Location-Based Moderation in Digital Marketing and E-Commerce: Understanding Gen Z’s Online Buying Behavior for Emerging Tech Products

Abstract

In an increasingly digitalized marketplace, understanding Generation Z’s (Gen Z) online consumer behavior has become a critical priority, particularly in relation to newly launched technological products. Although online consumer behavior has been widely studied, a gap remains in understanding how the location of the e-shop (domestic vs. international) moderates this behavior. Addressing this gap, the present study adopts a quantitative, cross-sectional design with data from 302 Gen Z participants, using a hybrid sampling method that combines convenience and systematic techniques. A structured questionnaire, grounded in 19 well-established behavioral theories, was employed to examine the influence of six key factors, behavioral and attitudinal traits, social and peer influences, marketing impact, online experience, brand perceptions, and Gen Z characteristics, across various stages of the consumer journey. Moderation analysis revealed that e-shop location significantly affects the strength of relationships between these factors and both purchase intention and post-purchase behavior. Notably, Gen Z’s values and marketing responsiveness were found to be more predictive in the context of international e-shops. These findings highlight the importance of marketing strategies that are both locally relevant and globally informed. For businesses, this research offers actionable insights into how digital engagement and brand messaging can be tailored to meet the unique expectations of Gen Z consumers across diverse e-commerce contexts, thereby enhancing consumer satisfaction, loyalty, and brand advocacy.

1. Introduction

In today’s rapidly evolving digital economy, e-commerce serves as a dominant channel for consumer interaction, particularly among Generation Z, a group raised in a highly connected and technology-driven environment [1,2]. Gen Z’s preferences and behaviors are especially important in the adoption of newly launched technological products, where innovation, digital interaction, and cultural relevance act as key drivers [3]. While prior research has examined online consumer behavior, few studies have explored how contextual elements such as whether an e-shop is domestic or international, moderate the impact of behavioral factors on Gen Z’s purchasing decisions. This gap is notable given the fluid nature of online shopping, where consumers frequently navigate both local and global platforms within the same digital space. Additionally, existing studies often evaluate behavioral variables in isolation and lack a cohesive theoretical framework. To address these gaps, the present study develops a multidimensional model grounded in nineteen established theories, including the Theory of Planned Behavior, Social Exchange Theory, Technology Acceptance Model, and Social Capital Theory. The study analyzes how psychological, social, and technological factors shape Gen Z’s online purchasing behavior for new technological products and examines whether e-shop location alters these relationships. It aims to offer both theoretical depth and practical insights for digital marketers and e-commerce practitioners. It is important to note that this manuscript represents a focused segment of a broader research program on Gen Z’s digital consumer behavior. The current analysis emphasizes the moderating role of e-shop location in the relationship between influential factors and behavioral outcomes. While this study offers a macro-level synthesis of key constructs, future stages of the research examine more granular mechanisms and specific theoretical pathways in depth. In alignment with the objective, the study is guided by the following research questions:

Research Question 1:

To what extent do influential factors affect the consumer behavior of Generation Z in the context of newly launched technological products?

Research Question 2:

How does the location of the e-shop (domestic vs. international) moderate the relationship between influential factors and the online consumer behavior of Generation Z?

By addressing these questions, the research aims to clarify not only the direct drivers of Gen Z’s behavior in response to technological innovation but also how the e-commerce context shapes or alters those drivers. The findings are expected to contribute meaningfully to the literature on consumer behavior and e-commerce, while also offering actionable insights for brands looking to tailor their digital strategies to the values, expectations, and purchasing patterns of this highly connected and increasingly influential generation. Despite the growing influence of Gen Z in shaping digital commerce, existing research tends to examine consumer behavior constructs in a fragmented manner. Many studies focus on isolated factors without considering contextual influences such as platform origin or cultural framing. Additionally, while prior studies have explored consumer motivations, trust, and technology adoption, they often assume these variables function uniformly across geographic contexts. This creates a gap in understanding how well-established consumer behavior drivers may shift in cross-border e-commerce environments. To address this gap, the present study integrates multiple behavioral and psychological constructs and examines how e-shop location (domestic vs. international) moderates their influence on Gen Z’s purchase and post-purchase behavior. This approach contributes a context-sensitive and multi-dimensional perspective to the literature on digital consumer behavior.

2. Literature Review

2.1. Generation Z’s Pre- and Post-Purchase Consumer Behavior in the Context of Technological Innovation

Understanding Generation Z’s consumer behavior requires a theoretical approach that considers the shared social and historical influences shaping generational identity. Generational Theory suggests that people who experience major cultural, political, and technological events during their formative years develop lasting values and behaviors [4,5,6]. Generation Z, born between 1995 and 2012, grew up during milestones such as the Internet revolution, social media, climate concerns, and the COVID-19 pandemic, which strongly influenced their identity [7,8,9]. These experiences have contributed to the emergence of a globally connected, socially aware, and digitally skilled generation; traits that influence both their purchasing decisions and brand interactions [10]. With Gen Z comprising 25 to 30 percent of the global consumer base in 2024 and controlling over 360 billion dollars in spending power [11,12], their economic influence is both substantial and expanding. Projections indicate they will become the dominant force in global consumption by 2030 [13]. Moreover, Gen Z influences not only their own purchases but also household buying decisions, acting as key influencers in the digital marketplace [14]. Their online brand engagement continues to reshape marketing strategies, service delivery models, and customer loyalty programs [15]. As such, businesses must actively understand and respond to their expectations and behaviors to remain relevant and ensure long-term success [16,17].

