Analyzing the Impact of Social Media Influencers on Consumer Shopping Behavior: Empirical Evidence from Pakistan

Abstract

This study provides valuable insights into the impact of social media influencers (SMIs) on consumer shopping behavior through the lens of social influence theory. It focuses on the mediating role of shopping intentions and the moderating effects of brand credibility and individual shopping values in Pakistan. Using online questionnaires, data were collected from 988 individuals with first-hand experience of social commerce. To investigate a moderated mediation model, we used Smart-PLS 4, which examines how SMIs affect shopping behavior through shopping intention in the case of social media buying. The moderating effects of brand credibility and individual shopping values, specifically hedonic and utilitarian, are also examined. The results show that brand credibility significantly moderates the relationship between SMIs and shopping intentions. Utilitarian shopping value significantly moderates the conversion of shopping intentions into actual shopping behavior, while hedonic shopping value is found to be insignificant. This study uses social influence theory to emphasize the importance of critical variables in digital consumer behavior. The findings suggest that marketers should emphasize brand credibility and practical values to boost social commerce and engagement, providing a practical guide for their strategies.

1. Introduction

Social media has seamlessly woven itself into the fabric of our daily lives [1], influencing not only communication but also consumer decisions, consumer behavior, and shopping models [2]. Integrating social media into commerce has reshaped retail experiences by adapting to changing consumer behaviors. This integration is not merely a trend; it reflects how consumers increasingly use digital platforms [3]. Social commerce transforms every interaction on platforms such as TikTok, Facebook, and Instagram into revenue opportunities within the same digital environment. For instance, Instagram Shopping and TikTok Shop enable users to make direct purchases within the apps. UK Shopify merchants can now integrate with TikTok Shop, allowing them to create ad campaigns, synchronize product catalogs, and sell through in-feed videos and live broadcasts on TikTok [4]. According to a report, the global influencer marketing market is projected to expand from $21.1 billion in 2023 to $47.80 billion in 2027, with a compound annual growth rate (CAGR) of 11.61%, driven by factors such as the increasing popularity of social media platforms, the rising influence of social media creators, and the growing demand for personalized marketing experiences from consumers, making it an attractive and cost-effective strategy for businesses of all sizes [5]. US adults are projected to spend 11.4% of their daily media time and 17.9% of their digital media time on social platforms. This extensive social media usage underpins the rise of social commerce, emphasizing its growing significance in the retail landscape [3].

With the rise of social media influencers (SMIs), who exercise substantial persuasive power over their followers, the landscape of social commerce is evolving rapidly [6]. SMIs influence consumer attitudes, intentions, and behaviors [7,8]. The intention to shop from social media (ISSM) refers to consumers’ plans to purchase products through social media [9], whereas social media shopping behavior (SMSB) encompasses their actual shopping behavior and preferences [10]. Existing literature on SMIs has substantially contributed to our understanding of their impact on consumer behavior. It incorporates different facets such as attitudes towards shopping behavior, SMI authenticity, development of parasocial relationships, and influence mechanisms [11,12,13,14,15]. However, notable gaps persist despite this progress, necessitating a more nuanced exploration of the relationships between SMIs, consumer behavior, brand credibility, and shopping values in the dynamic context of social commerce and fashion product purchases [10,16]. Scholars have identified these gaps, emphasizing the need for research incorporating mediating and moderating variables to provide a comprehensive understanding of the influence of SMIs on consumer behavior. While social influence theory has been extensively applied in marketing research, there remains a persistent knowledge gap regarding its application in measuring the influence of SMIs on consumer behavior [17].

Therefore, this study holistically examines the relationship between SMIs and their followers, particularly from the followers’ perspective, and explores how SMIs’ personal and behavioral traits impact the followers’ decision-making processes. By incorporating mediating and moderating variables, the study fills these gaps. It offers insights into the complex interplay between SMIs and social media shopping behavior, enriching the existing literature on this topic. Furthermore, this study explores the relationships between SMIs, the ISSM, and SMSB, highlighting their implications for sustainable consumer behavior in the digital age. The recent global pandemic has accelerated the shift towards digitalization and online shopping, with a notable increase in transactions for various products. This change drives consumers to use social media platforms as a preferred shopping avenue [18]. This trend has prompted researchers to investigate the multifaceted impact of social commerce on consumer behavior and retail experiences [19]. Grounded in social influence theory, this study provides a framework for understanding how external factors such as SMIs affect consumer intentions and behaviors, offering insights into the evolving digital marketplace [20].

In our pursuit of contributing towards exploring the complex relationship between social media influencers and shopping behavior within the realm of social commerce, we extend our inquiry from direct influences to the moderating influences shaping the relationships between key variables. This study investigates the nuanced dynamics by drawing upon the theoretical foundation of social influence theory and the significance of brand credibility and shopping values. In particular, we aim to investigate the moderating effect of brand credibility (BC), which includes the perceptions of truthfulness and reliability that consumers associate with brand reputation [21], on the relationship between SMIs and the ISSM. Additionally, our study delves into the moderating effects of utilitarian shopping value (USV) and hedonic shopping value (HSV) as second-stage moderators on the association between the ISSM and social media shopping behavior (SMSB) in a moderated mediation model. USV represents the practical and functional aspects of meeting specific needs through shopping, while HSV refers to the pleasure and enjoyment derived from the shopping experience [22]. This study seeks to address the following research questions (RQs):

RQ1. 

How do social media influencers impact consumer shopping behavior in the context of social commerce?

RQ2. 

What role does the intention to shop from social media play in mediating the relationship between social media influencers and actual social media shopping behavior?

RQ3. 

How does brand credibility moderate the relationship between social media influencers and consumers’ intentions to shop from social media?

RQ4. 

In what ways do individual shopping values, specifically hedonic and utilitarian shopping values, moderate the relationship between shopping intentions and actual social media shopping behavior?

Our research examines the complex relationships between SMIs, brand credibility, shopping values, and consumer behavior to improve theoretical and practical understanding. First, the study uses brand credibility and shopping values to moderate the relationship between social media influencers and consumer behavior, a novel approach. Second, the study uses a sophisticated moderated mediation model to understand consumer behavior and influencer dynamics, improving methodological rigor. Through this approach, the research advances social influence theory and has practical implications for marketers and businesses. Third, the research seeks to improve consumer behavior influence mechanism theories by actively contributing to social influence theory. This study helps us understand how SMIs affect consumer behavior and suggests future research by revealing the mechanisms behind consumer decisions in social commerce. Fourth, this study helps create effective social media marketing strategies. By identifying consumer responses to social media influencers, marketers can tailor their approach to boost brand credibility and align with consumer values, maximizing social media campaign effectiveness. Finally, this research’s focus on sustainability suggests that it considers the ethical and long-term effects of influencer dynamics, aligning with the growing emphasis on sustainable business practices. This study adds to the responsible marketing and corporate social responsibility debate by highlighting influencer marketing’s potential impact on sustainable consumer behavior.

Following the introduction, this article provides a comprehensive theoretical background that delves into various constructs and theoretical foundations, establishing the conceptual framework. Section 3 focuses on the research model and hypotheses development followed by methods employed in the study, detailing the measures, data collection, and demographic information. Section 5 contains data analysis and results, while Section 6 presents an in-depth discussion that systematically dissects the results, connecting them to the established theoretical and conceptual framework and outlining their practical consequences, thereby providing a comprehensive view of the research’s contributions. The article discusses these findings, recognizes the limitations, and concludes by integrating results and future research prospects.

