In age of technology, the function of social media in determining buying habits has grown progressively essential altering how individuals interface with brands and make buying decisions. The rise of social networking platforms has created an ecosystem where consumers are constantly exposed to information, recommendations, and persuasive communications that influence their perceptions and choices (Sulistiani & Towpek, 2025; Waworuntu et al., 2022). Social media has grown from basic communication to serve as the main platform for marketing and trust-building while connecting consumers and brands on a massive scale according to Pookulangara and Koesler (2011) and Farzin et al. (2022). Marketing research today focuses remarkably on social media dependence and social influence as the primary drivers of consumer decision-making stages because digitized platforms have become consumers' primary guidance source (Riaz et al., 2021; Chang et al., 2019; Husnain & Toor, 2017).

Research shows that when people heavily use social media their attitudes and actions change more strongly because of peer influence and online information credibility (Ngo et al., 2024; Liang et al., 2024; Kudeshia & Kumar, 2017). Social media dependence defined as users' platform reliance for information seeking and social connection and validation builds a framework which enhances consumers' sensitivity to network-transmitted cues and signaling inputs (Hsu & Lin, 2016; Malik et al., 2023). Social media dependence affects how consumers trust information from their networks which leads them to make purchase decisions (Saima & Khan, 2020; Al Kurdi et al., 2022). Research by Petan and Harwani (2024) and Khattak et al. (2024) confirms that the digital community influence jointly determines perceived credibility assessments and transaction-risk evaluations as well as readiness to complete transactions.

Online purchase intentions strongly depend on trust according to consistent findings in research (Lăzăroiu et al., 2020; Hajli et al., 2017; Wang et al., 2022; Lu et al., 2016) and multiple other studies alongside these. Through social commerce users establish trust through sociological evidence backed by reliable endorsements as well as transparent authentic user feedback (Pop et al., 2022; Siddiqui et al., 2021). When shopping online consumers heavily rely on trusted voices from friend’s influencers and third-party reviewers to make decisions (Kwakye 2024 Ahn & Lee 2024). Social influence works directly and indirectly to support or oppose what consumers already believe and want to do according to Beyari and Abareshi (2019) and Sheth and Kim (2017). Online reviews together with eWOM and influencer marketing exert strong influence on brand attitudes and purchase intentions particularly toward digital natives along with younger consumer segments according to Konale et al. (2025), Waworuntu et al. (2022) and Ngo et al. (2024). The relationship between online social platforms’ dependence and social influence on end-user’s trust and purchase behavior needs more research because it remains unclear. Research shows how each element works alone but few studies examine how the association of social variables and likelihood of purchase, and trust is impacted by social media dependence (Liang et al., 2024; Chang et al., 2019; Pop et al., 2022). The decision-making process becomes more complex because new social interaction methods including virtual fitting rooms and real-time influencer endorsements and live commerce have been added (Konale et al., 2025, Petan & Harwani, 2024). The investigation bridges identified gaps through structured research which analyzes social media dependence and social influence effects on consumer trust and purchase plans based on previous empirical and conceptual works (Riaz et al., 2021; Hsu & Lin, 2016; Malik et al., 2023).

The present analysis focuses on two key research questions: (1) Social media dependence impacts what extent consumers trust and intend to purchase.  (2) What is the mediating or moderating role of social influence in this relationship? Addressing these questions is not only of academic relevance, as it Fuels the broader exploration of digital consumer conduct and social commerce, but also of significant practical value for marketers seeking to leverage social media as a channel for building trust and driving sales (Sulistiani & Towpek, 2025; Al-Alawi et al., 2020). By integrating insights from an assorted collection of contexts—including travel, fashion, health, and retail—this research aims to deliver a comprehensive insight into the mechanisms at play, with direct implications for branding, content strategy, and customer relationship management (Pop et al., 2022; Gong et al., 2025; Waworuntu et al., 2022). This investigation holds great importance because digital interactions have evolved rapidly while consumers gain power through social influence (Kwakye et al., 2024; Kudeshia & Kumar, 2017; Farzin et al., 2022). The determination of trust factors and purchase intentions requires immediate attention from scholars and practitioners because marketing methods have become complex and organic and paid influence have merged (Saima & Khan, 2020; Wang et al., 2022). This article uses contemporary evidence and theory to study these phenomena with the goal of developing both theoretical and effective knowledge for marketing and social research (Liang et al., 2024; Siddiqui et al., 2021; Beyari & Abareshi, 2019; Chang et al., 2019; Khattak et al., 2024; Sheth & Kim, 2017; Ngo et al., 2024).

