focused on over two million young participants, divided into six target groups and split into two categories. The first category consisted of three groups of young participants from various public settings, while the second category encompassed three groups from universities across Jordan. Per Krejcie and Morgan’s (1970) recommendations, a sample size of three hundred and forty-eight is suitable for a population segment of one million youth participants. Therefore, the sample for this study was set at 550 individuals from the six target groups in both public settings and university environments. In the end, 510 complete and valid responses were obtained.
The survey was distributed across the three regions in Jordan, with approximately 180 to 183 samples allocated to each region. Within each region, the samples were divided between the main university and public areas, ensuring that part of the sample included non-students.
Northern Region: 93 samples were allocated within Yarmouk University and 90 samples in public areas.
Central Region: 93 samples were allocated within the University of Jordan and 90 samples in public areas.
Southern Region: 93 samples were allocated within Mutah University and 91 samples in public areas.
This distribution method ensured a balanced representation between universities and public areas across the different regions.

4.3. Measurement Scales

In this study, the measurement of variables related to social media was operationalized using a seven-point Likert scale, assessing various aspects of user interaction and psychological impact. The scales include the following:
  • Social media use (intensity scale): adapted from Ellison et al. (2007), this scale measures the frequency and emotional significance of social media in daily life, with items like how integral social media is to daily routines and feelings of disconnect when not logged on.
  • Social media engagement: this involves two sub-dimensions:
    • Individual Involvement assesses the perceived importance and relevance of social media to the user, including its interest and essential nature to their daily lives (Hatamleh et al. 2023).
    • Personal Meaning evaluates the emotional and psychological fulfillment derived from social media use, including feelings of accomplishment and energization from interactions.
  • Social media addiction: measured using the Bergen Social Media Addiction Scale (BSMAS; Andreassen et al. 2017), this scale includes indicators of compulsive use, such as excessive preoccupation, inability to reduce usage, and distress when access is restricted.
  • Subjective well-being: this scale, updated by Hatamleh et al. (2023), gauges overall personal happiness and satisfaction with life in relation to social media usage, including comparative happiness to peers and general life enjoyment (refer to Table 1).

5. Data Analysis and Results

5.1. Measurement Model Assessment

Measurement model assessment is the first step in the PLS-SEM analysis. Hair et al. (2011) outlined four steps in the assessment of the measurements’ reliability and validity. These are (1) indicator reliability, which is assessed using a Cronbach’s alpha (α) > 0.70; (2) composite reliability (CR) > 0.70; (3) convergent validity using average variance extracted (AVE) values of > 0.50; and (4) discriminant validity, which can be evaluated by using the Heterotrait–Monotrait (HTMT) correlation and the Fornell–Larcker correlation. The measurement model loadings (Cronbach’s alpha and composite reliability) presented in Table 2 and Table 3 were largely above the 0.70 thresholds, and the average variance extracted (AVE) was above the 0.50 threshold.
Concerning discriminant validity, it is defined as “the extent to which a construct is truly distinct from other constructs by empirical standards” (Hair et al. 2014, p. 104), which is determined by Heterotrait–Monotrait correlations (Roemer et al. 2021) and Fornell–Larcker correlations (Hair et al. 2014). The foremost alternative method to evaluate discriminant validity in PLS-SEM is using Heterotrait–Monotrait correlations, which should have thresholds of less than 0.90, as Roemer et al. (2021) suggested. Accordingly, Table 4 and Table 5 show the results of Heterotrait–Monotrait correlations within the recommended range. In addition, it is displayed that the Fornell–Larcker correlation was satisfied, as the square of each variable’s AVE was greater than the intercorrelations.