2. Literature Review and Theoretical Background

2.1. Social Influence Theory

Social influence has become a prominent focus in contemporary consumer behavior research, particularly social commerce [14,23]. Multiple studies have explored the dynamics of social commerce, fostering new business models integrating social interactions with e-commerce and emphasizing consumer engagement and credibility [24,25]. This study draws upon Kelman’s social influence theory as a foundational framework [20,26], enriching our understanding of how SMIs, as independent variables, shape individuals’ intentions and behaviors related to social media shopping. Integrating social influence theory adds depth to our exploration, elucidating direct and indirect influences and highlighting the nuances within individuals’ responses to social influence [20]. Direct influence entails a clear, immediate impact, while indirect influence involves a more subtle, complex effect, often manifesting through shared group norms, beliefs, and expectations [26]. The theory posits that other members of society influence an individual’s behavior through processes such as conformity, compliance, and internalization [20]. Conformity aligns behavior with prevailing trends endorsed by external factors such as SMIs, even if it deviates from personal preferences, while compliance entails making purchases based on direct recommendations. Internalization involves a deep alignment of individual values and style with those of SMIs [20]. Social influence theory can provide a theoretical framework for understanding how SMIs, intentions, behaviors, brand credibility, and shopping values influence consumer decision-making processes in social commerce. These variables influence each other through social comparison, conformity, and informational influence, ultimately impacting consumer attitudes, intentions, and behaviors in the digital marketplace.

The application of social influence theory in marketing research has extensively elucidated changes in consumer behavior resulting from interactions with external factors, including advertisements, user-generated content, online reviews, informativeness, and creativity [27,28]. Despite the richness of existing research, limitations persist [29], emphasizing the need for nuanced investigations of consumer-related characteristics. These studies have predominantly focused on direct influences, neglecting the moderating factors and contextual elements that shape and complicate the effectiveness of SMIs. Classic studies such as Asch’s conformity experiments [30] and Milgram’s obedience experiments [31] provide valuable insights into the mechanisms of social influence, which can further inform our understanding of how SMIs and social influence affect consumer behavior in the digital age. Our research contributes to a more comprehensive understanding of the interplay between SMIs and social media shopping behavior, highlighting the importance of considering moderating variables in shaping consumer decisions in social commerce.

2.2. Social Media Influencers

Multiple scholars consistently highlight social media influencers’ (SMIs) role in shaping consumer intentions and subsequent behavior [14,32]. SMIs operate as agents of social influence, leveraging compliance, conformity, and internalization processes to impact consumer behavior. Compliance occurs when consumers make decisions based on direct recommendations from SMIs, while conformity involves aligning behaviors with trends endorsed by SMIs. Internalization reflects a deeper alignment with the values and styles promoted by SMIs [20].

2.3. Brand Credibility

Brand credibility is introduced as a crucial moderating variable, recognizing its role in influencing the strength of the relationship between SMIs and intention. High brand credibility enhances conformity, as consumers are more likely to align their attitudes and behaviors with credible brands’ endorsements. It also reinforces compliance, increasing the likelihood that consumers will follow the recommendations of SMIs. Furthermore, high brand credibility facilitates internalization, where consumers adopt the values and beliefs of the influencer as their own, amplifying the persuasive power of SMIs on consumer intentions [14]. Conversely, low brand credibility may weaken these social influence mechanisms, underscoring the importance of perceived brand credibility in moderating social influence effects.

2.4. Shopping Values

Hedonic and utilitarian shopping values serve as second-stage moderators, reflecting different motivational aspects of shopping behavior. Social influence theory provides a framework for understanding how these motivations interact with conformity, compliance, and internalization processes [20]. Hedonic shopping value (HSV) relates to the pleasure and enjoyment derived from the shopping experience, encouraging conformity as consumers seek to emulate the enjoyable behaviors endorsed by influencers. Utilitarian shopping value (USV) pertains to the practical and functional benefits of shopping, aligning with compliance as consumers follow influencers’ recommendations for valid and valid reasons [33]. Internalization occurs when consumers adopt the influencers’ values and beliefs, whether hedonic or utilitarian, as their own.

3. Research Framework and Hypothesis Development

3.1. Research Framework

Although several theories, such as UTAUT or TPB, can help explore these variables and develop a comprehensive research framework [34,35], we primarily base our research on social influence theory [20]. However, to enhance the robustness of our model development, we also draw some insights from UTAUT, TPB, and TRA [36,37]. While the UTAUT framework includes several variables, we specifically relate to social influence in our context. Similarly, TPB and TRA can help provide a perspective on examining intention and behavior only. Our research framework posits that SMIs directly influence users’ intentions to engage in social commerce, aligning with the combined theoretical frameworks of social influence theory and UTAUT. The model, depicted in Figure 1, intricately captures the multifaceted factors influencing consumer behavior in the context of SMIs and social commerce, incorporating a broader theoretical foundation. It posits a direct impact of SMIs on social media users’ intentions, revealing the pathway through which influencers shape user intentions. Notably, the model establishes a clear link between SMIs, user intentions, and subsequent actions in social commerce. The model proposes that brand credibility moderates the relationship between SMIs and shopping intention (ISSM), and individual values moderate the relationship between shopping intentions (ISSM) and actual shopping behavior (SMSB). The influence of intentions on behavior varies based on whether consumers seek pleasure or practical benefits.

The proposed research framework aligns with established theoretical underpinnings and integrates scientific rigor by precisely delineating intricate relationships between variables. Each element is carefully justified based on the existing literature, forming a solid foundation for our study. Based on our model, we posit that SMIs significantly shape users’ intentions for online shopping, and these intentions translate into tangible consumer behavior on social media platforms. By incorporating moderators, we postulate that the effectiveness of SMIs in shaping user intentions and subsequent behavior varies. It varies based on the associated brand’s perceived credibility and users’ specific shopping motivations.

3.2. Hypothesis Development

3.2.1. Social Media Influencers, Shopping Intention, and Shopping Behavior

SMIs exert a substantial influence on consumer shopping intentions and shopping behaviors [38]. Intention encompasses consumers’ desires and expectations related to buying products within various time frames [39]. On the other hand, shopping behavior incorporates specific shopping patterns, preferences, frequency, and the effect of external factors on shopping decisions. Intention and behavior have been extensively studied across diverse contexts; in some studies, scholars explore how individual attitudes shape intentions [40], while others examine factors such as the influence of live streaming, online consumer reviews, the persuasive influence of SMIs, and dynamic promotion displays on purchase intention [14,41,42].

SMIs’ ability to connect with and engage their audience makes them trusted advisors. They share a common thread of possessing a devoted follower base, expertise, accessibility, and authenticity [43]. Understanding SMIs’ dynamics is crucial for comprehending the broader phenomenon of shopping behavior, emphasizing their direct influence on consumer actions. Recent studies have explored the factors contributing to SMIs’ influence, such as follower count, content sharing, and sponsorship disclosures [44]. Additionally, research has emphasized how SMIs craft tailored messages to appeal to specific audiences using attributes such as entertainment, informativeness, and credibility [44,45]. Further, the informative value of influencer-generated content, alongside the influencer’s credibility and attractiveness, positively influences trust in branded posts, impacting brand awareness and purchase intentions [46].

