The proliferation of social media platforms has fundamentally transformed the practice of marketing and the structure of online retail markets. Platforms such as Facebook, Instagram, YouTube, and TikTok have evolved from purely social networking sites into fully functional digital marketplaces where consumers discover products, interact with brands, and complete purchases without leaving the platform interface (Kaplan & Haenlein, 2010; Hajli, 2015). As a consequence, social media marketing (SMM) has emerged as a critical strategic tool for firms seeking to enhance visibility, build relationships with customers, and drive online sales growth.

The rapid expansion of e-commerce has strengthened the relevance of social media in shaping customer journeys. Global e-commerce revenues have increased consistently over the last decade, driven by mobile commerce, digital payments, and algorithm-based recommendation systems (Laudon & Traver, 2023). In parallel, social media has become a primary source of product information and peer communication, substantially influencing consumer awareness, trust formation, and purchase decisions (Mangold & Faulds, 2009). Unlike traditional marketing channels, social media enables two-way communication, allowing firms to engage customers through personalized content, influencer collaborations, and real-time feedback mechanisms.

Empirical research has demonstrated that social media marketing significantly influences customer attitudes and behaviors. Prior studies show that interactive content, influencer endorsements, and electronic word-of-mouth (e-WOM) have positive effects on brand perception, customer engagement, and purchase intention (Alalwan, 2018; Duffett, 2017; Erkan & Evans, 2016). Consumers increasingly rely on peer reviews, recommendations, and digital creators for product evaluation, highlighting a shift in trust from corporate branding to social credibility (Lou & Yuan, 2019). Influencer marketing, in particular, has emerged as an effective strategy for translating online engagement into purchasing behavior, especially among younger consumers and lifestyle-oriented markets (De Veirman et al., 2017).

The integration of commerce functionalities directly into social media platforms has further intensified the influence of SMM on sales performance. Social commerce features such as in-app checkouts, shoppable posts, and livestream selling have reduced transaction friction and shortened the customer decision cycle (Hajli, 2015). These technological advancements have enabled firms to convert consumer interest into immediate purchases, increasing conversion rates and impulse buying behavior (Zhang & Benyoucef, 2016). Research indicates that perceived convenience and trust in social platforms are essential drivers of online purchase behavior in social commerce environments (Kim & Park, 2013; Liang & Turban, 2011).

Despite growing academic interest, much of the existing literature focuses primarily on purchase intention rather than actual sales performance (Yadav & Rahman, 2018; Dwivedi et al., 2021). Moreover, marketing effectiveness is often studied in isolation without incorporating business performance indicators such as sales growth, repeat purchases, or customer lifetime value. Studies examining firm-level outcomes are limited and often constrained to specific industries or regional contexts (Tajvidi & Karami, 2021). Additionally, “social media marketing” is frequently treated as a single construct, neglecting the differential impact of content strategy, influencer activity, paid advertising, and customer engagement mechanisms.

Another limitation is the concentration of research on developed markets, with comparatively fewer empirical investigations addressing social commerce behavior in emerging economies where mobile usage and social media adoption are growing rapidly. Consumer behavior in these contexts differs due to infrastructure constraints, income disparity, and cultural influences, making generalization problematic (Chatterjee et al., 2020). There is thus a need for integrated empirical research that directly examines the impact of social media marketing on online sales growth using a measurable business perspective.

In response to these gaps, this study aims to empirically analyze the impact of social media marketing activities on online sales growth. The objectives are:

• to assess consumer perceptions of social media marketing practices,
• to examine the relationship between SMM and online sales growth, and
• to identify the most influential components of SMM such as influencer marketing, content strategy, and brand engagement.

By focusing on observable sales-related outcomes, this study extends theoretical discourse beyond consumer intention into business performance analytics. The findings are expected to offer both academic contribution and practical relevance by providing evidence-based guidance for marketing strategists and digital commerce leaders.

CONCEPTUAL BACKGROUND

2.1 Social Media Marketing as a Strategic Resource

Social media marketing (SMM) refers to the use of social networking platforms to promote products and services through content creation, digital advertising, influencer partnerships, and customer interaction. Unlike traditional marketing communication, SMM facilitates real-time engagement and co-creation of brand meaning with consumers (Kaplan & Haenlein, 2010; Mangold & Faulds, 2009). Firms deploy SMM to support brand awareness, community building, and customer relationship management, all of which are fundamental to long-term sales performance (Tuten & Solomon, 2018).

