Social media has fundamentally reshaped how people communicate and shop, fueling the rapid growth of social commerce form of online retail enriched by user-generated content, peer reviews, and real-time interaction. Unlike traditional e-commerce, social commerce thrives on digital engagement dynamics that shape consumer decisions (Alhumud and Elshaer, 2024; Attar et al., 2022; Lin and Wang, 2023). However, much of the existing research treats these elements branding, usability, or influencer tactics as standalone variables, lacking a unified theoretical structure. This fragmentation obscures how different digital cues combine to influence consumer behavior. This study investigates how three key digital stimuli social media trends (SMT), quality of information (QTI), and influencer cues (ICR) impact consumer behavior in Saudi Arabia. It focuses on the mediating role of social media interaction (SMI) and examines whether age moderates these relationships. By integrating the S-O-R model with UGT and TPB, this research contributes to filling the theoretical gap in understanding how diverse digital cues translate into consumer action, especially in digitally mature environments.

The Saudi market provides a valuable context for examining these trends. With over 90% internet penetration and a predominantly youthful, digitally active population, Saudi Arabia has experienced a fast transition from traditional to digital media platforms (Almuammar et al., 2021; Wahabi et al., 2023), with internet penetration over 95% and widespread adoption of platforms like Instagram, Snapchat, and TikTok (Sanam et al., 2024). The Kingdom’s youthful demographic where nearly 70% are under 35 makes it a strategic context for examining digital interactions (Katooa, 2024). Notably, Saudi females demonstrate higher engagement levels on visual platforms, making them a vital segment for consumer-centric marketing studies (Aldhahery et al., 2018). These cultural, technological, and demographic conditions justify the country’s selection for investigating social media-driven consumer behavior.

As digital platforms become more integrated into everyday routines, consumer decisions are shaped not just by functional goals, but also emotional and social needs (Alatawy, 2021). Viral trends and peer-generated content increasingly influence purchasing intentions, often more than conventional advertising. Yet, concerns about credibility, such as fake reviews and influencer cues transparency, remain salient particularly in contexts like Saudi Arabia where trust and community values strongly affect decision-making (Onofrei et al., 2022; Palalic et al., 2020; Shahbaznezhad et al., 2021).

While SMT and QTI are widely accepted as core drivers of interaction, influencer cues (ICR) represent a more context-sensitive factor, with their influence potentially shaped by issues such as trust erosion, saturation, and regional norms. This integrated framework offers both theoretical insight and actionable value for digital marketers, particularly those targeting socially driven, mobile-first consumers in the MENA region.

This study extends the Stimulus–Organism–Response (S-O-R) model by integrating insights from the Uses and Gratifications Theory (UGT) and the Theory of Planned Behavior (TPB), framing social media interaction as both a behavioral and epistemic response to digital stimuli.

While numerous studies have explored how social media content and influencer cues shape consumer behavior, this study fills a key gap by examining how multiple digital cues interact through social media interaction (SMI) as a unified cognitive-emotional process. Most prior studies focus on isolated effects or test direct paths, such as content quality directly affecting purchase intention (Onofrei et al., 2022) or influencer trust influencing decisions (Coutinho et al., 2023). However, few have modeled the mediating role of interaction across diverse stimuli types or explored how user characteristics like age moderate these effects within a structured theoretical framework.

This research integrates the Stimulus–Organism–Response (S-O-R) model with the Uses and Gratifications Theory (UGT) and Theory of Planned Behavior (TPB). UGT helps identify why users are drawn to certain stimuli (to fulfill social, cognitive, or emotional gratifications). S-O-R models how these stimuli are processed, and TPB explains how internal states translate into purchase behavior. Each hypothesis is grounded in this theoretical synthesis.

Social media interaction (SMI) is treated as a digital organismic response that reflects user-level engagement and emotional-cognitive processing. SMI encompasses both active behaviors (e.g., commenting, sharing, participating in challenges or polls) and passive behaviors (e.g., viewing, liking, browsing), all of which contribute to engagement intensity. Although treated as a unified construct in this study, these forms differ in depth and motivation suggesting a potential avenue for future research to explore their discrete effects on consumer behavior.

