The Triple Helix of Digital Engagement: Unifying Technology Acceptance, Trust Signaling, and Social Contagion in Generation Z’s Social Commerce Repurchase Decisions

Abstract

This study investigated Generation Z’s repurchase intention in social commerce environments by integrating three theoretical frameworks: the unified theory of acceptance and use of technology, signaling theory, and herding behavior. The research addressed critical gaps in understanding continued engagement behaviors of digitally native consumers in socially embedded commerce platforms. Data were collected from 542 Generation Z consumers using a structured questionnaire, and relationships were tested using partial least squares structural equation modeling. Results demonstrated that all UTAUT factors significantly influenced repurchase intention. Return policy leniency, as a quality signal, positively impacted repurchase intention both directly and indirectly through enhanced online trust. Fear of missing out demonstrated significant direct effects on repurchase intention and operated indirectly through imitative behaviors. This research advances the theoretical understanding of Generation Z’s continued engagement with social commerce by validating an integrated framework that simultaneously accounts for technological, informational, and social-psychological dimensions. The findings provide practical guidance for social commerce platforms seeking to enhance Generation Z’s loyalty through balanced strategies addressing functional performance, trust-building signals, and social validation mechanisms.

1. Introduction

The exponential growth of social commerce represents one of the most significant transformations in the digital marketplace over the past decade, fundamentally reshaping how consumers discover, evaluate, and purchase products online [1]. Social commerce—defined as the integration of social networking features with traditional e-commerce functionalities—has created hybridized commercial environments where transactions occur within socially embedded contexts, facilitating a seamless fusion of social interaction and commercial activity [2,3]. This evolution has been particularly resonant with Generation Z consumers, born between 2001 and 2010, who demonstrate distinctive patterns of digital engagement characterized by intensive social media usage and technologically mediated consumption behaviors [4]. Generation Z, as digital natives who have never experienced a world without internet connectivity, exhibit unique characteristics in their approach to online consumption. Unlike preceding generations, these consumers demonstrate heightened comfort with mobile technology, greater susceptibility to social influence, and elevated expectations regarding user experience and platform performance [5]. Their purchasing patterns are characterized by frequent digital engagement, preference for socially validated products, and substantial influence from peer recommendations and trending content [6]. Furthermore, Generation Z’s consumption behaviors are increasingly concentrated on social commerce platforms, where commercial transactions occur within familiar social media environments that align with their communication preferences and interaction patterns [7].

Although many studies have looked at first adoption behaviors around technology platforms, social commerce sustainability has become more and more important via ongoing involvement via repurchase intention [8]. Repurchase intention—defined as customers’ desire to engage in repeated transactions with the same platform or vendor—represents a fundamental sign of customer loyalty and platform sustainability in the competitive digital market [9]. Given Generation Z’s growing buying power, technical knowledge, and possibility for lifelong customer value, knowing the factors of repurchase intention becomes more vital for social commerce platforms targeting this group [10]. Generation Z’s repurchase decision making in social commerce settings is so complicated that it calls for many theoretical frameworks to concurrently capture technical, informational, and social-psychological aspects [11]. Valuable insights on the technical elements affecting ongoing platform engagement are provided by the unified theory of acceptance and use of technology (UTAUT), including performance expectation, effort expectancy, social influence, and enabling circumstances [5]. Signaling theory shows how credibility signals like return policy leniency help to offset information asymmetry issues in digital settings by lowering perceived risk and strengthening trust [12]. Herding behavior theories, especially with reference to social influence and fear of missing out (FoMO), help to explain how Generation Z’s purchase choices are formed by group movements and worry over possible exclusion from valued events [13].

Understanding Generation Z’s repurchase intentions in social commerce settings—where technological functionality, information issues, and social dynamics converge to affect ongoing engagement choices—is especially relevant at the junction of these theoretical viewpoints [9]. Understanding how Generation Z’s repurchase behavior is shaped by these factors taken together will be crucial for both theoretical development and practical implementation as social commerce platforms change to include more complex technology features, clear signaling systems, and socially embedded shopping experiences [14].

Notwithstanding significant study in related fields, certain important gaps remain in the knowledge of Generation Z’s repurchase intentions in social commerce settings. First, although the UTAUT model has been widely used for first technology adoption choices, its application for ongoing engagement behaviors in socially embedded commerce settings is still rather restricted, especially for Generation Z consumers, whose technological orientation varies radically from prior generations [8]. This constraint raises doubt about whether conventional technology acceptance models sufficiently reflect the complex elements affecting Generation Z’s repurchase choices in changing social commerce settings [15]. Second, although signaling theory has highlighted several quality signals in e-commerce settings, particular research on return policy leniency as a trust-building tool in social commerce environments is lacking [10]. Although Kharouf, Lund, Krallman and Pullig [9] point out that the foundations of the theory have a strong relevance to marketing communications with customers, thorough models investigating how signals affect relationship outcomes in socially embedded commerce settings are still lacking. Particularly for Generation Z consumers negotiating information asymmetry issues in social commerce environments, the mediation function of online trust in the link between signaling mechanisms and repurchase intentions needs further empirical confirmation [12]. Third, while swarming behavior and FoMO have been studied in investing settings, its relevance to social commerce repurchase choices remains scattered [16,17]. Although there are no thorough frameworks looking at how these psychological mechanisms affect ongoing interaction with social commerce platforms, Rahmawati and Raharja [4] noted that Generation Z often depends on social media as a main source of investment information, maybe causing biases or decision-making mistakes. Moreover, the possible mediating function of imitative behaviors in the link between FoMO and repurchase intentions calls for empirical research to create more complex knowledge of the psychological routes via which social-psychological elements affect Generation Z’s ongoing engagement choices [18].

The present study aims to address these research gaps by developing and empirically validating an integrated theoretical framework that comprehensively explains Generation Z’s repurchase intentions in social commerce environments. First, we aim to extend the UTAUT framework to Generation Z’s continued engagement with social commerce platforms by examining how performance expectancy, effort expectancy, social influence, and facilitating conditions influence repurchase intentions in these technologically and socially embedded environments. By testing the applicability of this established technology acceptance model to Generation Z’s repurchase behavior, we seek to enhance theoretical understanding of post-adoption decisions among this distinctive demographic cohort [5]. Second, we aim to advance signaling theory by empirically investigating the role of return policy leniency as a quality signal that influences Generation Z’s repurchase intentions in social commerce contexts. Furthermore, we seek to examine the mediating role of online trust in this relationship, thereby illuminating the psychological mechanisms through which signaling processes influence continued engagement decisions in environments characterized by substantial information asymmetry challenges [9,12]. Third, we aim to enhance understanding of herding behavior in social commerce contexts by examining how fear of missing out (FoMO) and social influence shape Generation Z’s repurchase intentions, with particular attention to the mediating role of imitative behaviors in these relationships. By investigating these social-psychological pathways, we seek to develop more nuanced insights into the emotional and social drivers of Generation Z’s continued engagement with social commerce platforms [4,16]. Last but not least, through the integration of these theoretical perspectives, we aim to develop a comprehensive model that simultaneously accounts for technological, informational, and social-psychological factors influencing Generation Z’s repurchase intentions in social commerce environments. This integrated approach seeks to advance the theoretical understanding of the multifaceted nature of continued engagement decisions while providing practical guidance for social commerce platforms targeting this demographically distinctive consumer segment [10].

The structure of this document is as follows: By establishing the theoretical underpinnings and suggested relationships, Section 2 offers the literature evaluation and hypothesis building. The study methodology—including measuring instruments, sample processes, and data collecting techniques—is covered in Section 3. Results of data analysis using partial least squares structural equation modeling (PLS-SEM) are shown in Section 4 and Section 5 to discuss the finding of this study. Theoretical and practical consequences are covered in Section 6; it also notes study constraints and offers ideas for further research.

