Understanding Social Media Addiction through the S-O-R Framework: The Mediating Roles of Cognitive and Behavioral Paths

1 Introduction

As social media has expanded rapidly, growing scholarly attention has focused on the negative consequences associated with overuse [1], known as the “dark side” of digital engagement [2]. Social media addiction (SMA) is commonly understood as one manifestation of a broader class of technology-related behavioral addictions, which also includes problematic internet use, smartphone addiction, and gaming disorder. Within this broader domain, SMA is marked by difficulties in controlling use, resulting in impairments in social, academic, and occupational functioning [3]. Beyond core symptoms such as compulsive use and withdrawal [4], SMA is potentially linked to various adverse outcomes, including social media fatigue [5], declines in psychosocial well-being [6], sleep disturbances [7], and poorer academic performance [8].

Given its growing prevalence, researchers have employed diverse theoretical perspectives to explain the mechanisms underlying SMA [9]. These include the I-PACE (Interaction of Person-Affect-Cognition-Execution) model for mapping the psychological stages of addiction [10], attachment theory for exploring interpersonal dependencies [11], uses and gratifications theory for understanding motivational drivers [12]. While these frameworks have identified various antecedents, for instance fear of missing out (FoMO) [13], dark triad personality traits [14], perceived stress [15], and other related factors, many studies examine these predictors in isolation. This fragmented approach often overlooks the integrated processes through which internal traits and online cognitive-behavioral experiences jointly drive addiction. Furthermore, limited consensus exists regarding the specific pathways that differentiate cognitive and behavioral mediating processes within a unified framework.

Targeting this deficiency, the study uses the Stimulus–Organism–Response (S-O-R) framework to examine how internal predispositions are associated with SMA through distinct intermediate processes. Specifically, boredom proneness, social comparison orientation, and anxiety are conceptualized as internal stimuli, while the organism stage is differentiated into cognitive and behavioral components, operationalized as information overload and social media stalking. By distinguishing between these two pathways, this study aims to offer a more refined perspective on how habitual monitoring and cognitive strain collectively fuel the onset of social media addiction.

2 Literature Review

2.1 The Stimulus-Organism-Response (SOR) Model

The S-O-R model, originally rooted in environmental psychology, provides a structured approach for analyzing how external or internal cues shape human behavior. It posits that environmental inputs (S) impact inner cognitive and psychological conditions (O), which eventually dictates the observable response (R) [16]. Within this framework, stimuli refer to various cues or conditions that trigger individuals’ cognitive and emotional reactions, while the organism represents internal processes operating between environmental inputs and later actions. These processes involve cognitive appraisals and affective experiences that shape how stimuli are interpreted. The response reflects the observable behaviors that result from these internal processes [17].

Although the S-O-R model was initially developed to explain human responses in environmental and consumer contexts, its utility has significantly expanded into the realms of technology use and digital behavior [18]. Its core logic lies in linking stimuli to behavioral responses through mediating organismic states, thereby establishing an integrative analytical framework. While the S-O-R framework traditionally focuses on environmental stimuli, recent research has extended it to incorporate individual psychological and dispositional characteristics [19]. Owing to its theoretical flexibility, the S-O-R framework has been widely employed to investigate various addictive behaviors, including live-streaming addiction [20], artificial intelligence addiction [21], gaming disorder [22], and alcohol and other drug addiction [23]. However, many existing studies treat the organism component as a unified internal state, with limited attention to the differentiation of underlying psychological processes. As a result, the specific mechanisms through which addictive behaviors develop remain insufficiently specified. Distinguishing between these processes is crucial for formulating more precise and effective response strategies.

In response to this deficiency, the research broadens the S-O-R framework by differentiating between two types of organismic processes: cognitive strain and habitual behavior. Specifically, information overload is conceptualized as a form of cognitive strain, whereas social media stalking reflects a pattern of habitual monitoring. By incorporating this dual-path structure, the present research seeks to offer deeper insights into how psychological predispositions relate to SMA.

