The rise of social commerce (s-commerce) has transformed how consumers discover, evaluate, and purchase products online. Unlike traditional e-commerce, s-commerce platforms integrate social features such as reviews, likes, shares, recommendations, and live interactions, creating a socially enriched shopping environment that merges social networking with commercial activity (Liang & Turban, 2011; Hajli, 2015). The convergence of social interaction and commerce has encouraged consumers to engage in more personalized and emotionally engaging shopping experiences, leading to novel forms of decision-making behavior (Chen, Su, & Widjaja, 2016; Lin, Li, & Wang, 2017).

Emerging markets—such as India, Indonesia, Brazil, and Nigeria—have witnessed rapid social media adoption, making them fertile grounds for s-commerce expansion (Dey, Yen, & Samuel, 2020). In these regions, increasing smartphone penetration, digital payment availability, and social connectivity have accelerated the shift from traditional retail to socially embedded online purchasing (Akram et al., 2018; Xu, Wang, & Zhao, 2022). However, despite the growing importance of these markets, the mechanisms driving impulse purchases within their social commerce contexts remain underexplored (Bai, Yao, & Dou, 2015; Luo, Chen, & Wu, 2021).

Impulse buying is characterized by spontaneous and emotionally driven decisions that occur without prior purchase planning (Rook & Fisher, 1995). In online and social commerce environments, this tendency is often amplified by social cues, peer influence, visual stimulation, and real-time engagement (Verhagen & van Dolen, 2011; Wang & Yu, 2017). Features such as user-generated content (UGC), product recommendations, influencer marketing, and live streaming have been shown to trigger emotional arousal and enjoyment, resulting in unplanned purchases (Wongkitrungrueng & Assarut, 2020; Zhang et al., 2020).

Therefore, understanding how specific social commerce features influence impulse purchasing behavior among consumers in emerging markets has significant theoretical and managerial value. By exploring the psychological and social dynamics of these environments, this study extends existing consumer behavior theories to new, fast-growing digital economies (Chen, Huang, & Davison, 2020; Xu et al., 2022).

Research Objectives

1. To identify key social commerce features that stimulate impulse purchasing (Hajli, 2015; Luo et al., 2021).
2. To analyze the mediating roles of trust, enjoyment, and social influence (Gefen, Karahanna, & Straub, 2003; Lim, Cheah, & Wong, 2020).
3. To examine cross-cultural variations in emerging market consumer responses to social commerce stimuli (Dey et al., 2020; Akram et al., 2018).

Social Commerce Features

Social commerce, rooted in Web 2.0 technologies, integrates social networking functions with online shopping, allowing users to interact, generate content, and influence each other’s purchase decisions (Liang & Turban, 2011; Lin, Li, & Wang, 2017). It differs from traditional e-commerce by emphasizing social interaction, user engagement, and community participation rather than mere transactional efficiency (Hajli, 2015). Core social commerce features include social interaction, recommendation systems, user-generated content (UGC), and social proof, all of which shape consumer perceptions and behavior (Chen, Su, & Widjaja, 2016; Kim & Park, 2013).

Social interaction functions—such as chats, comments, and live-streaming sessions—enable real-time communication between sellers, consumers, and peers. These interactions increase trust and emotional engagement, fostering stronger purchase intentions (Wongkitrungrueng & Assarut, 2020). Similarly, recommendation systems and peer-based suggestions influence decision-making by reducing perceived uncertainty and cognitive effort during the purchase process (Lim, Cheah, & Wong, 2020; Bai, Yao, & Dou, 2015).

User-generated content (UGC), including customer reviews, photos, and testimonials, enhances authenticity and credibility by serving as social proof (Hajli, 2015; Luo, Chen, & Wu, 2021). The visible indicators of approval, such as likes, shares, and endorsements, signal trustworthiness and popularity, influencing others to engage in similar behaviors (Wang & Yu, 2017). Together, these features form an interconnected system that promotes engagement, trust, and impulsive consumption through social validation and emotional stimulation (Chen, Huang, & Davison, 2020; Zhang, Qin, Wang, & Luo, 2020).