2. Literature Review

2.1. Generation Z and Social Commerce Context

Comprising people born between 2001 and 2010, Generation Z is a unique demographic group defined by their heavy use of digital technology and social media channels, which greatly influences their buying choices and consumption patterns [19]. From an early age, this tech native generation has been surrounded by digital settings, showing unique behavioral tendencies in their online consuming activities [20]. Unlike other generations, Generation Z customers have different views on digital buying, showing more comfort with mobile technology and social media integration in their shopping experiences [21]. Social commerce—a subcategory of electronic commerce that uses social media features to enable online commercial transactions—has emerged as an especially relevant purchasing option for this demographic [19].

Combining social networking elements with conventional e-commerce functionality, social commerce platforms provide a hybrid commercial arena where transactions take place in a social setting [2]. This combination allows people to participate in product research, assessment, and buying activities even while they communicate with peers and opinion leaders [22]. The social aspect of commerce is especially important for Generation Z customers as they tend to depend rather a lot on peer validation and social proof in their decision-making processes [20]. Therefore, a multi-theoretical framework that considers both technological acceptance elements and social-psychological aspects of customer behavior can help one to grasp the elements affecting Generation Z’s repurchase intentions within social commerce environments.

2.2. Unified Theory of Acceptance and Use of Technology (UTAUT)

The unified theory of acceptance and use of technology (UTAUT) is a thorough framework for grasping how people embrace and keep utilizing technological systems [23]. UTAUT provides useful insights regarding the technical elements affecting Generation Z’s ongoing involvement and repurchase patterns in the framework of social commerce platforms [24]. The model consists of four main constructs—performance expectation, effort expectancy, social influence, and facilitating conditions—each of which contributes uniquely to users’ behavioral intents and actual use patterns [25].

Performance expectation is the extent to which people think using a certain technology would improve their performance in certain tasks [26]. In social commerce, this shows up as customers’ views on how well platforms support their shopping goals, including product discovery, comparison, and purchase completion [27].

Studies show that customers’ intentions to participate in recurrent buying behaviors rise dramatically when they see social commerce platforms as offering large utilitarian advantages—including time efficiency, product diversity, and competitive pricing—especially when they view social commerce platforms [28]. Performance expectancy is especially important for Generation Z customers, who value efficiency and effectiveness in their technology interactions, since it affects their repurchase choices [8]. Performance-related elements are emphasized by Jebarajakirthy, Yadav and Shankar [5] when they say that, for a successful acquisition to occur, technology-related firms should invest in market orientation during the early stages of the firm’s life cycle, stressing the need for these elements to keep technologically native consumers.

In competitive market settings, where many platforms compete for customer attention and loyalty, the link between performance anticipation and repurchase intention becomes more pronounced [29]. In such situations, Generation Z customers’ assessments of platform performance in comparison with substitutes greatly influence their ongoing involvement choices [30]. Social commerce platforms that regularly outperform in supporting shopping goals are thus better positioned to foster repurchase habits among Generation Z consumers [31]. Therefore, this study proposed the following hypothesis:

H1: 

Performance expectancy positively impacts Generation Z’s repurchase intention in social commerce.

Effort expectation relates to the perceived simplicity linked to utilizing a technological system [23]. For Generation Z consumers of social commerce platforms, effort expectation includes the intuitiveness of interfaces, streamlined checkout procedures, and smooth movement between social and commercial functionalities [26]. Though they are technically savvy, Generation Z customers still place great importance on cognitive economy when choosing and using platforms [9,32].

Studies show that consumers of social commerce sites defined by low learning needs, simple design features, and easy checkout procedures have more repurchase intentions [33]. Khoa and Thanh [34] point out that perceived control is lower on personalized websites than on non-personalized websites, which leads to privacy concerns during online transactions. Transparent information and simplified processes, meanwhile, may help to reduce these worries and improve consumers’ readiness to participate in recurrent buying activities [35].

Specifically for Generation Z customers, the cognitive resources saved by simple platform interactions may be transferred towards more interesting elements of the buying process, like product assessment and social involvement [10]. This change improves general shopping process satisfaction, hence increasing repurchase intentions [36]. Furthermore, the favorable link between effort expectation and repurchase intention should intensify as users become more acquainted with platform features over time because ease of use changes from being just appreciated to being anticipated [37]. Therefore, this study proposed the following hypothesis:

H2: 

Effort expectancy positively impacts Generation Z’s repurchase intention in social commerce.

Social influence is the degree to which people feel that important others think they should adopt a certain technology [23]. Particularly in social commerce settings, where peer recommendations and social validation greatly affect Generation Z’s buying choices, this aspect is very important [38]. This demographic group shows more sensitivity to peer views and social validation, which makes them more vulnerable to influence from reference groups in their buying choices [39].

Social influence in Generation Z’s consumption contexts operates through multiple distinct mechanisms requiring deeper theoretical exploration. Beyond the general definition provided by UTAUT, social influence manifests through (1) normative processes, where conformity is driven by desire for social approval [40]; (2) informational processes, where peers’ actions serve as information shortcuts under uncertainty [41]; and (3) identification processes, where consumption aligns with reference group identity [42]. For Generation Z specifically, social influence operates through unique digital mechanisms including algorithmic recommendations, visibility metrics (likes, shares), and peer-generated content that serves as consumption cues [43]. These influence pathways are particularly potent in social commerce environments where identity signaling through consumption is embedded within social networking structures [44]. The platforms’ architecture amplifies social influence by making peer preferences highly visible and reducing barriers between observing others’ consumption and one’s own purchase actions [45].

In social commerce settings, when commercial operations are integrated into social networking environments, the impact of peers, opinion leaders, and larger social networks becomes amplified [46]. Studies show that herding is a behavior in which investors follow the actions of other investors, leading to herd effects and potentially irrational investment decisions [18]. Social commerce environments also follow this concept; Generation Z customers often adjust their buying behavior based on seen trends among peers [47].

Social influence manifests itself via many routes within social commerce platforms, e.g., explicit recommendations, implicit social evidence via purchase data, and visibility of peers’ spending patterns [44]. Generation Z customers’ personal repurchase intentions for certain platforms or items grow markedly when they feel important persons within their reference networks support them [48].

Where consumption patterns act as vehicles for social communication and group affiliation, this impact is especially noticeable in product categories with high social visibility or identity signaling value [6]. The social dimensions of consumption are especially important for Generation Z customers negotiating identity construction as they make social influence a key factor in their repurchase choices in social commerce settings [43]. Therefore, this study proposed the following hypothesis:

H3: 

Social influence positively impacts Generation Z’s repurchase intention in social commerce.

Facilitating conditions include users’ views on the availability of a technological and organizational infrastructure that supports system use [23,49]. For social commerce platforms, these criteria include device compatibility, customer support services, and payment security methods [50].

Studies show that Gen Z customers, even though they are tech-savvy, are nonetheless aware of the existence of strong enabling factors when deciding to repurchase on social commerce sites [51]. Users’ trust in platform reliability is greatly increased by the availability of thorough customer support services, safe payment systems, and cross-device capability, therefore reinforcing their repurchase intentions. Meilatinova [52] underlines the need for several facilitating components in creating consumer trust by saying that building a corporate image is more valuable to strong corporate image retailers while showrooms play a critical role for weak corporate image retailers.

For Generation Z customers in particular, the smooth integration of social commerce platforms with their current technology environment is a particularly relevant enabling factor [53]. Users feel less friction in transaction procedures when platforms show interoperability with preferred devices, social networking platforms, and payment methods, therefore improving their likelihood of repeat purchase [30]. Furthermore, for Generation Z customers who appreciate openness and accessibility in their business transactions, the openness of information about accessible assistance resources is a major enabling element. Therefore, this study proposed the following hypothesis:

H4: 

Facilitating conditions positively impact Generation Z’s repurchase intention in social commerce.

2.3. Fear of Missing out and Herding Behavior

2.3.1. Social Influence, FoMO, and Imitative Behaviors

While social influence, fear of missing out, and imitative behaviors share conceptual territory as social-psychological constructs affecting consumer decision making, they represent distinct theoretical mechanisms with important differences in their operational definitions and psychological foundations.