5.2. Structural Model Assessment

Structural model assessment is the second step in the PLS-SEM analysis to assess the inner model and the significance level of the path coefficients, β, for hypothesis testing (Hair et al. 2011). As presented in Table 6 and Figure 2, the findings confirm that all the hypotheses were supported.
Table 7 presents the R-squared (R²) values for two dependent variables: social media addiction (0.643) and subjective well-being (0.887). R-squared is a statistical measure indicating the proportion of variance in the dependent variable explained by the independent variables in a regression model. The R² of 0.643 for social media addiction means that 64.3% of its variance is explained by the model, while the R² of 0.887 for subjective well-being indicates that 88.7% of its variance is explained by the model. This suggests that the model for subjective well-being has stronger explanatory power compared to the model for social media addiction.
Socsci 13 00351 g002
Figure 2. Result of hypothesis testing (p-values and R-squared).
Figure 2. Result of hypothesis testing (p-values and R-squared).
Socsci 13 00351 g002
Table 6 outlines the results of the hypothesis testing using path coefficients (β) from a partial least squares structural equation modeling (PLS-SEM) analysis. This method is typically used to assess relationships between variables, particularly in complex models. Here is a breakdown and interpretation of the data given in the table:
H1. 
(A positive relationship exists between social media use and social media addiction):
  • Path coefficient (β): 0.307, which indicates a positive and moderate relationship between social media use and social media addiction.
  • Standard deviation: 0.053, which suggests that the variability around the estimated path coefficient is relatively low.
  • T-statistics: 5.760, which is significantly higher than typical critical values (e.g., 1.96 for a 95% confidence level), indicating a statistically significant relationship.
  • p-value: 0.000, which is less than the common alpha level of 0.05, supporting statistical significance.
  • Result: accepted, confirming that social media use has a significant correlation with social media addiction.
H2. 
(A positive relationship exists between social media engagement (Individual Involvement and Personal Meaning) and social media addiction):
  • Path coefficient (β): 0.540, which suggests a strong positive relationship.
  • Standard deviation: 0.052.
  • T-statistics: 10.326, demonstrating a very significant relationship.
  • p-value: 0.000.
  • Result: accepted, indicating that higher levels of engagement with social media lead to greater addiction.
H3. 
(A positive relationship exists between social media use and subjective well-being):
  • Path coefficient (β): 0.131, which indicates a positive but weak relationship.
  • Standard deviation: 0.019.
  • T-statistics: 6.781, again showing statistical significance.
  • p-value: 0.000.
  • Result: accepted, suggesting that social media use positively and significantly impacts subjective well-being, although the effect is relatively small.
H4. 
(A positive relationship exists between social media engagement (Individual Involvement and Personal Meaning):
  • Path coefficient (β): 0.384, which points to a moderate positive relationship.
  • Standard deviation: 0.108, which indicates higher variability in this estimate compared to others.
  • T-statistics: 3.550, which is above the critical value, indicating significance.
  • p-value: 0.000.
  • Result: accepted, indicating that engaging more with social media can significantly improve subjective well-being to some extent.
H5. 
(A negative relationship exists between social media addiction and subjective well-being):
  • Path coefficient (β): −0.501, which indicates a moderate-to-strong negative relationship.
  • Standard deviation: 0.077.
  • T-statistics: −6.480, showing that the negative impact is statistically significant.
  • p-value: 0.000.
  • Result: accepted, confirming that social media addiction negatively and significantly influences subjective well-being.