Despite extensive research, there is a need to study indirect influences and balance the perspective by considering followers’ requirements and how SMIs fulfill these requirements [14,23,45]. Moreover, discerning the role of SMIs has practical implications within sustainable consumer behavior, emphasizing conscious decisions to select environmentally friendly products and services [47]. SMIs can promote eco-friendly habits among followers, incorporating sustainability into daily lives through reciprocity and social validation messages [48].

Building on these insights, our study emphasizes the importance of understanding SMIs’ dynamics, especially considering geographical variations. This comprehension is crucial for deciphering shopping behavior. SMIs serve as the independent variable, shaping consumer intentions and behaviors on social media platforms. Thus, we hypothesize as follows:

H1. 

Social media influencers have a direct influence on consumers’ intention to shop through social media.

H2. 

Social media influencers have a direct influence on consumers’ social media shopping behavior.

Continuing with the context of consumers’ intentions and behavior, theories such as the theory of reasoned action (TRA) and the theory of planned behavior (TPB) emphasize the role of intentions in shaping human behavior [36,37]. According to these theories, intention is the immediate antecedent of behavior, indicating that individuals with solid intentions are more inclined to engage in corresponding actions [36,39]. While these theories do not explicitly frame intention as a statistical mediator, they suggest that certain variables contribute to intention formation, subsequently driving behavior. Research highlights the direct influence of the intention to shop from social media (ISSM) on actual social media shopping behavior (SMSB). Studies indicate that stronger shopping intentions predict increased online shopping activities, aligning with the theory of planned behavior’s premise that intention significantly predicts behavior [36]. Thus, individuals with a stronger intention to shop through social media are more likely to engage in activities such as browsing, adding items to carts, and making purchases.

Regarding our research, these studies inform us that individuals with a stronger intention to shop through social media are more likely to engage in actual social media shopping behavior, such as browsing for products, adding items to their carts, and making purchases. This hypothesizes as follows:

H3. 

Intention to shop from social media has a direct influence on social media shopping behavior.

The concept of intention to shop from social media (ISSM) is central to our research, directly impacting SMSB and serving as a mediator between SMIs and SMSB. SMIs influence SMSB by meditating on the ISSM and leveraging their perceived authenticity, credibility, and expertise to shape consumer intentions. SMIs create relatable content that resonates with their followers, which enhances the followers’ intention to engage in shopping activities on social media platforms. This intention, in turn, directly influences their actual shopping behavior (SMSB). The mediation effect of the ISSM can be understood through the process where SMI endorsements and recommendations lead to increased shopping intentions, which subsequently result in higher instances of actual purchases and other shopping behaviors on social media [38,45].

This synthesis positions our following hypothesis within a robust theoretical and empirical foundation, contributing to understanding the interplay between intention and behavior. This hypothesis posits that intention significantly shapes a consumer’s actual shopping behavior on social media platforms. Understanding this mechanism is imperative for marketers leveraging social media for sales and engagement in specific geographic areas.

H4. 

The relationship between social media influencers and social media shopping behavior is mediated by the intention to shop through social media.

3.2.2. Brand Credibility, Social Media Influencers, and Shopping Intention

Brand credibility, encompassing a brand’s trustworthiness and its influence on consumers’ assessments of consistent promise delivery [49], is integral to shaping perceptions and behaviors [50]. While many studies have recognized the importance of brand credibility in the context of influencers and shopping intention [14,51], only a few have specifically emphasized its role as a mediator in the relationship between influencers and intention [52]. The distinct role of brand credibility as a moderator in the relationship between SMIs and the ISSM remains insufficiently explored in existing literature with Guo et al.’s [21] study as an exception that focuses on the moderation of brand credibility between informational support and shopping intention.

Drawing on these insights, brand credibility, although internalized, could act as a catalyst in shaping the relationship between SMIs and the ISSM through mechanisms outlined in social influence theory, such as conformity, compliance, and internalization [20]. Under social influence theory, internalization refers to consumers accepting an influence because it aligns with their values and beliefs. In this context, perceived brand credibility, shaped by external cues such as SMI endorsements, enhances or diminishes the impact of SMIs on user intentions. Conformity and compliance also play roles: conformity occurs when consumers align their attitudes and behaviors with those endorsed by SMIs due to perceived social norms, while compliance involves consumers adopting behaviors to gain approval or avoid disapproval from others. High brand credibility strengthens these influences by increasing trust in the information presented by SMIs, thereby making consumers more likely to conform to and comply with the endorsed behaviors.

This study contributes to the existing literature by focusing on brand credibility as a moderator, addressing gaps, and extending our understanding of how internalized perceptions interact with external influences. We provide a nuanced view of this dynamic by empirically examining how brand credibility moderates the relationship between SMIs and consumers’ intentions to shop on social media.

H5. 

Brand credibility moderates the relationship between social media influencers and the intention to shop through social media.

3.2.3. Shopping Intention, Shopping Values, and Shopping Behavior

Shopping value refers to consumers’ shopping experience encompassing two fundamental categories of values: USV and HSV [53,54]. USV is the practical, task-oriented value that consumers derive from shopping experiences. It is characterized by rationality, efficiency, and a focus on the functionality and utility of products or services. This value is evident in everyday purchases such as groceries or household essentials [53,54]. HSV, conversely, can be considered a mental construct deeply rooted in emotional desires, excitement, dream gratification, and consumers’ subjective interpretations of their shopping experiences [53,55]. It goes beyond a product’s tangible features, centering on the emotional and imaginative elements of the shopping experience. Shoppers pursue hedonic value to fulfill their emotional needs, infusing their shopping experience with subjective significance that surpasses the product’s physical attributes [56].

The boundary between hedonic and utilitarian shopping values can be unclear, as products often encompass elements of both [57], and the perception of necessity varies among individuals [58]. Additionally, these values influence the ease of justifying spending, with hedonic consumption being more challenging to explain due to its subjective nature, while utilitarian consumption is more straightforward to justify with tangible benefits [59]. Existing research has explored the direct impact of USV and HSV on attitude [60], intention [61], satisfaction [62], preferences [63], and behavior [64] in various shopping contexts. A few studies have explored the mediating role of such values in the context of mobile and social commerce as well [65,66]. However, the specific moderating role of these values in the connection between shopping intention and shopping behavior has yet to be adequately investigated. Das G [67] investigated this moderation of such values as regulatory focus orientation on various consumer retail shopping behavior dimensions.

Under the framework of social influence theory, the moderation of USV and HSV can be explained through the mechanisms of conformity, compliance, and internalization [20]. Conformity refers to consumers aligning their behaviors with social norms, which can influence their shopping values. For instance, consumers with high HSV may conform more to social trends and recommendations from SMIs due to the pleasure and excitement they seek from shopping. Compliance involves adopting behaviors to gain social approval, and consumers with strong USV might comply with practical and efficient shopping recommendations from SMIs to maintain social acceptance. Internalization, the most profound social influence mechanism occurs when consumers accept an influence because it aligns with their values and beliefs. Here, USV and HSV play critical roles: consumers internalize the shopping behaviors recommended by SMIs based on how well these behaviors fulfill their utilitarian or hedonic needs. High USV can enhance the perceived practicality and functionality of the recommendations, while high HSV can amplify the emotional and experiential appeal.