Understanding the distinction between active and passive engagement is critical for conceptualizing Social Media Interaction (SMI) within digital consumer behavior. Active engagement refers to participatory actions such as commenting, sharing, or contributing user-generated content, which signal higher levels of cognitive and emotional investment (Lai Cheung et al., 2021). These behaviors reflect a user’s willingness to engage with content beyond consumption, often indicating deeper processing, stronger attitudinal alignment, and a higher likelihood of behavioral outcomes (Palalic et al., 2020).

Conversely, passive engagement encompasses less participatory but still meaningful behaviors, including browsing, liking, or silently consuming content. Although passive users do not overtly contribute, their behaviors can be indicative of interest, exploratory intent, or cognitive resonance (Onofrei et al., 2022). Research shows that passive engagement may still reflect affective responses and can be a precursor to more active behaviors under the right contextual stimuli (Mahoney and Tang, 2024). However, the psychological depth and conversion potential of passive engagement are typically lower, making the distinction theoretically and practically relevant.

This distinction matters because the cognitive-emotional processing involved in active engagement often strengthens the mediation effect of SMI on behavioral outcomes, such as consumer buying behavior. By integrating both engagement modes under a unified construct, the current study reflects the blended nature of digital interaction, while acknowledging that underlying motivational dynamics may differ (Lina et al., 2022; Rather and Hollebeek, 2021). Recognizing this duality allows for a more nuanced understanding of how stimuli like social media trends and influencer cues shape consumer decision-making across varied user types and contexts.

Data analysis utilized SmartPLS 4 software applying the partial least squares (PLS) approach for structural equation modeling (SEM). PLS was preferred over traditional SEM due to its focus on maximizing variance explained by independent variables. It has minimal sample size requirements while delivering reliable results for both measurement and structural models, aligning well with the study’s goals (Hair and Alamer, 2022). Additionally, SmartPLS 4’s PLS-SEM is ideal for exploratory research and complex models, especially when assumptions of normality are not met (Chin, 1998; Hair et al., 2011).

This study contributes to a growing body of literature exploring how digital cues influence consumer decision-making by testing a multi-theoretical model combining the Stimulus–Organism–Response (S-O-R) framework, Uses and Gratifications Theory (UGT), and Theory of Planned Behavior (TPB). It investigated how the triad of Social Media Trends (SMT), Quality of Information (QTI), and Influencer Cues (ICR) affect Social Media Interaction (SMI) and, ultimately, Consumer Buying Behavior (CBB).

The results highlight several critical insights. First, both SMT and QTI had significant and positive effects on SMI, suggesting that exposure to trending content and access to reliable information increase digital engagement. These findings align with prior research indicating that digital media content fulfilling cognitive and emotional gratifications prompts deeper involvement (Mahoney and Tang, 2024; Wang and Yan, 2022; Zolkepli et al., 2018). The effect sizes, although modest (f² = 0.077 for SMT, f² = 0.079 for QTI), confirm that these forms of content act as effective stimuli in triggering interaction.

Trending Content was conceptualized as media material that gains rapid visibility within a short timeframe due to high engagement. SMT items captured perceptions of content virality and social proof. Examples include viral hashtags (e.g., #NewDrop), influencer challenges (e.g., dance trends), meme-based content, real-time news reactions, and promotional campaigns amplified by algorithms. These formats were chosen because they embody emotionally appealing, socially validated, and time-sensitive stimuli that trigger spontaneous interaction (Mahoney and Tang, 2024). This clarification supports the construct’s grounding in UGT and S-O-R theory as a stimulus with both cognitive and affective triggers.

From a theoretical standpoint, ICR did not significantly influence SMI or CBB, with a negligible effect size for ICR → SMI (β = 0.003) and a non-significant indirect path to CBB, as confidence intervals crossed zero. This finding diverges from prior assertions in the UGT and TPB literature that position influencers as persuasive cues shaping attitudes and behaviors (Coutinho et al., 2023; Croes and Bartels, 2021). A plausible explanation lies in the evolving phenomenon of “influencer fatigue,” where consumers become desensitized to repetitive influencer content or view it as commercially motivated (Kim et al., 2025; Lina et al., 2022). In digitally mature markets like Saudi Arabia, especially among younger users, persuasion may place greater trust in peer-generated content or algorithmically ranked posts than in celebrity endorsements. Yet, this interpretation should be approached with caution, as the study did not capture fine-grained distinctions in influencer types (e.g., micro vs. macro) or message framing, and the generalizability of findings may be constrained by cultural and contextual nuances.