5.3. Dissections: Key Findings and Comparisons with Previous Studies

H1. 
A positive relationship exists between social media use and social media addiction.
This research postulates an assumed correlation between social media usage and addiction, as observed by other scholars, including Kuss and Griffiths (2017) and Nazir et al. (2020b). They point out that the use of these platforms can be addictive and lead to a dependency on them. Several investigations, including studies on the effects of push notifications, support this claim. Nevertheless, understanding why such an association occurs requires an in-depth analysis of the underlying psychological and behavioral processes involved.
Another factor is the release of chemicals in the brain associated with the reward pathway. The development of the Internet, particularly through social media, has amplified this effect by stimulating dopamine secretion through mechanisms such as likes, comments, and shares. This specific activation can lead to more frequent checking and prolonged use of social media, which tends to create addictive habits (Stapleton et al. 2017). Additionally, websites exploit the Fear of Missing Out (FOMO) phenomenon, making users feel compelled to constantly check social media, thereby increasing their addiction to these sites. (Przybylski et al. 2013). Moreover, the validation of self-esteem through the use of positive feedback on social media can lead to dependency on social media posts, which can spiral into addiction (Stapleton et al. 2017).
Another important aspect that favors the propagation of social media is habit formation due to the habitual use of social media, which becomes as natural as breathing. Supported by the theory of planned behavior, such automatic behaviors help increase the potentiality of addiction (LaRose et al. 2003). Furthermore, the fundamental reason that most people rely on social media is that it provides a means of fun and entertainment, and there can be no better time to embark on fun than a time of stress or boredom (Kardefelt-Winther 2014). Such psychological and behavioral factors substantiate the findings of a study where the path coefficient (β = 0.307) was revealed to be moderately positive. This path coefficient indicates that there is a positive correlation between increased use of social media and higher addiction levels due to the integrated activities within the designed framework of social media platforms among users.
H2. 
A positive relationship exists between social media engagement (Individual Involvement and Personal Meaning) and social media addiction.
The hypothesis that increased social media engagement leads to addiction is also supported in this research, similar to previous research by Al-Samarraie et al. (2021). This relationship is demonstrated in the earlier table, with the strong positive path coefficient (β = 0.540) indicating that more intense engagement with social media significantly increases the likelihood of addiction. However, understanding why this association appears requires exploring the underlying psychological and behavioral mechanisms.
One significant factor is that social media engagement often involves activities that provide Personal Meaning and Individual Involvement. When users find personal relevance and a sense of involvement in social media activities, they are more likely to invest time and emotional energy into these platforms. This emotional investment can lead to compulsive checking and prolonged usage, similar to behaviors seen in addiction (Kuss and Griffiths 2017). Additionally, personalized content and algorithms designed to keep users engaged can lead to a cycle of continuous interaction, reinforcing addictive behaviors (LaRose et al. 2003).
Moreover, social media engagement often fulfills psychological needs such as the need for social connection and self-presentation. When users perceive that their social media presence and activities hold significant personal meaning, they may experience heightened anxiety over maintaining their online persona, leading to increased use. This intense involvement can create a dependency on social media for social validation and self-worth, further escalating the risk of addiction (Stapleton et al. 2017). These factors explain the strong positive path coefficient (β = 0.540) found in this study, highlighting the powerful influence of Individual Involvement and Personal Meaning on the development of social media addiction.
H3. 
A positive relationship exists between social media use and subjective well-being.
This study found a positive relationship between social media use and subjective well-being, which aligns with findings by Hatamleh (2024) and Hatamleh et al. (2023). These studies argue that social media can enhance social capital and reduce loneliness. However, understanding why this relationship appears requires exploring the underlying psychological and social mechanisms.
One significant factor is the role of social media in enhancing social connections. Social media platforms provide opportunities for users to maintain and strengthen relationships, thereby increasing social support and perceived social capital. This can lead to improved subjective well-being, as strong social connections are a key component of mental health (Ellison et al. 2007). Additionally, social media use can provide a sense of belonging and community, which can mitigate feelings of loneliness and isolation, further contributing to subjective well-being (Lin and Utz 2015).
However, the impact of social media use on well-being is relatively weak, as indicated by the path coefficient (β = 0.131). This may be due to the dual nature of social media, where positive effects on well-being are often counterbalanced by negative experiences, such as cyberbullying, social comparison, and the pressure to present a curated life. These negative aspects can diminish the overall positive impact of social media on well-being, resulting in a weaker but still statistically significant relationship (Verduyn et al. 2017). Therefore, while social media can enhance subjective well-being by fostering social connections and reducing loneliness, its benefits are tempered by the potential for negative experiences.
H4. 
A positive relationship exists between social media engagement (Individual Involvement and Personal Meaning) and subjective well-being.
Supported by a positive path coefficient (β = 0.384), this hypothesis in the document aligns with research suggesting that engaging with social media can have beneficial effects on an individual’s subjective well-being by providing outlets for emotional expression and social interaction (Hollebeek 2011; Brodie et al. 2013). However, understanding why this relationship appears requires exploring the underlying psychological and social mechanisms.
One significant factor is that social media engagement often involves activities that provide Personal Meaning and Individual Involvement. When users find personal relevance and a sense of involvement in social media activities, they are more likely to experience emotional satisfaction and fulfillment. This emotional engagement can lead to improved subjective well-being as individuals use social media to express their emotions, share experiences, and receive social support from their networks (Smith and Yang 2017). Additionally, engaging meaningfully with social media allows individuals to build and maintain social connections, fostering a sense of community and belonging that positively impacts their well-being (Oh et al. 2014).
Moreover, social media platforms can serve as a venue for self-presentation and identity exploration, allowing individuals to express themselves and explore different aspects of their identity in a supportive environment. This can enhance self-esteem and contribute to overall well-being (Stapleton et al. 2017). These psychological and social benefits explain the positive path coefficient (β = 0.384) found in this study, indicating that meaningful engagement with social media can significantly enhance subjective well-being by providing emotional and social support.
H5. 
A negative relationship exists between social media addiction and subjective well-being.
This study and previous studies such as those by Andreassen et al. (2017) and Marino et al. (2018a) note the negative impact of social media addiction on well-being. The significant negative path coefficient (β = −0.501) strongly supports this, indicating that addiction detracts significantly from personal well-being. However, understanding why this relationship appears requires exploring the underlying psychological and social mechanisms.
One significant factor is the way social media addiction can lead to negative emotional states. Individuals who are addicted to social media often experience increased levels of anxiety, depression, and stress due to excessive use of and constant exposure to online content (Mieczkowski et al. 2020). This continuous engagement can disrupt sleep patterns, reduce time for offline social interactions, and create a dependency that negatively impacts overall mental health (Vannucci et al. 2017). Additionally, the pressure to maintain an idealized online presence and the tendency to engage in social comparison can exacerbate feelings of inadequacy and low self-esteem, further detracting from well-being (Stapleton et al. 2017).
Moreover, social media addiction often results in a neglect of real-life responsibilities and relationships, leading to social isolation and reduced life satisfaction (Andreassen et al. 2017). Excessive time spent online can replace meaningful offline activities and interactions, which are crucial for emotional and psychological well-being. This isolation can create a vicious cycle where individuals turn to social media to fill the void, only to find their well-being deteriorating further (Kross et al. 2013). These factors explain the significant negative path coefficient (β = −0.501) found in this study, indicating that social media addiction has a substantial adverse effect on subjective well-being.