2.2 Social Media Stalking and Information Overload

Social media stalking (SMS) involves the repeated and impulsive monitoring of other users’ profiles to track their recent online activities. Such behaviors may occur either intentionally or unintentionally and are often driven by curiosity and a desire to remain informed about others’ lives [24].

Because individuals engaged in SMS often devote excessive time to social media, such engagement can interfere with daily routines, including nocturnal sleep [25]. This pattern creates a cycle of habitual checking and increased time investment, which may facilitate the emergence of compulsive tendencies. Research indicates that SMS strongly predicts excessive social media use [24], as individuals who monitor others’ information tend to spend more time on platforms, a behavior associated with addictive tendencies [26].

Although SMS involves frequent engagement, it should not be equated with SMA itself. Instead, it is conceptualized as an intermediate behavioral process characterized by habitual monitoring, which may increase individuals’ exposure to social media content and repeated interaction with the platform, without necessarily implying a loss of control. By distinguishing this habit-based process from addiction, this perspective helps clarify how routine monitoring behaviors may contribute to the development of problematic engagement. Therefore, the study postulates that:

Hypothesis 1 (H1): Social media stalking is positively associated with social media addiction.

Information overload (IO) is triggered when people receive more data than they can effectively process [27]. While prior research often views IO as a consequence of addiction, this perspective overlooks the antecedent impact of the social media environment on cognitive processes. In contemporary digital contexts, where users are continuously exposed to multi-source and rapidly updating streams, IO functions as a pervasive environmental condition rather than a mere symptomatic outcome. Consequently, it is essential to reframe IO as a cognitive mechanism active during the usage process rather than a secondary result variable.

According to Cognitive Load Theory, people have finite mental resources. When exposed to large volumes of complex information, processing demands increase substantially, and IO can hinder the efficient and effective use of information. This may lead to a diminished sense of control over one’s situation and trigger a range of negative responses, including uncertainty, stress, and reduced motivation [28]. As information volume keeps expanding, distinguishing useful, accurate, and high-quality content becomes progressively more challenging. Consequently, individuals may perform below expected levels, abandon tasks, procrastinate, or rely on superficial and low-quality information processing strategies [28]. These patterns of engagement may increase susceptibility to problematic use.

Thus, beyond being a potential consequence of excessive use, IO can also function as a cognitively mediated source of pressure arising from information environments. By weakening self-regulatory capacity, it may indirectly shape patterns of social media engagement and increase susceptibility to addictive behavior.

Hypothesis 2 (H2): Information overload is positively associated with social media addiction.

2.3 Boredom Proneness, Anxiety Proneness, and Social Comparison Orientation

2.3.1 Boredom Proneness

Boredom proneness is an aversive emotional state characterized by a consistent lack of internal stimulation and difficulty maintaining attention. Individuals high in this trait frequently seek external sensory input to alleviate feelings of monotony. Social media platforms, with their continuous stream of interpersonal updates and interactive affordances, offer a convenient venue for such stimulation seeking [29].

SMS represents a low-effort behavioral strategy for mitigating boredom, as it provides constant informational novelty with relatively minimal cognitive investment. Consequently, boredom-prone individuals may utilize social media stalking as a habitual means of escaping. Accordingly, we propose:

Hypothesis 3a (H3a): Boredom proneness is positively associated with social media stalking behaviors.

Beyond behavioral monitoring, boredom proneness significantly influences how individuals process digital content. To escape under-stimulation, boredom-prone users often engage intensively with diverse and novel online experiences, even when such behaviors impair long-term well-being [29,30]. This intensive engagement frequently results in excessive exposure to high-density information environments. As these individuals consume vast volumes of content to sustain interest, the resulting cognitive demands may exceed their finite processing limits, leading to information overload (IO). Thus, the pursuit of emotional stimulation through social media inadvertently increases cognitive burden by depleting available mental resources over time.

Hypothesis 3b (H3b): Boredom proneness is positively associated with information overload in social media use.

