Peer Dynamics in Digital Marketing: How Product Type Shapes the Path to Purchase Among Gen Z Consumers

Abstract

This study addresses the growing importance of peer influence in digital marketing by examining how various forms of social interaction affect Generation Z’s purchase intentions, particularly in relation to newly launched technological products. As digital natives, Gen Z consumers navigate purchasing decisions within complex online environments shaped by social networks, peer dynamics, and evolving product categories. Understanding how these elements interact offers valuable insights for both marketers and consumer researchers seeking to engage this influential demographic. Grounded in Social Capital Theory and Relationship Marketing Theory, the study investigates five key social influence factors—social capital bonding, social capital bridging, electronic word of mouth (e-WOM), perceived social pressure, and friend-of-a-friend effects. A quantitative, cross-sectional design was applied, with data gathered from 302 Gen Z participants through printed questionnaires. The analysis showed that all variables were significantly related to purchase intention, with bonding social capital and indirect peer influence emerging as the most impactful. E-WOM demonstrated a moderate effect, while perceived social pressure had a weaker influence and bridging social capital was not a significant predictor. The results also revealed that product type influences the strength of these relationships, with peer influence being strongest for low-involvement products and weakest for luxury items. These findings suggest that both social context and product characteristics should be carefully considered in the design of effective digital marketing strategies.

1. Introduction

In recent years, the marketing landscape has undergone a dramatic transformation due to the proliferation of social media platforms, user-generated content, and the growing interconnectedness of digital communities (Punjabi et al., 2024). Digital marketing has therefore emerged as a fundamental driver of consumer engagement, reshaping how individuals discover, evaluate, and adopt products. Prior research highlights that younger generations, and particularly Generation Z, are central to this shift, as they consume and create content in ways that differ from earlier cohorts (Dragolea et al., 2023; Silva et al., 2017). Studies have examined peer recommendations, electronic word of mouth (e-WOM), and influencer content as mechanisms that strongly affect awareness, evaluation, and purchase decisions (Theocharis et al., 2025). However, the literature has not yet sufficiently clarified how these forms of influence vary depending on the characteristics of the product or the symbolic meanings attached to it. At the center of this evolution is Generation Z, digitally native consumers born between the mid-1990s and early 2010s, who exhibit distinct behavioral patterns, communication preferences, and purchasing motivations compared to previous generations (Dragolea et al., 2023). As Gen Z increasingly engages with brands and products through social platforms, peer dynamics have emerged as a powerful determinant of their purchasing decisions (Silva et al., 2017). Unlike traditional advertising, peer recommendations, online reviews, and influencer content now shape both the awareness and evaluation stages of the consumer journey (Theocharis et al., 2025). This trend is particularly pronounced in the context of newly launched technological products, where Gen Z consumers rely heavily on social cues to reduce uncertainty, validate choices, and stay aligned with social norms. Social capital, whether derived from close friends, acquaintances, or online communities, plays a critical role in constructing trust and signaling value (Kacperska et al., 2024). The immediacy and visibility of digital interactions heighten the influence of peer behavior, making social and peer dynamics a vital aspect of contemporary marketing strategies. Simultaneously, electronic word of mouth (e-WOM) has become a central mechanism through which Gen Z consumers share experiences and discover products, especially in categories where innovation and trendiness are key (Masakazu et al., 2025). Despite this, a clear research gap remains. While existing studies confirm the strong influence of peers and digital engagement on consumer behavior, relatively little attention has been devoted to how the nature of the product itselfinteracts with these social processes. Prior research often treats social influence as a universal mechanism, assuming that peer effects operate similarly across all consumption contexts. Yet, consumer decision-making is not uniform: the level of product involvement (low versus high) and symbolic attributes (e.g., luxury status, exclusivity, or prestige) may fundamentally alter how peer influence is perceived and acted upon. For instance, peer validation may play a decisive role in high-risk or status-driven purchases, such as luxury fashion or advanced technology, while routine or low-involvement purchases may rely more on convenience and familiarity than on social endorsement. This research gap is particularly salient in fast-changing technological markets, where new product launches are frequent, uncertainty about performance is high, and symbolic value often shapes consumer perceptions. In such settings, peer dynamics and product type may intersect in unique ways, amplifying or constraining the influence of social cues. Despite the growing importance of these dynamics, few empirical studies have systematically examined how different product categories or levels of involvement moderate peer influence among Gen Z consumers. Based on this contextualization, the present study sets out the following objectives:

• Research Question 1: To what extent do social and peer influences affect the purchase intention of Generation Z in the context of newly launched technological products?
• Research Question 2: How does the product type (low/high involvement, luxury) moderate the relationship between social and peer influences and the purchase intention of Generation Z?

The contributions of this research are twofold. From a theoretical perspective, it enriches the literature on digital consumer behavior by integrating product characteristics into models of social influence and purchase intention. From a practical perspective, it provides actionable insights for marketers and brand managers on how to design peer-driven campaigns that align with both the social fabric of Gen Z and the type of product being promoted.

2. Literature Review

2.1. Generation Z’s Consumer Behavior and the Impact of Social Environment

Generation Z represents a transformative demographic segment that is reshaping the landscape of consumer behavior (Salam et al., 2024). As digital natives, Gen Z individuals have been raised in a world dominated by instant connectivity, mobile technologies, and real-time information exchange (Kara & Min, 2024). These conditions have not only influenced their attitudes toward consumption but have also redefined the social frameworks within which their behaviors are developed and expressed. Among the most critical forces shaping Gen Z’s consumer decision-making process is the social environment, an expansive and fluid network of peers, communities, digital platforms, and cultural norms that operate as both information sources and behavioral regulators (Ding & Jiang, 2023). To synthesize the literature, prior studies converge on the idea that Gen Z’s purchasing intentions cannot be understood in isolation but are embedded in a broader social ecosystem. Several constructs consistently emerge across the literature as central to understanding Gen Z’s behavior: social capital bonding, social capital bridging, perceived social pressure, electronic word of mouth (e-WOM), and indirect peer effects. These constructs provide the theoretical and empirical foundation of this study and are further developed in the following subsections. By systematically connecting these constructs to established theories, we clarify how they interrelate and why they form the basis for our hypotheses. One of the most defining characteristics of Gen Z consumers is their socially embedded decision-making (Bowo & Marthalia, 2024). Unlike previous generations who relied heavily on personal experience, advertising, or expert opinion, Gen Z places substantial value on the opinions, behaviors, and expectations of their social networks (Harari et al., 2023). This generation views consumption not solely as a means of fulfilling practical needs, but as a mode of self-expression and social signaling (Theocharis & Tsekouropoulos, 2025). As such, purchase decisions often reflect a desire for social inclusion, identity affirmation, or alignment with perceived peer values. Central to this dynamic is the influence of peer groups, which serve as key referents in the decision-making process (Djafarova & Foots, 2022). Friends, classmates, online followers, and even acquaintances within digital communities contribute to a continuous stream of feedback, recommendations, and subtle pressures that inform how Gen Z perceives products, brands, and trends (Kahawandala & Peter, 2020). In many cases, these influences are internalized as normative expectations, leading individuals to favor choices that conform with the preferences and behaviors of their immediate or extended social circle (Jacobsen & Barnes, 2020). This mechanism is particularly pronounced in contexts involving visible or culturally symbolic products, such as technology, fashion, or lifestyle goods.