Social influence, as conceptualized within UTAUT, represents the degree to which individuals perceive that important others believe they should use a particular system or engage in specific behaviors [23]. This construct primarily operates through normative pressures and perceived social expectations rather than actual behavioral imitation. In social commerce contexts, social influence manifests as the perception of others’ expectations regarding platform usage, which may exist independently of the observation of others’ actions [26].

Fear of missing out (FoMO), by contrast, is fundamentally an affective state characterized by anxiety and apprehension about missing rewarding experiences that others might be having [54]. Unlike social influence, which centers on normative perceptions, FoMO involves emotional responses to potential exclusion from valued social experiences. Bonaparte and Fabozzi [55] establish that FoMO operates through anticipated regret mechanisms distinct from general social pressure, representing a specific anxiety-based pathway of influence.

Imitative behaviors represent the behavioral outcome of observational learning and mimicry of others’ actions [41]. While potentially resulting from either social influence or FoMO, imitative behaviors are distinct in representing the actual behavioral manifestation rather than the psychological antecedent. Zhu, et al. [56] demonstrate that imitative behaviors can occur through multiple cognitive and emotional pathways beyond simple normative compliance, including uncertainty reduction, inferential learning, and behavioral contagion.

These constructs are further distinguished by their measurement approaches. Social influence items assess perceptions of others’ expectations, FoMO measures capture anxiety about potential exclusion, while imitative behavior items evaluate conscious mimicry of observed actions. Each construct thus captures a different aspect of social-psychological processes influencing Generation Z’s social commerce behavior.

2.3.2. Fear of Missing Out, Social Influence, Imitating Others, and Repurchase Intention

A major psychological element affecting Generation Z’s social commerce behaviors is herding behavior, which is defined as people’s inclination to mimic the activities of bigger groups instead of making autonomous choices based on personal information [57]. Herding in online shopping environments is the tendency of customers to follow the buying trends of peers and opinion leaders, especially under information ambiguity or choice complexity [58]. Herding habits could significantly affect Generation Z customers’ repurchase choices in social commerce settings as they are more sensitive to social approval [59].

Defined as the anxiety that arises when one feels they are missing out on valuable experiences others are having, driving a constant desire to stay connected and participate in others’ activities, fear of missing out is a psychological concept closely linked to herding behavior [54]. In social commerce settings, FoMO shows up as customers’ anxiety over possible exclusion from advantageous buying chances or trending items liked by peers [60]. FoMO could be especially strong for Generation Z customers, who show more sensitivity to social comparison and peer influence, since it might lead to hasty buying and repurchase actions [4]. Studies indicate that FoMO might increase irrationality in decision making as people try to guarantee they stay socially and economically on par with others. For Generation Z customers negotiating identity development, the dread of losing socially recognized purchasing possibilities has particular psychological significance [61]. Social commerce platforms trigger FoMO processes that greatly affect users’ repurchase choices when they strategically emphasize limited-time deals, trending goods, or peer involvement with certain items [60]. In highly competitive marketplaces defined by fast changing trends and limited-availability items, FoMO’s impact on repurchase intention grows [55]. In such settings, Generation Z customers’ knowledge of peers’ spending habits via social media integration increases their vulnerability to FoMO-driven repurchase actions [62]. Therefore, this study proposed the following hypothesis:

H5: 

Fear of missing out positively impacts Generation Z’s repurchase intention in social commerce.

Studies show that herding happens when investors follow collective market actions or mimic other investors’ reactions without considering available information [57], a concept that applies to customer behavior in social commerce settings. The impact of reference groups on imitative behaviors is clearer for Generation Z consumers, who show more sensitivity to peer approbation and social integration [63].

Multiple channels in social commerce platforms show the interaction between social influence and imitation [41,64]. Among Generation Z customers, explicit endorsements from trusted peers, quantitative measures of popular items, and observability of others’ buying patterns all represent powerful sources of social influence that propel imitative reactions [65]. Social impact toward imitation becomes stronger for Generation Z consumers in product categories with notable identity signaling value, where consumption decisions convey social affiliation and status [42]. In such situations, the apparent convergence of peer consumption patterns generates strong normative pressures toward conformity, hence greatly raising the probability of imitative buying behaviors [40]. Therefore, this study proposed the following hypothesis:

H6: 

Social influence positively impacts imitating others in social commerce.

Imitative behaviors among Generation Z customers seem to considerably influence the link between FoMO and repurchase intention in social commerce settings [56]. FoMO activates increased awareness of others’ spending behaviors, hence driving imitative reactions that finally enhance repurchase intentions [4].

Studies show that FoMO acts as a mediator that strengthens the influence of availability on investment decision making [60], implying its function in cognitive processes before behavioral reactions. Psychological pain linked to possible exclusion from valued consumption experiences drives Generation Z consumers with FoMO to become more aware of their friends’ buying habits [18]. People want to reduce FoMO by engaging in socially approved purchasing habits, so their increased awareness later enables imitative reactions [66]. In social commerce settings, where the combination of commercial and social functions increases the exposure of peers’ spending patterns, the mediating effect of imitation becomes clearer [56]. Imitative buying reactions help Generation Z customers to manage FoMO when they see others’ good experiences with certain items or platforms, hence increasing their intent to repurchase these entities [67].

Where consumption patterns have major social signaling functions, the mediating impact of imitation may be more pronounced in product categories with social visibility and trend sensitivity [68]. For Generation Z consumers negotiating identity formation processes, the psychological temptation to reduce FoMO via imitative buying becomes clearer in such situations [4]. Therefore, this study proposed the following hypothesis:

H7: 

Imitating others positively mediates the relationship between fear of missing out and repurchase intention in social commerce.

2.4. Signaling Theory and Return Policy Leniency

Signaling theory offers a paradigm for comprehending how parties address in-formation imbalance in market transactions [69]. Effective signaling systems become very important in social commerce settings when physical product inspection is impractical and vendor authenticity may be questionable for building customer confidence and promoting repurchase behavior [9]. Of many market signals, return policy leniency has surfaced as a particularly strong tool for lowering perceived risk and improving buying intentions [12].

Return policy leniency is the degree of flexibility and ease of the return process of the vendor’s product, including time frame allowances, reimbursement assurances, and procedural simplicity [70]. Lenient return policies act as trust signals that reduce doubts about product quality and vendor dependability for Generation Z customers negotiating social commerce settings [71]. Studies show that, when online retailers adopt a lenient return policy, consumers have higher perceived quality and lower perceived risk, which in turn leads to a higher intention to purchase [72]. In the setting of recurrent purchase choices, when customers’ previous encounters with return procedures greatly affect their readiness to participate in following transactions, this signaling mechanism becomes more important [73].

Return policy leniency is especially important for Generation Z customers, who show more risk sensitivity in digital buying settings, as a strong predictor of their repurchase intentions [8]. Knowing that undesired purchases may be undone with few financial or procedural repercussions greatly lowers perceived transaction risks, hence increasing the likelihood of these customers making repeat purchases [74]. In product categories marked by changeable quality, size uncertainty, or subjective assessment criteria, the link between return policy leniency and repurchase intention intensifies [75]. In such situations, Generation Z consumers, when considering recurrent purchases, find risk reduction tools especially useful in forgiving return policies [76]. Therefore, this study proposed the following hypothesis:

H8: 

Return policy leniency positively impacts Generation Z’s repurchase intention in social commerce.

Online trust among Generation Z customers in social commerce settings seems to considerably moderate the link between return policy leniency and repurchase intention [77]. This mediation mechanism signals vendor dependability and product quality via permissive return policies, hence fostering confidence that finally enhances repurchase intentions [75].

Studies show that firms signal trustworthiness through transparent business practices [70], with return policy leniency being an especially obvious example of such openness. Generous return policies provide credibility signals for Generation Z customers negotiating information asymmetry issues in social commerce settings, hence improving views of vendor trustworthiness [71]. This increased confidence then lowers psychological obstacles to repurchase, hence promoting customers’ readiness to participate in many transactions with certain vendors [9].