Boredom is deeply rooted in attentional deficits and a widespread deficiency of interest in primary tasks. Boredom-prone individuals are highly distractible, often abandoning utilitarian activities in favor of hedonic alternatives like social media [29]. While these platforms are intentionally designed to capture attention and satisfy the need for novelty [31], repetitive reliance on digital stimulation can weaken an individual’s self-regulatory capacity. Addiction research suggests that such habitual coping mechanisms reinforce excessive usage patterns, gradually evolving into problematic engagement [32]. Over time, the habitual reliance on social platforms as the chief instrument for boredom regulation fosters a psychological craving, which in turn catalyzes the transition into addictive usage. Therefore, we propose:

Hypothesis 3c (H3c): Boredom proneness is positively associated with social media addiction.

2.3.2 Anxiety Proneness

Anxiety, particularly in social contexts, involves a heightened concern regarding social evaluation and a sensitivity to interpersonal feedback. According to the Theory of Compensatory Internet Use (TCIU), individuals experiencing psychological distress often utilize online environments to mitigate negative emotions or compensate for perceived offline social deficiencies [2]. Within this framework, social media stalking functions as a strategic coping mechanism. Anxious individuals may repeatedly monitor others’ profiles to interpret social dynamics and reduce uncertainty about their standing. While initially a form of self-protection, this persistent surveillance reinforces a reliance on digital cues for emotional stabilization. Thus, we hypothesize:

Hypothesis 4a (H4a): Anxiety is positively associated with social media stalking behaviors.

As a future-oriented affective state, anxiety drives individuals to anticipate potential negative outcomes and seek constant updates to remain informed [33]. This need for reassurance often leads anxious users to maintain active presences across multiple digital platforms [34]. While mediated communication reduces immediate social pressure compared to face-to-face interaction, it simultaneously intensifies exposure to diverse and rapidly updating information streams [35]. Given the finite nature of cognitive resources, this heightened input increases the likelihood of experiencing information overload. Thus, the anxious pursuit of digital reassurance inadvertently elevates cognitive demands, leading to a state of informational saturation.

Hypothesis 4b (H4b): Anxiety is positively associated with information overload in social media use.

Anxiety is a common emotional state characterized by nervousness, worry, and anticipation of potential negative events, manifesting in both cognitive and physiological distress [34]. Empirical evidence consistently links elevated anxiety to problematic technology use, including smartphone and Internet addiction [36,37]. For anxious individuals, social media provides an accessible avenue for distraction and social connection [34], often manifesting in intensified usage patterns [38]. However, when individuals habitually rely on these platforms to regulate their affective states, this coping strategy can weaken self-regulatory capacity and foster psychological dependence. Consequently, the persistent engagement driven by anxiety-related distress is expected to facilitate the development of addictive behaviors. Thus, we hypothesize:

Hypothesis 4c (H4c): Anxiety is positively associated with social media addiction.

2.3.3 Social Comparison Orientation

Social comparison theory (SCT) holds that people tend to gauge their personal proficiency and social position by measuring themselves against their peers [39]. Those characterized by a strong tendency toward social comparison are inherently motivated to seek information about peers to facilitate these evaluations. Social media platforms, characterized by an abundance of accessible personal data, provide an ideal context for such comparative processes. Under the TCIU framework, social media stalking functions as a strategic mechanism for acquiring evaluative information [2]. By repeatedly monitoring others’ profiles, individuals with high social comparison orientation can efficiently gather the social cues necessary to navigate their interpersonal environment and satisfy their need for self-assessment. Accordingly, we propose:

Hypothesis 5a (H5a): Social comparison orientation is positively associated with social media stalking behaviors.

Social comparison on digital platforms requires users to constantly process, evaluate, and switch between diverse types of social information [40]. Social media platforms have become primary environments for social comparison, as they prominently display curated information about others’ achievements, lifestyles, and social interactions. Research indicates that upward social comparison often occurs automatically when users are exposed to others’ content [41]. According to Cognitive Load Theory, such continuous evaluative processing and cognitive switching consume substantial mental resources. For individuals with a strong social comparison orientation, the persistent drive to compare their lives with the idealized lifestyles of others creates an intense informational demand. This heightened cognitive activity frequently exceeds finite processing limits, leading to information overload. Accordingly, we propose:

Hypothesis 5b (H5b): Social comparison orientation is positively related to information overload during social media use.