Impulse Purchasing Behavior

Impulse purchasing behavior refers to unplanned buying driven by emotional arousal, spontaneous desire, and immediate gratification (Rook & Fisher, 1995). In digital contexts, impulse buying is amplified by visual stimulation, persuasive content, and instant purchasing options, all of which reduce cognitive control and increase emotional engagement (Verhagen & van Dolen, 2011). Social commerce environments—unlike static e-commerce websites—facilitate constant exposure to stimuli through multimedia content, peer discussions, and time-limited promotions (Akram et al., 2018; Luo et al., 2021).

Visual and interactive elements such as product videos, influencer live sessions, and augmented product displays can trigger hedonic motivation and drive immediate purchase actions (Wongkitrungrueng & Assarut, 2020). Furthermore, social endorsement mechanisms, including follower counts and influencer credibility, create normative pressure that encourages conformity and impulsive spending (Wang & Yu, 2017; Lim et al., 2020). In emerging market settings, where online consumers are still developing digital consumption norms, these cues exert particularly strong influence (Dey, Yen, & Samuel, 2020). Consequently, the emotional, cognitive, and social triggers embedded in social commerce environments make them fertile grounds for studying impulse purchasing behavior.

Theoretical Framework

This study draws upon two foundational models—the Stimulus–Organism–Response (S-O-R) framework and the Theory of Planned Behavior (TPB)—to explain how social commerce features affect impulse purchasing.

The S-O-R model, originally proposed in environmental psychology, suggests that external stimuli (S) influence internal organismic states (O), which then generate behavioral responses (R). In the context of social commerce, stimuli include interactive features such as recommendations, reviews, and live-streaming; organismic factors represent consumers’ internal emotional and psychological states—such as trust, enjoyment, and arousal; and the response manifests as impulsive purchasing behavior (Verhagen & van Dolen, 2011; Luo et al., 2021). Several studies have successfully applied the S-O-R model to explain how online environments evoke emotional engagement and drive impulsive consumption (Akram et al., 2018; Chen et al., 2016).

The Theory of Planned Behavior (TPB) complements this perspective by emphasizing the roles of attitude, subjective norms, and perceived behavioral control in shaping behavioral intentions (Gefen, Karahanna, & Straub, 2003; Lim et al., 2020). In social commerce, subjective norms—driven by social influence, peer pressure, and online reputation—can significantly increase purchase intentions, especially when reinforced by perceived social trust (Hajli, 2015). By integrating S-O-R and TPB, this study captures both the emotional (impulsive) and cognitive (intentional) dimensions of online consumer decision-making, providing a robust theoretical basis for understanding social commerce dynamics in emerging economies.

Research Gap

Although the influence of social commerce features on consumer behavior has been widely studied in developed economies, research focusing on emerging markets remains limited (Dey et al., 2020; Xu, Wang, & Zhao, 2022). Much of the existing literature has examined Western or East Asian consumers, whose digital behaviors are shaped by relatively mature e-commerce ecosystems (Bai et al., 2015; Chen et al., 2016). However, in emerging markets such as India, Brazil, and Indonesia, cultural norms, social capital, and economic structures significantly affect how consumers engage with social commerce platforms (Akram et al., 2018; Dey et al., 2020).

Moreover, while prior studies have explored the direct relationships between social features and purchase intentions, limited attention has been given to the mediating roles of trust, enjoyment, and emotional arousal—factors critical to impulse buying (Lim et al., 2020; Luo et al., 2021). Additionally, there is a lack of comparative studies examining cross-cultural differences and contextual influences across emerging economies, where social media penetration and consumer maturity vary greatly (Chen, Huang, & Davison, 2020; Xu et al., 2022). Therefore, this study addresses these gaps by investigating how social commerce features influence impulsive purchase behavior through psychological and social mediators, specifically within the emerging market context.