6. Research Contribution

The research contribution of this study is significant, as it bridges existing gaps in the understanding of social media’s psychological impacts within the Jordanian context. The key contributions are as follows:
  • Theoretical enhancement: This study innovatively applies a combined first- and second-order model in the analysis of social media use, engagement, addiction, and subjective well-being. This model provides a more granular understanding of the interactions between these variables, enhancing the theoretical frameworks used in previous research.
  • Cultural contextualization: By focusing on Jordanian youth, the research addresses a notable gap in the literature concerning the Middle Eastern context, where cultural nuances play a critical role in the adoption and effects of technology. This adds depth to the global understanding of social media impacts across different cultural settings.
  • Methodological rigor: By utilizing structural equation modeling (SEM), the study offers a robust analysis of the pathways linking social media use and subjective well-being. This methodological approach allows for a sophisticated exploration of direct and indirect effects, providing a comprehensive view of the dynamics at play.
  • Practical implications for stakeholders: The findings from this study are crucial for practitioners and policymakers who are aiming to mitigate the adverse effects of social media addiction while promoting its benefits. The research outlines the specific factors of social media engagement that could be leveraged to enhance subjective well-being, offering a strategic basis for developing targeted interventions.
  • Contribution to the empirical literature: The empirical evidence provided by this study adds to the body of knowledge by confirming and expanding upon previous findings regarding the positive aspects of social media use and engagement on well-being, alongside the contrasting negative impacts of addiction.
This study significantly enriches the discourse on social media’s dualistic role in modern society, especially highlighting how its use in a culturally specific context like Jordan influences youth well-being and societal dynamics.

7. Conclusions

This article provides valuable insights into the complex dynamics of social media use, engagement, and addiction and their various effects on subjective well-being. The findings are generally consistent with previous studies, providing further evidence that while social media can enhance subjective well-being through use and engagement, addiction to these platforms can have detrimental effects on well-being. This research enriches the understanding of these relationships in a Jordanian context, suggesting that the implications might vary with cultural nuances.

Author Contributions

Methodology, I.H.M.H. and R.A.; software, I.H.M.H.; validation, I.H.M.H. and R.A.; investigation, I.H.M.H.; writing—original draft, I.H.M.H.; writing—review and editing, I.H.M.H. and R.A.; supervision, I.H.M.H. and R.A.; project administration, I.H.M.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Human participants were included in the study, which utilized questionnaires from a social sciences perspective. This study was neither clinical nor physical in nature. In this research, researchers obtained permission to distribute surveys from Department of Information and Communication Technology at Jadara University.

Informed Consent Statement

This study did not involve the use of any chemicals, procedures, or equipment that pose significant hazards. Additionally, no disability human participants, animals, or individuals with disabilities were involved in the research, thus eliminating the necessity for informed consent.

Data Availability Statement

In line with Jordanian privacy laws, the dataset for this study is not publicly available. However, the corresponding author can provide the relevant data upon reasonable request, ensuring compliance with all legal and ethical standards.

Conflicts of Interest

The authors affirm that there are no conflicts of interest that could affect the integrity, objectivity, or impartiality of this research.