Social comparison orientation describes an enduring inclination to perform self-appraisals through the lens of interpersonal contrasts [42]. In social media environments, users frequently encounter idealized portrayals that can evoke negative affective states, including jealousy and dissatisfaction [43]. These emotional discrepancies create psychological discomfort and cognitive dissonance [44]. Social comparison orientation captures an individual’s propensity to engage in such comparative processes. According to TCIU, such negative experiences function as stressors that may paradoxically motivate increased platform engagement as a coping mechanism [2]. When individuals repeatedly utilize social media to manage the emotional fallout of skewed comparisons, this habitual reliance can weaken self-regulatory capacity and reinforce addictive usage patterns. Consequently, a strong orientation toward social comparison is expected to facilitate one’s susceptibility to social media addiction. Accordingly, we propose:

Hypothesis 5c (H5c): Social comparison orientation is positively associated with social media addiction.

2.4 The Mediating Roles of Information Overload and Social Media Stalking

The S-O-R logic dictates that intrinsic or extrinsic triggers evoke specific internal conditions, which in turn influence behavioral responses [45]. A critical distinction within this framework lies in its emphasis on indirect relationships, where the impact of a stimulus is transmitted through mediating mechanisms [46]. This study identifies social media stalking and information overload as the primary cognitive and behavioral pathways through which psychological predispositions are transformed into addictive outcomes.

Social media stalking represents a behavioral pathway characterized by habitual monitoring and repeated checking of others’ activities. Although such behaviors involve frequent engagement, they do not necessarily imply a loss of control and are therefore conceptually distinct from social media addiction. Instead, they can be understood as intermediate behavioral patterns that increase exposure to social media and reinforce routine interaction with the platform. Prior research has shown that cyberstalking as a mediator, linking dark personality traits to dysfunctional social media engagement [47]. Moreover, stalking behaviors have been shown to correlate positively with Internet addiction [48] and compulsive social media use [24]. By establishing a cycle of constant profile checking and heightened platform engagement, SMS serves as the behavioral bridge that reinforces the transition from initial psychological needs to compulsive usage patterns.

Parallel to behavioral pathways, information overload functions as a cognitive mediator. This study conceptualizes information overload as a state of cognitive constraint where excessive informational input exhausts an individual’s processing capacity [49]. Rather than being solely a consequence of heavy use, information overload can function as a cognitive condition that shapes how individuals engage with social media. When cognitive resources are excessively consumed, users may rely on superficial and low-quality information processing strategies [28], which can increase susceptibility to problematic use. Existing studies have linked information overload to fatigue [50], stress [51], and maladaptive usage patterns [52], suggesting its role as a mechanism underlying behavioral outcomes.

By distinguishing these behavioral and cognitive processes, the study conceptualizes social media stalking and information overload as intermediate mechanisms that precede addictive use. This distinction helps clarify how psychological predispositions are translated into problematic engagement through different pathways.

Accordingly, we propose:

Hypothesis 6 (H6): Social media stalking mediates the relationships between psychological antecedents and social media addiction.

Hypothesis 7 (H7): Information overload mediates the relationships between psychological antecedents and social media addiction.

Aligning with the S-O-R logic, the current study examines how specific psychological predispositions are associated with SMA through the dual pathways of cognitive strain and habitual monitoring. Fig. 1 presents the conceptual framework and the hypothesized paths between these variables.

Figure 1: Structure of the proposed model. Note: BP, Boredom Proneness; ANX, Anxiety; SC, Social Comparison; SMS, Social Media Stalking; IO, Information Overload; SMA, Social Media Addiction.

3 Methods

3.1 Participants

Using convenience sampling, this study recruited Chinese adults aged 18 years and above who had prior experience with social media and reported using at least one social media platform daily. Data were collected in January 2026, primarily through Credamo, a commonly used online survey platform in China, with a small proportion of responses obtained through offline channels. All individuals participated voluntarily after providing informed consent. This study was reviewed and approved by the Institutional Review Board of the School of Journalism and Communication, Beijing Normal University (Approval No.: BNUJ&C20250408005), where the corresponding author was affiliated at the time of ethics application. After excluding responses that exhibited patterned answering, contained missing values, or were completed in an unusually short time, 258 valid responses were kept for subsequent analyses, satisfying the minimum threshold of 200 necessary for structural equation modeling (SEM) models with moderate complexity [53].