3.1. Research Design

This study adopts a quantitative, cross-sectional research design aimed at empirically examining the relationships between social commerce features and consumers’ impulse purchase intentions in emerging markets. Quantitative analysis allows for systematic testing of hypothesized relationships through measurable constructs derived from established theories and validated scales. A survey-based approach is employed to capture respondents’ perceptions and behaviors related to social commerce platforms such as Instagram, Facebook Marketplace, and TikTok Shop. The design is consistent with prior studies investigating online consumer behavior (Hajli, 2015; Verhagen & van Dolen, 2011).

3.2. Sample and Data Collection

The target population comprises active users of social commerce platforms in selected emerging markets, namely India, Indonesia, and Brazil, which represent diverse cultural and digital environments. A purposive sampling method is used to ensure participation of respondents with direct experience in online purchasing via social platforms. Data are collected through a structured online questionnaire distributed via social media channels, online communities, and messaging groups.

A sample size of approximately 400 respondents is determined to provide sufficient statistical power for Structural Equation Modeling (SEM) analysis. Respondents are screened to ensure they have made at least one online purchase influenced by social features (e.g., reviews, likes, or influencer recommendations). Ethical considerations, including voluntary participation and confidentiality, are strictly maintained.

3.3. Measurement Scales

All measurement items are adapted from validated scales in previous studies to ensure content validity and reliability. Responses are measured on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree.

• Social Interaction: Items adapted from Hajli (2015) measure perceived social engagement through communication, feedback, and participation features on social commerce platforms.
• Trust: Adapted from Gefen, Karahanna, and Straub (2003), assessing users’ belief in the reliability and integrity of sellers and platforms.
• Emotional Arousal and Enjoyment: Adapted from Verhagen and van Dolen (2011), evaluating affective responses triggered by interactive and visually appealing content.
• Impulse Purchase Intention: Based on Rook and Fisher (1995), assessing the degree of spontaneous purchase behavior driven by emotional and situational cues.

Each construct comprises 3–5 items, refined through a pilot test with 30 participants to ensure clarity and cultural appropriateness.

3.4. Data Analysis

Data analysis proceeds in multiple stages using SPSS and AMOS (or SmartPLS) software tools.

• Reliability Analysis: Cronbach’s alpha and Composite Reliability (CR) are computed to confirm internal consistency of constructs.
• Validity Testing: Convergent and discriminant validity are assessed through Confirmatory Factor Analysis (CFA) based on factor loadings, Average Variance Extracted (AVE), and inter-construct correlations.
• Hypothesis Testing: Structural Equation Modeling (SEM) is employed to test the hypothesized relationships between social commerce features and impulse purchase intention.
• Mediation Analysis: The indirect effects of trust and enjoyment as mediators are examined using the bootstrapping method to identify the strength and significance of their mediating roles.

This methodological approach provides a robust statistical foundation for understanding the mechanisms through which social commerce features influence impulse buying tendencies in emerging digital economies.

4.1. Demographic Profile

A total of 400 valid responses were collected from active users of social commerce platforms in emerging markets, including India, Indonesia, and Brazil. The sample was fairly balanced by gender, with 52% female and 48% male respondents. The majority of participants were aged between 21 and 35 years (64%), representing the digital-native population most active on social media. Approximately 58% reported a monthly income within the middle-income bracket, reflecting typical emerging market consumer segments. Regarding shopping behavior, 72% indicated making at least one social commerce purchase per month, demonstrating their familiarity with s-commerce platforms. The demographic composition aligns with prior studies suggesting that young, digitally engaged consumers are primary drivers of social commerce adoption in developing economies (Hajli, 2015; Liang & Turban, 2011).

4.2. Reliability and Validity

All measurement constructs demonstrated high internal consistency and satisfactory validity. Cronbach’s alpha (α) values for all variables exceeded 0.70, indicating strong reliability. Composite Reliability (CR) values were above 0.80, confirming construct consistency, while the Average Variance Extracted (AVE) values surpassed the recommended threshold of 0.50, confirming convergent validity. Discriminant validity was established as each construct’s AVE square root exceeded inter-construct correlations. These results confirm the robustness of the measurement model and support its suitability for subsequent structural analysis (Gefen et al., 2003; Verhagen & van Dolen, 2011).