In cross-border e-commerce settings, when geographical distance increases information asymmetry worries, the mediating function of online trust becomes clearer [78]. International sellers that use lax return policies show dedication to client happiness despite logistical issues, hence greatly boosting confidence among Generation Z customers [79]. Shao and Chen [80] point out that, when a consumer purchases a product that ships from a domestic bonded warehouse or a product without a product traceability code present, the effect of the leniency of the return policy on perceived quality and perceived risk is stronger, stressing contextual elements affecting this mediation link.

The trust-building role of return policy leniency is especially important for Generation Z customers, who show more skepticism towards unknown internet businesses [81]. When these customers see lax return policies as signs of vendor confidence and dependability, the ensuing trust greatly increases their likelihood of platform reengagement and repeat buying behavior [9]. Therefore, this study proposed the following hypothesis:

H9: 

Online trust positively mediates the relationship between return policy leniency and repurchase intention in social commerce.

From the research hypotheses above, this study proposed the research model shown in Figure 1.

3. Research Methodology

3.1. Measurement Scales

To guarantee construct validity and dependability, this paper uses accepted measurement scales modified from the pertinent literature. Consistent with other research in technology adoption and social commerce settings, all items were assessed using a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). Rigorous piloting and expert validation helped to enhance the measuring scales, hence guaranteeing contextual relevance for Generation Z respondents in social commerce settings.

Three questions modified from Venkatesh, Morris, Davis and Davis [23] and contextually honed after Sarker, Hughes, Malik and Dwivedi [27] were used to gauge performance expectation (PEE). This scale measures Generation Z’s view on the usefulness and efficacy of social commerce platforms in improving their buying experience. Sample items include “Using social commerce improves my shopping efficiency” and “Social commerce helps me complete shopping tasks more quickly”. Four items modified from Venkatesh, Morris, Davis and Davis [23] and contextualized for social commerce using Sarker, Hughes, Malik and Dwivedi [27] were used to operationalize effort expectation (EFE). This measure assesses the perceived simplicity of using and navigating social commerce platforms, e.g., “Learning to operate social commerce plat-forms is easy for me” and “My interaction with social commerce platforms is clear and understandable” are among sample items. Three items modified from Venkatesh, Morris, Davis and Davis [23] and honed for the social commerce environment using Sarker, Hughes, Malik and Dwivedi [27] were used to gauge social influence (SOI). This scale measures how much Generation Z customers feel that significant people think they need to use social commerce tools, e.g., “People whose opinions I value prefer that I use social commerce” and “People who influence my behavior think I should use social commerce”. Four items modified from Venkatesh, Morris, Davis and Davis [23] and contextually refined following Sarker, Hughes, Malik and Dwivedi [27] were used to gauge facilitating conditions (FACs). This scale assesses Generation Z’s view of accessible resources and support for using social commerce tools, e.g., “A particular person or group is available for help with social commerce challenges” and “I have the required resources to use social commerce platforms”.

To operationalize fear of missing out (FoMO), four items were taken from Przybylski, Murayama, DeHaan and Gladwell [54] and contextualized for social commerce based on Phuong, et al. [82]. This scale gauges Generation Z’s worry about missing worthwhile purchasing chances or experiences, e.g., “I get worried when I find out my friends are shopping for something without me” and “It is important that I understand the shopping references people are talking about”. Three modified items were used to gauge imitating others (IMO). This scale evaluates Generation Z’s inclination to copy other people’s buying habits in social commerce settings, e.g., “Other users’ purchasing decisions affect my buying choices” and “I often respond fast to changes in other users’ shopping decisions” [4].

Four items modified from Shao, et al. [12] were used to operationalize return policy leniency (RPL). This scale measures Generation Z’s view of ease and flexibility in product return processes, e.g., “The return process on this platform is simple and convenient” and “This social commerce platform offers a sufficiently long time period for returns”. Three questions modified from Khoa and Thanh [34] were used to gauge online trust (ONT). This scale gauges Generation Z’s trust in the integrity and dependability of social commerce platforms, e.g., “This social commerce platform is honest and truthful with its customers” and “This social commerce platform can be trusted at all times” are among the sample items.

Three items modified from Meilatinova [52], Reksoprodjo [83] were used to operationalize repurchase intention in social commerce (REI). This scale measures Generation Z’s desire to participate in repeat buying practices on social commerce platforms, e.g., “I plan to keep buying from this social commerce platform in the future” and “I will routinely buy from this social commerce platform in the future” [6].

3.2. Sample and Data Collection

The target audience for this research consisted of Generation Z customers (born between 2001 and 2010) who had prior exposure to social commerce platforms [63]. Consistent with sample techniques used in comparable research, a purposive sampling method was used to guarantee that respondents had relevant experience with social commerce transactions. Given the particular demographic and behavioral requirements for participant selection, this method was considered suitable.

Guidelines for structural equation modeling, which advise a minimum of 10 observations per estimated parameter, helped to define the sample size of 542 respondents. G*Power analysis was also performed to guarantee a statistical power of 0.95 with a medium effect size of f² = 0.05, hence confirming the sufficiency of the sample size [84]. Recent research in social commerce and consumer behavior studies, which use comparable sample sizes to explore intricate structural interactions, support this strategy [85].

An online survey was sent out via many channels, including social media, academic networks, and snowball sampling methods, and data collecting was carried out. This multi-channel strategy was used to maximize respondent variety and reduce any sample bias [86]. Established scales from the literature served as the foundation for the survey instrument, which was then contextualized for social commerce settings using exhaustive pretesting techniques.

A pilot research involving 30 Generation Z consumers was performed to evaluate questionnaire clarity, relevance, and completion time before the full-scale rollout. Feedback from the pilot research guided small changes to question wording and survey design to improve understanding and contextual relevance. The last questionnaire included three parts: (1) screening questions to verify eligibility requirements, (2) major construct measuring items, and (3) demographic information.

Periodic monitoring to guarantee demographic variety within the Generation Z group helped to guide data collection over six months (June to December 2024). Attention check questions were included and completion time was tracked to find possible sloppy replies, hence improving answer quality. Originally, 652 replies were received, but, following data cleaning processes, including the removal of incomplete responses, outlier detection, and verification of attention checks, a final sample of 542 valid responses was kept for analysis, representing an effective response rate of 83.1%.

Table 1. Demographic profile of respondents (N = 542).

Characteristic	Category	Frequency	Percentage
Gender	Male	226	41.70%
	Female	316	58.30%
Age	15–17	196	36.20%
	18–22	346	63.80%
Education	High School	221	40.80%
	Undergraduate	321	59.20%
Monthly Purchase Frequency	Once	62	11.50%
	2–3 times	225	41.50%
	4–5 times	148	27.30%
	More than 5 times	107	19.70%
Product Categories	Fashion and Accessories	208	38.40%
	Electronics	123	22.70%
	Beauty and Personal Care	105	19.30%
	Home and Lifestyle	63	11.60%
	Others	43	8.00%
Preferred Social Commerce Platform	Instagram Shopping	183	33.80%
	Facebook Marketplace	149	27.50%
	TikTok Shop	117	21.60%
	Pinterest Shopping	50	9.20%
	Others	43	7.90%

shows the demographic profile of respondents, offering a thorough picture of the sample’s traits

4. Results

4.1. Confirmatory Factor Analysis and Model Fit Assessment

Prior to structural model evaluation, we conducted confirmatory factor analysis to validate the measurement model. Outer loadings ranged from 0.713 to 0.921, exceeding the recommended threshold of 0.708 [86].

Table 2. Reliability and convergent validity.

Construct	Outer Loadings	Cronbach’s Alpha	CR	AVE
Performance Expectancy (PEE)	0.893–0.916	0.897	0.937	0.832
Effort Expectancy (EFE)	0.834–0.898	0.886	0.922	0.746
Social Influence (SOI)	0.842–0.886	0.827	0.896	0.742
Facilitating Conditions (FACs)	0.807–0.875	0.863	0.907	0.710
Fear of Missing Out (FoMO)	0.813–0.881	0.871	0.913	0.724
Imitating Others (IMO)	0.713–0.834	0.806	0.866	0.623
Return Policy Leniency (RPL)	0.824–0.892	0.887	0.922	0.747
Online Trust (ONT)	0.872–0.921	0.887	0.930	0.815
Repurchase Intention (REI)	0.864–0.917	0.879	0.925	0.805

presents the detailed measurement model results, including factor loadings, Cronbach’s alpha, composite reliability, and average variance extracted.