Social Media Dependence and Consumer Behavior

It has become a very relevant concept in the research of how today’s digital consumer behaviors change. This represents the level to which persons take components of social media globally and integrate them into their everyday lives and, in their decision, making processes that may result in repetitive and even dependant types of utilisations. In addition to the frequency and duration of platform use, this dependence is also characteristic of the psychological reliance on social media in order to attain information, social validation, and emotional support (Pookulangara & Koesler, 2011). It has been observed by scholars that customers reliance onto social media influenced they become by their connections' opinions, recommendations, and experiences (Farzin et al., 2022) that stylize their attitudes towards brands and products. Not only are the effects of social media dependence on passivity, but they encourage, increase, and change consumer engagement, support brand loyalty, and influence decision processes. User engaged engagement on social media helps shape collective consumer sentiments because this involves participation in online discussions, sharing content, and participation in review or feedbacks, which will lead users to engage more on these social networking platforms, hence, amplifying the influence of online social platforms (Husnain & Toor, 2017). And also, because of the integration of social networking into the mobile applications, consumers find it easy to stay connected hence making it easy for them to stick to the platform, somewhat increasing the chances of in app purchases or being the brand advocates (Hsu & Lin, 2016). Social media platforms are turning out to be influential spaces on which marketing strategies prove rich to convert engagement to actual purchasing behavior,

Social Influence Mechanisms on Social Media

In social media context social influence mechanisms are very important to form consumer preferences and behaviors. Normative, informational, and referent social influences are the mechanisms through which these can be classified. Normative influence refers to the situation where individuals conform to the expectations of their social groups to be accepted, while informational influence is when consumers look to others for guidance to reduce uncertainty in decision making (Liang et al., 2024). Nevertheless, referent influence is seen when consumers regard some individuals or groups as role models whose opinions and choices they want to follow. Overall, recent studies have shown that such influences are moderated by group similarity, self construal, and perceived expertise of data sources (Ahn & Lee, 2024). Amongst other things, digital influencers and electronic word of mouth (eWOM) are particularly salient forms of virtual influence on social media platforms. As influencers have an array of followers as well as another level of perceived credibility, they can make consumers' attitudes and purchase intentions swayed when they share personal endorsements, product reviews or posting life style content related to their followers (Malik et al., 2023; Saima & Khan, 2020; Pop et al., 2022). Findings show that credibility of the digital creators as well as the perceived authenticity of eWOM communications increase message persuasiveness, encourage engagement with the brand, and a subsequent increase in brands’ purchase intentions. Moreover, the tone and words of reviewer comments and online reviews hold a profound influence in shaping consumer reliance and acquisition decisions, which is nourishing or clear feedbacks have significant impacts in purchasing (Kwakye, 2024; Kwakye, Ertugan, & Tashtoush, 2024).