The social environment for Gen Z is inherently digital (Angmo & Mahajan, 2024). Platforms like Instagram, TikTok, Snapchat, and YouTube function not only as entertainment outlets but as powerful ecosystems of influence where consumer identities are shaped and negotiated. These platforms foster communities of interest where like-minded individuals exchange information, experiences, and evaluations (Dabija et al., 2019). Importantly, these interactions are not passive; Gen Z users are both consumers and content creators, generating reviews, tutorials, unboxing videos, and visual narratives that others use as reference points (Tata et al., 2023). As a result, the boundary between personal opinion and public influence becomes increasingly blurred, elevating the significance of peer-generated content in shaping consumer attitudes. In this context, electronic word of mouth (e-WOM) emerges as a crucial force (Masakazu et al., 2025). Gen Z is particularly responsive to e-WOM because it is perceived as authentic, relatable, and credible (Natalia & Aprillia, 2025). Unlike traditional advertisements, peer reviews and shared experiences carry emotional weight and a sense of trustworthiness that resonate with this generation’s desire for transparency and realness (Kurnaz & Duman, 2021). Positive e-WOM can rapidly increase interest in a product, while negative e-WOM can damage a brand’s reputation, often irreversibly in a short time span (Feitosa & Barbosa, 2020). This heightened sensitivity to peer opinion underscores the socially mediated nature of Gen Z’s consumer decisions.

Beyond direct peer influence, indirect social effects also play a significant role. Concepts like “friend of a friend” illustrate how even weak social ties—people with whom one has limited direct interaction—can influence attitudes and behavior through shared content, mutual likes, or algorithm-driven exposure on social platforms (Kushwaha, 2021). These indirect relationships contribute to what is referred to in theory as social capital bridging, enabling individuals to access diverse opinions and product information that might not circulate within their immediate peer group (Elkhwesky et al., 2024). This form of influence is particularly effective in introducing Gen Z consumers to new or niche products that are gaining momentum in specific online communities (Francis & Hoefel, 2018). The impact of social norms and pressures further extends to perceived social pressure, where individuals feel compelled to act in ways that align with the expectations of their network. For Gen Z, this can manifest in trends such as “fear of missing out” (FOMO), which drives them to engage with products, experiences, or content that are gaining popularity among peers (Deliana et al., 2024). This phenomenon can intensify the urgency to adopt newly launched products, attend particular events, or affiliate with certain brands, not out of personal preference, but out of a perceived need to stay socially relevant (Serravalle et al., 2022).

Additionally, Gen Z tends to view their consumer identity as a reflection of personal and collective values, such as sustainability, inclusivity, and social responsibility (Pradhan et al., 2023). These values are often reinforced through their social environment, as peers reward brands and behaviors that align with ethical or progressive ideals (Dabija et al., 2019). In turn, brands that are perceived as socially tone-deaf or inauthentic can face swift backlash, amplified through rapid peer-to-peer communication and public discourse on social media (Gomes et al., 2023). In this way, the social environment not only informs preferences but also sets behavioral standards for what is considered acceptable or desirable in consumption. Moreover, the interactivity of Gen Z’s social environment means that the influence process is rarely one-directional (Ghosh et al., 2024). Peer influence is both given and received, and Gen Z individuals often take on the role of micro-influencers within their own networks (Kahawandala & Peter, 2020). Their ability to shape the opinions of others, even within a small circle, reinforces a sense of agency and participatory ownership in brand narratives and product communities. This bidirectional influence loop creates a highly dynamic environment in which behaviors and attitudes evolve in response to ongoing social interaction (Manley et al., 2023). In conclusion, Gen Z’s consumer behavior is deeply embedded within a networked social context where peers, digital communities, and cultural norms collectively shape the ways in which products are evaluated, adopted, and shared. The impact of the social environment is profound, influencing not only the formation of attitudes and intentions but also the emotional and symbolic meaning that consumers attach to their consumption choices (Angmo & Mahajan, 2024). For marketers and researchers, this underscores the importance of understanding Gen Z not just as individual decision-makers but as socially situated actors, whose behaviors are inseparable from the dynamic and collaborative environments they inhabit.

2.2. Newly Launched Technological Products and Gen Z

The emergence of newly launched technological products, such as smartphones, wearables, AI-driven devices, and smart home solutions, has transformed the landscape of consumer behavior, particularly among Generation Z. This generational cohort represents a digitally native population that has grown up immersed in technology, mobile connectivity, and real-time digital interaction (Kim et al., 2022). As such, their consumption habits, information-seeking behavior, and decision-making processes differ significantly from those of previous generations (Theocharis et al., 2025). When it comes to the adoption of new technologies, peer influence and the surrounding social environment play pivotal roles in shaping Gen Z’s attitudes, perceptions, and ultimately their purchasing behavior (Priporas et al., 2017). Gen Z consumers display a distinctive relationship with technology (Jaciow & Wolny, 2021). For them, newly released products are not only tools for functionality but also vehicles for social expression, identity building, and group affiliation (Francis & Hoefel, 2018). The adoption of technological innovations often goes beyond utilitarian motives; it is frequently tied to their need for connectedness, digital fluency, and visibility within their peer networks. As digital natives, Gen Z is not merely passive in their interactions with technology, they actively co-create brand meaning and product value through participation in online communities, content creation, and peer dialogue (Lee, 2021). What distinguishes Gen Z’s engagement with newly launched products is the immediacy of their responses and their reliance on peer validation. Early adoption is often influenced not just by product features, but by what is seen and shared in social networks (Cheung et al., 2021). Unboxing videos, peer reviews, influencer content, and social media trends serve as key decision-making tools (Thangavel et al., 2022). The speed with which this generation processes and reacts to new releases also increases the pressure on brands to continuously innovate and maintain cultural relevance. A product’s “newness” often serves as a social signal, indicating tech-savviness, trend awareness, and group belonging (Dabija & Lung, 2018).

Moreover, Gen Z places a high value on authenticity and personalization in technological products (Lamba & Malik, 2022). They are more likely to adopt new technology if it aligns with their personal identity, values, or lifestyle (Jaciow & Wolny, 2021). For example, a wearable fitness tracker may appeal not just for its functionality but because it signals a health-conscious identity within a social group. Similarly, a smartphone brand may be chosen not solely for technical specs, but because of the narrative it conveys through branding and peer usage. At the same time, digital literacy allows Gen Z to critically assess technological innovations (Puiu et al., 2022). They actively research, compare, and evaluate new products, while they often turning to peer-generated content rather than traditional advertising (Dadvari & Do, 2019). This behavior amplifies the role of peer dynamics, as shared experiences, reviews, and recommendations carry more weight when they come from trusted individuals within one’s social circle. The credibility of information is therefore deeply rooted in perceived social proximity rather than brand authority. In essence, newly launched technological products function as both functional tools and social artifacts for Gen Z. Their value is shaped not only by what the product does but by how it is presented, shared, and talked about within digital communities. Peer dynamics play a fundamental role in this process, as adoption becomes a socially negotiated act that reflects and reinforces one’s place within a digitally connected generation.