Based on such insights, we set the foundation for Hypotheses 6 and 7 in recognition of the dual dimensions of shopping values and their potential influence on the relationship between the ISSM and SMSB. Building on established literature that underscores the importance of these values in shaping consumer behavior, these hypotheses posit that the nature of one’s shopping values moderates the strength of the link between the ISSM and SMSB. By exploring the moderating effects of both hedonic and utilitarian values, this study aims to unravel insights into how distinct shopping value orientations can shape the translation of intention into behavior in social commerce.

H6. 

Utilitarian shopping value moderates the relationship between the intention to shop through social media and social media shopping behavior.

H7. 

Hedonic shopping value moderates the relationship between the intention to shop through social media and social media shopping behavior.

4. Methodology

4.1. Sample and Data Collection

The population for this study is defined as the total number of social media users in Pakistan who are actively engaged in social commerce. As of early 2024, Pakistan has 111.0 million internet users, with an internet penetration rate of 45.7%. Among these, 71.70 million are social media users, representing 29.5% of the total population. Market research indicates that approximately 50% of these social media users are involved in social commerce activities, such as browsing, purchasing, and reviewing products through social media channels. Therefore, the population size for this study is estimated to be 35.85 million [68]. Given the large population size, we calculated the required sample size using the formula

n = Z² × p·(1 − p)/e²

With a 95% confidence level (Z = 1.96), an estimated proportion (p) of 0.5, and a 5% margin of error (e = 0.05), the required sample size (n) is approximately 384 respondents. The complexity of the model and the number of variables necessitate a larger sample size to ensure statistical power and reliability of results [69]. We targeted collecting one thousand responses, with 988 qualifying respondents, significantly exceeding the minimum sample size requirement. We are confident in the statistical significance, and our study is well-equipped to handle the complexity and provide robust insights.

Our questionnaire was meticulously designed to align with the study’s objectives and the diverse characteristics of the target population—Pakistani consumers with varying backgrounds, with ethical considerations as the guide of our approach. Our questionnaire comprised five sections. The first section introduced the concept of social commerce, research purpose, and shopping experience through social commerce, filtering out unqualified respondents having no such shopping experience. The second section included the measurement items for intention and behavior. The third section introduced SMIs with various examples and relevant measurement items. The fourth section explained hedonic and utilitarian shopping values and brand credibility and gathered corresponding responses. The final section consisted of demographic questions.

To gather a comprehensive dataset, the researchers employed a multi-phased approach combining online and offline techniques to reach a broad and diverse audience while ensuring a representative sample. An online survey was conducted using Google Forms (https://forms.google.com) and promoted across social media platforms. Secondly, invitations were sent to specific organizational groups and contacts through organizational and personal emails and other social networks. Reminders were also sent to the potential respondents after every 15 days. Thirdly, in-depth structured interviews were conducted with a smaller, targeted sample to fill in the questionnaire based on their responses. This approach aligns with the study’s aim to gather comprehensive insights into consumer behavior from various demographic groups and perspectives. The researchers carefully considered potential limitations associated with online data collection, strategically targeting their data collection efforts to ensure a diverse sample.

We aimed to gather one thousand responses, but due to the low response rate, we invited over 40,000 individuals to participate voluntarily across various recruitment phases. The questionnaire was designed to filter the respondents based on their social commerce shopping experience. Those without such experience were automatically redirected to an exit page, precluding them from completing the rest of the questionnaire. All questions for qualifying respondents were compulsory to answer; a questionnaire with repetitive respondents and identical answers to all questions was dropped. We received 988 responses that qualified for analysis. The survey was conducted from 16 May to 17 September 2023, remaining accessible to all eligible participants for a more inclusive representation of opinions.

4.2. Demographic Information on Respondents

Our primary focus was on factors such as age, gender, income, and education to examine the direct and moderating influences on social commerce behavior. Including other important demographics such as location, marital status, and profession would have required a more detailed geographic breakdown, potentially complicating the analysis and lengthening the questionnaire. Additionally, these factors were deemed less critical for our specific research objectives, which aimed to identify broad patterns and trends across a diverse sample rather than regional differences.

The sample, predominantly aged below 40 (63%), comprised 51.8% females. Educational diversity is notable, with 42.7% holding university degrees. Most (51.82%) were employed or engaged in entrepreneurial activities, ensuring a consistent monthly income. Regarding net household income, 38.2% reported earnings between 20,000 to 60,000 PKR (approximately 70 to 210 USD or 4.67 USD per day), 27.5% between 60,001 and 100,000 PKR (approximately 210 to 350 USD or 9.34 USD per day), 16.4% between 100,001 and 150,000 PKR (approximately 350 to 522 USD or 14.5 USD per day), and 17.8% exceeding 150,001 PKR (approximately 522 USD or 17.4 USD per day). Exchange rates as of 24 September 2023 were used for conversion. Refer to

Table 1. Demographic Information (N = 988).

Measure	Category	Frequency	Percentage
Age	19 years and below	103	10.42
	20–29 years	308	31.17
	30–39 years	213	21.56
	40–49 years	136	13.77
	50–59 years	105	10.63
	60+ years	123	12. 45
Household Income	PKR 20,000 to 60,000	378	38.26
	PKR 60,000 to 100,000	272	27.53
	PKR 100,000 to 150,000	162	16.40
	Greater than PKR 150,000	176	17.81
Gender	Male	476	48.18
	Female	512	51.82
Education	Below High School	101	10.22
	High School	209	21.16
	College Education	256	25.91
	University Degree	422	42.71
Occupation	Employee/Business	512	51.83
	Students	365	36.94
	Unemployed	111	11.23

for detailed demographics.

4.3. Measures

In this study, we meticulously adapted all items from established research sources, ensuring their relevance and robust psychometric properties. Experts in the field conducted a content validity analysis to evaluate the validity of the adapted items. The comprehensive details of this analysis are presented in Table 2. The items related to SMSB were adapted by incorporating three pertinent questions for scale augmentation. After this enhancement, the reliability and validity of the items were rigorously evaluated and confirmed.

We employed a 6-point Likert scale to measure participants’ responses. The deliberate use of an even-numbered scale avoids a neutral midpoint, encouraging respondents to express their agreement or disagreement more distinctly. Each point on the scale has specific significance: 1 indicates strong disagreement, reflecting a very firm negative response; 2 represents disagreement, showing moderate negative sentiment; 3 signifies slight disagreement, indicating a mild negative response; 4 represents a slight agreement, indicating a mildly positive response; 5 indicates agreement, reflecting moderate positive sentiment; and 6 signifies strong agreement, reflecting a very firm positive response. This approach enhances the granularity of our data, providing clearer insights into participants’ perceptions and attitudes toward the constructs under study. The methodology is supported by different researchers, who discuss the benefits of excluding a neutral midpoint to capture more definitive responses and provide insights into using and analyzing Likert scales [70].

For increased questionnaire clarity and precision, each question was presented in English, Pakistan’s official language, and Urdu, its national language. For translation purposes, we followed a modified direct translation method involving discussions with translation experts to reach a consensus on wording [71]. Furthermore, feedback was actively sought from professors, practitioners, and PhD candidates in the relevant field to ensure the questionnaire’s clarity and comprehensibility. After this feedback, necessary adjustments were made. Additionally, a pilot test was conducted with 55 respondents (not included in the formal survey and analysis) to refine the questionnaire’s effectiveness further.