References

  1. Abd Rahman, Mohd Shaari, Manaf Al-Okaily, Wan Nur Syahida Wan Ismail, and Ajbor Ali. 2020. Measuring success of accounting information system: Applying the DeLone and McLean model at the organizational level. Journal of Theoretical and Applied Information Technology 98: 2697–706. [Google Scholar]
  2. Agarwal, Shivani, and Rekha Mewafarosh. 2021. Linkage of social media engagement with fomo and subjective well being. Journal of Content, Community & Communication 13: 46–57. [Google Scholar]
  3. Al-Dmour, Hani, Ra’ed Masa’deh, Amer Salman, Mohammad Abuhashesh, and Rand Al-Dmour. 2020. Influence of social media platforms on public health protection against the COVID-19 pandemic via the mediating effects of public health awareness and behavioral changes: Integrated model. Journal of Medical Internet Research 22: e19996. [Google Scholar] [CrossRef]
  4. Al-Okaily, Manaf. 2022. Toward an integrated model for the antecedents and consequences of AIS usage at the organizational level. EuroMed Journal of Business, 45–94. [Google Scholar] [CrossRef]
  5. Al-Okaily, Manaf. 2023. The influence of esatisfaction on users’ eloyalty toward ewallet payment apps: A mediatedmoderated model. International Journal of Emerging Markets, 22–13. [Google Scholar] [CrossRef]
  6. Al-Okaily, Manaf. 2024a. Assessing the effectiveness of accounting information systems in the era of COVID-19 pandemic. VINE Journal of Information and Knowledge Management Systems 54: 157–75. [Google Scholar] [CrossRef]
  7. Al-Okaily, Manaf. 2024b. So What About the Post-COVID-19 Era?: Do Users Still Adopt FinTech Products? International Journal of Human–Computer Interaction 59–92: 1–15. [Google Scholar] [CrossRef]
  8. Al-Samarraie, Hamid, Khaled Amin Bello, Amer Iram Alzahrani, Aomar Polo Smith, and Cam Emele. 2021. Young users’ social media addiction: Causes, consequences and preventions. Information Technology & People 35: 2314–43. [Google Scholar]
  9. Al-Sous, Nida, Dmaithan Almajali, and Abdullah Alsokkar. 2023. Antecedents of social media influencers on customer purchase intention: Empirical study in Jordan. International Journal of Data and Network Science 7: 125–30. [Google Scholar] [CrossRef]
  10. Andreassen, Cecilie Schou, and Stale Pallesen. 2014. Social network site addiction-an overview. Current Pharmaceutical Design 20: 4053–61. [Google Scholar] [CrossRef]
  11. Andreassen, Cecilie Schou, Ståle Pallesen, and Mark D. Griffiths. 2017. The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. Addictive Behaviors 64: 287–93. [Google Scholar] [CrossRef]
  12. Barger, Victor, James W. Peltier, and Don E. Schultz. 2016. Social Media and Consumer Engagement: A Review and Research Agenda. Journal of Research in Interactive Marketing 10: 268–87. [Google Scholar] [CrossRef]
  13. Bougie, Roger, and Uma Sekaran. 2019. Research Methods for Business: A Skill-Building Approach. Hoboken: John Wiley & Sons. [Google Scholar]
  14. Brodie, Roderick J., Ana Ilic, Biljana Juric, and Linda Hollebeek. 2013. Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research 66: 105–14. [Google Scholar] [CrossRef]
  15. Calder, Bobby J., Edward C. Malthouse, and Ute Schaedel. 2009. An experimental study of the relationship between online engagement and advertising effectiveness. Journal of Interactive Marketing 23: 321–31. [Google Scholar] [CrossRef]
  16. Carter, Michael, David Bickham, Elizabth Hunt, and Michael Rich. 2023. Exploring the Nuances of Social Media Use and Experiences. Boston: Boston Children’s Hospital Digital Wellness Lab. Available online: https://digitalwellnesslab.org (accessed on 1 August 2023).
  17. Coyne, Sarah M., Adam A. Rogers, Jessica D. Zurcher, Laura Stockdale, and McCall Booth. 2020. Does time spent using social media impact mental health?: An eight year longitudinal study. Computers in Human Behavior 104: 106160. [Google Scholar] [CrossRef]
  18. Ellison, Nicole B., Charles Steinfield, and Cliff Lampe. 2007. The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication 12: 1143–68. [Google Scholar] [CrossRef]
  19. Freire, Teresa, and Gabriela Ferreira. 2020. Do I Need to Be Positive to Be Happy? Considering the Role of Self-Esteem, Life Satisfaction, and Psychological Distress in Portuguese Adolescents’ Subjective Happiness. Psychological Reports 123: 1064–82. [Google Scholar] [CrossRef]
  20. Frost, Rachel L., and Debra J. Rickwood. 2017. A Systematic Review of the Mental Health Outcomes Associated with Facebook Use. Computers in Human Behavior 76: 576–600. [Google Scholar] [CrossRef]
  21. Griffiths, Mark D., Daria J. Kuss, and Zsolt Demetrovics. 2014. Social Networking Addiction. In Behavioral Addictions. Amsterdam: Elsevier, pp. 119–41. [Google Scholar] [CrossRef]
  22. Guo, Yu, Yiwei Li, and Naoya Ito. 2014. Exploring the Predicted Effect of Social Networking Site Use on Perceived Social Capital and Psychological Well-Being of Chinese International Students in Japan. Cyberpsychology, Behavior and Social Networking 17: 52–58. [Google Scholar] [CrossRef]