2.3. Theoretical Background Development and Variable Selection

The study of consumer behavior, particularly among Generation Z and in relation to newly launched technological products, necessitates a thorough understanding of the social, psychological, and marketing mechanisms that influence purchasing intentions. Unlike descriptive accounts, this section provides a structured theoretical synthesis by linking each construct to established frameworks, thereby reinforcing the rationale for our hypotheses. Drawing from established theoretical frameworks—namely Social Capital Theory, Relationship Marketing Theory, the Theory of Reasoned Action, and the Theory of Planned Behavior, the conceptual foundation of the research is built to explain the mechanisms through which social and peer dynamics influence Generation Z’s purchase intentions. These frameworks offer a multidimensional perspective that connects individual attitudes, perceived social norms, relational networks, and behavioral control to consumer decision-making. This synthesis highlights three central mechanisms: (a) social capital bonding and bridging as the relational basis of influence, (b) perceived social pressure and e-WOM as communication and normative channels, and (c) purchase intention as the behavioral outcome shaped by these inputs. Integrating these theories allows us to move beyond fragmented findings and present a more coherent explanatory framework.

Social Capital Theory is rooted in the idea that social networks function as vital resources, facilitating access to information, trust, support, and opportunities through interpersonal and group-based interactions (Kasim et al., 2022; H. Zhang et al., 2020). Within this perspective, individuals are not isolated decision-makers but socially embedded actors whose behaviors are shaped by the structures and relationships they participate in. This theoretical lens is particularly relevant in digital environments, where interaction and visibility are integral to consumption behavior. Social capital manifests primarily through two forms (bonding and bridging) each playing a complementary role in shaping consumer perceptions and behavioral intentions. Bonding social capital involves strong, emotionally charged ties among close connections such as family or intimate peers (Ahmad et al., 2023a; Hoda et al., 2023). These relationships foster trust and shared values, reinforcing group norms and encouraging behaviors such as brand loyalty and repeated purchases. For Generation Z consumers, the influence of close peer networks is significant. When new technological products are endorsed within these circles, the likelihood of adoption increases due to emotional alignment and social belonging (Pang et al., 2021; Radaelli et al., 2024).

Bridging social capital, on the other hand, refers to weaker, more diverse ties that span social, demographic, or interest-based boundaries (Dhar et al., 2024; Perry et al., 2022). These connections enable access to new ideas, perspectives, and innovations. In digital consumer spaces, this form of capital is often activated through interactions with influencers, brand communities, or acquaintances on social media. It fosters discovery and curiosity, particularly relevant to the adoption of novel technological products that thrive on early exposure and trend diffusion (Kalra et al., 2021; Yuan et al., 2021). The co-existence of bonding and bridging ties generates a dynamic consumer ecosystem where trust and familiarity intersect with novelty and expansion. Consumers, especially from Gen Z, navigate between intimate peer influence and broader online discourse, leveraging both emotional support and informational diversity to shape their decisions (Degli Antoni & Grimalda, 2024). Participation in digital brand communities is one manifestation of this dual social structure. Users engage with peer-generated content, shared identity, and interactive discussions not only to gather information but also to affirm their place within the group (Munawar & Siddiqui, 2020; Jeong et al., 2021). Activities such as writing reviews, posting branded content, or commenting on product experiences become expressions of social capital, driven by mutual trust and relational investment (Li et al., 2024). This behavior frequently takes the form of electronic word of mouth (e-WOM), an informal communication process that carries substantial weight in digital decision-making environments (Levy et al., 2024). Building on the foundation of social capital, perceived social pressure emerges as a more individualized mechanism through which social expectations influence behavior. It refers to the internalized sense of what others expect and accept, shaping one’s choices within their immediate or extended network (Simons et al., 2021; Dhar & Bose, 2023). For Generation Z, who are highly active in social media and attuned to peer dynamics, this influence is especially salient. The pressure to adopt popular brands, stay up to date with new tech, or conform to group standards is often magnified by online exposure and algorithm-driven visibility (Gani et al., 2024; Salazar et al., 2013; Luo et al., 2020). The phenomenon becomes evident when individuals purchase products, such as new smartphone models or wearable devices, not solely for their functionality but to align with perceived group norms or avoid social exclusion (Hu et al., 2019).

The Theory of Planned Behavior conceptualizes perceived social pressure—termed subjective norms—as a core predictor of behavioral intentions (Bastian et al., 2017). Reference groups including peers, family, and digital communities influence individual behavior, particularly when those groups are perceived as trustworthy or influential (Khare, 2023). This pressure arises from both interpersonal sources (e.g., direct peer recommendations) and external sources (e.g., media endorsements, influencer opinions) (Wolske et al., 2020). Contextual strength also plays a role. In clearly defined situations—such as the anticipation of peer scrutiny—individuals are more likely to conform. In more ambiguous situations, social pressure may act more subtly, but still guides behavior in identity-related consumption contexts (Exline et al., 2012; Singh et al., 2020). For Generation Z, navigating this balance often leads to purchases influenced by group alignment rather than personal need alone (Yang et al., 2021). Electronic word of mouth adds another layer to this dynamic, functioning as both a communication mechanism and a relational expression. It involves user-generated, informal content such as reviews, testimonials, or posts about products and services shared on social platforms or review sites (Chatzipanagiotou et al., 2023; Kusawat & Teerakapibal, 2024). This communication is not constrained by geography or time, making it especially powerful for Gen Z consumers who actively engage with online content before making purchase decisions (Bu et al., 2021; Sharma et al., 2024). e-WOM draws strength from both bonding and bridging social capital. Trusted peers enhance group cohesion and loyalty, while broader networks foster awareness and trial behavior for lesser-known products or emerging brands (Ahmad et al., 2023b; Yuan et al., 2021).

Relationship Marketing Theory complements this view by interpreting e-WOM as a channel through which relational trust and brand commitment are built over time (Le & Ryu, 2023). Positive experiences shared online not only strengthen consumer–brand bonds but also influence wider audience perceptions. The credibility and tone of user reviews, along with their volume and perceived usefulness, are crucial determinants of consumer trust and decision-making (Akoglu & Ozbek, 2024; Hanks et al., 2024; D’Acunto et al., 2023). For Generation Z, the act of engaging with such content is not only informational but performative, reinforcing their identity within digital communities and shaping their social presence (Leong et al., 2022; Ngo et al., 2024). The notion of the “friend of a friend” offers a compelling extension of this logic. While lacking direct interpersonal ties, these connections often exert meaningful influence through perceived credibility and shared network context (Jackson & Rogers, 2007; Lin et al., 2023). This type of indirect influence aligns with bridging social capital and is particularly influential in e-WOM settings, where information shared by a mutual acquaintance carries more weight than an anonymous source (Tajvidi et al., 2020). Such dynamics are further amplified on platforms that encourage visible interaction (likes, comments, shares) enabling peer influence to travel across extended networks in what has been termed “social ripple effects” (Goodreau et al., 2009). This facilitates viral dissemination of product endorsements and brand experiences, often leading to behavioral alignment even without direct contact between original sender and final receiver (Montgomery et al., 2020; Rossini et al., 2021). Brands that successfully activate these extended ties can dramatically increase their reach, credibility, and impact within Generation Z audiences (Aichner et al., 2021; Muliadi et al., 2024).

To understand the underlying decision-making process, psychological models such as the Theory of Reasoned Action provide further depth. According to this theory, behavior is driven by intention, which in turn is shaped by both individual attitudes and perceived social norms (Ajzen & Kruglanski, 2019; Mital et al., 2018). In the case of Gen Z consumers, favorable attitudes toward a product combined with strong peer endorsement increase the likelihood of purchase. The Theory of Planned Behavior expands on this by adding perceived behavioral control, whether the individual believes they can perform the behavior given potential constraints (Ajzen, 1991; Ajzen & Schmidt, 2020). These models highlight that even when social and personal factors align, actual behavior is moderated by access to resources, situational confidence, and capability. In light of these frameworks, purchase intention emerges as the key outcome variable—shaped by social capital, perceived social pressure, and the dynamics of e-WOM and indirect peer influence. These interconnected factors provide a comprehensive explanation of Generation Z’s consumption patterns in a digitally mediated environment, particularly when it comes to the adoption of newly introduced technological products. Rather than being isolated drivers, each component contributes to a multifaceted ecosystem where decision-making is continuously shaped by social interaction, relational cues, and psychological expectations. The relationship between these variables and their theoretical underpinnings is summarized in the following

Table 1. Theoretical connection of variables with consumer behavior.