To assess the overall measurement model fit, we examined multiple goodness-of-fit indices following recommendations by Henseler et al. [87]. The standardized root mean square residual (SRMR) value was 0.057, below the conservative threshold of 0.08, indicating a good fit. The normed fit index (NFI) was 0.912, exceeding the recommended value of 0.90. Additionally, we calculated the d_ULS (squared Euclidean distance) at 1.324 and d_G (geodesic distance) at 0.876, both falling below the 95% quantile of their corresponding reference distributions (1.486 and 0.937, respectively), further confirming an appropriate model fit.

The goodness-of-fit (GoF) index, which simultaneously assesses the overall fit of both measurement and structural models, was calculated at 0.712, substantially exceeding the threshold for large effect sizes (0.36) suggested by Wetzels, et al. [88]. These comprehensive fit statistics collectively validate the measurement model’s robustness and appropriateness for subsequent structural analysis.

4.2. Measurement Model Assessment

Indicator reliability was assessed by examining the outer loadings of each measurement item on its respective construct. As shown in Table 2, all indicators exhibited outer loadings exceeding the recommended threshold of 0.708 [86], ranging from 0.713 to 0.921. This confirms that each indicator shared more variance with its associated construct than with error variance, thus demonstrating adequate indicator reliability.

Values shown in Table 2 were used to assess internal consistency reliability using Cronbach’s alpha and composite reliability (CR). Comfortably above the suggested level of 0.7, Cronbach’s alpha scores varied from 0.806 to 0.897. Likewise, composite reliability scores varied from 0.866 to 0.937, well over the recommended threshold of 0.7 but below 0.95, suggesting ideal internal consistency without duplication issues. The strong reliability values show that the measurement items together consistently reflected their respective conceptions, therefore verifying sufficient internal consistency reliability for all constructs.

Average variation extracted (AVE) was used to evaluate convergent validity, which measures the degree to which a concept converges to account for the variation of its indicators. Ranging from 0.623 to 0.832, AVE values for all constructions surpassed the advised threshold of 0.5, as shown in Table 2. These figures show that every construct accounted for more than 50% of the variation in its related metrics, hence verifying sufficient convergent validity. Performance expectancy showed significant convergent validity, with an AVE of 0.832, suggesting good alignment between its assessment items. Imitating others (AVE of 0.623), the construct with the lowest AVE, easily surpassed the minimal requirement, hence verifying acceptable convergent validity across all constructs.

Both the Fornell–Larcker criteria and the heterotrait–monotrait ratio (HTMT) were used to evaluate discriminant validity, which measures the degree to which constructs are empirically different from one another. Presented in

Table 3. Fornell–Larcker criterion results.

Constructs	EFE	FACs	FoMO	IMO	ONT	PEE	REI	RPL	SOI
EFE	0.864
FACs	0.582	0.843
FoMO	0.394	0.436	0.851
IMO	0.367	0.402	0.624	0.789
ONT	0.458	0.596	0.329	0.341	0.903
PEE	0.617	0.531	0.406	0.325	0.443	0.912
REI	0.563	0.642	0.574	0.589	0.676	0.586	0.897
RPL	0.435	0.548	0.347	0.318	0.757	0.432	0.632	0.864
SOI	0.483	0.517	0.529	0.723	0.397	0.496	0.583	0.364	0.861

Note: The bold diagonal elements represent the square root of the AVE for each construct. Off-diagonal elements represent the correlations between constructs.

, the Fornell–Larcker criteria evaluated the square root of each construct’s AVE (given in bold on the diagonal) against its correlations with other constructs (off-diagonal components). According to this criterion, all diagonal values surpassed the relevant correlation values in the same row and column, indicating acceptable discriminant validity [89].

We also looked at the HTMT ratio for more rigorous evaluation; it reflects a more consistent way to test discriminant validity in PLS-SEM. All HTMT values in

Table 4. Heterotrait–monotrait ratio (HTMT) results.

Constructs	EFE	FACs	FoMO	IMO	ONT	PEE	REI	RPL	SOI
EFE
FACs	0.672
FoMO	0.456	0.512
IMO	0.458	0.497	0.764
ONT	0.529	0.683	0.378	0.429
PEE	0.694	0.612	0.470	0.403	0.510
REI	0.646	0.739	0.661	0.734	0.765	0.674
RPL	0.501	0.636	0.395	0.397	0.833	0.493	0.724
SOI	0.584	0.624	0.637	0.846	0.472	0.585	0.688	0.429

, from 0.378 to 0.846, below the conservative criterion of 0.85, verified sufficient discriminant validity between all construct pairings [90]. Theoretically, the greatest HTMT value (0.846) was found between social influence and imitating others given their conceptual closeness in signaling theory settings. Still, this result was below the essential threshold confirming discriminant validity.

A discriminant validity investigation verifies that every concept catches phenomena not represented by other model constructs, hence proving the distinctiveness of all theoretical constructs investigated in this work. This strong foundation of discriminant validity increases confidence in following hypothesis testing and structural model studies.

We performed a complete collinearity assessment to find any shared method bias, given that data for both independent and dependent variables were gathered concurrently from the same respondents. We looked at variance inflation factor (VIF) values for all construct combinations using the suggested procedure of Kock [91]. Ranging from 1.000 to 2.843, all VIF values in

Table 5. Collinearity assessment (VIF) and effect sizes (f²).

Relationship	VIF	f2	f2 Interpretation
PEE → REI	1.832	0.057	Small
EFE → REI	1.766	0.031	Small
SOI → REI	2.207	0.046	Small
SOI → IMO	1.000	1.092	Large
FACs → REI	1.926	0.037	Small
FoMO → REI	1.838	0.041	Small
FoMO → IMO	1.388	0.156	Medium
IMO → REI	2.427	0.091	Small
RPL → REI	2.437	0.033	Small
RPL → ONT	1.000	1.344	Large
ONT → REI	2.843	0.077	Small

were below the threshold of 3.3, suggesting that common technique bias was not a major issue in this research. We also used Harman’s single-factor test as a supplementary tool to evaluate common technique bias [92]. The unrotated principal components analysis showed that the first factor contributed 38.75% of the total variance, below the criterion of 50%, thereby supporting the finding that common method bias was not a major issue in this research.

4.3. Structural Model Assessment

Before judging the structural linkages, we looked at the VIF values for all predictor constructs in the structural model to identify any collinearity concerns. All VIF values in Table 5, from 1.000 to 2.843, were below the advised threshold of 5 [86]. These numbers suggest that multicollinearity was not a major issue in the structural model, hence guaranteeing the stability and dependability of path coefficient estimations. All structural connections were evaluated for effect size (f²), which showed the influence of a predictor construct on an endogenous construct. Social influence had a significant impact on imitating others (f² = 1.092), while return policy leniency had a significant impact on online trust (f² = 1.344), thereby underlining the great significance of these connections.

The coefficient of determination (R²), which reflects the degree of variation in endogenous constructs explained by all exogenous constructs related to it, was used to evaluate the explanatory power of the structural model. The model accounted for significant variation in every endogenous component, as seen in

Table 6. Explanatory power and predictive relevance.

Endogenous Construct	R2	R2 Adjusted	Interpretation	Q2	Interpretation
IMO	0.563	0.561	Substantial	0.347	Strong
ONT	0.573	0.572	Substantial	0.460	Strong
REI	0.764	0.759	Substantial	0.612	Strong

. With a R² value of 0.764, the model’s predictors roughly accounted for 76.4% of the variation in Generation Z’s repurchase intention in social commerce. This significant explanatory power implies that the integrated theoretical framework integrating UTAUT, signaling theory, and herding behavior offers a complete knowledge of Generation Z’s repurchase intentions in social commerce settings. Indicating significant explanatory power for these mediating factors, the model also accounted for 56.3% of the variation in imitating others (R² = 0.563) and 57.3% of the variance in online trust (R² = 0.573). While return policy leniency explains a major part of variance in online trust, these results imply that social influence and fear of missing out together account for a considerable amount of variance in imitative behaviors among Generation Z consumers.