The final sample included 155 females (60.1%) and 103 males (39.9%), averaging 31.45 years of age (Mean = 31.45, SD = 8.65). Regarding age distribution, participants fell into the following brackets: 18–27 (40.3%), 28–37 (40.7%), 38–47 (12.0%), and 48 years or older (7.0%). Regarding employment status, the majority of participants were employed full-time (71.7%), followed by students (13.6%), freelancers (8.5%), and entrepreneurs (3.5%), with a small proportion identifying as workers, unemployed, or retired. With respect to educational attainment, most participants held an undergraduate degree (69.8%), while 16.7% reported a postgraduate degree. The remaining participants reported education levels ranging from junior college to middle school. Participants’ average daily engagement with social media was also recorded. Approximately 29.1% indicated using social media for 2–3 h per day, followed by 21.7% for 3–4 h, 15.1% for 4–5 h, and 10.1% for 5–6 h. Smaller proportions reported either relatively light use (less than 2 h per day) or heavy use (more than 6 h per day). The distribution of demographic variables across the final sample is illustrated in Table 1.

3.2 Instruments

To ensure cultural and linguistic equivalence, scales adopted from previous literature underwent a Chinese translation process. All items were rated on a five-point Likert scale (1 = strongly disagree; 5 = strongly agree).

3.2.1 Boredom Proneness Scale

This research utilized the Short Boredom Proneness Scale, with the Chinese adaptation and translation provided by Peng et al. [54]. It comprises eight items assessing individuals’ feelings of monotony and emptiness. A sample item is: “I need to do repetitive and monotonous things”.

3.2.2 Anxiety Proneness Scale

Anxiety was assessed with five items adapted from Jabeen et al. [34]. This scale assesses anxiety as an emotional response characterized by unpleasant feelings triggered by fear or concern in social contexts. Example item: “I am afraid that others will not like me”.

3.2.3 Social Comparison Orientation Scale

A six-item scale adapted from Jabeen et al. [34] was used to measure social comparison orientation, defined as individuals’ tendency to compare oneself with others in online settings. Example item: “When I am on social media, I tend to compare myself with others”.

3.2.4 Social Media Stalking Scale

To evaluate social media stalking, we utilized a six-item instrument derived from the work of Tandon et al. [2]. It captures the degree of persistent observation regarding other users’ social media engagement and content. Example item: “I usually check others’ social media profiles to see what they are up to”.

3.2.5 Information Overload Scale

Information overload was assessed with five items adapted from Qaisar et al. [52] and Shi et al. [55], reflecting users’ perceptions that incoming information surpasses what they can process or personally need. Example item: “I am overwhelmed by the amount of information that I process on a daily basis from social media”.

3.2.6 Social Media Addiction Scale

Social media addiction was assessed with items adapted from Qaisar et al. [52], covering core features of problematic use, including preoccupation, unsuccessful attempts to reduce usage, withdrawal-related anxiety, and interference with academic and social functioning. Example item: “I feel anxious if I cannot access social media”.

3.3 Data Analysis

Statistical analysis was conducted using AMOS 26.0 and SPSS 27.0 (IBM Corp., Armonk, NY, USA). Consistent with the two-step SEM procedure, the measurement model was first evaluated for reliability and validity, after which the structural paths were tested.