Generation Z, having grown up immersed in technology, perceives digital tools and platforms as integral to everyday life [18]. Their post-purchase behavior reflects high expectations for speed, transparency, and interactivity in brand interactions [19]. This is especially evident with newly launched technological products, where Gen Z tends to engage in active and visible post-purchase practices [20]. They often express their opinions through online reviews, social media content, unboxing videos, and participation in online communities, activities that contribute to the digital credibility and social presence of brands and products [8,21,22,23]. Research has shown that Gen Z places greater trust in peer-generated content and independent third-party reviews than in messages directly from brands, a trend observed across different cultural contexts [2,24]. This behavior is consistent across cultures, as recent research in Vietnam has shown how cultural factors and peer influence significantly shape Gen Z’s online shopping motivations and trust in digital platforms [25]. These behaviors go beyond functional feedback; they are also means of expressing identity, shared values, and group belonging [26]. Theoretical models such as the Theory of Planned Behavior and the Elaboration Likelihood Model provide insight into these patterns, explaining how attitudes, perceived norms, and cognitive consistency influence post-purchase responses, including endorsements, product evaluations, and returns [27].

Generation Z places considerable importance on values such as sustainability, inclusivity, and brand transparency, and these priorities strongly influence their post-purchase behavior [22]. They are more inclined to support and advocate for brands that align with these ideals, while actively distancing themselves from those that do not. They often generate online buzz, both organically and through direct brand interaction, which significantly enhances a brand’s credibility and digital presence [28]. Post-purchase engagement for Gen Z goes beyond mere reaction; it is deliberate and participatory. Individuals contribute to brand narratives as both consumers and content creators. Their loyalty is not automatic; it depends on a brand’s consistent delivery of value, ethical conduct, and relevance to their evolving expectations [29]. This conditional loyalty becomes even more evident with innovative and emerging technologies, where the post-purchase experience, such as product performance, customer support, and responsiveness, directly influences whether loyalty is maintained or lost. These loyalty patterns reflect the principles of Social Exchange Theory, where ongoing engagement continues only when the consumer perceives meaningful returns. These returns include functional benefits, social affirmation, personalized experiences, and ethical congruence [30]. Ultimately, Generation Z seeks brand relationships that offer both practical utility and alignment with their environmental and cultural values [9,16]. Recent cross-cultural studies comparing Gen Z consumers in the U.S. and France highlight these ideological drivers, showing that values such as individualism, social responsibility, and digital engagement patterns differ by cultural background but similarly influence loyalty and purchase intention [31]. Generation Z expects brands to be authentic and accountable, fostering a consumer-brand relationship based on trust that must be consistently maintained. Influencers play a vital role in this process, reinforcing satisfaction and promoting deeper engagement after the purchase [32,33]. Their presence in Gen Z’s digital communities builds trust-based micro-environments where loyalty is shaped by social interaction [34]. Alongside loyalty programs, personalized communication, and interactive content, these factors contribute to stronger emotional bonds and brand advocacy beyond the point of sale [35]. Although Gen Z is somewhat price-sensitive due to economic uncertainty [36], they prioritize value over cost and are willing to pay more for brands committed to sustainability and social justice if the value aligns with their expectations [37,38]. When brands deliver both strong performance and ethical alignment, Gen Z consumers show long-term loyalty and often become brand advocates [12,39]. This trend is reflected in studies from South Asia, where Gen Z’s use of mobile shopping apps is shaped not only by technology adoption factors but also by situational influences such as fear of health crises and trust in platform reliability, underscoring the need to contextualize behavior within both cultural and technological ecosystems [40]. Generation Z’s consumer behavior in the post-purchase and after-purchase stages is influenced by their digital proficiency, commitment to social values, and economic awareness [41]. Shaped by technological advancements and global change, their engagement with brands extends beyond isolated transactions to ongoing interaction, feedback, and co-creation [42]. Through activities like product reviews and loyalty shaped by values, Gen Z is redefining how companies must approach marketing, innovation, and customer relationships [43]. As their economic and cultural impact grows, understanding this generation has become essential for brands seeking long-term relevance in a fast-changing, value-driven marketplace.