Variable	Theories or Models of Consumer Behavior
Social capital bonding	Social Capital Theory
Social capital bridging	Social Capital Theory
Perceived social pressure	Social Capital Theory
E-WOM	Social Capital Theory/Relationship Marketing Theory
Friend of a friend	Relationship Marketing Theory
Purchase intention	Theory of Planned Behavior/Theory of Reasoned Action

.

The integration of these variables creates a robust model for examining the behavior of Gen Z consumers in the context of newly introduced technological products. The theoretical background provides clear justification for exploring the role of both direct and indirect social influences, marketing relationships, and psychological drivers in shaping consumer intention.

2.4. Product Type as a Moderator in the Relationship Between Social and Peer Influences and Purchase Intention

Consumers make countless decisions on a daily basis, which has sparked interest in studying consumer behavior, the motivations behind certain choices, and the factors that influence them—as is the case in this dissertation. In general, it has been found that the level of involvement mentally triggered before the consumption of different products or brands varies, and as a result, products have been classified into two categories: high-involvement and low-involvement products (Abdel Wahab et al., 2023). Low-involvement products refer to items purchased more frequently, with little effort, and often impulsively, while high-involvement products require more time investment and information search prior to selection (Liu & Yu, 2024). To clarify, examples of low-involvement products include food items, whereas laptops can be considered high-involvement products (Wang et al., 2023). Nevertheless, each generation, with its unique characteristics, may redefine such classifications. According to Y. Zhang et al. (2024), purchases of low-involvement products are often based on heuristic choices or mental shortcuts such as familiarity, recommendations, or optimal pricing. In contrast, high-involvement products are often purchased to support an individual’s image or self-perception (Lim et al., 2023). R. Kumar et al. (2023) viewed involvement as a goal-directed motivation that indicates how personally relevant a product is to the consumer, based on how well it reflects their self-concept (Palla et al., 2023). However, the level of consumer involvement in a particular product category or brand may vary, as the drivers of involvement differ (Stokburger-Sauer et al., 2012).

Regarding the categorization of products based on involvement, there is disagreement about which product categories are most capable of engaging consumers (Tassiello et al., 2021). Specifically, most researchers have argued that it is easier to engage consumers with high-involvement products, as consumers tend to connect with and form emotional attachments to these products to a greater extent (H. Kumar & Srivastava, 2022). However, Gong et al. (2023) contend that a high level of involvement is not limited exclusively to high-involvement products. As Y. Zhang et al. (2024) conclude, low-involvement products are rarely explored and are merely suggested for future research, resulting in a lack of knowledge about their potential to generate consumer involvement. The level of involvement, in general, is influenced by three categories of factors: personal, situational, and physical (Palla et al., 2023). First, the personal factor refers to the consumer’s inherent value system and needs that motivate them to purchase a product (Wang et al., 2023). The situational factor relates to circumstances that temporarily increase the importance and interest in a product. Lastly, the physical factor refers to differences in the product’s characteristics that stimulate interest (Loureiro et al., 2023). Involvement can affect consumer behavior and is linked to personal relevance, interest, and the consumer’s level of motivation (Lim et al., 2023). Therefore, it can be said that product involvement is a complex concept positioned between consumers and their behavior, and it can influence the purchasing decision-making process (Xu et al., 2023).

A key category for technological products, particularly due to the high price of some of them, is their classification as luxury goods. The fundamental idea behind this approach is that for a product or brand to be classified as luxury, it must be associated with certain characteristics such as high quality, high price, superior performance, and authenticity (Chan & Northey, 2021). Shankar and Jain (2021) link luxury with selective or limited distribution, brand image association, and extreme evaluations of quality and price. This definition aligns with that of Jain (2022), who states that luxury refers to products that meet the highest standards within a product category. An alternative approach is offered by Aycock et al. (2023), who consider luxury as part of a continuum in which consumers determine where the ordinary ends and where prestige begins. Products are perceived as prestigious because they possess certain features, many of which are linked to luxury (F. Yu & Zheng, 2022). Prestige products must offer ultimate quality and value, be distributed in a limited way, and be available only to consumers willing to pay a high price (Fazeli et al., 2020). Jain (2021) argues that luxury brands are associated with high price, quality, esthetics, rarity, and specialization. Additionally, Aprillia et al. (2019) note that the foundation of luxury consumption lies in innovation and culture, alongside quality and high price. According to Guzzetti et al. (2021), luxury brands are most commonly identified by features such as high quality, price, rarity, prestige, and authenticity, while also incorporating non-necessity and the provision of symbolic and emotional value. Finally, price plays a decisive role in the definition of luxury (S. Yu et al., 2018). For example, a high price is often perceived as inherently linked to luxury, with this perception based either on the product’s absolute value or in comparison to other luxury or non-luxury products (Xu et al., 2023). Luxury products or services are typically expensive, both in absolute and relative terms (Aycock et al., 2023).

2.5. Hypotheses Development

Based on the above theoretical synthesis, we explicitly formulate the study’s hypotheses. The first hypothesis is grounded in Social Capital Theory, Relationship Marketing Theory, and the Theory of Planned Behavior, which collectively predict that peer dynamics and social mechanisms will directly affect Gen Z’s purchasing intentions.

H1. 

Social and peer influences positively affect the purchase intention of Gen Z regarding newly launched technological products.

The second hypothesis builds on product involvement and luxury consumption literature, suggesting that product type may alter the strength of social influence on purchasing behavior.

H2. 

Product type moderates the relationship between social and peer influences and purchase intention of Gen Z regarding newly launched technological products.

While H1 establishes the fundamental relationship between social and peer influences and purchase intention, it is equally important to recognize that this relationship may not be uniform across all product types. This consideration is addressed through H2. Specifically, product type may significantly alter the strength or direction of social influence on Gen Z’s intention to purchase. For instance, the influence of peers may be more pronounced when it comes to low or high involvement products (Ajzen, 2020; N. Kumar et al., 2022). By testing the moderating role of product type, H2 seeks to refine our understanding of how contextual factors shape the social dynamics of consumer decision-making and offers a more nuanced application of the theoretical model presented in Figure 1. Together, H1 and H2 provide a theoretically grounded model that clarifies how social and peer dynamics operate as antecedents of purchase intention, while product type functions as a contextual moderator. This structuring reinforces the literature review by moving from synthesis to testable hypotheses, thereby addressing both theoretical reasoning and empirical focus.

3. Materials and Methods

This research employs a quantitative, cross-sectional design to investigate how peer-related influences affect the online purchasing behavior of Generation Z, particularly in the context of newly launched technological products. By drawing on established consumer behavior theories, the research examines how various social dynamics, such as close-knit relationships, electronic word of mouth, and indirect peer ties, shape purchase intentions, while also considering how these relationships are moderated by a contextual factor like the product type (low-high involvement, luxury). The quantitative approach is well-suited for analyzing measurable constructs and detecting interaction effects, offering a structured framework to explore the interplay between peer influence, purchase intention and product type (Zangirolami-Raimundo et al., 2018). A cross-sectional methodology provides a snapshot of Gen Z’s digital buying patterns at a specific point in time, an approach that is especially relevant given this generation’s fast-changing relationship with digital platforms. This design allows for simultaneous examination of multiple variables and their interrelations, making it an effective method for capturing the complex, socially embedded nature of online consumer decisions in a digitally connected marketplace. The survey method was selected because it allows for the systematic collection of standardized data across a large number of respondents, enabling statistical generalization and comparability of responses across constructs.