  23. Hair, Joe F., Christian M. Ringle, and Marko Sarstedt. 2011. PLS-SEM: Indeed a Silver Bullet. The Journal of Marketing Theory and Practice 19: 139–52. [Google Scholar] [CrossRef]
  24. Hair, Joe F., Marko Sarstedt, Lucas Hopkins, and Volker G. Kuppelwieser. 2014. Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European Business Review 26: 106–21. [Google Scholar] [CrossRef]
  25. Hatamleh, Islam Habis Mohammad. 2024. Exploring the Multifaceted Influences of Social Media Motivation on Online Relationship Commitment among Young Adults in Jordan: An Empirical Study. Human Behavior and Emerging Technologies, 2510500. [Google Scholar] [CrossRef]
  26. Hatamleh, Islam Habis Mohammad, Amjad Omar Safori, Amer Khaled Ahmad, and Neibal Moh’d Ibrahim Al-Etoum. 2023. Exploring the Interplay of Cultural Restraint: The Relationship between Social Media Motivation and Subjective Happiness. Social Sciences 12: 228. [Google Scholar] [CrossRef]
  27. Hollebeek, Linda D. 2011. Demystifying customer brand engagement: Exploring the loyalty nexus. Journal of Marketing Management 27: 785–807. [Google Scholar] [CrossRef]
  28. Hormes, Julia M., Brianna Kearns, and C. Alix Timko. 2014. Craving Facebook? Behavioral addiction to online social networking and its association with emotion regulation deficits. Addiction 109: 2079–88. [Google Scholar] [CrossRef]
  29. Hou, Yubo, Dan Xiong, Tonglin Jiang, Lily Song, and Qi Wang. 2019. Social media addiction: Its impact, mediation, and intervention. Cyberpsychology, Behavior, and Social Networking 22: 431–37. [Google Scholar] [CrossRef]
  30. Kardefelt-Winther, Daniel. 2014. A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior 31: 351–54. [Google Scholar] [CrossRef]
  31. Kim, Yonghwan, Bumsoo Kim, Ha-Sung Hwang, and Dohyung Lee. 2020. Social media and life satisfaction among college students: A moderated mediation model of SNS communication network heterogeneity and social self-efficacy on satisfaction with campus life. The Social Science Journal 57: 85–100. [Google Scholar] [CrossRef]
  32. Knupfer, Helena, Ariadne Neureiter, and Jörg Matthes. 2023. From social media diet to public riot? Engagement with “greenfluencers” and young social media users’ environmental activism. Computers in Human Behavior 139: 107527. [Google Scholar] [CrossRef]
  33. Koc, Mustafa, and Seval Gulyagci. 2013. Facebook addiction among Turkish college students: The role of psychological health, demographic, and usage characteristics. Cyberpsychology, Behavior, and Social Networking 16: 279–84. [Google Scholar] [CrossRef]
  34. Krejcie, Robert, and Daryle Morgan. 1970. Determining Sample Size for Research Activities. Educational and Psychological Measurement 30: 607–10. [Google Scholar] [CrossRef]
  35. Kross, Ethan, Philippe Verduyn, Emre Demiralp, Jiyoung Park, David Seungjae Lee, Natalie Lin, Holly Shablack, John Jonides, and Oscar Ybarra. 2013. Facebook use predicts declines in subjective well-being in young adults. PLoS ONE 8: e69841. [Google Scholar] [CrossRef]
  36. Kuss, Daria J., and Mark D. Griffiths. 2017. Social networking sites and addiction: Ten lessons learned. International Journal of Environmental Research and Public Health 14: 311. [Google Scholar] [CrossRef] [PubMed]
  37. LaRose, Robert, Carolyn A. Lin, and Matthew S. Eastin. 2003. Unregulated Internet usage: Addiction, habit, or deficient self-regulation? Media Psychology 5: 225–53. [Google Scholar] [CrossRef]
  38. Lazaraton, Anne. 2005. Quantitative research methods. In Handbook of Research in Second Language Teaching and Learning. Abingdon: Routledge, pp. 209–24. [Google Scholar]
  39. Lin, Ruoyun, and Sonja Utz. 2015. The emotional responses of browsing Facebook: Happiness, envy, and the role of tie strength. Computers in Human Behavior 52: 29–38. [Google Scholar] [CrossRef]
  40. Marino, Claudia, Gianluca Gini, Alessio Vieno, and Marcantonio M. Spada. 2018a. A comprehensive meta-analysis on problematic Facebook use. Computers in Human Behavior 83: 262–77. [Google Scholar] [CrossRef]
  41. Marino, Claudia, Gianluca Gini, Alessio Vieno, and Marcantonio M. Spada. 2018b. The associations between problematic Facebook use, psychological distress and well-being among adolescents and young adults: A systematic review and meta-analysis. Journal of Affective Disorders 226: 274–81. [Google Scholar] [CrossRef] [PubMed]
  42. McDaniel, Brandon T. 2015. “Technoference”: Everyday Intrusions and Interruptions of Technology in Couple and Family Relationships. In Family Communication in the Age of Digital and Social Media. Edited by C. J. Bruess. New York: Peter Lang Publishing. [Google Scholar]
  43. Mieczkowski, Hannah, Angela Y. Lee, and Jeffrey T. Hancock. 2020. Priming effects of social media use scales on well-being outcomes: The influence of intensity and addiction scales on self-reported depression. Social Media+ Society 6: 2056305120961784. [Google Scholar] [CrossRef]