2.2. Newly Launched Technological Products

The success of new product launches is essential to business growth, competitiveness, and sustainability, but it remains difficult to predict due to the constant evolution of consumer behavior and shifting market demands [44,45,46,47]. This challenge is especially pronounced in industries driven by technological innovation, such as electronics, smart devices, and gaming, where consumer responses depend on a range of personal, cultural, and social factors [48]. Generation Z has emerged as a pivotal consumer group in this context, exerting strong influence over market trends and maintaining a distinctive relationship with technology and innovation [49,50]. Raised in a fully digital environment, Generation Z is both highly informed and comfortable with technology [51,52]. They actively seek newly released products that reflect the latest advancements and align with their fast-paced, connected lifestyles [50]. For them, products like smartphones, smart home devices, and wearables serve not only as functional tools but also as expressions of identity and channels for social interaction [53]. Gen Z’s product discovery and evaluation take place primarily in digital environments, particularly through platforms like TikTok, Instagram, YouTube, and Reddit, where peer reviews, influencer content, and creative marketing significantly shape their perceptions [51,54,55,56]. This generation values authenticity, personalization, and ethical responsibility. Their evaluations go beyond technical features or price, considering whether a product aligns with personal values and expectations. They especially appreciate user-friendly design, sustainable materials, and transparent brand messaging [53,54,55,56,57]. In addition, Generation Z expects convenience and efficiency in digital shopping experiences. Fast delivery, responsive customer support, and seamless service are non-negotiable, and brands that fall short often face immediate backlash on social media platforms [49,56,58]. In contrast, brands that engage Gen Z through interactive content, influencer marketing, or gamified promotions tend to see rapid adoption and enthusiastic support [48,59]. Exclusivity is another powerful motivator for this group. Limited editions, early access, and immersive digital campaigns that create a sense of urgency or belonging effectively capture their attention [54,60,61]. These strategies become even more impactful when supported by technologies like augmented reality and community-driven social media approaches that build emotional connections with the brand. Social influence remains deeply embedded in Gen Z’s product evaluations, with peer recommendations and influencer endorsements often carrying more weight than traditional advertisements [62,63]. In conclusion, launching technological products in today’s digital marketplace requires a deep understanding of Generation Z’s expectations, behaviors, and values. Their influence, amplified through digital platforms and peer networks, makes them essential to a product’s reputation and long-term success. Brands that align with Gen Z’s preferences—delivering innovation, transparency, ethical practices, and digital fluency—are more likely to achieve both rapid market entry and sustained consumer loyalty.

2.3. E-Shop Location as a Moderator in the Relationship Between Influential Factors and Consumer Behavior

In the evolving world of online commerce, understanding consumer behavior is essential for businesses aiming to remain competitive and relevant [64]. While much research has focused on direct relationships between factors such as trust, loyalty, brand image, and purchase intention, researchers are increasingly examining the role of moderating variables. One important moderator is the location of the e-shop, referring to whether an online store is domestic or international. This factor plays a critical role in shaping consumer perceptions of trust, risk, and brand authenticity, which in turn influence how consumers respond to marketing and branding efforts. Local e-shops often convey cultural familiarity, language consistency, and logistical convenience, factors that matter especially for low involvement products where practicality and service ease are key [65,66,67]. In contrast, international e-shops may offer greater variety, uniqueness, or perceived quality, but they also raise concerns about product authenticity, customer support, and delivery reliability [68,69]. As a result, consumers often seek additional validation, such as influencer endorsements or customer reviews, to feel confident in their decisions. The e-shop’s location moderates the effects of brand image and trust on consumer behavior. Perceptions of brand identity and authenticity shift based on whether the store is domestic or international. Domestic platforms tend to align more closely with local values and expectations, which builds initial trust and acceptance [70,71,72]. On the other hand, international platforms often benefit from associations with innovation, style, or prestige, particularly in product categories such as fashion, cosmetics, and technology [73,74]. These differing perceptions require tailored marketing strategies. Campaigns that emphasize community relevance may be more effective on domestic platforms, while those focusing on global excellence or exclusivity may resonate more with international shoppers.

The moderating influence of e-shop location is evident not only in cognitive responses but also in behavioral outcomes. Consumers tend to show greater brand trust and engagement when purchasing from local online stores, which enhances the effectiveness of loyalty programs and increases overall satisfaction [67,75]. For international e-shops, trust often depends on external validation through social media, influencer marketing, and user reviews [76]. In digital settings where physical cues are absent, brands must build trust through visual design, secure payment systems, and transparent policies [77,78]. Incorporating e-shop location into consumer behavior models allows marketers to develop more targeted and effective strategies. Businesses can tailor their messaging based on store origin, focusing on emotional and cultural alignment for domestic markets and global prestige for international ones. This approach not only improves conversion rates and customer experience but also fosters stronger long-term relationships [79,80]. In summary, the location of an e-shop significantly shapes consumer perceptions and behaviors, making it a valuable moderating variable in digital marketing research and practice.