From a strategic management perspective, the Resource-Based View (RBV) explains how SMM functions as an intangible organizational capability. Brand storytelling skills, audience analytics, and influencer networks represent firm-specific resources that can generate sustained competitive advantage when they are valuable, rare, inimitable, and non-substitutable (Barney, 1991). Firms that develop superior digital communication skills are better positioned to convert engagement into revenue (Tajvidi & Karami, 2021).

2.2 Uses and Gratifications Theory

The Uses and Gratifications Theory (UGT) posits that individuals actively choose media platforms to satisfy specific needs such as information seeking, entertainment, social interaction, and identity reinforcement (Katz et al., 1973). Applied to SMM, consumers engage with social platforms to obtain product knowledge, seek reviews, communicate experiences, and develop brand associations (Park et al., 2009).

This theory helps explain why content characteristics such as creativity, interactivity, and relevance significantly affect purchase intention (Alalwan, 2018). Consumers who derive functional and emotional value from brand interactions on social media demonstrate higher trust and stronger willingness to purchase, linking user motivation directly to sales potential.

2.3 Technology Acceptance and Consumer Adoption

The Technology Acceptance Model (TAM) and its extension, UTAUT2, provide explanatory frameworks for understanding online shopping adoption behavior. These models emphasize perceived usefulness, ease of use, hedonic motivation, and social influence as key determinants of consumer acceptance of digital platforms (Venkatesh et al., 2012).

In social commerce contexts, perceived ease of navigating brand pages, eyewitness reviews, and seamless transaction interfaces significantly impact consumer confidence and purchasing behavior (Hajli, 2015; Kim & Park, 2013). When users view social media platforms as both entertaining and efficient, transaction probability increases.

2.4 Consumer Trust and Electronic Word-of-Mouth (e-WOM)

Trust is a critical variable in online commercial success due to limited physical interaction and higher perceived risk (Gefen et al., 2003). Social media influences trust through peer communication and social validation mechanisms. User-generated content, reviews, and influencer endorsements reduce information asymmetry and build confidence (Erkan & Evans, 2016).

Electronic word-of-mouth (e-WOM) significantly influences attitudes and purchase decisions by enhancing credibility and perceived authenticity (Cheung & Thadani, 2012). Influencer marketing operates as a modern form of e-WOM, where content creators serve as trusted opinion leaders (Lou & Yuan, 2019). This explains the high persuasive power of influencer endorsements in shaping buying behavior.

2.5 Brand Engagement and Relationship Marketing

Brand engagement theory views consumers as active participants who interact emotionally, cognitively, and behaviorally with brands (Hollebeek et al., 2014). Social media enables real-time two-way interactions that strengthen customer-brand relationships and improve retention (Yadav & Rahman, 2018).

Relationship marketing suggests that engagement-driven loyalty increases long-run customer value (Morgan & Hunt, 1994). Active brand engagement transforms transactional relationships into loyalty networks, leading to repeat purchases and referrals.

2.6 Social Commerce and Purchase Conversion

Social commerce refers to commerce activities facilitated by social platforms that allow online shopping within the same ecosystem (Liang & Turban, 2011). Features such as shoppable posts, in-app payment systems, and live-stream marketing bridge the gap between browsing and buying (Zhang & Benyoucef, 2016).

Social presence theory explains that interactive social environments increase user comfort and emotional engagement, fostering impulse buying and conversion (Hajli, 2015). By reducing procedural barriers and enhancing realism, social commerce accelerates the sales cycle.

2.7 Conceptual Framework

Guided by the Resource-Based View, Uses and Gratifications Theory, and technology acceptance perspectives, this study conceptualizes social media marketing as a strategic capability that influences online sales growth through consumer exposure, engagement, and interaction mechanisms. Social media marketing activities such as content quality, influencer marketing, brand interaction, and user-generated content enhance consumer awareness, trust, and perceived value, which collectively strengthen purchase behavior.

In this framework, social media marketing represents the independent construct, while online sales growth reflects the outcome variable capturing consumer purchase behavior and perceived sales performance. The proposed relationship assumes that increased effectiveness of social media marketing activities leads to higher levels of consumer responsiveness and purchasing activity, which is observable through growth in online sales.

3.1 Research Design

This study employed a quantitative, explanatory, cross-sectional survey design to examine the influence of social media marketing activities on online sales growth. The design was chosen to establish statistically testable relationships between variables using numerical data collected at a single point in time. The approach allows objective measurement of consumers’ perceptions, attitudes, and purchasing behavior related to social media influences.

3.2 Population and Sampling

The target population consisted of individuals who actively use social media platforms and engage in online shopping. The study focused on consumers aged 18 years and above who had completed at least one online purchase influenced by social media in the past six months.