We used the blindfolding procedure to compute Stone–Geisser’s Q² values with an omission distance of 7, therefore evaluating the predictive usefulness of the model. All Q² values, as shown in Table 6, were significantly above 0, from 0.347 to 0.612, suggesting good predictive significance for all endogenous constructs in the model. Repurchase intention (0.612) had the highest Q² value, indicating that the model had very significant predictive validity for this focus construct.

Bootstrapping, using 5000 subsamples, as suggested by Hair, Black, Babin and Anderson [86], helped to evaluate the importance and relevance of the structural model linkages. Presented in

Table 7. Path coefficients and hypothesis testing.

Hypothesis	Relationship	β	t-Value	p-Value	Result
H1	PEE → REI	0.187	4.653	0.000	Supported
H2	EFE → REI	0.142	3.426	0.000	Supported
H3	SOI → REI	0.178	3.814	0.000	Supported
H4	FACs → REI	0.159	3.573	0.000	Supported
H5	FoMO → REI	0.165	3.742	0.000	Supported
H6	SOI → IMO	0.723	21.872	0.000	Supported
H7	FoMO → IMO → REI	0.096	3.547	0.000	Supported
	FoMO → IMO	0.240	6.031	0.000
	IMO → REI	0.399	8.268	0.000
H8	RPL → REI	0.151	3.217	0.001	Supported
H9	RPL → ONT → REI	0.114	3.982	0.000	Supported
	RPL → ONT	0.757	30.264	0.000
	ONT → REI	0.151	3.321	0.001

, the findings show that all supposed direct correlations were statistically significant with p-values under 0.05 and t-values above 1.96, hence validating all nine hypotheses.

Supporting H1, performance expectation showed a significant positive impact on repurchase intention (β = 0.187, t = 4.653, p < 0.001). This result is consistent with other studies, indicating that Generation Z customers’ ongoing use intentions are greatly influenced by their perception of usefulness and efficacy of technology [5] Likewise, effort expectation showed a notable favorable effect on repurchase intention (β = 0.142, t = 3.426, p < 0.001), thereby supporting H2. This finding supports the idea that, even among tech-savvy Generation Z customers, perceived ease of use is a key factor influencing technology adoption and ongoing use.

Supporting H3, social influence had a significant favorable impact on repurchase intention (β = 0.178, t = 3.814, p < 0.001). This result emphasizes how peer pressure and social approval help to shape Generation Z’s ongoing involvement with social commerce platforms. Social influence also showed a significant favorable effect on imitating others (β = 0.723, t = 21.872, p < 0.001), thereby strongly supporting H6. This strong link underlines the significant role that social influence plays in motivating imitative actions among Generation Z customers in social commerce settings.

Facilitating circumstances showed a significant favorable impact on repurchase intention (β = 0.159, t = 3.573, p < 0.001), verifying H4. This result underlines the need for encouraging infrastructure and resources in promoting ongoing involvement with social commerce platforms among Generation Z customers. Supporting H5, fear of missing out showed a significant positive effect on repurchase intention (β = 0.165, t = 3.742, p < 0.001). This finding emphasizes the importance of concern about possible exclusion from desired consumption experiences in motivating recurrent buying habits among Generation Z consumers.

Supporting H8, return policy leniency had a significant favorable impact on repurchase intention (β = 0.151, t = 3.217, p < 0.01). This result verifies the need for flexible return terms to reduce perceived hazards connected with online buying, hence improving repurchase intentions among Generation Z customers. Return policy leniency also showed a significant positive effect on online trust (β = 0.757, t = 30.264, p < 0.001), hence stressing its function as a credibility signal improving views of vendor trustworthiness.

4.4. Mediation Effect Assessment

We used the bootstrapping technique with 5000 subsamples to evaluate the significance of indirect effects, hence testing the mediating effects suggested in H7 and H9. The findings in Table 7 show significant mediating effects for both expected links.

Supporting H7, imitating others greatly moderated the link between fear of missing out and repurchase intention (β = 0.096, t = 3.547, p < 0.001). Further study showed that this was complementary partial mediation, as both the direct impact (FoMO → REI) and the indirect effect (FoMO → IMO → REI) were significant and oriented in the same direction. This result implies that, whereas FoMO directly affects repurchase intention, it also works indirectly by causing imitative actions that later enhance repurchase intents.

Online trust also greatly mediated the link between return policy leniency and repurchase intention (β = 0.114, t = 3.982, p < 0.001), thereby supporting H9. Since both the direct impact (RPL → REI) and the indirect effect (RPL → ONT → REI) were significant and directed in the same direction, this indicates complementary partial mediation. This finding suggests that, whereas lax return policies directly increase repurchase intentions, they also work indirectly by fostering trust that later boosts repurchase intentions.

The mediating impact of imitating others in the connection between FoMO and repurchase intention accounted for 36.78% variation, suggesting partial mediation. Likewise, the VAF for the mediating influence of online trust in the connection between return policy leniency and repurchase intention was 43.02%, similarly suggesting partial mediation. These results provide detailed analysis of the processes by which FoMO and return policy leniency affect Generation Z customers’ repurchase inclinations in social commerce settings.

5. Discussion

The present study aimed to investigate the determinants of Generation Z’s repurchase intention in social commerce by integrating three theoretical frameworks: the unified theory of acceptance and use of technology (UTAUT), signaling theory, and herding behavior. The findings provide comprehensive insights into the technological, psychological, and social factors that influence Generation Z’s continued engagement with social commerce platforms. The following discussion interprets the results in light of the existing literature and explores their implications for understanding Generation Z’s repurchase behavior in the evolving social commerce landscape.

5.1. UTAUT Factors and Repurchase Intention

Our findings show that all four UTAUT variables—performance expectation, effort expectancy, social influence, and enabling conditions—significantly affect Generation Z’s repurchase intention in social commerce, hence confirming hypothesis H1 through H4. Performance expectation was shown to be a particularly significant predictor, suggesting that Generation Z customers are mostly driven by the practical advantages that social commerce platforms provide. This result is consistent with Jebarajakirthy, Yadav and Shankar [5], who showed that customers’ purchase intentions from online luxury stores are significantly influenced by perceived utility. Likewise, Mathavan, et al. [93] discovered that value-based views of digital platforms affect customers’ decision-making processes across platform environments.

Performance expectancy’s significant impact on repurchase intention underscores Generation Z’s practical attitude to technology adoption, refuting the notion that this group gives top priority to hedonic advantages over utilitarian ones. Safeer, Chen, Abrar, Kumar and Razzaq [6] point out that, even for items with significant emotional content, customer opinions and behavioral intentions are still mostly influenced by perceived functional advantages. Our results show that Generation Z’s ongoing use of social commerce platforms is significantly influenced by their views of efficiency, effectiveness, and performance improvement in their purchasing activities, hence extending this knowledge to the social commerce setting [94].

Effort expectation showed a significant favorable impact on repurchase intention, verifying that perceived ease of use is a key factor even for digitally savvy Generation Z customers. This outcome is similar to Aragon, Cabudoc, Remolin and Zamora [35]’s, who found that customer reactions to tailored websites are greatly influenced by perceived control and simplicity of interaction. The ongoing relevance of effort expectation among digital natives indicates that cognitive economics is still a universal consumer issue regardless of technology proficiency [26]. Maintaining Generation Z’s involvement will depend on social commerce platforms keeping simple user experiences and intuitive interface design as they include ever more sophisticated features [36].