  44. Nazir, Faridah, Norliza Omar, Muhamad Afzamiman Aripin, and Mohd Hizwan Mohd Hisham. 2020a. A case study of social media addiction among Malaysians. Solid State Technology 63: 1839–51. [Google Scholar]
  45. Nazir, Tamer, Aahm Raza, Manr Yousaf, and Sami Ghazal. 2020b. Social media addiction and its impact on academic performance of university students. Journal of Environmental and Public Health 2020: 7638965. [Google Scholar]
  46. Oh, Hyun Jung, Elif Ozkaya, and Robert LaRose. 2014. How does online social networking enhance life satisfaction? The relationships among online supportive interaction, affect, perceived social support, sense of community, and life satisfaction. Computers in Human Behavior 30: 69–78. [Google Scholar] [CrossRef]
  47. Pang, Hua. 2018. Exploring the beneficial effects of social networking site use on Chinese students’ perceptions of social capital and psychological well-being in Germany. International Journal of Intercultural Relations 67: 1–11. [Google Scholar] [CrossRef]
  48. Phu, Becky, and Alan J. Gow. 2019. Facebook use and its association with subjective happiness and loneliness. Computers in Human Behavior 92: 151–59. [Google Scholar] [CrossRef]
  49. Przybylski, Andrew K., Kou Murayama, Cody R. DeHaan, and Valerie Gladwell. 2013. Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior 29: 1841–48. [Google Scholar] [CrossRef]
  50. Robinson, Anthony, Aaron Bonnette, Krista Howard, Natalie Ceballos, Stephanie Dailey, Yongmei Lu, and Tom Grimes. 2019. Social comparisons, social media addiction, and social interaction: An examination of specific social media behaviors related to major depressive disorder in a millennial population. Journal of Applied Biobehavioral Research 24: e12158. [Google Scholar] [CrossRef]
  51. Roemer, Ellen, Florian Schuberth, and Jörg Henseler. 2021. HTMT2–an Improved Criterion for Assessing Discriminant Validity in Structural Equation Modeling. Industrial Management + Data Systems 121: 2637–50. [Google Scholar] [CrossRef]
  52. Ryan, Tracii, Andrea Chester, John Reece, and Sophia Xenos. 2014. The uses and abuses of Facebook: A review of Facebook addiction. Journal of Behavioral Addictions 3: 133–48. [Google Scholar] [CrossRef]
  53. Smith, Graeme D., and Fang Yang. 2017. Stress, Resilience and Psychological Well-Being in Chinese Undergraduate Nursing Students. Nurse Education Today 49: 90–95. [Google Scholar] [CrossRef]
  54. Stapleton, Peta, Gabriella Luiz, and Hannah Chatwin. 2017. Generation validation: The role of social comparison in use of Instagram among emerging adults. Cyberpsychology, Behavior, and Social Networking 20: 142–49. [Google Scholar] [CrossRef]
  55. Statista. 2022. Number of Social Media Users Worldwide from 2010 to 2026 (In Billions). Available online: https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/ (accessed on 19 February 2023).
  56. Sun, Yalin, and Yan Zhang. 2021. A review of theories and models applied in studies of social media addiction and implications for future research. Addictive Behaviors 114: 106699. [Google Scholar] [CrossRef] [PubMed]
  57. Sutanto, Taufik, and Richi Nayak. 2018. Fine-Grained Document Clustering via Ranking and Its Application to Social Media Analytics. Social Network Analysis and Mining 8: 29. [Google Scholar] [CrossRef]
  58. Tandoc, Edson C., Jr., Patrick Ferrucci, and Margaret Duffy. 2015. Facebook Use, Envy, and Depression among College Students: Is Facebooking Depressing? Computers in Human Behavior 43: 139–46. [Google Scholar] [CrossRef]
  59. Tang, Jih-Hsin, Ming-Chun Chen, Cheng-Ying Yang, Tsai-Yuan Chung, and Yao-An Lee. 2016. Personality Traits, Interpersonal Relationships, Online Social Support, and Facebook Addiction. Telematics and Informatics 33: 102–8. [Google Scholar] [CrossRef]
  60. Utz, Sonja, and Johannes Breuer. 2017. The Relationship between Use of Social Network Sites, Online Social Support, and Well-Being: Results from a Six-Wave Longitudinal Study. Journal of Media Psychology 29: 115–25. [Google Scholar] [CrossRef]
  61. Vannucci, Anna, Kaitlin M. Flannery, and Christine McCauley Ohannessian. 2017. Social media use and anxiety in emerging adults. Journal of Affective Disorders 207: 163–66. [Google Scholar] [CrossRef]
  62. Verduyn, Philippe, Oscar Ybarra, Maxime Résibois, John Jonides, and Ethan Kross. 2017. Do social network sites enhance or undermine subjective well-being? A critical review. Social Issues and Policy Review 11: 274–302. [Google Scholar] [CrossRef]
  63. Wheatley, Daniel, and Sarah L. Buglass. 2019. Social network engagement and subjective well-being: A life—Course perspective. The British Journal of Sociology 70: 1971–95. [Google Scholar] [CrossRef] [PubMed]
  64. Zhao, Lei. 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. [Google Scholar] [CrossRef]
Socsci 13 00351 g001
Figure 1. Research framework.