A total sample of 250 respondents was selected through a non-probability convenience sampling method. This technique was chosen due to time limitations and the large, geographically dispersed nature of online consumers. Eligibility screening questions were included to ensure respondents met the study criteria.

3.3 Instrument Development

A structured questionnaire was developed after an extensive review of relevant literature. The instrument comprised four major sections:

Each construct was operationalized using multiple items to improve reliability. All perceptual items were measured using a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).

3.4 Variables and Measurement

Validity and Reliability

• The questionnaire was reviewed by subject experts in marketing and consumer behavior to ensure conceptual relevance and clarity.
• Internal consistency was assessed using Cronbach’s Alpha, which yielded a value of 86, indicating acceptable reliability.
• A pilot study involving 20 respondents was conducted, and unclear statements were revised accordingly.

3.6 Data Collection Procedure

• Data were collected through an online survey platform over a period of four weeks. The survey link was circulated via email, WhatsApp, and social media platforms.
• Participation was entirely voluntary. Respondents were informed about the purpose of the study, and consent was obtained electronically prior to access to the questionnaire.

3.7 Data Analysis Techniques

Data were analyzed using SPSS version 26. The following statistical methods were applied:

• Descriptive statistics to summarize responses

• Correlation analysis to measure relationships
• Regression analysis to determine predictive power
• Hypothesis testing at a 5% significance level

Regression analysis was employed to examine the predictive relationship between social media marketing and online sales growth. Consistent with prior empirical studies in digital marketing research, regression enables assessment of the extent to which variations in online sales performance can be explained by social media marketing activities (Tajvidi & Karami, 2021; Yadav & Rahman, 2018). Given the study’s objective of quantifying the influence of social media marketing on sales outcomes, regression analysis was considered an appropriate statistical technique for testing the proposed relationship.

3.8 Hypothesis Formulation

• Based on the conceptual framework and prior empirical evidence, the following hypothesis was formulated.
• H₀: H₁: Social media marketing has no significant effect on online sales growth
• H₁: H₁: Social media marketing has a significant and positive effect on online sales growth.

3.9 Model Specification

The research model can be expressed as:

Where:
Y = Online sales growth

X = Social media marketing

β₀ = Intercept

β₁ = Coefficient

ε = Error term

Table 1 presents the descriptive statistics for Social Media Marketing (SMM) and Online Sales Growth (OSG).

Table 1: Descriptive Statistics of Social Media Marketing and Online Sales Growth (N = 250)

Note: Scale ranges from 1 = Strongly Disagree to 5 = Strongly Agree.

The mean score for Social Media Marketing (M = 4.12, SD = 0.54) indicates that respondents generally perceived social media as a strong influence on their purchasing decisions. Similarly, Online Sales Growth recorded a high mean value (M = 4.05, SD = 0.59), suggesting that respondents experienced increased online purchasing activity associated with social media exposure. The relatively low standard deviation values indicate consistency in responses and reflect a shared perception among consumers.

Table 2 reports the mean scores of individual dimensions of social media marketing.

Table 2: Mean Scores of Social Media Marketing Components

Note: Higher scores indicate stronger perceived influence.

Influencer marketing emerged as the most influential component (M = 4.24, SD = 0.52), indicating that endorsements and recommendations from influencers strongly shape consumer purchasing behavior. Content quality (M = 4.18, SD = 0.55) also demonstrated a high level of influence, underscoring the importance of informative and engaging content. In contrast, paid advertising recorded a comparatively lower mean score (M = 3.96, SD = 0.61), suggesting that consumers perceive organic and peer-driven content as more persuasive than traditional promotional advertising.

Table 3 presents the correlation between Social Media Marketing and Online Sales Growth.

Table 3: Correlation Between Social Media Marketing and Online Sales Growth

*Note: *p < .01.

The correlation analysis revealed a strong and statistically significant positive relationship between Social Media Marketing and Online Sales Growth (r = .71, p < .01). This finding indicates that higher engagement with social media marketing activities is associated with increased online sales performance.

Consistent with the proposed conceptual framework, regression analysis was conducted to assess whether social media marketing significantly predicts online sales growth.

A simple linear regression analysis was conducted to examine the predictive effect of Social Media Marketing on Online Sales Growth. The results are presented in Table 4.