Table 2. Measurement.

Constructs and Source	Items
Social Media Influencers (SMI) Ki et al. Pangarkar et al. [1,72]	SMI1. [SMIs] inspire me to discover something new about fashion products from social media. SMI2. [SMIs] have tastes and preferences similar to mine regarding fashion products. SMI3. [SMIs] seem to have a lot in common with me regarding fashion products. SMI4. [SMIs] are knowledgeable regarding fashion products. SMI5. [SMIs] make me feel like a mirror image of the person I would like to be through wearing similar fashion products. SMI6. [SMIs] make me feel the kind of person I want to be in life by making me wear similar fashion products. SMI7. [SMIs] make me feel a personal connection toward them through wearing similar fashion products. SMI8. [SMIs] make me feel an emotional connection toward them through wearing similar fashion products. SMI9. I would likely consider buying one of the same fashion products and brands the [SMIs] posted on their social media. SMI10. I would likely consider using one of the same fashion products and brands the [SMIs] posted on their social media.
Intention to Shop through Social Media (ISSM) Ajzen & Fishbein. Bian & Forsythe. Dodds et al. Patel et al. [10,39,73,74]	ISSM1. I would love to buy fashion products from social media. ISSM2. I will buy fashion products from social media in the future. ISSM3. I intend to buy fashion products from social media within the next year. ISSM4. There is a high probability that I would buy fashion products from social media within 6 months.
Social Media Shopping Behavior (SMSB) Lin. Patel et al. [10,75]	SMSB1. I prefer buying fashion products from social media. SMSB2. I frequently use social media to buy fashion products. SMSB3. On a scale of 1 (strongly disagree) to 6 (strongly agree), please indicate your agreement with the statement: ‘I am a frequent buyer of fashion products from social media.’ SMSB4. On a scale of 1 (strongly disagree) to 6 (strongly agree), please indicate your agreement with the statement: ‘I frequently purchase fashion products based on recommendations from Social Media Influencers.’ SMSB5. On a scale of 1 (strongly disagree) to 6 (strongly agree), please indicate your agreement with the statement: ‘I believe the role of brand reputation in my decision to purchase fashion products is very significant.’
Brand Credibility (BC) Erdem et al. Guo & Luo. [21,49]	BC1. The brands shown on social media can’t deliver on their promises. BC2. The brands’ claims shown on social media are believable. BC3. The brands shown on social media deliver what they promise. BC4. The brands shown on social media are reliable.
Utilitarian Shopping Value (USV) De-hu, Hanafiza-deh et al. Qu et al. [22,76,77]	USV1. I think the products recommended by the social media platform or social media influencers are of good quality and trustworthy. USV2. I think the social media platform or social media influencers helped me choose the right product, which is a good bargain. USV3. I think the interactive features set by the platform are attractive and exactly what I want.
Hedonic Shopping Value (HSV) De-hu, Hanafizadeh et al. Qu et al. [22,76,77]	HSV1. Browsing through social media platforms when I’m bored makes me happy. HSV2. It’s fun to browse, express opinions, discuss, and communicate with other users on social media platforms. HSV3. Participating in topic comments and other activities on the social media platform makes me feel relaxed and happy.

Table 2. Measurement.

Constructs and Source	Items
Social Media Influencers (SMI) Ki et al. Pangarkar et al. [1,72]	SMI1. [SMIs] inspire me to discover something new about fashion products from social media. SMI2. [SMIs] have tastes and preferences similar to mine regarding fashion products. SMI3. [SMIs] seem to have a lot in common with me regarding fashion products. SMI4. [SMIs] are knowledgeable regarding fashion products. SMI5. [SMIs] make me feel like a mirror image of the person I would like to be through wearing similar fashion products. SMI6. [SMIs] make me feel the kind of person I want to be in life by making me wear similar fashion products. SMI7. [SMIs] make me feel a personal connection toward them through wearing similar fashion products. SMI8. [SMIs] make me feel an emotional connection toward them through wearing similar fashion products. SMI9. I would likely consider buying one of the same fashion products and brands the [SMIs] posted on their social media. SMI10. I would likely consider using one of the same fashion products and brands the [SMIs] posted on their social media.
Intention to Shop through Social Media (ISSM) Ajzen & Fishbein. Bian & Forsythe. Dodds et al. Patel et al. [10,39,73,74]	ISSM1. I would love to buy fashion products from social media. ISSM2. I will buy fashion products from social media in the future. ISSM3. I intend to buy fashion products from social media within the next year. ISSM4. There is a high probability that I would buy fashion products from social media within 6 months.
Social Media Shopping Behavior (SMSB) Lin. Patel et al. [10,75]	SMSB1. I prefer buying fashion products from social media. SMSB2. I frequently use social media to buy fashion products. SMSB3. On a scale of 1 (strongly disagree) to 6 (strongly agree), please indicate your agreement with the statement: ‘I am a frequent buyer of fashion products from social media.’ SMSB4. On a scale of 1 (strongly disagree) to 6 (strongly agree), please indicate your agreement with the statement: ‘I frequently purchase fashion products based on recommendations from Social Media Influencers.’ SMSB5. On a scale of 1 (strongly disagree) to 6 (strongly agree), please indicate your agreement with the statement: ‘I believe the role of brand reputation in my decision to purchase fashion products is very significant.’
Brand Credibility (BC) Erdem et al. Guo & Luo. [21,49]	BC1. The brands shown on social media can’t deliver on their promises. BC2. The brands’ claims shown on social media are believable. BC3. The brands shown on social media deliver what they promise. BC4. The brands shown on social media are reliable.
Utilitarian Shopping Value (USV) De-hu, Hanafiza-deh et al. Qu et al. [22,76,77]	USV1. I think the products recommended by the social media platform or social media influencers are of good quality and trustworthy. USV2. I think the social media platform or social media influencers helped me choose the right product, which is a good bargain. USV3. I think the interactive features set by the platform are attractive and exactly what I want.
Hedonic Shopping Value (HSV) De-hu, Hanafizadeh et al. Qu et al. [22,76,77]	HSV1. Browsing through social media platforms when I’m bored makes me happy. HSV2. It’s fun to browse, express opinions, discuss, and communicate with other users on social media platforms. HSV3. Participating in topic comments and other activities on the social media platform makes me feel relaxed and happy.

5. Results and Discussion

5.1. Analytical Strategy and Descriptive Statistics

In our study, we used Smart-PLS 4 to analyze the quantitative data for examining the associations between different variables [78]. The use of this strategy becomes advantageous in cases when the hypothesized model integrates higher-order formative elements [79]. Before testing the hypotheses, we conducted preliminary tests of the data assumptions, including measures of central tendency (mean), median, dispersion (standard deviation), excess kurtosis, skewness, and factor loading, as shown in

Table 3. Descriptive Statistics and Factor Loading.