Figure 1. Research framework.
Socsci 13 00351 g001
Table 1. Measurement scale.
Table 1. Measurement scale.
Variables
Social media use(intensity scale)SMU1—“Social media is part of my everyday activity”SMU2—“I am proud to tell people I’m on social media”SMU3—“social media has become part of my daily routine”SMU4—“I feel out of touch when I haven’t logged onto social media for a while”(Ellison et al. 2007)
Social media engagementIndividual Involvement—first orderII1—“social media is Important”II2—“social media is Relevant to me”II3—“social media is Interesting”II4—“social media is Essential”(Hatamleh et al. 2023)
Personal Meaning—first orderPM1—“My experience with social media is deeply fulfilling”PM2—“When I look to social media, I feel satisfaction of really having accomplished something”PM3—“I feel that I am really going to attain what I want from social media”PM4—“I get so excited by what I am doing in social media that I find new stores of energy I didn’t know that I had”
Social media addictionSMD1—“spent a lot of time thinking about social media or planned use of social media?”SM2—“felt an urge to use social media more and more?”SMD3—“used social media to forget about personal problems?”SMD4—“tried to cut down on the use of social media without success?”Bergen Social Media Addiction Scale (BSMAS; Andreassen et al. 2017)
Subjective well-beingSWB1—“In general, I consider myself”SWB2—“Compared to most of my peers, I consider myself”SWB3—“Some people are generally very happy. They enjoy life regardless of what is going on, getting the most out of everything. To what extent does this characterization describe you?”SWB4—“Some people are generally not very happy. Although they are not depressed, they never seem as happy as they might be. To what extent does this characterization describe you?”(Hatamleh et al. 2023)
Table 2. Convergent validity results for second-order construct.
Table 2. Convergent validity results for second-order construct.
Construct NameCronbach’s AlphaComposite ReliabilityAVE
Second OrderFirst Order
Social Media EngagementIndividual Involvement0.9230.9460.814
Personal Meaning0.8540.9010.697
Table 3. Convergent validity results for first-order construct.
Table 3. Convergent validity results for first-order construct.
Construct NameCronbach’s AlphaComposite ReliabilityAverage Variance Extracted
Social media use0.9680.9750.886
Social media addiction0.8880.9090.630
Subjective well-being0.8420.8820.599
Table 4. Discriminant Validity—Heterotrait–Monotrait criterion.
Table 4. Discriminant Validity—Heterotrait–Monotrait criterion.
Construct NameIndividual InvolvementPersonal MeaningSocial Media AddictionSocial Media EngagementSocial Media UseSubjective Well-Being
Individual Involvement
Personal Meaning0.358
Social Media Addiction0.7400.295
Social Media Engagement0.7320.8300.666
Social Media Use0.8110.2860.7930.708
Subjective Well-Being0.2230.4950.1830.4410.143
Table 5. Discriminant Validity—Fornell–Larcker criterion.
Table 5. Discriminant Validity—Fornell–Larcker criterion.
Construct NameIndividual InvolvementPersonal MeaningSocial Media AddictionSocial Media EngagementSocial Media UseSubjective Well-Being
Individual Involvement0.902
Personal Meaning0.3360.835
Social Media Addiction0.7260.2930.794
Social Media Engagement0.8820.7410.6650.710
Social Media Use0.7650.2820.7580.6880.941
Subjective Well-Being0.2230.4620.1160.3910.1320.774
Table 6. Results of hypothesis testing (path coefficients—β).
Table 6. Results of hypothesis testing (path coefficients—β).
HypothesesOriginal SampleStandard DeviationT
Statistics		p-ValuesResults
(H1) Social Media Use -> Social Media Addiction0.3070.0535.7600.000Accepted
(H2) Social Media Engagement -> Social Media Addiction0.5400.05210.3260.000Accepted
(H3) Social Media Use -> Subjective Well-Being0.1310.0196.7810.000Accepted
(H4) Social Media Engagement -> Subjective Well-Being0.3840.1083.5500.000Accepted
(H5) Social Media Addiction -> Subjective Well-Being−0.5010.077−6.4800.000Accepted
Table 7. R-squared.
Table 7. R-squared.
Dependent Variables R2
Social media addiction0.643
Subjective well-being0.887
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Hatamleh, I.H.M.; Aissani, R. Investigating the Dynamics of Social Media Addiction and Well-Being in Jordan: An Empirical Analysis. Soc. Sci. 2024, 13, 351. https://doi.org/10.3390/socsci13070351

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Hatamleh IHM, Aissani R. Investigating the Dynamics of Social Media Addiction and Well-Being in Jordan: An Empirical Analysis. Social Sciences. 2024; 13(7):351. https://doi.org/10.3390/socsci13070351

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Hatamleh, Islam Habis Mohammad, and Rahima Aissani. 2024. "Investigating the Dynamics of Social Media Addiction and Well-Being in Jordan: An Empirical Analysis" Social Sciences 13, no. 7: 351. https://doi.org/10.3390/socsci13070351

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