Table 4: Regression Analysis Predicting Online Sales Growth

Model Statistics:

R² = .468

F(1, 248) = 139.7, p < .001

The regression results indicate that Social Media Marketing is a significant predictor of Online Sales Growth (β = .68, t = 11.82, p < .001). The unstandardized coefficient (B = 0.74) suggests that a one-unit increase in social media marketing perception is associated with a 0.74-unit increase in online sales growth. The coefficient of determination (R² = .468) indicates that social media marketing explains 46.8% of the variance in online sales growth, demonstrating strong explanatory power.

Table 5 summarizes the outcome of hypothesis testing.

Table 5: Hypothesis Testing Outcome

Since the p-value associated with the regression model was below the 5% significance level (p < .001), the null hypothesis was rejected. This confirms that social media marketing has a statistically significant and positive effect on online sales growth.

This study examined the role of social media marketing (SMM) in driving online sales growth using a survey-based empirical approach. The findings provide strong evidence that SMM significantly influences consumer purchasing behavior and sales performance in digital environments. The discussion interprets the results in light of existing theories and prior research.

5.1 Social Media Marketing and Online Sales Growth

The results revealed a strong positive correlation between social media marketing and online sales growth (r = .71), confirming that social media is not merely a communication tool but a revenue-generating mechanism. This supports earlier research which suggests that social media activities directly enhance firm performance and sales outcomes (Tajvidi & Karami, 2021; Liadeli et al., 2020).

The regression analysis further showed that social media marketing explained 46.8% of the variance in online sales growth (R² = .468), indicating a substantial predictive effect. This aligns with findings by Tajvidi and Karami (2021), who reported that effective social media usage contributes significantly to business performance through customer relationship management and market responsiveness. Similarly, Yadav and Rahman (2018) observed that consistent social media engagement positively affects loyalty and repurchase behavior, which directly contributes to sustained revenue.

From a strategic perspective, these results reinforce the view that social media has evolved into a primary distribution and conversion channel rather than remaining limited to promotional messaging (Mangold & Faulds, 2009).

The regression results support the study’s conceptual framework by demonstrating that social media marketing functions as a strategic driver of online sales growth, consistent with theoretical expectations derived from resource-based and consumer behavior perspectives.

5.2 Influence of SMM Components on Consumer Behavior

Among the studied dimensions, influencer marketing recorded the highest mean score (M = 4.24), highlighting the persuasive power of influencers in shaping purchase decisions. This is consistent with De Veirman et al. (2017), who reported that influencers significantly affect consumer perceptions by creating aspirational identification and enhancing brand credibility. Lou and Yuan’s (2019) study further supports this finding, demonstrating that message credibility and trust in influencers strongly predict purchase intention.

The strong role of content quality (M = 4.18) confirms the importance of creative and informative brand communication. This aligns with Alalwan (2018), who found that content relevance and informativeness significantly improve customer engagement and advertising effectiveness. In parallel, Duffett (2017) noted that visual-based content on social media generates higher recall and emotional connection, which positively affects consumer attitudes.

Conversely, social media advertising received a relatively lower mean score (M = 3.96), indicating that consumers are becoming more resistant to overt promotional messaging. This shift aligns with Erkan and Evans (2016), who argue that peer-generated information (e-WOM) is perceived as more credible than company-sponsored advertisements. This result emphasizes the need for brands to combine paid promotions with authentic storytelling and influencer collaboration.

5.3 Role of Trust and Engagement in Social Media–Driven Purchase Behavior

The observed importance of brand interaction and engagement (M = 4.11) highlights the role of trust as a key psychological mechanism that strengthens the relationship between social media marketing exposure and consumer purchase behavior. Prior research has consistently shown that trust is a fundamental determinant of online consumer acceptance, as it reduces perceived risk and uncertainty associated with digital transactions (Gefen et al., 2003). The present findings align with this view and support Kim and Park’s (2013) conclusion that frequent and meaningful interactions between brands and consumers enhance confidence and willingness to purchase in online environments.

User-generated content (UGC) also demonstrated a high level of influence (M = 4.09), reinforcing the role of electronic word-of-mouth in shaping consumer attitudes and decision-making. As suggested by Cheung and Thadani (2012), peer-generated reviews and shared experiences increase perceived credibility and informational value, thereby influencing purchasing behavior. In social media contexts, such content provides social proof that partially compensates for the absence of physical product evaluation.

These findings are consistent with customer engagement theory, which emphasizes emotional, cognitive, and behavioral involvement as drivers of stronger consumer–brand relationships (Hollebeek et al., 2014).

5.4 Theoretical Implications

This study contributes to theory by extending the conversation beyond purchase intention and linking SMM directly to online sales growth. The findings validate the Uses and Gratifications Theory by confirming that consumers engage with social media for information and interaction, leading to purchase behavior (Katz et al., 1973; Alalwan, 2018).