Consumer Trust in Online Environments

In general, trust is universally regarded as a vital aspect in online commerce and has the potential to affect consumers’ willingness to partake in purchase pursuits and to supply private information. In the case of social commerce, trust mitigates the perception of risk, increases the perceived worth and has a direct impact on the decision to buy a good or service (Lăzăroiu et al., 2020; Wang et al., 2022; Hajli et al., 2017). A company has to go through a series of channels that are associated with credible recommendations, transparent interaction and consistent brand performance to manage online trust. In particular, its presence is crucial in information asymmetry and limited physical interaction environments. Perceived Website Quality, Having Secure Payment System and Authenticity of Social Cues like Testimonials and User Generated Content (Chang et al., 2019) are antecedents of online trust. Moreover, social presence, understood as the impression of being related to others via technology, contributes to trust making the virtual experience more friendly and encouraging significant communication (Lu, Fan and Zhou, 2016). In addition of purchase intentions, trust leads to higher customer loyalty, positive eWOM and long-term brand advocacy.

Purchase Intention in the Digital Age

Inclination to purchase is one of the central constructs assumed in consumer behavior research and is often explained as the probability of a purchase of a good or service by a consumer considering their attitudes, beliefs and perceptions. In the digital era, unlike other environments, prominent modesl  like the Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) have evolved their own logic, developed specific focus areas considering the unique features present in the online environment (Riaz et al., 2021; Khattak, Ali, & Khan, 2024). The complex and dynamic ways in which purchase intentions are shaped by perceived practicality, user-friendliness, social factors, and reliability, as factors, depend upon combinations of trust and engagement in varying ways. In recent literatures, social media dependence, peer influence and confidence are the instantiations of previously defined interrelated concepts that impact purchase intention. Social influence relies on trust to affect consumers’ response to recommendations and endorsements about the product, and consumers who trust both the platform and information source would be more likely to act on these recommendations and endorsements (Petan & Harwani, 2024). Additionally, eWOM credibility and persuasiveness and visibility of others’ positive experiences increase purchase intentions (Kudeshia & Kumar, 2017). Firstly, this shows that online information and social influence have a greater effect on Gen Z and digital natives, thus a need to provide targeted marketing solutions to this group of customers.

Conceptual Model and Hypotheses Development

From the previous review, it is undeniable that social media dependence, social influence and consumer trust are interrelated constructs that jointly influence purchase intention in the digital marketplace. We advance based on this insight a possible conceptual framework that social media dependence will amplify the effect of social influence in influencing the propensity to purchase; social influence will, in turn, enhance trust. The direct and indirect the consequences of social media reliance regarding purchase likelihood, the function of trust as a mediator, and the contingent effect of eWOM, credibility are empirical hypotheses (Konale et al., 2025; Beyari & Abareshi, 2019; Al Kurdi et al., 2022). The intention of this integrative model is to be able to give a differentiated look on the mechanisms of behavior of online shoppers and direct the future research on this subject.

Research Design

With a quantitative research methodology, the collected responses from participants is through a structured survey. Hypotheses regarding the accord of social platform dependence, social influence, consumer trust with purchase intention would be best tested by using a quantitative approach. Given that it is a robust tool for testing complex, multivariate relationships and for both irect and intervening effects, situated within a theoretical framework, Structural Equation Modeling (SEM) is used as the primary analytical technique. The survey-based SEM strategy permits the researchers to gauge the measurement models (to authenticate the credibility and appropriateness of the metrics) and the structural models (to test the proposed links between the variables) simultaneously.

Sampling and Data Collection

The active social media users who have completed an e-commerce transaction within the past six months fall within the confines of the intended group for this research. This criterion guarantees that respondents have experience in social media engagement and digital commerce. The participants are selected using purposive sampling technique, since the selection criteria are likely to ensure relevance and accuracy of the findings. Recommendations for SEM suggest a minimum of 10 respondents per observed variable or item, so the number of subjects is ascertained on this basis. However, given intricacy of the model that is anticipated, we aim to have a sample size of 400 valid responses for the sake of a sufficient statistical power. This number is sufficient for confirmatory factor analysis and has enough variability for mediation and moderation analyses. A self-administered web-based survey was shared via a prominent online social platform (Facebook, Instagram, Twitter) and e commerce forums to collect data. The questionnaire is user friendly and can be accessed on any device. Before the main data collection, 30 respondents are used for a preliminary test to assess the clarity of items, refine the wording, and estimate the completion time. The survey instrument is finalized using feedback from the pilot phase.