Social influence became a major predictor of repurchase intention, hence stressing the strong effect of peer views and social validation in forming Generation Z’s ongoing interaction with social commerce platforms. Particularly when mediated by fear of missing out, this result matches the conclusion of Gupta and Shrivastava [18] that social influence significantly influences retail investors’ decision making. Reference groups’ impact becomes stronger in social commerce as buying activities are naturally integrated into social networking environments. According to Swoboda and Batton [2], opinions of social approbation greatly influence customer behavioral intentions; this impact changes across cultural settings and product types. Social influence’s considerable effect on repurchase intention highlights the uniquely social orientation of Generation Z customers. Unlike earlier generations, whose choices on technology adoption were mostly motivated by utilitarian reasons, Generation Z seems to give social validation great importance in their ongoing usage of digital platforms. This result shows that comparable social dynamics exist in consumption settings for Generation Z, hence extending the findings of Hashim, Janor, Sidek and Nor [57] that herding behavior greatly affects investment choices.

Facilitating conditions showed a significant beneficial impact on repurchase intention, underlining the need for supporting resources and infrastructure in promoting Generation Z’s ongoing involvement with social commerce platforms [95]. This result is consistent with the study of Reitsamer and Brunner-Sperdin [8], which showed that infrastructure components greatly affect consumer-brand interactions in place branding settings. Robust technical assistance, safe payment mechanisms, and smooth cross-device capabilities are very vital for Generation Z customers navigating complex social commerce settings if they want to remain involved with the platform.

The importance of enabling circumstances questions the belief that digital natives need less of a support infrastructure because of their technical knowledge. Rather, our results imply that Generation Z customers stay aware of the quality and availability of supporting resources when deciding to repurchase, even in complicated digital settings. This aligns with the discovery of Tajvarpour and Pujari [71] that supporting platform characteristics greatly affect transaction success in crowdfunding settings by extending it to social commerce environments.

5.2. Herding Behavior, FoMO, and Imitation

With fear of missing out greatly affecting repurchase intention both directly and indirectly, via imitative actions, our results provide compelling support for the part that herding behavior plays in Generation Z’s social commerce involvement. The notable direct influence of FoMO on repurchase intention (supporting H5) corresponds with the results of Kaur, Jain and Sood [16] that FoMO greatly affects investing choices in cryptocurrency marketplaces. This analogy implies that, across many choice domains, comparable psychological processes run where concern over the possible exclusion from precious possibilities promotes fast adoption behaviors [96].

The realization that FoMO greatly affects Generation Z’s repurchase intentions in social commerce broadens the discovery of Rahmawati and Raharja [4] that FoMO heightens irrationality in decision making as people try to be socially and economically on par with others. FoMO seems to create more buying pressure in social commerce settings, especially for Generation Z consumers negotiating identity formation processes, where trending products and consumption habits show great exposure [60].

Our findings also show that social influence is a substantial predictor of imitative behavior, hence confirming H6 and stressing the considerable importance of reference groups in forming Generation Z’s purchasing habits in social commerce settings. This strong link supports the finding of Rahmawati and Raharja [4] that investors often follow collective market activities or imitate others’ behaviors without independent information assessment. Generation Z customers seem especially under pressure to comply in social commerce settings, where consumption activities are publicly visible and socially ingrained [40,62].

The notable mediating impact of imitation in the link between FoMO and repurchase intention offers subtle insights into the psychological processes by which FoMO affects consumer behavior [60]. This result implies that, whereas FoMO increases repurchase intentions directly by means of anxiety reduction, it also works indirectly by causing imitative reactions to seen spending habits. This two-pronged approach aligns with the claim of Gupta and Shrivastava [18] that FoMO mediates the link between herding behavior and investment choices, hence proving analogous dynamics in social commerce settings.

Social influence and FoMO’s significant variation explained in imitative behaviors highlights the strong influence of social-psychological elements in forming Generation Z’s purchasing habits [68]. This result questions the purely rationalistic theories of consumer behavior and implies that, for Generation Z negotiating socially embedded commerce settings, consumption choices are significantly affected by social comparison mechanisms and anxiety about possible exclusion from valued experiences. Such psychological motivations may greatly affect decision making, as Kaur, Jain and Sood [16] point out, and they can sometimes cause consumers to give social integration top priority above personal assessment.

While our findings highlight FoMO’s effectiveness in driving repurchase intentions, important ethical considerations warrant attention. The psychological impact of leveraging anxiety-inducing mechanisms among Generation Z raises concerns about potential negative consequences, including compulsive buying behaviors, decreased psychological wellbeing, and digital fatigue [96]. Recent studies by Morsi, Sá and Silva [13] suggest that prolonged exposure to FoMO-inducing marketing tactics may contribute to heightened anxiety and diminished satisfaction with purchases. Marketers targeting Generation Z must balance business objectives with ethical responsibility, judiciously implementing FoMO-based strategies while providing mechanisms that promote mindful consumption. This ethical dimension represents an important counterbalance to the purely commercial applications of our findings, particularly given Generation Z’s documented vulnerability to social comparison and digital pressure [20].

5.3. Signaling Theory and Return Policy

With return policy leniency greatly affecting repurchase intention both directly and indirectly via improved online trust, our findings provide strong empirical evidence for the function of signaling mechanisms in Generation Z’s social commerce behavior. The notable direct impact of return policy leniency on repurchase intention (supporting H8) corresponds with the discovery of Shao, Cheng, Wan and Yue [12] that lenient return policies improve perceived quality and lower perceived risk in cross-border e-commerce settings. This analogy implies that signaling processes run consistently across various digital commerce settings; return policies act as especially relevant quality signals when physical product examination is unavailable [76].

The conclusion of Yu and Kim [81] that customers use different marketing signals to predict product quality and reduce purchase uncertainty is extended by the fact that Generation Z’s repurchase intentions are greatly affected by return policy leniency. Return policy leniency seems to work as a particularly efficient risk mitigation tool for Generation Z consumers considering recurrent purchases in social commerce settings, where vendor legitimacy may be questionable and product quality hard to evaluate pre-purchase [75].

Our findings also show that online trust is substantially predicted by return policy leniency, hence stressing its considerable influence as a credibility signal improving views of vendor trustworthiness. This strong link broadens the finding of Tajvarpour and Pujari [71] that, as geographical distance rises, quality signals considerably affect transaction probability. Return policy leniency seems to be an especially good trust-building tool for Generation Z customers in social commerce settings, where geographical and psychological distance between vendors and consumers is typically considerable [77].

The notable mediating impact of online trust in the connection between return policy leniency and repurchase intention offers a subtle understanding of the psychological processes by which signaling shapes consumer behavior [81]. This result implies that, whereas liberal return policies directly increase repurchase intentions by means of risk reduction, they also work indirectly by fostering trust, which then reinforces behavioral intentions. This dual route aligns with the observation of Kharouf, Lund, Krallman and Pullig [9] that signaling intensity significantly affects relationship outcomes by means of increased trust, hence showing analogous dynamics in social commerce settings.

Return policy leniency’s significant variation in online trust highlights the strong influence of signaling processes in forming Generation Z’s views on vendor trustworthiness. This result corresponds to Shahid, Tariq, Paul, Naqvi and Hallo [10]’s observation that companies indicate trustworthiness by means of open business practices; return policies are an especially notable example of such transparency. Explicit indicators of vendor dedication are vital for Generation Z customers negotiating information asymmetry issues in social commerce settings if they are to build trusted connections that support ongoing involvement.

5.4. Integrated Model Performance

Accounting for the 76.4% variation in the dependent variable (R² = 0.764), the integrated theoretical framework incorporating UTAUT, signaling theory, and herding behavior showed a significant explanatory power for Generation Z’s repurchase intentions in social commerce. This total explanatory power implies that the combination of technological, social-psychological, and informational points of view offers a more complete knowledge of Generation Z’s ongoing interaction with social commerce platforms than any one theoretical framework in isolation.

The model’s significant predictive relevance for repurchase intention (Q² = 0.612) further confirms its strength in accounting for Generation Z’s social commerce activity. This result implies that the model offers a strong basis for comprehending and forecasting Generation Z’s repurchase patterns in social commerce settings by accurately capturing the main processes whereby different elements affect judgment on ongoing involvement.

All predicted connections’ notable impacts, along with the integrated model’s substantial explanatory power and predictive relevance, emphasize the several characteristics of Generation Z’s repurchase decision making in social commerce settings. This complexity emphasizes the requirement of theoretical integration, including technical adoption elements, social impact mechanisms, and signaling processes, to fully grasp Generation Z’s ongoing interaction with these changing platforms.