2.4. Theoretical Background Development and Variable Selection

The selection of variables in this study is grounded in a comprehensive integration of major consumer behavior theories to support a well-rounded understanding of online decision-making. The theoretical basis ensures that each variable represents specific psychological, social, or technological mechanisms that influence consumer actions in digital environments. Social Capital Theory, which focuses on the value generated through social networks and trust, supports variables such as bonding and bridging social capital and perceived social pressure, highlighting the importance of interpersonal influence on consumer choices [81,82,83,84,85]. Relationship Marketing Theory emphasizes the value of long-term consumer–brand relationships and informs variables like electronic word of mouth (e-WOM), information usefulness, and friend-of-a-friend effects, which also intersect with Social Capital Theory in the context of trust and communication [86,87,88,89,90,91,92,93]. Social Impact Theory, which focuses on the strength, immediacy, and number of influencers, explains peer behavior and online social dynamics, aligning with variables like online experience and peer influence [94,95,96,97]. This theory, along with the Theory of Reasoned Action, which connects intentions to attitudes and norms, clarifies how beliefs and social expectations guide behavior [98,99]. Brand-related variables such as image, trust, loyalty, awareness, and engagement draw support from Social Exchange Theory, which views consumer-brand relationships as value exchanges, and from Consumer Culture Theory, which emphasizes identity expression through consumption [100,101,102,103,104,105,106,107,108].

The Theory of Planned Behavior extends the Theory of Reasoned Action by introducing perceived control as a determinant of behavior. This is especially relevant in digital contexts, where users evaluate trust, usability, and risk [109,110,111]. Variables like brand experience, behavioral intention, and advertising awareness reflect this model’s applicability [112,113,114,115]. Schema Theory and Hierarchy of Effects Models further explain how consumers cognitively and emotionally respond to advertising content [116,117,118,119]. The Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology inform technology-related variables, including website quality, technology acceptance, online shopping attitudes, and intention to shop online. These models emphasize perceived usefulness, ease of use, and facilitating conditions [120,121,122,123,124]. Perceived innovativeness links to these frameworks and the Diffusion of Innovations Theory, which categorizes adopters based on readiness and risk tolerance [125,126]. Trust-Based Consumer Behavior Theory and the Elaboration Likelihood Model serve as the foundation for variables related to privacy, security, and information processing, showing how motivation and context shape attention to persuasive content [127,128]. Utility Theory and Consumption Value Theory underpin perceived product value, recognizing the role of functional, emotional, and social benefits in consumer choice [129,130,131]. Attachment Theory explains emotional bonds with social media and brands, which in turn influence long-term engagement [132,133,134]. The Elaboration Likelihood Model also explains how task ambiguity affects cognitive effort, while Conditioned Learning Theory accounts for brand associations developed through repeated exposure to online advertising [135,136,137,138].

Behavioral outcomes such as purchase intention, actual purchase, post-purchase actions, and loyalty are explained through the Theory of Planned Behavior, Theory of Reasoned Action, and Technology Acceptance Model. Together, these frameworks describe how attitudes and perceived control shape behavior [108,110,111,139,140,141,142,143,144,145,146,147,148,149,150]. Finally, lifestyle traits, innovativeness, and early adopter tendencies are interpreted through the Diffusion of Innovations Theory, which highlights how individual characteristics influence openness to new products [151,152,153]. Appendix A provides detailed analytical connections between variables and their theoretical foundations.

In summary, the selection of variables in this study is firmly grounded in established consumer behavior theories, allowing for a comprehensive investigation of the cognitive, emotional, social, and technological factors that shape online consumption. By integrating these theoretical perspectives, the research offers a nuanced understanding of the internal processes and external influences affecting digital consumer behavior. Focusing on Generation Z and their engagement with newly launched technological products, the study examines a wide range of variables, including behavioral attitudes, peer and social influences, marketing effectiveness, online experience, and brand perceptions, all of which collectively influence purchasing behavior. Additionally, the study introduces e-shop location, whether domestic or international, as a moderating factor, recognizing its influence on consumer trust, expectations, and engagement in digital retail settings. This consideration is particularly relevant in today’s globalized e-commerce landscape. Based on these foundations, a set of research hypotheses has been developed and is presented in Figure 1.

H1. 

Influential factors positively affect consumer behavior of Gen Z, regarding newly launched technological products.

H1_a. 

Behavioral and attitudinal factors positively influence consumer behavior of Gen Z.

H1_b. 

Social and peer influences positively influence consumer behavior of Gen Z.

H1_c. 

Brand-related factors positively influence consumer behavior of Gen Z.

H1_d. 

Online experience factors influence consumer behavior of Gen Z.

H1_e. 

Marketing and advertising impact influences consumer behavior of Gen Z.

H1_f. 

Gen Z characteristics influence consumer behavior of Gen Z.

H2. 

E-shop location moderates the relationship between influential factors and consumer behavior of Gen Z, regarding newly launched technological products.

H2_a. 

E-shop location moderates the relationship between Behavioral and attitudinal factors and consumer behavior of Gen Z.

H2_b. 

E-shop location moderates the relationship between Social and peer influences and consumer behavior of Gen Z.