Name	Items	Mean	Median	Standard Deviation	Excess Kurtosis	Skewness	Factor Loading
Social Media Influencers	SMI1	3.403	4	1.652	−1.231	−0.113	0.748
	SMI2	3.428	4	1.492	−1.014	−0.132	0.853
	SMI3	3.38	4	1.471	−0.973	−0.065	0.837
	SMI4	3.818	4	1.426	−0.67	−0.422	0.741
	SMI5	3.315	3	1.593	−1.179	0.006	0.879
	SMI6	3.266	3	1.572	−1.167	0.02	0.875
	SMI7	3.225	3	1.567	−1.133	0.081	0.881
	SMI8	3.107	3	1.557	−1.111	0.179	0.86
	SMI9	3.439	4	1.57	−1.13	−0.106	0.841
	SMI10	3.442	4	1.54	−1.08	−0.108	0.832
Intention to Shop through Social Media	ISSM1	4.206	4	1.352	0.046	−0.827	0.883
	ISSM2	4.418	5	1.308	0.581	−1.004	0.899
	ISSM3	4.32	5	1.382	0.149	−0.927	0.915
	ISSM4	4.16	4	1.433	−0.322	−0.698	0.886
Social Media Shopping Behavior	SMSB1	3.93	4	1.467	−0.737	−0.472	0.872
	SMSB2	3.907	4	1.53	−0.944	−0.412	0.876
	SMSB3	3.492	4	1.621	−1.143	−0.133	0.839
	SMSB4	3.238	3	1.623	−1.189	0.04	0.764
	SMSB5	4.59	5	1.515	0.086	−1.035	0.558
Utilitarian Shopping Value	USV1	3.698	4	1.357	−0.624	−0.363	0.894
	USV2	3.671	4	1.406	−0.702	−0.352	0.921
	USV3	3.761	4	1.344	−0.549	−0.393	0.882
Hedonic Shopping Value	HSV1	4.315	5	1.337	0.179	−0.828	0.86
	HSV2	4.106	4	1.385	−0.373	−0.612	0.902
	HSV3	3.757	4	1.483	−0.852	−0.298	0.866
Brand Credibility	BC1	3.794	4	1.356	−0.71	−0.209	0.188
	BC2	3.781	4	1.342	−0.583	−0.307	0.899
	BC3	3.741	4	1.362	−0.57	−0.324	0.914
	BC4	3.793	4	1.326	−0.476	−0.392	0.916

.

5.2. Reliability, Validity, and Collinearity

Afterward, we assessed the convergent validity of the measurement model, which evaluates how well items that are supposed to measure the same construct correlate with each other. A popular measure of convergent validity is average variance extracted (AVE), which measures the percentage of variation in an observable variable explained by its hidden counterpart. An acceptable AVE is 0.50 or greater [80].

Table 4. Reliability, Convergent Validity, and Collinearity.

Construct	α	rho_a	rho_c	AVE	VIF
Social Media Influencers (SMI)	0.899	0.902	0.937	0.832	0.899
Intention to Shop through Social Media (ISSM)	0.918	0.918	0.942	0.802	0.918
Social Media Shopping Behavior (SMSB)	0.845	0.878	0.891	0.625	0.845
Brand Credibility (BC)	0.899	0.902	0.937	0.832	0.899
Utilitarian Shopping Value (USV)	0.882	0.882	0.927	0.809	0.882
Hedonic Shopping Value (HSV)	0.848	0.849	0.908	0.767	0.848

shows that the measurement model has sufficient convergent validity, since all latent variable AVE values surpassed this criterion.

The structural model’s reliability was evaluated by reliability tests such as Cronbach’s alpha (α), composite reliability for equally reliable (rho_a), and differentially reliable (rho_c) models. These metrics quantify the measuring scale’s internal consistency or how much items load on their hidden variable. All latent variable reliability and composite reliability values are above 0.70, indicating robust data internal consistency [81]. We computed each independent variable’s variance inflation factor (VIF) to detect multicollinearity. Table 4 shows that these values range between 0.899 and 0.848, which means they are closer to 1 with low multicollinearity, which leads to more stable, interpretable, and reliable results in regression analysis [82].

Existing research suggests two different methods to evaluate the discriminant validity: the heterotrait–monotrait (HTMT) ratio and the Fornell–Larcker criterion (FLC) [83]. HTMT compares the relationship between items of different constructs (heterotrait) and the same construct (monotrait). A higher value of the HTMT ratio indicates a lower discriminant validity, suggesting the constructs are not sufficiently linked to each other. The maximum acceptable threshold for the HTMT ratio is taken as 0.85. The FLC, however, requires the square root of the AVE for each construct to be higher than its highest correlation with other variables [84,85]; details are provided in

Table 5. Discriminant Validity (Heterotrait–Monotrait) (Fornell–Larcker).

	SMI	ISSM	SMSB	BC	USV	HSV	SMI	ISSM	SMSB	BC	USV	HSV
SMI							0.836
ISSM	0.510						0.479	0.896
SMSB	0.637	0.869					0.581	0.783	0.791
BC	0.607	0.608	0.663				0.561	0.554	0.588	0.912
USV	0.777	0.626	0.690	0.777			0.713	0.565	0.602	0.692	0.899
HSV	0.599	0.497	0.500	0.554	0.641		0.537	0.440	0.427	0.484	0.556	0.876

.

5.3. Structural Model Evaluation and Hypothesis Testing

Afterward, a confirmatory factor analysis (CFA) was conducted to assess the model’s overall fit and to identify potential issues. Figure 2 explains that the two items had lower factor loadings, indicating that they may not adequately represent their respective constructs. One item was BC1, and the other one was SMSB5. For BC1, since the factor loading was lower than 0.4, the item had to be deleted before further analysis. For the SMSB5, the item was not deleted since the model fit indices (SRMR should be less than 0.08 and NFI should be greater than 0.80) depicted in

Table 6. Model Fit Index Values.

	Saturated Model	Estimated Model
SRMR	0.053	0.058
d_ULS	1.235	1.465
d_G	0.55	0.562
Chi-square	3135.307	3192.884
NFI	0.867	0.864

remain good before and after the deletion of the variable [80,85].

The acquired model fit values met the model implementation requirements. Thus, structural equation modeling was used to test the model and hypotheses. Following existing literature, the bootstrap approach was used for modeling to enhance the comprehension of the developed model [80].

Table 7. Path Coefficient and the Significance of the Structural Model.

Relationship	β	SD	T	p	LLCI	ULCI	Hypothesis
SMI -> ISSM	0.257	0.034	7.629	0.000	0.195	0.327	H1 Accepted
SMI -> SMSB	0.210	0.038	10.071	0.000	0.15	0.276	H2 Accepted
ISSM -> SMSB	0.652	0.023	28.076	0.000	0.605	0.696	H3 Accepted
SMI -> ISSM -> SMSB	0.171	0.022	7.762	0.000	−0.127	0.214	H4 Accepted
BC × SMI -> ISSM	−0.088	0.027	3.447	0.002	−0.141	−0.03	H5 Accepted
USV × ISSM -> SMSB	0.045	0.017	1.104	0.010	0.011	0.078	H6 Accepted
HSV × ISSM -> SMSB	−0.018	0.017	2.587	0.270	−0.051	0.013	H7 Not Accepted

provides insights into the strength of relationships through path coefficients (β), where values closer to 0 indicate weaker ties. Significance is determined based on p-values (<0.05) and bias-corrected confidence intervals (BCCI). In this study, all causal relationships, except for the moderating effect of HSV, were found to be statistically significant. The details of the relationships, their respective path coefficients, standard deviations, t-values, p-values, and confidence intervals are presented in the table.