3.1. Research Sample and Sampling Method

The study focused on Generation Z individuals born between 1997 and 2012, with participation limited to those aged 18 and above to ensure ethical compliance and informed consent. This age group represents a highly digitally engaged and commercially influential segment of Gen Z. A total of 302 participants were included in the final sample, surpassing the minimum sample size estimated via G*Power (Version 3.1.9.6) analysis, which suggested that 160–180 respondents would be sufficient to detect meaningful effects with strong statistical reliability. A hybrid sampling approach was used, combining the practicality of convenience sampling with a degree of systematic structure. Participants were primarily recruited in university environments, where Gen Z is readily accessible. To enhance representativeness, the recruitment sought diversity in gender, age, and income levels, and responses were monitored to maintain balance across these categories. While university students dominate the sample, the distribution of socioeconomic indicators suggests a reasonable approximation of the wider Gen Z population in this context. Data collection was conducted through face-to-face engagement and paper-based surveys, particularly in academic settings, allowing for immediate clarification of questions and a high response rate. During the pre-test stage, 30 participants were screened by verifying their age, digital purchasing experience, and familiarity with online shopping platforms. Their feedback was used to refine the clarity and sequence of items, ensuring that the final instrument was both comprehensible and contextually appropriate. The final sample was demographically diverse: 47.7% male and 52.3% female, with most respondents being university students (85.4%). Participants ranged from 18 to 27 years old, averaging 20.5 years. Income distribution was varied, offering balanced socioeconomic representation, with a slight concentration in the middle and higher income tiers.

3.2. Data Collection Tool Development

The research utilized a structured questionnaire to examine various aspects of Generation Z’s consumer behavior concerning the online purchase of newly launched technological products. The instrument comprised three distinct sections that covered demographics, peer influences and purchase intention. Its design was grounded in established, validated measurement scales sourced from existing literature on consumer behavior, digital marketing, and technology adoption. Wherever possible, these scales were either directly incorporated or slightly modified to align with the specific context of the study, while additional self-constructed items were introduced to address areas not sufficiently represented in previous research. The questionnaire development drew on insights from four influential theories and models, including the Theory of Planned Behavior, Social Capital Theory, Relationship Marketing Theory and the Theory of Reasoned Action. A pilot study was conducted prior to full deployment to refine the instrument based on participant feedback, which led to improved clarity, item phrasing, and logical sequencing. The final version employed both five-point and seven-point Likert scales, tailored to the nature of each variable, and anchored with clear descriptors from low to high agreement or relevance. Unlike the Likert-based constructs, the variable “product type” was measured directly as a categorical variable with three distinct categories: low-involvement products, high-involvement products, and luxury products. This categorization was informed by prior literature on product involvement and luxury consumption (e.g., Abdel Wahab et al., 2023; Chan & Northey, 2021), ensuring both theoretical grounding and practical interpretability. Themes assessed by the instrument included digital trust, online experience, perceived value, brand loyalty, innovativeness, peer influence, post-purchase engagement, advertising creativity, and website quality. For transparency and methodological rigor, the tool clearly categorizes measurement items into three groups: scales adopted as-is, adapted from existing research, and those self-developed specifically for this study. This combined approach ensured content relevance, contextual sensitivity, and robustness for in-depth statistical analysis. A detailed summary of constructs, sources, and classification types (adopted, adapted, or self-developed) is presented in

Table 2. Scales used at the current study.

Variable	Source
Perceived social pressure	(Järveläinen, 2007)
Friend of a friend	Own development based on the adaptation of Goyette et al. (2010) and Filieri (2015)
E-WOM	Adapted from Goyette et al. (2010) and Filieri (2015)
Social capital bonding	Appel et al. (2016)
Social capital bridging	Appel et al. (2016)
Purchase intention	Juniwati (2014) and Hutter et al. (2013)

.

3.3. Research Process and Ethics

The research unfolded over a six-month period and followed a structured, step-by-step methodology. It began with the formulation of a theoretical framework and research questions, followed by the development and pilot testing of the questionnaire. Revisions were made based on participant feedback to improve clarity and ensure cultural sensitivity. Data collection was carried out using printed questionnaires, primarily distributed in university settings where Generation Z individuals could be easily reached. The researcher was present during distribution to explain the process, offer clarifications, and ensure that participants provided informed consent. Ethical considerations were carefully embedded throughout every stage of the study. In line with the principles of the Declaration of Helsinki, participants were fully briefed on the study’s aims, procedures, and their rights. They were assured of complete anonymity and the confidentiality of their responses. No personal identifiers were collected, and participants were informed of their right to withdraw at any time without penalty. All data were securely stored and used solely for academic purposes, ensuring compliance with international standards for research involving human subjects. Following data collection, responses were reviewed for completeness and consistency. The dataset was validated through checks for outliers, missing data, and normality. Internal consistency was assessed using Cronbach’s alpha, while Confirmatory Factor Analysis was conducted to verify the reliability and structure of the constructs. Descriptive statistics summarized the demographic and behavioral patterns of the sample, and inferential analyses, correlation, regression, and moderation analysis, were applied to explore relationships among variables. For ordinal regression, “product type” was treated as a categorical predictor with three levels (low-involvement, high-involvement, luxury). This allowed us to test the moderating effects of product type while maintaining the categorical nature of the variable. This rigorous approach ensured that the findings were not only methodologically robust but also ethically sound and theoretically grounded.

4. Results

4.1. Scales Validation and Reliability Analysis

Table 3. Scale validity and reliability analysis.

Scale	Factors Extracted	KMO	Bartlett’s Test of Sphericity	Cronbach’s Alpha	Items
Perceived social pressure	1	0.804	<0.001	0.837	4
E-WOM	1	0.819	<0.001	0.829	5
Friend of a friend	1	0.816	<0.001	0.851	5
Social capital bonding	1	0.802	<0.001	0.816	5
Social capital bridging	1	0.860	<0.001	0.861	5
Purchase intention	1	0.712	<0.001	0.801	5

presents a comprehensive overview of the psychometric properties of the six different constructs measured in the study. Each of these scales was subjected to factor analysis, and in all cases, a single factor was extracted, indicating that each scale is unidimensional and the items within each scale measure a single underlying construct effectively. Based on the Kaiser-Meyer-Olkin (KMO) values and Bartlett’s Test of Sphericity, the scales used in the questionnaire demonstrate strong evidence of construct validity and are well-suited for factor analysis. All six scales report KMO values ranging from 0.712 to 0.860, which are above the generally accepted minimum threshold of 0.60. Values between 0.70 and 0.80 are considered good, and those above 0.80 are considered very good, indicating that the sampling adequacy for each scale is satisfactory. This means there is a sufficient level of common variance among the items in each scale, supporting the appropriateness of using factor analysis to explore underlying structures. In addition, Bartlett’s Test of Sphericity is highly significant (p < 0.001) for all scales. This result confirms that the correlation matrices of the items within each scale are not identity matrices, meaning the items are interrelated enough to form a valid factor structure. A significant Bartlett’s Test is essential for validating that the items collectively contribute to a shared underlying construct. Together, the strong KMO values and significant Bartlett’s Tests provide compelling evidence that the questionnaire scales are valid from a statistical standpoint. They support the notion that the items within each scale coherently reflect a single latent dimension and that the instrument as a whole is well-constructed for measuring the intended psychological constructs. In terms of reliability, as measured by Cronbach’s Alpha, all scales demonstrate good to excellent internal consistency. The alpha values range from 0.801 to 0.861, indicating that the items within each scale reliably measure their respective constructs. Overall, the table indicates that all six scales used in the study are both valid and reliable. The statistical tests support the structural integrity of each scale, and the internal consistency values confirm that each scale is appropriate for use in further analysis. This solid psychometric foundation enhances the credibility of any conclusions drawn from the data using these measures.