H2_c. 

E-shop location moderates the relationship between Brand-related factors and consumer behavior of Gen Z.

H2_d. 

E-shop location moderates the relationship between Online experience and consumer behavior of Gen Z.

H2_e. 

E-shop location moderates the relationship between Marketing and advertising impact and consumer behavior of Gen Z

H2_f. 

E-shop location moderates the relationship between Gen Z characteristics and consumer behavior of Gen Z.

Although this study synthesizes constructs from nineteen behavioral theories, the aggregation into six higher-order factors was driven by both empirical grouping (via factor analysis) and theoretical alignment. Each composite factor captures a coherent set of theoretically linked variables. The expanded hypotheses provided above serve to articulate the directional pathways between these factors and Gen Z’s behavioral responses in more detail.

Theoretical Integration and Contribution

This study offers a novel contribution by integrating insights from nineteen well-established theories—spanning behavioral intention (e.g., Theory of Planned Behavior, Theory of Reasoned Action), technology adoption (e.g., Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology), post-purchase behavior (e.g., Social Capital Theory), and branding, trust, and social capital frameworks—into six composite constructs. While prior studies have tended to apply these models in isolation, this synthesis enables a more comprehensive understanding of the multiple, interrelated forces shaping Gen Z’s online behavior. The theoretical integration allows us to examine how combinations of psychological, social, and technological factors jointly influence consumer decision-making. Importantly, this model is tested within a moderated structure that introduces e-shop location (domestic vs. international) as a contextual boundary condition. This contributes a significant advancement to the literature by revealing how the predictive power of these constructs varies across geographic settings—an area that remains underexplored in cross-border e-commerce research. By uniting broad theoretical foundations with a context-specific moderator, this study delivers both conceptual depth and practical relevance.

3. Materials and Methods

This study applies a quantitative, cross-sectional research design to explore the online consumer behavior of Generation Z in the context of newly launched technological products. The research investigates how influential factors derived from established theories of consumer behavior shape Gen Z’s purchasing decisions, and how these relationships are potentially moderated by the location of the e-shop (domestic or international). Given the study’s aim to produce measurable and comparable results, a quantitative approach was selected as the most suitable methodology. Quantitative research enables the use of standardized tools that allow for objectivity and replicability [154]. It also supports statistical analysis capable of identifying potential moderation effects, providing a robust foundation for examining complex behavioral relationships [155]. The cross-sectional design, commonly used in marketing and social sciences, offers a snapshot of consumer attitudes, perceptions, and behaviors at a specific point in time [156]. This makes it ideal for examining the fast-evolving online purchasing habits of Generation Z, a group characterized by their dynamic engagement with digital technologies and platforms. This methodological approach is well aligned with the nature of the research questions, as it allows for the simultaneous examination of multiple variables and the interactions among them, including the effect of contextual moderators such as e-shop location.

3.1. Research Sample and Sampling Method

The target population for this study was Greek individuals belonging to Generation Z, specifically individuals born between 1997 and 2012. To ensure ethical compliance and valid consent, only participants aged 18 years and older were included. This age range represents the legally autonomous, consumer-active segment of Gen Z, who are not only digitally literate but also have independent or semi-independent purchasing power. This subset of Gen Z was selected because it represents a digitally literate and influential consumer demographic with significant engagement in online commerce. The final sample consisted of 302 respondents, exceeding the minimum requirement determined through a G*Power analysis (version 3.1.9.6.). The analysis indicated that a sample of approximately 160 to 180 participants would be adequate to detect moderate to large effect sizes with a power of 0.80 and a significance level of 0.05. The decision to recruit a larger sample was made to increase statistical precision, ensure model stability in multivariate analysis (especially moderation models), and reduce the margin of error. Additionally, the larger sample size improves the representativeness of findings across subgroups within Gen Z and supports more robust generalization of results in the context of exploratory factor analysis and regression-based testing. Thus, the sample size used ensures statistical robustness for the analyses undertaken. Sampling was conducted using a hybrid approach that combined convenience sampling with elements of systematic sampling. Participants were primarily selected based on accessibility, particularly in environments where Gen Z individuals are known to be concentrated, such as university campuses. This convenience-based strategy allowed for efficient access to a digitally engaged population who frequently interact with e-commerce platforms. To reduce selection bias and introduce structure into the sample, a systematic element was incorporated: participants were selected from a sampling frame by choosing every odd-numbered entry (1st, 3rd, 5th, and so on). The systematic component helped minimize the bias typically associated with convenience sampling, ensuring greater variability and representativeness in the respondent pool. The rationale for this strategy is grounded in the study’s focus on young adult consumers with consistent online engagement. While alternative probability-based methods, such as stratified or cluster sampling, could potentially enhance generalizability, they require comprehensive population-level data and logistical resources beyond the scope of this study. Thus, the hybrid method provided a pragmatic yet defensible approach for obtaining valid insights from the target group. Data collection took place both through in-person interactions and paper-based questionnaires, particularly in university settings where respondents could be approached directly. This method offered the advantage of clarifying any participant’s doubts in real-time and ensured a high response rate. While alternative sampling methods such as stratified or cluster sampling could potentially enhance generalizability, they require detailed population data and broader access, which were not feasible within the resource constraints of this study. Therefore, the chosen hybrid sampling method offered a pragmatic yet structured solution for collecting high-quality data from Gen Z consumers.