4.3. Structural Model Results

The structural model was tested using Structural Equation Modeling (SEM). Model fit indices met the recommended thresholds (e.g., CFI > 0.90, RMSEA < 0.08), indicating a good fit between the hypothesized and observed data. The results are summarized in Table 1.

Table 1. Structural Model Results

The results indicate that social interaction positively influences consumer trust (β = 0.45, p < 0.001), suggesting that communication and engagement features foster relational confidence between consumers and sellers. Similarly, trust significantly predicts impulse purchase behavior (β = 0.37, p < 0.001), consistent with the Technology Acceptance Model (Gefen et al., 2003).

Additionally, UGC enhances perceived enjoyment (β = 0.41, p < 0.001), indicating that user reviews, photos, and testimonials increase emotional engagement. Finally, enjoyment significantly affects impulse buying intention (β = 0.33, p < 0.001), supporting the Stimulus-Organism-Response (S-O-R) framework where emotional states mediate behavioral outcomes.

Figure 1: Displays the standardized path coefficients (β) for each hypothesis, showing the strength of relationships among constructs.

Figure 2 Illustrates the corresponding t-values, confirming the statistical significance of each path in your structural model.

The findings demonstrate that trust and enjoyment act as key mediators linking social commerce features to impulse purchase intentions. Consistent with the S-O-R model, social interaction and user-generated content serve as stimuli that activate positive emotions (organism), resulting in spontaneous purchasing (response).

The positive association between social interaction and trust supports prior evidence that relational engagement on social platforms enhances consumer confidence (Hajli, 2015). In line with Rook and Fisher’s (1995) conceptualization of impulsive buying, the study highlights the emotional triggers—enjoyment and excitement—evoked by interactive social environments.

Furthermore, the results underscore the sociocultural dynamics of emerging markets, where peer influence, authenticity, and visual engagement drive purchase decisions more strongly than traditional advertising. These insights suggest that social proof mechanisms (likes, shares, comments) and community-driven interactions can significantly amplify impulse buying behavior, reinforcing the central role of social trust and affective experience in digital commerce ecosystems.

DISCUSSION AND IMPLICATIONS

5.1. Theoretical Implications

This study contributes to the social commerce and consumer behavior literature by extending theoretical frameworks into the context of emerging markets. First, the findings validate and extend the Stimulus–Organism–Response (S-O-R) framework (Mehrabian & Russell, 1974) by demonstrating how social commerce features act as external stimuli, evoking emotional and cognitive responses (trust and enjoyment) that, in turn, shape impulse purchase intentions. While prior studies (e.g., Hajli, 2015; Verhagen & van Dolen, 2011) focused largely on developed economies, this research underscores that similar mechanisms operate in emerging digital ecosystems—albeit influenced by cultural and technological contexts.

Second, the study enhances understanding of emotion-driven consumer behavior in online environments. The results show that emotional arousal and enjoyment significantly mediate the relationship between social stimuli (e.g., user-generated content, interaction) and impulse buying, reaffirming Rook’s (1987) and Rook and Fisher’s (1995) conceptualization of impulsive buying as an emotionally charged, spontaneous response.

Third, the study provides empirical support for integrating social and technological constructs into consumer behavior models in emerging markets. Trust—originally central to e-commerce adoption models (Gefen et al., 2003)—is reconceptualized here as a socially constructed belief shaped by peer engagement, social proof, and digital community dynamics. This reinforces the evolving theoretical intersection of social influence, emotion, and digital interaction within the social commerce landscape.

5.2. Managerial Implications

From a managerial perspective, the findings offer actionable insights for social commerce practitioners and digital marketers operating in emerging markets.