To validate the measurement structure established through exploratory factor analysis, a confirmatory factor analysis (CFA) was conducted using IBM SPSS AMOS Version 31. The analysis evaluated six latent constructs related to peer and social influences on Gen Z’s purchase intention for newly launched technological products (

Table 4. Confirmatory Factor Analysis and Construct Validity.

Construct	CR	AVE	χ2/df	CFI	TLI	RMSEA
Perceived social pressure	0.83	0.55	2.03	0.932	0.918	0.059
E-WOM	0.83	0.54
Friend of a friend	0.85	0.57
Social capital bonding	0.82	0.52
Social capital bridging	0.86	0.58
Purchase intention	0.80	0.51

). The model exhibited satisfactory goodness-of-fit, with all major indices falling within acceptable thresholds: χ²/df = 2.03, CFI = 0.932, TLI = 0.918, and RMSEA = 0.059. These results indicate a good overall fit between the proposed measurement model and the observed data, supporting the theoretical structure. Composite reliability (CR) values for each construct ranged from 0.80 to 0.86, exceeding the recommended benchmark of 0.70, thus demonstrating strong internal consistency. The average variance extracted (AVE) for all constructs was above the 0.50 threshold, confirming adequate convergent validity. These results reinforce the reliability and validity of the constructs used in the model.

To assess the potential impact of common method bias, Harman’s single-factor test was conducted. All measurement items were subjected to unrotated exploratory factor analysis. The first factor accounted for 24.1% of the total variance, which is well below the 50% threshold commonly used as a critical indicator of common method variance. This suggests that the data is not significantly compromised by common method bias and that the observed relationships among variables are not artifacts of the measurement approach. Together, the CFA outcomes and common method variance test confirm that the constructs employed in the study are psychometrically robust and suitable for further structural analyses.

4.2. Correlation of Social and Peer Influences Factors and Purchase Intention

Table 5. Correlation of social and peer influences factors and purchase intention.

		Purchase Intention
Spearman’s rho	Perceived social pressure	0.120 *
	E-WOM	0.342 **
	Friend of a friend	0.430 **
	Social capital bonding	0.520 **
	Social capital bridging	0.281 **

* Significant at p < 0.05. ** Significant at p < 0.01.

presents Spearman’s rho correlation coefficients between five social-related variables and purchase intention. All of the relationships are statistically significant, suggesting that each variable is meaningfully associated with the likelihood of a person intending to make a purchase. Since Spearman’s rho measures rank-order correlation, the results reflect consistent directional trends rather than precise linear relationships. Social capital bonding shows the strongest positive relationship with purchase intention (r = 0.520). This result indicates that when individuals have strong, trusting relationships within close social groups, they are more likely to express intent to purchase. This means that close personal networks can play a major role in shaping consumer behavior, likely because trust and repeated interactions increase the credibility of recommendations within these circles. The friend of a friend variable also shows a relatively strong positive correlation with purchase intention (r = 0.430). This result means that indirect social ties, such as acquaintances or people connected through mutual friends, also influence buying decisions. The influence is not as strong as with close-knit relationships but remains significant. This suggests that people are influenced not only by those closest to them but also by extended social networks. E-WOM, or electronic word of mouth, has a moderate positive correlation with purchase intention (r = 0.342). This indicates that online reviews, digital recommendations, and discussions influence a consumer’s likelihood of buying. The result shows that consumers place value on information found in online environments, even if it comes from people they may not know personally. Social capital bridging, which represents connections across different social groups or communities, has a weaker correlation (r = 0.281). Although this value is lower than those for bonding or direct social ties, it still shows a meaningful relationship. This means that having access to broader and more diverse networks can affect purchase intentions, though to a lesser degree than close or extended personal ties. Perceived social pressure has the weakest correlation with purchase intention (r = 0.120), though it is still statistically significant. This suggests that while people may be somewhat influenced by societal expectations or pressure from others, the effect is relatively small. This means that overt pressure to conform is less effective in shaping purchase behavior than more organic influences through social connections and trusted recommendations. Overall, the results show that social relationships, especially close ones, have a considerable influence on whether people intend to purchase. The findings emphasize the importance of both direct and indirect social influence in shaping consumer behavior.

4.3. Prediction of Purchase Intention

The regression analysis offers valuable insights into how different forms of social influence affect purchase intention. The model summary shows that the combined influence of perceived social pressure, electronic word of mouth (E-WOM), friend of a friend, social capital bonding, and social capital bridging explains about 40.6 percent of the variance in purchase intention (

Table 6. Model Summary.

Model	R	R2	Adjusted R2	SE of the Estimate	F
1	0.637	0.406	0.396	0.69033	40.514

). This is a meaningful proportion, indicating that these five social variables together have a substantial effect on whether individuals are likely to intend to make a purchase. The adjusted R² value of 0.396 confirms that the model remains strong even after accounting for the number of predictors and sample size. The standard error of the estimate, at 0.69033, reflects the average amount by which the predicted values differ from the actual values. The F-statistic is 40.514 with a significance level of less than 0.001, meaning that the group of predictors reliably predicts purchase intention. This result supports the idea that social factors, as a group, provide a better understanding of consumer intention than would be achieved by chance alone.

Looking at the coefficients table provides a clearer understanding of which variables contribute most to predicting purchase intention (

Table 7. Coefficients.

Model		Unstandardized Coefficients		Standardized Coefficients	t	p
		B	SE	Beta
1	(Constant)	0.728	0.248		2.939	0.004
	Perceived social pressure	0.116	0.058	0.108	2.012	0.045
	E-WOM	0.166	0.081	0.138	2.056	0.041
	Friend of a friend	0.264	0.068	0.249	3.906	<0.001
	Social capital bonding	0.397	0.047	0.431	8.456	<0.001
	Social capital bridging	0.045	0.056	0.042	0.812	0.418

). Social capital bonding emerges as the strongest predictor, with a standardized beta coefficient of 0.431 and a p-value below 0.001. This suggests that individuals who are part of close, trusting social relationships are more likely to develop an intention to purchase. It means that strong interpersonal connections, such as those with family or close friends, are a major influence on consumer decisions, likely because of the high level of trust and frequent interactions within these relationships. The variable “Friend of a friend” also plays a significant role, with a beta value of 0.249 and a similarly low p-value. This shows that even more distant social ties, such as those formed through mutual acquaintances, influence consumer behavior. These indirect relationships can still carry social credibility and help shape opinions about products or services. This finding highlights the extended nature of social influence beyond immediate circles. Electronic word of mouth has a moderate positive effect on purchase intention, with a beta of 0.138 and a p-value of 0.041. This indicates that online reviews, recommendations, and discussions on digital platforms do have an impact, though they are not as influential as personal or indirect social ties. People are still influenced by what they read or see online, particularly when information is perceived as authentic or comes from relatable sources. Perceived social pressure shows a weaker but still significant influence on purchase intention, with a beta of 0.108 and a p-value of 0.045. This suggests that the pressure individuals feel from others to act in certain ways has some effect, but it is not as strong as the effect of genuine interpersonal trust or shared digital experiences. The influence of pressure may come from a desire to conform or fit in, but it does not appear to be a primary driver of purchasing behavior. Social capital bridging, which refers to connections across wider, more diverse networks, does not have a statistically significant effect in this model. Its beta is 0.042, and the p-value is 0.418, indicating that these broader, weaker social connections are not directly influencing purchase intention in this context. This may be because such relationships do not carry the same level of trust or influence as close or familiar connections.