The results also support Technology Acceptance Theory, particularly UTAUT2, demonstrating that usability and engagement convenience significantly contribute to adoption (Venkatesh et al., 2012). Moreover, the Resource-Based View is reinforced as firms with advanced digital marketing capabilities gain a competitive advantage through superior engagement strategies (Barney, 1991).

5.5 Managerial Implications

The findings offer actionable insights for practitioners:

• Firms should prioritize influencer marketing and organic content strategies over excessive advertising expenditure.
• Customer engagement metrics should be integrated into revenue dashboards.
• Brands must emphasize community-building and trust creation rather than transactional messaging alone.
• Analytics should track consumer journeys from exposure to conversion to improve performance attribution.

These insights align with Dwivedi et al. (2021), who emphasize the importance of data-driven digital strategy integration.

5.6 Limitations and Future Research

Despite strong conclusions, some limitations must be acknowledged. The use of convenience sampling may affect generalizability. The cross-sectional nature of the study limits causal inference. Future research should adopt longitudinal or experimental designs and integrate real sales data rather than relying solely on self-reported perceptions.

Cross-country comparisons and industry-specific models are also recommended to understand platform and sector effects (Chatterjee et al., 2020; Yadav & Rahman, 2018).

5.7 Contribution of the Study

This study contributes to the existing literature on social media marketing and digital commerce in several important ways.

First, the study adds to earlier research by focusing on online sales growth rather than only on purchase intention or attitudes. Many previous studies have examined how social media affects consumer interest or intention to buy, but this study directly links social media marketing to sales-related outcomes. This helps in understanding the actual business impact of social media marketing.

Second, the study provides a clearer understanding of social media marketing by examining its different components separately. By analyzing content quality, influencer marketing, advertising, brand interaction, and user-generated content, the study shows that influencer marketing and content quality have a stronger influence on consumer purchasing behavior than paid advertising. This offers a more detailed view of which social media activities are most effective in driving online sales.

Third, the findings support well-known theories such as Uses and Gratifications Theory, Technology Acceptance perspectives, and the Resource-Based View in the context of social commerce. The results indicate that engagement, interaction, and perceived usefulness play an important role in strengthening the relationship between social media marketing activities and online sales growth. This confirms that these theoretical frameworks remain relevant in today’s digital marketing environment.

Finally, the study contributes by providing empirical evidence from an emerging market context, where social media use and online shopping are growing rapidly. Since much of the existing research is based on developed countries, this study helps broaden understanding of social media marketing effects across different economic and market settings.

This study examined the role of social media marketing in driving online sales growth and provides strong empirical evidence that social media functions as a central performance driver in digital commerce rather than merely a promotional channel. The findings confirm that social media marketing has a significant and positive influence on consumer purchasing behavior and online sales performance.

The strong correlation between social media marketing and online sales growth highlights the importance of strategic engagement, content quality, influencer partnerships, and brand interaction as key contributors to revenue generation in online marketplaces. Regression results further demonstrate that nearly half of the variance in online sales performance is explained by social media marketing, emphasizing its strategic relevance for contemporary businesses. Influencer marketing and content quality emerged as the most influential components, reflecting a shift in consumer preference away from conventional advertising toward peer-driven communication and creator-led content. Brand interaction and user-generated content also play an important role in strengthening the relationship between social media marketing activities and consumer purchasing confidence, reinforcing the effectiveness of relationship-driven marketing approaches over purely transactional promotion.

The findings further support the relevance of established theoretical frameworks. Uses and Gratifications Theory is reinforced through evidence of consumers’ information-seeking and social interaction behavior on social media platforms. Technology acceptance perspectives are supported by the role of convenience and perceived usefulness in facilitating online purchasing behavior. The Resource-Based View is also validated, as firms that develop advanced social media marketing capabilities gain competitive advantage through enhanced engagement and digital innovation. From a managerial perspective, the results highlight the need for organizations to integrate social media marketing into core business strategy rather than treating it as a supplementary communication activity. Firms that invest in high-quality content, influencer collaborations, engagement analytics, and platform-native commerce tools are better positioned to achieve sustainable online growth.

Despite the robustness of the findings, certain limitations should be acknowledged. The study relies on self-reported data, employs a cross-sectional research design, and uses convenience sampling, which may limit generalizability. Future research should employ longitudinal or experimental designs, incorporate objective transactional sales data, and examine platform-specific and cross-market differences to further strengthen understanding of social media–driven sales performance.

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