• Leverage User-Generated Content (UGC): Businesses should actively encourage customers to share authentic experiences through photos, testimonials, and short videos. Such content enhances perceived authenticity and enjoyment, thereby stimulating impulse purchases.
• Enhance Real-Time Social Interactions: Features such as live streaming, in-app chat, and interactive comment sections foster a sense of community and immediacy, which can emotionally engage consumers and trigger spontaneous buying.
• Build and Maintain Consumer Trust: Transparent product reviews, verified seller profiles, and influencer endorsements can enhance consumer confidence, particularly in markets where online transaction security remains a concern (Gefen et al., 2003).
• Cultural Relevance in Campaigns: Marketing strategies should resonate with local cultural values and consumption patterns. For example, collectivist cultures prevalent in many emerging economies respond positively to social proof and group-oriented messaging.
• Optimize Platform Design for Emotional Engagement: Visual storytelling, gamified features, and real-time feedback mechanisms can enhance enjoyment, translating to higher impulse purchase likelihood.

Collectively, these implications suggest that successful social commerce strategies in emerging markets depend not only on technological functionality but also on the integration of emotional, cultural, and social dynamics that drive consumer engagement and trust.

This study examined the influence of social commerce features on impulse purchasing behavior within emerging market contexts, emphasizing the mediating roles of trust and enjoyment. The empirical findings confirm that interactive and socially enriched online environments significantly stimulate spontaneous purchasing decisions. Among the key predictors, social interaction and user-generated content emerged as powerful stimuli that enhance emotional engagement and perceived trust, thereby driving impulse buying tendencies.

The results reinforce the applicability of the Stimulus–Organism–Response (S-O-R) framework in explaining consumer behavior in social commerce settings, extending its validity to developing digital economies. Moreover, the study underscores that psychological factors such as emotional arousal, perceived enjoyment, and social trust are central to understanding impulse purchasing beyond traditional rational decision-making models. These insights align with prior research emphasizing the fusion of social and affective processes in online shopping (Hajli, 2015; Rook & Fisher, 1995; Verhagen & van Dolen, 2011).

From a contextual standpoint, this research demonstrates that consumers in emerging markets are highly responsive to social and cultural cues embedded in digital platforms. The blend of peer influence, authenticity, and immediacy enhances consumer trust and accelerates unplanned purchasing decisions. This reflects the growing significance of community-driven digital ecosystems in shaping consumption behavior across regions such as India, Indonesia, and Brazil.

Future research can build on these findings by adopting longitudinal designs to examine behavioral changes over time, cross-platform analyses to compare consumer responses across different social commerce environments, and AI-driven personalization studies to assess how algorithmic recommendations influence impulsive tendencies. Additionally, integrating qualitative methods could deepen understanding of emotional and cultural dimensions underlying social commerce participation.

In conclusion, the study provides both theoretical advancement and managerial insight into the evolving landscape of digital consumption. By highlighting how social commerce features trigger psychological responses that culminate in impulse buying, it offers a foundation for designing more engaging, trustworthy, and emotionally resonant online shopping experiences in emerging markets.

LIMITATIONS AND FUTURE RESEARCH

While this study provides valuable insights into the influence of social commerce features on impulse purchasing behavior, several limitations should be acknowledged.

First, the research focused on a select group of emerging markets—primarily India, Indonesia, and Brazil—which limits the generalizability of findings to other developing or developed economies. Cultural, economic, and technological differences across regions may moderate the strength and direction of relationships among social interaction, trust, enjoyment, and impulse buying. Future research could expand the sample to include a broader range of countries, allowing for comparative cross-cultural analysis.

Second, the study utilized a cross-sectional survey design, capturing consumer perceptions at a single point in time. Although Structural Equation Modeling (SEM) established significant associations among variables, causal relationships cannot be firmly inferred. Longitudinal or experimental designs could provide more robust evidence of causality and reveal how consumer behaviors evolve over time in dynamic social commerce environments.

Third, the study relied on self-reported data, which may be subject to social desirability bias and response inaccuracies. Future research could integrate behavioral analytics, clickstream data, or AI-based tracking mechanisms to capture real-time decision-making patterns. Such approaches would deepen understanding of how algorithmic recommendations, personalization, and social influence dynamically shape impulsive behaviors.

Finally, incorporating qualitative methods—such as interviews or focus groups—would help explore the psychological and cultural subtleties underlying social trust, emotional engagement, and impulse purchasing. This mixed-method approach could enrich theoretical insights and enhance the ecological validity of findings in rapidly evolving digital commerce ecosystems.

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