The measurement model exhibited a satisfactory fit, indicating that the proposed conceptual structure aligns well with the empirical data: χ2/df = 1.45, CFI (comparative fit index) = 0.97, TLI (Tucker-Lewis index) = 0.97, RMSEA (root mean square error of approximation) = 0.04. Three items (SMS3, IO5, and SMA1) displayed factor loadings under the suggested 0.70 cut-off and were consequently omitted from later analyses [56]. The choice to discard these items was informed by both empirical data and theoretical grounds. Specifically, retaining items with low loadings may weaken construct validity and introduce measurement error. After removal, all constructs maintained adequate content coverage, suggesting that the exclusion did not compromise the conceptual integrity of the measures. Furthermore, both Cronbach’s alpha and composite reliability (CR) for all constructs exceeded 0.70, indicating robust reliability and internal consistency [57]. Although several constructs showed relatively high Cronbach’s alpha values, which may indicate potential item redundancy, this also reflects a high degree of internal consistency among items capturing similar underlying dimensions. Given that the retained items represent theoretically coherent aspects of each construct, the reliability levels were considered acceptable for the purposes of this study. Detailed results for factor loadings, Cronbach’s alpha, composite reliability, and average variance extracted (AVE) are reported in Table 2. Discriminant validity was assessed using the Fornell–Larcker criterion. As shown in Table 3, the square root of the AVE for each construct was greater than its correlations with all other constructs, thereby supporting the adequacy of discriminant validity [58].

4 Results

4.1 Descriptive Statistical Analysis and Correlation Analysis

We conducted a descriptive statistical analysis on the dataset. As illustrated in Table 3, the average scores of the six primary variables ranged from 2.634 to 3.478. Specifically, SMS exhibited the highest mean (Mean = 3.478, SD = 0.943), while BP showed the lowest (Mean = 2.634, SD = 1.023). These results suggest that participants experienced moderate levels of psychological traits and social media-related behaviors. The standard deviations, which varied between 0.943 and 1.136, indicate a reasonable degree of dispersion in the responses, providing a suitable basis for further SEM analysis.

Pearson correlation analysis examined links among the variables. Table 3 shows that all constructs were positively related at the 0.01 significance level. In particular, boredom proneness, anxiety, and social comparison orientation were significantly associated with both information overload and social media stalking. Moreover, information overload and social media stalking were strongly correlated with social media addiction. Overall, the correlations were of moderate to strong magnitude, providing preliminary support for the proposed hypotheses and satisfying the prerequisite for subsequent structural equation modeling. In addition, multicollinearity among the constructs was examined using variance inflation factors (VIFs). The VIF values ranged from 1.998 to 2.691, all of which were below the recommended threshold of 5 [59], indicating that multicollinearity was not a serious concern in this study. Moreover, we used the common latent factor technique to test common method bias (CMB). The one-factor constrained model exhibited poor indices, with χ2 = 2985.54, df = 495, CFI = 0.66, TLI = 0.64, RMSEA = 0.14, while the standardized regression weights in this model displayed a difference of less than 0.2 [60]. More importantly, the one-factor constrained model demonstrated inferior fit in comparison to the six-factor unconstrained model (Δχ2 (df) = 2288.57 (15), p < 0.001). This affirms that CMB does not pose a significant concern in our study.

4.2 Structural Model

The structural model demonstrated a good overall fit to the data (χ2/df = 1.46, CFI = 0.97, TLI = 0.97, RMSEA = 0.04). The results of the hypothesis testing are shown in Table 4. Regarding the mediating variables, both social media stalking (β = 0.245, p < 0.01) and information overload (β = 0.553, p < 0.01) were significantly and positively associated with social media addiction, providing support for H1 and H2. Boredom proneness showed significant positive associations with social media stalking (β = 0.112, p < 0.05) and information overload (β = 0.577, p < 0.01), supporting H3a and H3b. However, its direct relationship with social media addiction was not significant (β = 0.137, p > 0.05), leading to the rejection of H3c. Anxiety did not exhibit significant associations with social media stalking (β = 0.084, p > 0.05), information overload (β = 0.097, p > 0.05), or social media addiction (β = 0.116, p > 0.05); therefore, H4a, H4b, and H4c were not supported. Social comparison was strongly associated with social media stalking (β = 0.651, p < 0.01) and was also positively associated with information overload (β = 0.165, p < 0.05), supporting H5a and H5b. However, its direct link to social media addiction was not significant (β = 0.041, p > 0.05), resulting in the rejection of H5c.