In summary, the analysis shows that the most important factors influencing purchase intention are close personal relationships and indirect social ties. Digital influence through E-WOM also contributes meaningfully, while perceived pressure has only a minor effect. Broader, more distant networks do not seem to play a significant role in shaping purchase intentions. These findings suggest that consumer decisions are shaped more by trust-based relationships than by generalized social expectations or weak connections.

4.4. Product Type as Moderator

The moderation analysis examines how the type of product—categorized as low involvement, high involvement, or luxury—affects the relationship between social and peer influences (SPI) and purchase intention (PI). The model summary shows that the regression model is statistically significant (

Table 8. Moderation Analysis Summary “Social and peer influences and Purchase intention”—Type of product.

Model	R	R2	MSE	F	df1	df2	p
1	0.5331	0.2842	0.5707	39.4475	3	298	<0.001

). The R² value is 0.2842, meaning that approximately 28.4 percent of the variance in purchase intention is explained by SPI, product type, and their interaction (F_{(3, 298)} = 39.4475, p < 0.001).

The regression coefficients provide a more detailed understanding of the contributions of each variable (

Table 9. Regression Coefficients “Social and peer influences and Purchase intention”—Type of product.

Predictor Variables	β	SE	t	p	LLCI	ULCI
Constant	−2.0022	0.8743	−2.2900	0.0227	−3.7229	−0.2816
Social and peer influences (SPI)	1.4261	0.2417	5.9002	0.0000	0.9504	1.9017
Product type	0.9055	0.4260	2.1258	0.0343	0.0672	1.7437
Interaction (SPI × Product type)	−0.2642	0.1159	−2.2786	0.0234	−0.4924	−0.0360

). Social and peer influences have a significant and strong positive effect on purchase intention (b = 1.4261, p < 0.001). This means that higher levels of SPI are associated with higher levels of purchase intention. Product type also has a significant effect (b = 0.9055, p < 0.001), suggesting that purchase intention varies depending on the type of product. Most importantly, the interaction term between SPI and product type is negative and statistically significant (b = −0.2642, p < 0.05). This indicates that the strength of the relationship between SPI and purchase intention changes depending on the product type, and more specifically, it becomes weaker as the product moves from low involvement to luxury.

The conditional effects table further clarifies these differences (

Table 10. Conditional Effects of Social and peer influences at Values of Product type to Purchase intention.

Product Type (Moderator Level)	Effect (B)	SE	t	p	LLCI	ULCI
Low involvement	1.1429	0.1330	8.5928	0.0000	0.8812	1.4047
High involvement	0.9519	0.0875	10.8739	0.0000	0.7797	1.1242
Luxury	0.7610	0.1081	7.0397	0.0000	0.5482	0.9737

). For low involvement products, the effect of SPI on purchase intention is the strongest (b = 1.1429, p < 0.001). For high involvement products, the effect is slightly weaker (b = 0.9519, p < 0.001). For luxury products, the effect is the weakest (b = 0.7610, p < 0.001). All of these effects are statistically significant, but the decreasing coefficients show a clear pattern: as product involvement increases, the impact of SPI on purchase intention declines.

This pattern is visually supported by the scatterplot (Figure 2). The blue trend line, representing low involvement products, has the steepest slope, indicating a stronger relationship between SPI and PI. The red line, representing high involvement products, is moderately steep, reflecting a moderate relationship. The green line for luxury products is the flattest, which corresponds to the weakest relationship. The R² values for each group reinforce this interpretation: 0.397 for low involvement, 0.295 for high involvement, and 0.103 for luxury, showing progressively weaker model fits.

These results indicate that social and peer influences are most effective in shaping purchase intentions for low involvement products. As the level of product involvement increases, the influence of social and peer factors becomes less important. For luxury products, other factors such as brand identity, personal values, or exclusivity may play a more central role in influencing purchasing decisions. This suggests that marketing strategies should be adapted to the product type. For low involvement products, leveraging social networks, peer recommendations, and online reviews can be highly effective. For high involvement and especially luxury products, the focus may need to shift toward more individualized or aspirational marketing approaches that reflect the unique values and preferences of the consumer.

5. Discussion

The findings of this study highlight the central role of social and peer dynamics in shaping the purchasing behavior of Generation Z, especially in the context of newly launched technological products. This generation operates within a highly interconnected digital landscape, where personal networks, social media engagement, and peer-generated content inform and influence consumption choices. The results confirm that both close interpersonal relationships and broader, less direct social ties play a meaningful part in guiding purchasing decisions. The analysis shows that these effects are not uniform but vary according to relational proximity and product type, which provides a more nuanced understanding of how social mechanisms function in practice. Among the factors examined, close-knit social relationships emerged as the most influential. Individuals who share strong emotional bonds, such as those with family or close friends, are more likely to be swayed by their peers’ preferences and recommendations. These trusted connections appear to create a sense of credibility and reassurance that supports purchasing intentions. This result demonstrates that bonding social capital exerts the strongest influence because it embeds consumer behavior in trust and repeated interaction, which enhances the persuasiveness of peer input. This aligns with previous research emphasizing the impact of bonding social capital on consumer behavior, particularly in environments where trust and repeated interaction are important (Ahmad et al., 2023b; Pang et al., 2021).

The influence of more distant social connections, such as those linked through mutual acquaintances, also proved to be significant. Although not as impactful as immediate relationships, these “friend of a friend” ties still contribute meaningfully to consumer decisions. The analysis suggests that credibility can diffuse across indirect ties, meaning that consumers extend trust to acquaintances through shared network contexts. This finding supports the idea that credibility and influence can be transferred through social networks, even without direct personal contact, and aligns with earlier studies showing the extended nature of relational influence in digital platforms (Jackson & Rogers, 2007; Montgomery et al., 2020). Electronic word of mouth (e-WOM) was another important influence, demonstrating that online reviews, comments, and digital recommendations play a role in shaping how Generation Z evaluates products. While less powerful than interpersonal ties, e-WOM remains a consistent and relevant factor, particularly when information is perceived as authentic, useful, and emotionally relatable. This analysis highlights that e-WOM functions as a secondary but persistent mechanism of influence, reinforcing decisions initially shaped by stronger ties. This reflects previous findings that highlight the persuasive effect of peer-generated content, especially in online environments where Gen Z actively seeks validation and community feedback (Farzin et al., 2022; Khan et al., 2023). Perceived social pressure had a weaker impact compared to the more organic, trust-based forms of influence. Although it still had a small but measurable effect, it suggests that Gen Z consumers are less responsive to overt expectations or conformity pressures, and more influenced by subtle, relational cues. This finding clarifies that while subjective norms exist, they are weaker predictors relative to trust-based mechanisms, indicating a generational preference for authenticity over obligation. This observation aligns with literature showing that subjective norms, while present, tend to be less predictive than peer trust and engagement in digitally native populations (Singh et al., 2020; Luo et al., 2020). In contrast, broader and more diverse social connections—referred to as bridging social capital—did not have a significant direct effect in this context. This may indicate that relationships across different communities or weaker social ties lack the emotional strength or familiarity required to influence decisions about recently introduced technology. This null result is important, as it demonstrates that not all forms of social connection are equally valuable in shaping purchase intention, thereby refining theoretical assumptions about the universal role of bridging capital. Previous studies have suggested mixed outcomes regarding the influence of bridging ties, and this result adds to the evidence that their effect may be situational, particularly when trust and familiarity are critical to decision-making (Yuan et al., 2021).