Future studies should consider examining micro-influencers, AI-generated content, or peer-led validation as more culturally aligned alternatives.

The statistical insignificance of ICR in the structural model both directly and through SMI highlights an important theoretical implication: not all stimuli labeled as influential maintain their persuasive capacity across contexts. This aligns with recent studies showing that digital literacy and content discernment have equipped users to critically evaluate promotional content (Zhang et al., 2024). Thus, traditional influencer strategies may need reevaluation, particularly in digitally mature societies where consumer empowerment and authenticity drive decision-making. Future models should consider incorporating micro-influencer dynamics, AI-generated content, and peer-led credibility signals to better capture the evolving nature of online influence.

Social Media Interaction (SMI) itself was shown to be the strongest predictor of Consumer Buying Behavior (CBB) (β = 0.533, f² = 0.381), affirming that interactive engagement, rather than passive exposure, drives purchase intent. This supports the view that consumer decision-making is increasingly co-constructed in dynamic, socially embedded digital environments (Attar et al., 2022; Hassan and Sohail, 2021). These findings also validate SMI’s mediating role between stimuli and behavior, particularly for SMT and QTI (H5a and H5b supported), reinforcing its theoretical centrality.

Similarly, age-based moderation produced no statistically significant interaction effects. While slope graphs visually suggested potential differences in responsiveness between younger and older users, none of the interactions (H6a, H6b, H6c) reached significance thresholds (p > 0.05), and all confidence intervals crossed zero. This absence of significant moderation should not be taken as definitive evidence of age convergence in digital engagement behaviors. Rather, it may reflect limitations in the age distribution of the sample, which was not sufficiently detailed to allow for robust segmentation or subgroup comparisons. Future research with more stratified age samples is warranted to uncover potential age-driven differences in digital media processing and consumer behavior.

From a predictive standpoint, the model demonstrated acceptable predictive relevance and moderate explanatory power, with an adjusted R² of 0.276 for CBB and Q²predict values confirming out-of-sample performance.

This study advances theoretical and empirical understanding of how digital stimuli influence consumer behavior through interaction. Integrating the S-O-R model with UGT and TPB, it empirically tested the roles of social media trends, quality of information, and influencer cues in shaping interaction and driving consumer purchase intent.

Findings indicate that SMT and QTI significantly enhance SMI, which in turn strongly predicts buying behavior. These results highlight the power of trending content and credible information as key catalysts for engagement. ICR, however, showed neither significant nor substantive effects, suggesting that the influence of online personalities is waning, possibly due to credibility fatigue or the need for more personalized, context-sensitive approaches.

The model’s predictive strength and clarity of effect sizes particularly the dominant role of interaction (f² = 0.381) position it as a meaningful contribution to digital consumer behavior research. Importantly, the study challenges assumptions about influencer supremacy and raises questions about the cultural and generational consistency of digital responsiveness, particularly in digitally evolved societies.

The research reframes the conversation around social media influence from one centered on personalities to one anchored in interactivity, trust, and content relevance. Future research should explore these dynamics across varied cultural contexts, incorporate qualitative insights into trust perceptions, and consider other moderators such as digital literacy or platform-specific norms to deepen understanding of digital consumer intelligence.

First, the research was conducted in a single national context Saudi Arabia, which may limit the generalizability of findings to other cultural or digital ecosystems. Future studies should replicate the model in diverse cultural and regulatory environments to test cross-national consistency, especially regarding influencer skepticism and platform usage norms.

Second, while the moderation by age was conceptually justified and graphically explored, the lack of statistical significance suggests a need for more granular moderators such as digital literacy, trust orientation, or platform-specific behaviors. Including qualitative methods such as focus groups or digital ethnographies could also uncover deeper motivations underlying interaction and purchase behavior.

Third, although the study incorporated validated measurement scales, some constructs like ICR may have evolved beyond their original theoretical definitions. Future research should explore new forms of digital influence, including micro-influencers, AI-generated content, and community-driven endorsements.

Lastly, the cross-sectional design restricts causal inference. Longitudinal studies could provide richer insights into how user interaction evolves over time and how digital gratifications shift with market saturation or platform innovation.