The sample included 47.7% male and 52.3% female participants, ensuring a balanced gender representation. Regarding educational background, the majority were university students (85.4%), followed by vocational institute graduates (3.3%), university graduates (7.9%), and a smaller percentage of high school graduates (0.7%) and postgraduate degree holders (2.6%). This educational profile reflects a digitally literate and academically active segment of Generation Z. The age of participants ranged from 18 to 27 years, with a mean age of approximately 20.5 years and a standard deviation of 2.35, indicating a relatively homogeneous age group. In terms of household income, 19.2% reported earnings below EUR 10,000, 41.7% between EUR 10,000 and EUR 20,000, and 39.1% above EUR 20,000. Overall, the distribution suggests a well-balanced socioeconomic representation, with a slight concentration in the middle- and higher-income brackets.

3.2. Data Collection Tool Development

The research instrument employed was a structured questionnaire designed to assess various dimensions of Gen Z’s consumer behavior related to the online purchase of newly launched technological products. The questionnaire was composed of seven sections, each focusing on specific areas such as demographic characteristics, consumer attitudes, online shopping behavior, brand engagement, and perceptions of e-shop location. The questionnaire was developed using well-established and previously validated scales drawn from the literature on consumer behavior, digital marketing, and technology adoption. Where applicable, existing scales were directly adopted or slightly adapted to fit the context of this study, while a smaller number of items were self-developed to capture dimensions not adequately covered in prior research. The development of the tool was grounded in nineteen well-known theories and models, including the Theory of Planned Behavior, the Technology Acceptance Model, the Social Exchange Theory, and the Elaboration Likelihood Model. To ensure clarity and cultural relevance, a pilot study was conducted before the full administration of the questionnaire. Feedback from pilot participants led to modifications in the wording and sequencing of several items, ensuring better comprehension and higher-quality responses. The final questionnaire included both five-point and seven-point Likert scales with labeled anchors, ranging from strong disagreement to strong agreement or from not at all to very much, depending on the variable measured. Key themes covered by the questionnaire included online experience, digital trust, perceived value, task ambiguity, early adopter tendencies, website quality, brand attributes such as loyalty and trust, perceived product innovativeness, advertising awareness and creativity, peer influence, and post-purchase behavior. To support transparency and academic rigor, the final instrument distinguishes between three types of scale development: (1) directly adopted validated scales, (2) adapted scales from prior research to suit the study’s context, and (3) self-developed items created when no suitable validated measures existed. This hybrid approach ensures both content validity and context specificity, producing a detailed and multifaceted dataset suitable for complex statistical analysis. To enhance the reliability and validity of the research instrument, all measurement items used in the questionnaire were based on previously validated and widely recognized scales from the existing literature. Especially for the self-developed scales, additional steps were taken beyond the pilot study to support construct validity. After initial pilot testing, exploratory factor analysis (EFA) was conducted to examine the underlying structure of the items and confirm alignment with the intended constructs. Items with low factor loadings or cross-loadings were revised or removed to improve clarity and coherence. Internal consistency was assessed using Cronbach’s alpha, with all self-developed scales demonstrating acceptable reliability. To further ensure the stability of the measures over time, test–retest reliability was conducted with a subsample of participants, confirming consistent responses across time points. Together, these steps enhance the robustness and credibility of the self-developed scales used in the study. The following

Table 1. Scales used in the current study.

Variable	Source
TAM	[157,158,159]
Shopper lifestyle scale	[160]
Prior online experience	[161]
Task ambiguity	[162]
Perceived social pressure	[162]
Perceived brand innovativeness	[163]
Perceived risk	[164]
Perceived product value	[165]
Website security and privacy	[166]
Perceived website quality	[167]
Brand behavioral intention	[168,169]
Online brand engagement	Adapted from Rather & Hollebeek [168]
Online brand experience	[168,170]
E-WOM information usefulness	[157,171]
Intention to shop online	[158]
Early adopter mindset	Adapted from Zijlstra et al. [172]
Friend of a friend	Own development based on the adaptation of Goyette et al. [173] and Filieri [174]
Social media attachment	Own development
E-WOM	Adapted from Goyette et al. [173] and Filieri [174]
Prior experience with online advertisement	Own development
Advertising creativity	[175]
Advertising awareness	Own development
Brand awareness	Adapted from Yoo, Donthu & Lee [176]
Brand trust	[161,177]
Attitude towards online shopping	Adapted from Lim & Ting [158]
Social capital bonding	[178]
Social capital bridging	[178]
Brand knowledge	Adapted from Hanaysha [179] and Jin, Lee & Huffman [180]
Brand image	Adapted from Hanaysha [179] and Jin, Lee & Huffman [180]
Brand loyalty	Adapted from Hanaysha [179] and Jin, Lee & Huffman [180]
Gen Z	Own development based on relevant literature
Consumer behavior	Own development based on Voramontri & Klieb [181], Chopra, Gupta & Manek [182], and Akayleh [183]
Purchase intention	[159,184]
Actual purchase	Adapted from Wee et al. [185]
After-purchase consumer behavior	Own development based on Harrison & Shaw [186] and Hasan [187]
After-purchase Loyalty intentions	Adapted from Harris & Ezeh [188]