Our investigation delved into the effect sizes (f2) of direct relationships, demonstrating a spectrum from modest to substantial impacts. Notably, the association between SMIs and SMSB yielded the smallest effect size at 0.062, signifying a small to medium effect. In contrast, the connection between the ISSM and SMSB exhibited the most significant impact with a substantial effect size of 0.762, underscoring a strong and impactful relationship. With these findings, the study establishes a clear and direct impact of SMIs on both the intention to shop from social media and social media shopping behavior [38,44,45]. In line with prior research, the study found a positive relationship between users’ ISSM and their SMSB [36,37]. This means that consumers who have a stronger intention to shop through social media are more likely to actually make purchases through social media. By establishing these direct links, the research deepens our comprehension of the mechanism of how SMIs can influence their followers and other social media users [10,11,72].

Based on the statistical results, it is established that the ISSM acts as a partial mediator in the relationship between SMI and SMSB. This is because the direct effect of the SMI on SMSB is significant (β = 0.210, p < 0.001), indicating that the SMI affects SMSB even after controlling for the ISSM. The indirect effect of the SMI on SMSB through the ISSM is also found to be statistically significant (β = 0.171, p = 0.000), endorsing partial mediation. This means that SMIs partially influence SMSB by shaping consumers’ intention to shop through social media (ISSM). In simpler terms, when SMIs influence consumers, it does not immediately translate into shopping behavior; instead, their intention to shop through social media serves as the intermediary step [44,45]. In other words, exposure to social media influencers (SMIs) increases consumers’ intention to shop through social media (ISSM), and this increased intention leads to increased engagement in social media shopping behavior (SMSB).

Furthermore, regarding the moderators, the results establish that the moderating effect of brand credibility on the relationship between SMIs and the ISSM is statistically significant (β = −0.088, p = 0.002). This means that brand credibility has a significant negative moderating effect on the relationship between SMIs and the ISSM. This shows that the relationship between SMIs and the ISSM is stronger when brand credibility is higher, and the brands with higher credibility are more likely to benefit from social media marketing, as SMIs’ influence on consumer perceptions is amplified [15,46].

This relationship is due to the trust and reliability associated with high-credibility brands. Consumers are more likely to trust and be influenced by SMIs when these influencers endorse brands already perceived as credible. This trust in the brand enhances the persuasiveness of the SMI, making their endorsements more impactful. When a brand is highly credible, consumers are less skeptical about the SMI’s motivations and more likely to believe in the authenticity of the endorsement, thereby increasing their intention to engage in social media shopping.

Figure 3 further illustrates this moderating effect by plotting the relationship between the SMI and the ISSM at three levels of brand credibility: −1 SD below the mean, the mean, and +1 SD above the mean. As can be seen, the relationship between the SMI and the ISSM is strongest when brand credibility is high (mean and +1 SD above the mean) and weakest when brand credibility is low (−1 SD below the mean). This indicates that the higher the brand’s credibility, the more influential the SMI’s influence on consumer shopping intentions becomes due to enhanced trust and perceived authenticity.

Continuing with the moderators, the results establish that USV moderates the relationship between users’ intention and behavior (β = 0.045, p = 0.010), which indicates that consumers who prioritize practical benefits when shopping online are more likely to engage in social media shopping behavior when they have a stronger intention to do so [54,59]. This finding is consistent with the notion that utilitarian shoppers are more motivated to pursue tangible benefits, such as product quality, price, and convenience, when making purchasing decisions. In the context of Pakistan, where practicality and economic considerations often play a crucial role in consumer behavior, utilitarian values are particularly significant. When consumers have a strong intention to shop through social media, they are more likely to follow through with their intention because they perceive social media as a potential source of these utilitarian benefits. The effect size (0.005) is very small. This means the relationship between the ISSM and SMSB is only slightly stronger when USV is higher. Figure 4 explains this moderating impact by graphically representing the correlation between the ISSM and SMSB at three different levels of USV: one standard deviation below the mean, the mean, and one standard deviation above the mean. The correlation between the ISSM and SMSB is most pronounced when USV is high (at the mean or +1 standard deviation above the mean) and lowest when USV is low (−1 standard deviation below the mean).

While USV does moderate the relationship between the intention to shop through social media and social media shopping behavior, the hypothesis regarding the moderating effect of HSV on the relationship between the intention to shop through social media and social media shopping behavior was not supported at all (β = −0.018, p = 0.270). The effect size (0.001) was also found to be very small, which demonstrates that USV is a stronger determinant than HSV. In the Pakistani context, this may be attributed to the more practical and necessity-driven shopping behavior prevalent among consumers. Hedonic shopping value is characterized by its focus on the enjoyment, playfulness, and emotional aspects of shopping rather than solely on task completion [55,58,59]. It reflects the potential for entertainment and personal emotional gratification derived from shopping experiences. However, in Pakistan, where economic considerations and utilitarian needs often overshadow the pursuit of hedonic experiences, consumers exhibit different preferences for allocating time and money when acquiring hedonic versus utilitarian items. They show a willingness to invest more time in hedonic goods and more money in utilitarian goods, reflecting a more pragmatic approach to shopping. These distinctions are particularly evident in purchases made after basic needs have been met, where justifying spending on hedonic items can be more challenging, while utilitarian purchases are easier to justify [53]. Figure 5 illustrates this insignificant small moderating effect.

5.4. The Explanatory and Predictive Power of the Model

When evaluating the accuracy of structural model analysis, the Q² assesses predictive relevance and accuracy using sample reuse techniques. Values greater than zero indicate that exogenous variables have predictive relevance for endogenous variables [80]. For Q², the Smart-PLS prediction algorithm was employed, and the outcomes are documented in

Table 8. Values of R², R² Adjusted, and Q² for Endogenous Variables.

Endogenous Variables	R-Square	R-Square Adjusted	Q2-Predict
ISSM	0.357	0.355	0.349
SMSB	0.679	0.677	0.435

. Both endogenous variables have values above zero, signifying medium to strong predictive accuracy for the theoretical model. Furthermore, R² refers to the level of determination and measures the model’s explanatory power, with substantial values between 0.5 and 0.75, moderate values between 0.25 and 0.5, and weak values below 0.25 [80]. Even some prior studies have established that in the context of consumer behavior research, an R² value surpassing 0.2 is deemed a satisfactory result [86]. R² and R² adjusted indicate a moderate ISSM value and substantial SMSB values. This suggests that the framework possesses moderate to substantial explanatory power [80].

6. From Theoretical Insights to Applicable Contributions

This study delves into the complex dynamics of social commerce in the context of fashion products by examining the relationships between social media influencers (SMIs), social media users’ intentions, and their shopping behavior. It makes significant contributions to both theoretical and practical perspectives:

6.1. Theoretical Insights

Our study aims to explore the theoretical importance of social influence theory (SIT) by closely examining the relationship between the personal and behavioral characteristics of social media influencers (SMIs) and user intentions and behaviors in social commerce. Previous studies have utilized SIT to understand how external influencers impact intentions and behaviors, focusing on the direct effects of influencers’ characteristics on consumer behavior. However, those studies have often overlooked the indirect pathways and mediating factors through which SMIs influence consumer decisions [20,26]. Our research explains these indirect pathways, thereby highlighting the complexity of consumer decision-making processes in digital environments, a significant extension of existing theoretical models [11,14].