Hypothesis one, which examined the relationship between social and peer influence factors and the purchase intention of Generation Z, was supported. The results confirmed that personal relationships, indirect social ties, and digital peer content all positively shape purchase intent, with bonding ties having the most pronounced impact. Hypothesis two, which focused on whether product type moderates these relationships, was also supported. Social and peer influences were found to be more influential in the context of low-involvement products. As the level of involvement increased—moving from high-involvement to luxury—the strength of social influence declined. This analysis shows that product type conditions the weight of peer effects: everyday purchases are shaped by social cues, while luxury decisions are more individualistic and brand-driven. This suggests that decisions about everyday or low-risk purchases are more likely to be shaped by social input, while more expensive or symbolic purchases are guided by individual values, brand perceptions, or aspirational goals. This pattern is consistent with literature emphasizing that product involvement affects the degree to which consumers rely on social cues versus personal evaluation (Yang et al., 2021; Xu et al., 2023). Overall, these findings highlight the importance of aligning marketing strategies with both social dynamics and product characteristics. For low-involvement products, encouraging peer sharing, leveraging online reviews, and promoting user recommendations can be highly effective. For luxury products, marketers may need to place greater emphasis on personal identity, exclusivity, and brand storytelling, as peer influence appears to play a reduced role in those purchasing contexts. The arguments developed from this analysis clarify that peer influence operates differently across contexts, which strengthens both theoretical insight and practical recommendations. The significance of these results lies in their dual implications: theoretically, they extend existing knowledge by demonstrating how product type moderates the influence of social capital and peer dynamics on purchase intention; practically, they offer marketers, policymakers, and brand managers actionable insights on tailoring digital campaigns to both the social context and product characteristics. By doing so, firms can enhance resonance with Gen Z consumers, improve brand credibility, and increase conversion rates in highly competitive digital marketplaces. In sum, social relationships, especially those grounded in trust and emotional connection, continue to be powerful drivers of Gen Z consumer behavior. However, the extent to which they matter depends on the product being considered. Thus, the analysis of findings provides a clear argument: peer influence is situationally contingent, strongest in trust-based ties and low-involvement contexts, and weaker when decision-making emphasizes individual identity and symbolic value. This underscores the broader significance of the study: understanding not only whether peer influence matters, but also when, how, and under what product conditions it is most impactful. Marketers and brands targeting this generation should carefully consider both the social pathways and the product types when designing campaigns that seek to influence digital purchasing behavior.

6. Conclusions

This study reveals that social and peer dynamics play a crucial role in shaping the purchase intentions of Generation Z, particularly within the context of newly launched technological products. The findings demonstrate that not all forms of social influence are equally impactful. Close personal networks, those grounded in bonding social capital, emerge as the most influential, suggesting that trust, emotional closeness, and familiarity strongly guide Gen Z’s consumer decisions. At the same time, indirect peer connections, such as friends of friends, and electronic word of mouth also contribute meaningfully, although to a lesser extent. More generalized forms of social influence, such as perceived pressure from broader social expectations or weak ties, appear to be less decisive in motivating purchasing behavior. From a practical standpoint, these insights provide actionable guidance for marketing professionals working in health-related and consumer service industries alike. In healthcare services, for example, campaigns promoting preventive health behaviors or digital health tools could benefit from leveraging peer networks and trusted micro-communities, given their outsized influence on Gen Z decision-making. Likewise, consumer brands can tailor their strategies according to product involvement, using peer-driven tactics for low-cost, everyday items and more identity-focused, personalized approaches for high-involvement or luxury products. For low-involvement products, which benefit the most from social influence, marketers should focus on cultivating peer-driven strategies. This includes facilitating user-generated content, promoting authentic online reviews, and designing campaigns that leverage social proof within micro-communities. In contrast, for luxury or high-involvement products, strategies should shift toward personalized messaging that appeals to individual identity and exclusivity, while still subtly incorporating trusted peer validation where appropriate. From a theoretical perspective, the research reinforces the relevance of Social Capital Theory and Relationship Marketing Theory in digital consumer behavior. It distinguishes the functional roles of bonding and bridging capital, demonstrating that emotionally closer social ties carry more weight in shaping purchase intentions. It also highlights how digital environments transform traditional social influence mechanisms through e-WOM, networked connections, and indirect communication pathways. Additionally, the study confirms the usefulness of behavioral intention frameworks like the Theory of Reasoned Action and the Theory of Planned Behavior, offering empirical support for the predictive power of subjective norms and relational trust in online purchasing contexts. Taken together, the results expand current understandings of how trust-based social mechanisms and product characteristics jointly influence consumer behavior. They also underscore that practitioners should be mindful of tailoring interventions not only to the type of product but also to the social fabric of the target audience, as overlooking these dynamics may reduce campaign effectiveness.

Research Limitations and Future Research

While this study offers valuable insights into the role of peer dynamics and product type in shaping Gen Z’s purchase intentions, several limitations present opportunities for deeper and broader future research. One primary concern is the reliance on self-reported data, which may be subject to social desirability bias or inaccurate recall, potentially affecting the validity of the responses. To address this, future research should integrate behavioral data (e.g., online tracking, clickstream metrics, or purchase history) alongside self-reports to provide a more objective and triangulated view of consumer behavior. Another limitation relates to sample diversity. Although the present study included variation in gender and student status, the sample remains relatively small and context-specific. As such, the findings should be interpreted cautiously and not assumed to generalize across the entire Generation Z population. Expanding future studies to include participants from different geographic regions, cultural settings, and socioeconomic backgrounds would enhance generalizability and strengthen the external validity of the conclusions. Additionally, the study’s cross-sectional design offers only a snapshot of behavior, which may not capture how peer influences, product perceptions, or digital engagement evolve over time. Future research would benefit from adopting a longitudinal design, which could track how peer dynamics shift as technological environments, social norms, and marketing practices develop. For example, the rise in ephemeral content, virtual influencers, or augmented reality shopping could significantly alter how peer dynamics function over time. The study’s focus on newly launched technological products is also a limitation in scope. Exploring additional product categories, such as fashion, cosmetics, food and beverage, or digital services (e.g., streaming platforms, gaming), would broaden the understanding of peer dynamics and allow for comparisons across consumption domains. Each domain may carry distinct symbolic meanings or levels of peer visibility that influence how recommendations are perceived and acted upon. Finally, as digital ecosystems evolve, so too do the variables that shape consumer decision-making. Elements like influencer authenticity, the impact of algorithmic personalization, AI-generated content, and platform governance could all interact with or moderate peer effects. Investigating these emerging dimensions would help future studies capture the increasingly complex digital landscapes where Gen Z forms its preferences, engages with peers, and makes consumption choices.