The structural model demonstrated strong explanatory power, accounting for 56.7% of the variance in information overload (R2 = 0.567), 61.7% of the variance in social media stalking (R2 = 0.617), and 85.9% of the variance in social media addiction (R2 = 0.859). The notably high R2 for social media addiction should be interpreted with caution, as it may partly reflect the conceptual proximity between the mediators and the outcome, as further discussed in the limitations section.

4.3 Mediation Analyses

As shown in Table 5, social media stalking significantly mediated the relationship between boredom proneness and social media addiction (β = 0.027, 95% CI [0.003, 0.071]), supporting the proposed mediating effect. In addition, social media stalking also mediated the relationship between social comparison and social media addiction (β = 0.160, 95% CI [0.071, 0.278]). However, the indirect link from anxiety to social media addiction through social media stalking was not significant (β = 0.021, 95% CI [−0.020, 0.079]).

With respect to information overload, the results indicated a significant indirect association between boredom proneness and social media addiction (β = 0.319, 95% CI [0.209, 0.455]), suggesting that information overload played a substantial mediating role in this relationship. Similarly, information overload significantly mediated the relationship between social comparison and social media addiction (β = 0.091, 95% CI [0.005, 0.199]). In contrast, the indirect pathway from anxiety to social media addiction via information overload was not statistically significant (β = 0.054, 95% CI [−0.033, 0.153]).

5 Discussion

This research provides a nuanced perspective on social media addiction by identifying two empirically distinguishable pathways through which psychological predispositions are associated with problematic engagement. Utilizing the S-O-R framework, the study identifies cognitive strain and habitual behavioral monitoring as parallel mechanisms linking internal traits to social media addiction. The results suggest that boredom proneness and social comparison orientation are not directly associated with addictive outcomes but are related to such outcomes through these intermediary processes. This dual-path perspective extends prior research by moving beyond undifferentiated organism-level explanations and providing a more fine-grained account of how psychological tendencies are linked to problematic use patterns.

From the cognitive pathway perspective, boredom proneness and social comparison orientation were both linked to increased information overload, which subsequently correlated with social media addiction. Individuals with high boredom proneness tend to seek external stimulation [29], and social media provides a readily accessible source of continuous input [61]. However, sustained exposure to fragmented and high-density content may exceed cognitive processing capacity, thereby increasing cognitive strain [27]. Similarly, users possessing a more intense social comparison tendency are prone to assessing others’ digital engagements, a process requiring continuous processing of diverse social information [62]. This sustained demand on cognitive resources contributes to perceptions of overload. In this sense, information overload reflects a form of cognitive pressure associated with reduced self-regulatory capacity and increased vulnerability to problematic engagement.

From the behavioral pathway perspective, boredom proneness and social comparison orientation were also found to relate indirectly to social media addiction through social media stalking. For boredom-prone individuals, repetitive and low-effort browsing serves as a convenient means of alleviating under-stimulation, which may gradually develop into habitual patterns of use. For those high in social comparison orientation, social media stalking provides an accessible way to obtain evaluative information [61]. Repeated monitoring may shift from goal-directed behavior to habitual checking over time. In this regard, social media stalking represents a form of habitual engagement that increases repeated interaction with platforms and is associated with problematic use patterns [24].

Although repeated monitoring and perceptions of information overload may appear similar to manifestations of problematic use, the present study conceptualizes them as intermediate processes rather than the outcome itself. Specifically, cognitive strain and habitual monitoring are understood as transitional stages that precede the loss of control typically associated with addiction. This distinction explains why these variables are strongly associated with social media addiction while remaining theoretically separable. Simultaneously, the findings do not preclude the possibility that these processes emerge as consequences of prolonged engagement, suggesting a complex relationship between mechanisms and outcomes. Consequently, the model explains a high proportion of variance in social media addiction. While this reflects the close alignment between the proposed mechanisms and problematic engagement, it also warrants cautious interpretation. Self-report data obtained at a single time point may introduce shared method variance, potentially inflating observed associations. Additionally, the inclusion of proximal cognitive and behavioral processes contributes to the model’s explanatory strength. Therefore, the reported explanatory power should be interpreted with caution. Future research may consider adopting multi-method and longitudinal designs to further assess the robustness of these relationships.