presents a full overview of the constructs, their corresponding sources, and the classification (adopted, adapted, or self-developed) for each item.

To minimize the potential for common method bias (CMB), several procedural safeguards were implemented during questionnaire design and administration. These included assuring participants of anonymity and confidentiality, clarifying that there were no right or wrong answers, and emphasizing the academic purpose of the study. Additionally, item order was randomized where applicable, and different response scale formats (e.g., 5-point and 7-point Likert scales) were used across sections to reduce response pattern bias. Finally, the reliability analysis presented in

Table 2. Reliability analysis.

Scale	Cronbach’s Alpha	Items
Behavioral and Attitudinal Factors	0.864	4
Social and Peer influences	0.853	5
Brand-related factors	0.908	4
Online experience	0.830	5
Marketing and advertising impact	0.797	5
Gen Z characteristics	0.868	5
Customer Behavior	0.710	5
Purchase intention	0.801	5
Actual purchase	0.827	4
After-purchase consumer behavior	0.666	2
After-purchase Loyalty intentions	0.850	6

indicates that most scales used in the study demonstrate good to excellent internal consistency, with Cronbach’s alpha values ranging from 0.797 to 0.908, and overall, the results support the internal consistency of the measurement instruments used in the study.

3.3. Research Process and Ethics

The research was conducted over a six-month period and followed a systematic approach. After establishing the theoretical framework and research questions, a structured questionnaire was developed and refined through a pilot study to enhance clarity and cultural relevance. Data collection took place in university settings, targeting Generation Z respondents through printed questionnaires. The researcher was present during this process to provide clarification and ensure informed consent. Once the data were collected, responses were carefully reviewed for accuracy and completeness. The dataset was then validated through checks for outliers and normality, with Cronbach’s alpha used to assess internal reliability and Confirmatory Factor Analysis conducted to confirm construct validity. Descriptive statistics outlined the sample’s demographic and behavioral characteristics, while inferential analyses, including correlation, multiple regression, and moderation analysis, explored the relationships among variables and the moderating role of e-shop location. This comprehensive methodology allowed for the investigation of both direct and contextual influences on consumer behavior. Ethical standards were upheld throughout the study, following the Declaration of Helsinki [189,190]. Participants received full information about the study, were assured of anonymity and confidentiality, and informed of their right to withdraw at any time. No identifiable data were collected, and all information was securely stored and used solely for research purposes, ensuring participant rights were respected throughout the process.

4. Results

4.1. Grouping of the Factors That Influence Consumer Behavior

The results of the Exploratory Factor Analysis (EFA) provide a robust empirical foundation for understanding the multidimensional nature of Generation Z’s online consumer behavior. Factor analysis was utilized to identify and group the underlying constructs influencing this behavior. The adequacy of the dataset for factor analysis was confirmed by the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy, which was 0.711, indicating moderate sampling sufficiency. Furthermore, Bartlett’s Test of Sphericity was statistically significant (x² = 2639.450, df = 253, p < 0.001), supporting the suitability of the data for structure detection.

Table 3. KMO and Bartlett’s Test for grouping of the factors that influence consumer behavior.

Kaiser–Meyer–Olkin Measure of Sampling Adequacy.		0.711
Bartlett’s Test of Sphericity	Approx. Chi-Square	2639.450
	df	253
	Sig.	0.000
Factors extracted		5
Variance Explained Factor 1	%	12.639
Variance Explained Factor 2	%	12.317
Variance Explained Factor 3	%	10.865
Variance Explained Factor 4	%	8.971
Variance Explained Factor 5	%	8.540
Total Variance Explained	%	66.562

presents the core statistics from the EFA. A total of five factors were extracted, which together explain 66.562% of the total variance in the dataset. This percentage indicates that the model captures a significant portion of the variability in online consumer behavior, validating the grouping structure and theoretical assumptions behind the scale construction.

Table 4. Rotated Component Matrix for grouping of the factors that influence consumer behavior.