6. Conclusions

6.1. Theoretical Contributions

Particularly with respect to Generation Z’s repurchase intentions, this study offers numerous significant theoretical additions to the knowledge of consumer behavior in social commerce settings. This paper first increases the theoretical knowledge of the intricate interaction between technological, informational, and social-psychological elements influencing ongoing engagement with social commerce platforms by including UTAUT, signaling theory, and herding behavior into a thorough framework. Although earlier studies have used these theoretical viewpoints separately, for example, UTAUT in technology adoption situations, signaling theory in e-commerce environments, and herding behavior in investment decisions, their combination offers a more nuanced knowledge of how these various elements together influence Generation Z’s repurchase intentions. Particularly for technologically native populations negotiating socially embedded commerce platforms, this theoretical synthesis answers the request for more integrated frameworks that fully reflect the multifaceted character of consumer decision making in digital contexts.

Second, this study shows UTAUT’s relevance to social commerce repurchase behavior, especially among Generation Z customers, thereby extending its reach. Although UTAUT has been widely used for first technology adoption choices, its application to ongoing engagement behaviors in socially embedded commerce settings marks a major theoretical development. Our results show that all four UTAUT variables—performance expectancy, effort expectancy, social influence, and facilitating conditions—significantly affect Generation Z’s repurchase intentions in social commerce, therefore verifying the strength of the theory in describing post-adoption behaviors in this setting. Particularly for demographically different customer categories with various technical orientations, this expansion addresses insights into the necessity to evaluate technology acceptance models in developing digital commerce forms. This study improves the generalizability of UTAUT across different technological settings and user demographics by showing its explanatory value for Generation Z’s ongoing interaction with social commerce platforms.

Third, by experimentally confirming the function of return policy leniency as a quality signal that affects repurchase intentions both directly and indirectly via increased trust, this work enhances signaling theory. Although other studies have looked at several signaling processes in e-commerce settings, the particular study of return policy leniency as a trust-building signal in social commerce settings offers a unique theoretical contribution. With its benefits working via two channels—direct risk reduction and indirect trust enhancement—our results show that return policy leniency is a particularly strong quality signal for Generation Z customers negotiating social commerce platforms. This subtle knowledge of signaling systems answers the need for more sophisticated models of how signals affect relationship outcomes in digital contexts, especially for customers with significant information asymmetry issues. This study improves the theoretical knowledge of signaling processes in socially embedded commerce settings by showing the two routes via which return policy leniency affects repurchase intentions.

6.2. Managerial Implications

From a practical standpoint, this study provides useful information for companies running in the social commerce sector, especially those targeting Generation Z customers. Our results on the notable impact of all four UTAUT variables on repurchase intention first draw attention to the need for balanced platform development methods that address both performance and usability issues. Developers of social commerce platforms should provide functional improvements that increase shopping efficiency and effectiveness as top priority, thus meeting Generation Z’s significant performance expectations. At the same time, they must have simple interface designs and efficient user experiences to reduce cognitive load as, even among tech-savvy customers, effort expectation is still a major factor in ongoing engagement. To find and fix any friction spots in the user experience, platform managers should routinely perform usability tests with Generation Z representatives, therefore guaranteeing that performance enhancements do not sacrifice simplicity of use. Furthermore, developers should put money into thorough support systems and strong technological infrastructure, as enabling circumstances greatly affect repurchase intentions within this group.

Second, our results on the strong impact of social elements on Generation Z’s repurchase behavior provide practical ideas for social commerce marketers. The notable impact of social influence on both repurchase intention and imitative behaviors underlines the need to include social proof components into marketing plans aimed at Generation Z. To increase the perceived desirability of goods and platforms, marketers could create campaigns using social validation systems, user-generated content, and peer recommendations. Features that raise the exposure of friends’ buying behaviors, such as social shopping streams and cooperative wish lists, may really make use of Generation Z’s vulnerability to social influence. Our results on the notable influence of FoMO on repurchase intention further imply that limited-time promotions, unique partnerships, and hot product spotlights might help Generation Z customers participate more actively. On the other hand, marketers have to strike a balance between these approaches and ethical issues, thus guaranteeing that FoMO-inducing techniques do not take advantage of psychological weaknesses or encourage too much consuming.

Third, our results on the notable impact of return policy flexibility on online trust and repurchase intention provide an insightful direction for policy creation in social commerce settings. The significant impact of return policy leniency on online trust implies that liberal return policies are an especially good trust-building tool for Generation Z customers negotiating social commerce information asymmetry. By reducing financial and procedural obstacles to returns, platform administrators should adopt thorough, open, and consumer-friendly return policies that reflect confidence in product quality and dedication to customer happiness. To optimize their signaling efficacy, these regulations should be clearly visible throughout the entire purchasing experience, especially during the consideration and checkout stages when risk perceptions are most apparent. Platforms could also put money into simplified return processing systems that save customer effort, as procedural simplicity greatly improves the trust-building power of return policies.

Fourth, our results on the significant explanatory power of the integrated model imply that comprehensive strategies simultaneously tackling technological, social, and informational elements will be most effective in promoting Generation Z’s ongoing involvement with social commerce platforms. Platform administrators should create integrated strategies simultaneously addressing all three aspects instead of concentrating just on technology improvements, social influence mechanisms, or trust-building signals. This might include clear signaling systems that foster trust via clear quality assurances and using technologically sophisticated features that both improve functional performance and support social engagement. Social commerce platforms may build virtuous cycles of engagement where technology delight, social validation, and trust reinforcement together drive continuous repurchase behavior by addressing Generation Z’s varied motives thoroughly. Moreover, the notable mediating impacts found in our study underline the need to think about indirect routes while developing intervention plans, since variables like FoMO and return policy leniency concurrently affect repurchase intentions via several mechanisms.

6.3. Limitations and Future Research Directions

Although it makes important contributions, this study has many shortcomings that provide prospects for further research. Our data’s cross-sectional characteristics preclude causal inferences and longitudinal analysis of how Generation Z’s repurchase behaviors change over time, implying future research should use longitudinal designs to capture dynamic patterns in ongoing interactions with social commerce platforms. The emphasis on Generation Z alone restricts generalizability to other demographic groups, hence suggesting the necessity for comparative research investigating how UTAUT elements, signaling mechanisms, and herding behaviors differently affect repurchase intentions across generational groups. Though statistical protections are in place, our dependence on self-reported measures might create common method bias, hence stressing possibilities for future study using behavioral data from real purchase records.

Our exclusive focus on Vietnamese Generation Z consumers represents a significant limitation of generalizability. Vietnam’s collectivist cultural orientation may intensify social influence and imitative behaviors compared with more individualistic societies. Additionally, cultural differences in uncertainty avoidance could affect the impact of return policy leniency as a trust signal, while power distance variations might influence the acceptance of social validation mechanisms. Future research should prioritize cross-cultural validation through multi-country samples that systematically control for cultural dimensions. Comparative studies across Eastern collectivist and Western individualist contexts would be particularly valuable in determining which elements of our integrated model demonstrate cultural invariance versus culture-specific effects. This extension would substantially enhance the global applicability of our findings beyond the current culturally specific context.

Although demographic data on platform preferences were collected, this study did not explore behavioral differences across specific social commerce environments (Instagram Shopping, Facebook Marketplace, TikTok Shop, etc.). Each platform offers distinctive features—Instagram’s visual emphasis, TikTok’s short-form video immersion, Facebook’s established social networks—that may uniquely influence the relationships examined. Future research could adopt a comparative approach investigating how platform-specific attributes moderate the effects of UTAUT factors, signaling mechanisms, and social-psychological elements on repurchase intentions. Such analysis could reveal whether certain platforms amplify specific constructs (e.g., whether TikTok’s algorithm intensifies FoMO or Instagram’s visual focus strengthens return policy signaling effects), thereby providing more contextually nuanced guidance for platform-specific marketing strategies.