Additionally, our study introduces a novel perspective by highlighting the interplay between the influencer and brand credibility. Whereas earlier research has primarily focused on the influencer’s characteristics, our study suggests that the impact of SMIs is significantly contingent upon the perceived credibility of the endorsed brand. This offers a more dynamic and interactive model of social influence, demonstrating that social influence is not solely a function of the influencer’s traits but is also influenced by the context provided by brand credibility. This dual focus on both influencer and brand credibility contributes to SIT by expanding the scope of social influence to include contextual factors [14,49,52].

Furthermore, by exploring the moderating impact of Utilitarian Shopping Value (USV) and Hedonic Shopping Value (HSV), this study delves deeper into the complexity of consumer decision-making processes in digital environments. Prior research has identified different shopping motivations but has not fully examined their moderating effects on consumer behavior in social commerce. Our approach extends existing theoretical models by illustrating how different shopping motivations, such as practical benefits for utilitarian shoppers and enjoyment for hedonic shoppers, can affect consumer behavior in social commerce. This comprehensive examination of shopping motivations provides a richer understanding of consumer behavior and contributes to the literature by integrating motivational factors into the analysis of social influence [33,53,57].

6.2. Practical and Managerial Contributions

Our research highlights significant implications for businesses engaging with SMIs, emphasizing the importance of brand credibility and utilitarian shopping value in enhancing SMIs’ influence on consumer behavior. For instance, a fashion brand collaborating with an influencer known for sustainable practices can significantly boost the brand’s credibility among eco-conscious consumers. This strategic alignment ensures that the influencer’s credibility resonates with the brand philosophy, leading to more effective campaigns. Additionally, offering exclusive content and special discounts through SMIs can amplify direct consumer impact, enhancing shopping intentions and driving higher engagement rates.

Practical considerations are crucial in shaping consumer shopping behavior in the fashion sector. Businesses should highlight the utilitarian benefits of their products in marketing efforts to justify consumer spending on practical goods. For instance, promoting the durability, versatility, and functionality of clothing items can attract consumers looking for practical value in their purchases. This approach caters to diverse consumer segments by aligning promotional efforts with SMIs that reflect the brand’s values. Collaborating wisely with influencers whose personas resonate with the brand image can create synergies that boost product promotions. For example, a casual wear brand partnering with an influencer who promotes a laid-back, functional lifestyle can effectively reach its target audience. SMIs, in turn, should choose brand collaborations carefully to maintain their credibility and maximize promotional impact.

The evolving landscape of social media and online shopping in regions such as Pakistan and other developing countries presents unique opportunities for the fashion industry. Tailoring marketing strategies to highlight practical benefits and functional aspects aligns with Pakistani consumers’ preferences for utility-driven purchases over hedonistic indulgence. For example, a local fashion brand could emphasize the versatility and practicality of its clothing line to appeal to consumers’ preference for functional fashion. These actionable insights empower practitioners to apply research findings directly to real-world scenarios. Fashion businesses can effectively navigate the digital marketplace by optimizing influencer marketing strategies based on these insights. Collaborating with local influencers with a strong online presence in Pakistan can help brands reach a broader audience and build stronger consumer connections. These strategies, grounded in our research findings, can substantially enhance the effectiveness of influencer marketing campaigns in the fashion industry, leading to better engagement and higher conversion rates.

7. Limitations and Future Research

7.1. Limitations

This study has a few limitations to consider when interpreting its findings. Primarily, the study’s reliance on social influence theory may limit its ability to capture the complex dynamics of social commerce fully. SIT, being a general theory of social influence, does not specifically address the influence of SMIs on social media shopping intention and behavior. Additionally, the effectiveness of SMIs in influencing consumer behavior may vary depending on the type of product being promoted, the consumer’s perception of the product’s utilitarian value, and the cultural and economic context in which the product is being marketed. Another limiting factor is that the statistical significance of the results is context-specific to the time when the survey was conducted (16 May to 17 September 2023). This timeframe is crucial, as it ensures that other researchers can contextualize and compare our findings with their own. Changes in market dynamics, technological advancements, or shifts in consumer behavior over time could potentially influence the applicability and generalizability of our results beyond the specified period.

Moreover, the research model involves multiple interactions between various variables, making it challenging to assess the relative importance of each factor. Furthermore, the generalizability of the findings may be constrained, as they may not be universally applicable to countries characterized by distinct cultural and economic norms, particularly those emphasizing individualism and materialism. Lastly, the effectiveness of SMIs in influencing consumer behavior might exhibit fluctuations based on the industry within which they operate, introducing an additional layer of complexity to the study’s outcomes.

7.2. Future Research

Our study’s statistical significance underscores its potential to impact future research endeavors substantially. By integrating insights gleaned from our findings, researchers can enhance the theoretical framework to incorporate additional theories that provide a more comprehensive understanding of the factors influencing consumer behavior in social commerce. Incorporating user-centric theories, such as the theory of planned behavior (TPB) and the technology acceptance model (TAM), can offer valuable insights into users’ perceptions and attitudes. Additionally, integrating theories that explicitly consider cultural and economic factors, such as Hofstede’s cultural dimensions theory and the unified theory of acceptance and use of technology (UTAUT), can enrich the understanding of the research context.

Adopting a mixed-method research approach that integrates quantitative and qualitative data collection methods is recommended to capture a comprehensive view of the research problem. This approach enriches the depth of insights and facilitates the triangulation of findings, ensuring a robust understanding of the multifaceted dynamics at play. Moreover, employing a longitudinal design in future studies can elucidate the temporal aspects of consumer behavior, tracking changes over time to establish causality and discern the enduring effects of SMIs.

Future research should conduct studies in different product categories to enhance the generalizability of findings across diverse contexts. Examining the role of SMIs in influencing consumer behavior for varied products such as electronics, cosmetics, and automobiles can provide insights into the applicability of observed relationships to different industries. Expanding the research model to encompass various product categories will contribute to a more holistic understanding of these relationships in the social commerce landscape.

Lastly, future research could also examine the ethical implications of influencer marketing, particularly concerning transparency and authenticity. Understanding how consumers perceive the ethical conduct of SMIs and how it influences their trust and engagement can provide critical insights for developing ethical guidelines and practices in social commerce.

8. Conclusions

Our research concludes that the social influence theory serves as a robust framework for comprehending the intricate dynamics involving social media influencers (SMIs), brand credibility, and shopping values in shaping the intention and behavior of users in the realm of social commerce. The effectiveness of SMIs in influencing consumer behavior is contingent upon factors such as brand credibility and utilitarian shopping value. Moreover, this study reveals that the impact of SMIs on behavior is partially and indirectly mediated by intention. These findings underscore the pivotal role of perceived brand credibility, urging businesses and SMIs to meticulously assess this aspect when considering collaboration to promote certain products. SMIs are found to be more effective in influencing consumer behavior when they endorse products offering tangible benefits, aligning with consumers’ rational considerations. Businesses are advised to emphasize the utilitarian value of their offerings, particularly in markets where cultural and economic factors prioritize practicality. Additionally, this study highlights the variability in the relationship between SMIs, intention, and behavior across diverse product categories, signaling the need for tailored influencer marketing strategies based on the specific nature of the products targeted. The research results are valuable for academic researchers seeking to advance consumer behavior and social commerce theories, industry professionals aiming to refine marketing strategies and user experiences, policymakers shaping e-commerce regulations, educators teaching about digital commerce, and consumers navigating online platforms.