Measurement of Constructs

Validated multi-item scales are used for each of the key constructs, adopted from existing measures modified to fit to the context of social media and online purchasing.

• Social Media Dependence is described as the level to who utilizes social media for information, validation and decision making. It is measured via items assessing how often a user engages with the social media, how emotionally attached the user is to his social media and how he perceives the social media is important in his everyday life.
• Social Influence is characterized as the observed effect of others—like peers, near ones, marketers, and people online—on the response’s opinions and conduct regarding online shopping. The construct includes normative, informational, and referent influences, assessed by items that ask about agreement with statements about the importance and impact of others’ opinions.
• Social Influence refers to the impact of others (friends, family, influencers, etc. and online reviewers) on the respondent’s attitudes and behavior related to online shopping. The construct includes normative, informational, and referent influences, assessed by items that ask about agreement with statements about the importance and impact of others’ opinions.
• Likelihood of the respondent purchasing following exposure to information or recommendations on social media is measured as Purchase Intention. Items are items that probe the respondent’s intent to buy, recommend, or further engage with products featured on their social media feeds.

A 5-point Likert scale from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”) is conceived to evaluate all items. The digital marketing questionnaires are reviewed for content validity by panel of academic experts and practitioners in digital marketing. Due to the fact that instrument reliability and validity must be guaranteed, based on the value of Cronbach’s alpha and composite reliability coefficients, each construct is calculated with the acceptable criteria of 0.70 or higher. Average variance extracted (AVE) and inter-construct correlations are used to test the convergent and discriminant validity.

Analytical Techniques

The data analysis is carried out in several stages. First, descriptive statistics are summarized to provide the respondent demographics and usage patterns. After that, Confirmatory Factor Analysis (CFA) is employed o appraise the measurement model to substantiate the validity and reliability of the constructs. This step ensures that the items reflect the theoretical variables of interest. Subsequent to the measurement model is validated, SEM is employed to test the theoretical model. This approach permits the simultaneous estimation of multiple relationships, such as the direct relationships of dependence on social media and social influence with consumer dependability and buying propensity and the existence of indirect (mediating) relationships, respectively. Standard indices like CFI, TLI, RMSEA, and SRMR are used in evaluating the model fit. Mediation and moderation analyses are subsequently conducted in order to further explore the underlying theory. The aim of mediation analysis is to understand how consumer trust can be the mediator between social media dependence and social influence and so the purchase intention. Moderation analysis determines how these relationships differ depending on the demographic variables or the amount of social media usage. The analyses are run on the specialized statistical software, SPSS and AMOS or equivalent. Established guidelines are used in order to interpret the results with rigor and transparency, and the entire research process remains transparent.

Descriptive Statistics

An analysis was conducted on the responses from a total of 400 valid responses. The demographic record of the sample is presented in Table 1. Of those surveyed, 43.2% were men and 56.8% were women. 42.0% of the population was spanning the ages of 18 and 24, 38.5% was within the age spectrum of 25 and 34, 12.8% was in the age bracket of 35 and 44, and 6.7% was over the age of 44. Most, 64.5%, possessed a bachelor’s or more. In terms of online shopping frequency, 48.3% of those interviewed declared they make at least one online purchase each month, while 87.6% of them use social media daily. Most of them used Facebook (82%), Instagram (76%) and Twitter (48%).

Table 1. Demographic Profile of Respondents (N = 400)

Figure 1. Preferred Social Media Platforms among Respondents

As depicted in the graph in Figure 1, 82% of respondents prefer using Facebook and 76% of them prefer using Instagram for connecting and communicating through social media. TikTok and LinkedIn are at 36% and 29%, respectively, while Twitter follows with 48%. The pattern here highlights the importance of Facebook and Instagram as main gateways to digital routines in daily lives of consumers, and as preeminent platforms for interaction on line, receiving information and engaging with brands. The data indicate that marketing efforts on these platforms will have the most reach and impact within the studied population.