Contrary to expectations, anxiety was not significantly associated with the mediating variables or SMA. This result could be attributed to the particular way anxiety was operationalized in this research, which primarily reflects social-evaluative concerns. Highly anxious individuals may adopt avoidance-oriented strategies to limit exposure to potentially threatening social information, rather than increasing engagement through monitoring or extensive use [63]. Consequently, their involvement in both stalking and intensive information processing may be reduced [5]. Alternatively, the non-significant effect may reflect a suppression pattern, whereby stronger predictors like boredom proneness and social comparison orientation account for a large share of variance in the mediators. Furthermore, the impact of anxiety might be situational. Taken together, these results imply that anxiety could operate as a conditional factor instead of a general predictor, with its influence depending on measurement and contextual conditions.

5.1 Theoretical and Practical Implications

This study refines the theoretical understanding of SMA by distinguishing between cognitive and behavioral mediating pathways. While previous S-O-R research often overlooks specific organism-level processes, our findings identify information overload and social media stalking as two functionally distinct routes linking internal stimuli to addictive outcomes. By conceptualizing these variables as mechanistic precursors rather than mere symptoms, this research offers a granular account of how psychological predispositions translate into problematic engagement, thereby broadening the interpretive scope of the S-O-R model in digital contexts.

Regarding practical implications, these findings provide a strategic blueprint for mitigating social media addiction. Preventive measures might be more impactful when focusing not only on reducing screen time but also on addressing the underlying psychological drivers that initiate excessive engagement. For example, strategies that promote alternative offline activities during periods of boredom may lessen dependence on social media as a substitute coping outlet. Additionally, the significant role of information overload suggests that platform design should consider cognitive load management. Features such as usage reminders, adaptive content filtering, and information-density regulation may help reduce perceived overload, thereby interrupting the progression from frequent engagement to addictive use patterns.

5.2 Limitations and Directions for Future Research

Notwithstanding the insights provided by this work, certain constraints persist that offer valuable avenues for subsequent investigations. First, a notable limitation is the relatively high R2 values observed in the structural model, particularly regarding social media addiction. While diagnostic tests (e.g., VIF) ruled out severe multicollinearity, this high degree of explained variance may partly stem from the inherent behavioral overlap between habit-based monitoring and addiction symptoms. Furthermore, the use of a cross-sectional, self-reported approach during a single collection period might have introduced common method bias and potentially inflated the reported correlations. Prospective studies ought to utilize longitudinal frameworks or experimental methods to more robustly validate these effect sizes and capture the underlying dynamic mechanisms.

Second, the current model assumes a linear and unidirectional pathway; however, the relationships between information overload, social media stalking, and addiction may be reciprocal or self-reinforcing. Future studies are encouraged to explore these potential bidirectional dynamics to offer a more sophisticated insight into the developing digital landscape.

Finally, the employment of data from a solitary source may constrain the universal relevance of these insights. The non-significant results observed for certain variables, such as anxiety, further suggest that specific cultural or contextual mechanisms may not have been fully captured within the current scope. Future research should incorporate extensive contextual factors and more varied, broad-based datasets to improve the stability and cross-contextual generalizability of the results.

6 Conclusions

Drawing on the S-O-R framework, this investigation identifies differentiated cognitive and behavioral pathways through which psychological predispositions are associated with SMA. The results reveal that boredom proneness and social comparison orientation are not directly linked to addictive outcomes but are instead associated with problematic engagement through the intervening effects of information overload and social media stalking.

Specifically, the cognitive pathway via information overload highlights how cognitive strain is linked to addictive outcomes. Simultaneously, the behavioral pathway via social media stalking represents a mechanistic conduit through which boredom and comparison-driven motivations are associated with habitual or compulsive engagement. In contrast, the non-significant role of social evaluation anxiety suggests its influence may be conditional or secondary compared to more potent motivational drivers.

Overall, the primary contribution of this research lies in empirically distinguishing between cognitive strain and habitual monitoring as parallel intermediate processes. By reframing these variables as mechanistic precursors rather than mere symptoms, this study offers a granular account of the internal processes tied to SMA and provides a foundation for targeted digital wellness strategies.