Measurement Model

Confirmatory Factor Analysis (CFA) was implemented to measure the measurement model. Item reliability was supported by all factor loadings above the 0.70 threshold. Summarized in Table 2, Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) is reported for each of the constructs. Internal reliability and convergent accuracy were confirmed by all constructs having CR and Cronbach’s alpha coefficients higher than 0.80 and AVE scores above 0.50

Table 2. Construct Reliability and Validity

Following the Fornell-Larcker criterion, discriminant validity was established when the square root of the AVE for each concept was higher than its correlations with other components.

Regarding the model fit, model fit indices revealed an excellent fit: CFI = 0.968, TLI = 0.962, RMSEA = 0.041 and SRMR = 0.036 highlighting that all model fit indices were within the recommended thresholds. The results show that the model for measurement is not only robust but also appropriate for additional structural analysis.

Structural Model and Hypotheses Testing

Structural Equation Modeling (SEM) was employed to examine the hypothesized relationships.

Figure 2. Structural Model with Standardized Path Coefficients

The structural model depicting the standardized path coefficients among the key constructs namely social media dependence, consumers social influence, customers trust and purchase intention is given in figure 2. In this regard, the model proves that social media dependence significantly predicts both social influence and consumer trust whereas social influence in turn highly predicts both buyer assurance and intent to purchase. It should also be mentioned that buyer certainty has the greatest direct effect on purchase intention, thereby confirming its mediating role in the model. Some of the significant results of path coefficients are strong supporting traceback for the hypothesized and relationship between variables and demonstrate the interconnecting pathways which lead to consumer behavior in social media environment.

A summary of key results is given in Table 3. Consequently, social media dependence significantly contributed to social influence in a positive way (β = 0.54, p <0.001), and consumer trust (β = 0.32, p<0.001) Consumer trust (β = 0.41, p < 0.001) and purchase intention (β = 0.37, p < 0.001) were significantly social influence affected. Furthermore, consumer trust showed a high positive direct effect on purchase intention (β = 0.51, p < 0.001).

Table 3. Structural Model Path Coefficients

It was established by bootstrapping analysis that social influence plays a part in the interaction between social media dependence, customer trust, and purchase intention. Also, consumer trust was the mediator of the effects of social media dependence and social influence on the purchase intention.

Robustness and Additional Analyses

Robustness checks were performed in context of age and incidences of online purchase. On younger users (aged 18–24), social influence exerts a greater force on purchase intention (β = 0.44, p < 0.001) than on older cohorts (β = 0.30, p < 0.01). Exact analogs were seen with frequent (>=2/month) online shoppers, with sensitive social media dependence having an increased contributing function in forming trust (β = 0.38, p<0.001). Results were consistent with the main conclusions in sensitivity analyses with respect to the specification of alternative models and the exclusion of outliers.

Based on this study, findings provide key insights on social media dependence and social influence jointly impact the customer belief and tendency to purchase in the digital commerce setting. It turns out that the largest proportion of the respondents are youthful and educated individuals, who also regularly use social media channels. The results are contemporary and this profile is representative of a digitally savvy consumer segment, as the analysis of social media usage patterns shows that Instagram and Facebook are the main platforms in the daily lives of consumers. However, their high frequency of use implies that these channels can act as main sources of information, entertainment and interaction which are prime grounds for marketers to get in touch with their would be buyers. This preference for these services shows that they are effective marketing tools and that brands must stay active and strategic on these networks. The validity and consistency of the constructs used in this study are confirmed by the measurement model. All the key variables, namely social media dependence, social influence, consumer trust and purchase intention showed high internal consistency and high discriminant and convergent validity. The robust model fit indices additionally bolster confidence that the analytical approach written and the theoretical framework are well supported by the data.

Several noticeable relations are seen from the structural model. Both social influence and consumer trust are strongly and positively influenced by social media dependence. This infers that opinions and behaviors of others within the social network can have the greatest affect on those who are more heavily dependent on social media. Additionally, these individuals are also more likely to build trust with the platforms themselves and the brands that use these channels to interact with consumers. These relationships are not totally isolated from each other; on seeing each other in context, social media is not a medium where consumers simply receive information, but it’s one that actively affects consumer perception and behavior. Additionally, the research demonstrates a significant effect of social influence on both consumer trust and purchase intention. The consumer is exhibit a higher propensity to rely on the information if she perceives strong social cues i.e. recommended by a peer, or endorsed by an influencer or if she liked it based on an online review. In turn, this elevated trust leads them to act upon such recommendations and to have stronger intentions to make a purchase. These results demonstrate that digital communities provide persuasion power and that the influence on purchase intention goes through the direct path, pointing out the importance of viral marketing.

Social media dependence and social influence turn out to be channeled into actual purchase through the mechanism of consumer trust in the model. Such direct robust relationship of trust with purchase intention brings the importance of seizing the opportunity for brands to build, maintain and nurture their credibility and transparency, consistent with online communications. If consumers can trust the information they read about brands or peers on social media with the trust these consumers can turn the trust into buying actions. Further depth is given to the findings by robustness and sensitivity analyses that point to important subgroup differences. In the case of younger consumers in particular, social influence is a more effective factor in the formation of purchase intentions, which implies that marketers interested in reaching such consumers should implement marketing strategies based on peer networks and influencer partnership. Moreover, frequent online shoppers tend to be more reliant on social media dependence in their trust building, which means that brands looking to establish a strong relationship and customer loyalty need to continue investing time in such efforts. The mediation and moderation analyses advocate for the complication of the relationships studied. For example, social influence and consumer trust would mediate the partial effect of social media dependence on purchase intention. This further emphasizes the necessity of a holistic strategy to creating digital marketing, and that many psychological and social factors are combined.

The results as a whole are coherent and provide a comprehensive portrayal of an ecosystem that is digital via social media dependence and social influence in the formation of trust and eliciting purchase intentions. These results hold important implications for practitioners in which they learn that in order to gain presence on such key social platforms, but more importantly to foster meaningful engagement with consumers in a genuine, trust building manner. The findings are relevant to scholars, as they provide empirical support for contemporary theories of electronic word of mouth, digital consumer behavior, and consumer's inclination to pay during the initial development of social commerce, and open future research avenues for studying the fast-changing dynamics of the phenomenon. Results of this study show that for successful digital marketing, these social effects of individual reliance on social media, the power of social influence and the importance of trust as a basis for consumer behavior should be taken into account. Through recognition of and a strategic leveraging of these relationships one is able to better convert engagement to meaningful and lasting purchase intention.

This study presents an extensive analysis regarding how customer onlinetrust and purchasing decisions get influenced by social media dependency and social influence in digital markets. Research outcomes show that people dependent on social networks become more sensitive to public opinion thus affecting both their trust levels and purchasing readiness significantly. Trust and actual purchase actions both depend heavily on social influence which people experience through peer reviews and influencer promotions together with user-generated assessments. Consumer trust maintains an essential position that unites social media dependence effects with social influence impact on real purchase behaviors because it demonstrates how digital decision-making occurs interdependently.

The research results create important implications which affect both market practitioners and academic researchers. From this analysis marketers understand the need to establish genuine customer relationships by preserving their honesty while interacting with users on various social media channels. Cyber strategies need to expand past basic platform maintenance because they require sincere trust-building and social influence tools to produce sales conversion. The presented insights confirm to policymaking institutions that digital marketing should maintain ethical boundaries that put consumer protection alongside informed customer choice at their core. This study develops academic knowledge of social commerce by proving how digital engagement turns into economic behaviors through empirical testing. Research in the future should extend its investigation of these buyer-supplier relationships among various demographic categories while different cultural settings to verify novel marketing strategies function effectively through digital change.

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