	Comp. 1	Comp. 2	Comp. 3	Comp. 4	Comp. 5
Technology acceptance	0.820
Attitude toward online shopping	0.782
Brand behavioral intention	0.779
Perceived brand innovativeness	0.713
Intention to shop online	0.679
Online brand engagement	0.586
Shopper lifestyle	0.521
Early adopter mindset	0.435
Perceived social pressure		0.790
E-WOM		0.790
Social capital bridging		0.716
E-WOM information usefulness		0.712
Friend of a friend		0.602
Social capital bonding		0.460
Social media attachment		0.412
Brand knowledge			0.882
Brand loyalty			0.847
Brand image			0.642
Online brand experience			0.635
Brand trust			0.625
Brand awareness			0.539
Perceived product value			0.472
Website security and privacy				0.758
Perceived website quality				0.745
Task ambiguity				0.661
Prior online experience				0.521
Prior experience with online advertisement					0.834
Advertising awareness					0.764
Advertising creativity					0.587

Extraction Method: Principal Component Analysis.

outlines the variable loadings across the five extracted components. Each component represents a coherent construct derived from theoretical models and grouped through empirical analysis. Variables loading on Component 1 correspond to Behavioral and Attitudinal Factors, Component 2 captures Social and Peer Influences, Component 3 aligns with Brand-Related Factors, Component 4 comprises Online Experience variables, and Component 5 consists of Marketing and Advertising Impact indicators. The variable loadings range from 0.412 to 0.882, which suggests a strong contribution of individual items to their respective components and reinforces the validity of the theoretical categorizations.

The results of the Exploratory Factor Analysis (EFA) reveal five distinct dimensions that capture the latent structure of online consumer behavior. These dimensions reflect coherent thematic and theoretical groupings, validating the multidimensional nature of the constructs involved. Each factor encompasses a cluster of variables that share conceptual similarities and jointly define a particular aspect of digital consumer behavior—especially within the context of Generation Z. The first factor, labeled Behavioral and Attitudinal Factors, encapsulates individual dispositions, intentions, and personal traits that influence consumer behavior in online contexts. It includes variables such as perceived brand innovativeness, early adopter mindset, online brand engagement, brand behavioral intention, shopper lifestyle, attitude toward online shopping, technology acceptance, and intention to shop online. These variables collectively represent a consumer’s openness to innovation, their willingness to engage with brands, and their readiness to adopt digital shopping behaviors. Theoretical underpinnings include the Theory of Planned Behavior, Theory of Reasoned Action, and the Diffusion of Innovations Theory, which explain how attitudes, behavioral control, and innovativeness shape consumer intentions and actions. The second factor, Social and Peer Influences, brings together variables that emphasize the role of social networks, peer influence, and interpersonal communication in shaping consumer decisions. It includes social media attachment, perceived social pressure, electronic word-of-mouth (e-WOM), friend of a friend, e-WOM information usefulness, social capital bonding, and social capital bridging. This factor reflects how consumers, especially digital natives like Gen Z, are influenced by their online communities and interpersonal relationships. These variables are theoretically grounded in Social Capital Theory, Relationship Marketing Theory, and Social Impact Theory, which highlight the power of trust, shared norms, and relational influence in consumer behavior. The third factor, Marketing and Advertising Impact Factors, is focused on the influence of advertising exposure and creative messaging on consumer attitudes and awareness. It includes prior experience with online advertisements, advertising creativity, and advertising awareness. This factor captures how consumers perceive and process marketing communications. The theoretical background includes Schema Theory, Conditioned Learning Theory, and the Hierarchy of Effects Models, which explain how advertising can shape consumer memory, emotional engagement, and step-by-step movement from awareness to action. The fourth factor, Online Experience Factors, includes variables that describe users’ interaction with the digital shopping environment, specifically their perceptions of clarity, usability, trust, and prior exposure. It consists of task ambiguity, perceived website quality, website security and privacy, and prior online experience. These variables relate to consumers’ evaluation of the online interface and their confidence in using it safely and efficiently. Theoretical frameworks such as the Technology Acceptance Model, Elaboration Likelihood Model, and Trust-Based Consumer Behavior Theory help explain how website usability and perceived risk affect the likelihood of engaging in digital transactions. The fifth factor, Brand-Related Factors, captures perceptions, evaluations, and emotional connections consumers form with brands. It includes perceived product value, brand knowledge, brand image, brand trust, brand loyalty, brand awareness, and online brand experience. These variables reflect the depth and quality of the consumer-brand relationship, shaped by both cognitive evaluations and emotional experiences. Theoretical connections include Social Exchange Theory, Consumer Culture Theory, Theory of Reasonable Action, and Social Impact Theory, all of which highlight how perceived benefits, identity alignment, and social influence contribute to brand preference and loyalty.

Finally, a sixth factor, labeled Gen Z Characteristics, was added to capture behavior patterns and preferences that are particularly distinctive to the Generation Z consumer segment. This factor includes variables such as dependency on reviews, influencers’ impact, comments dependency, visual aspects dependency, sustainable image dependency, price dependency, and brand community dependency. These variables reflect how Gen Z consumers rely heavily on digital cues, peer validation, and social consciousness when making purchasing decisions. The inclusion of this factor is both theoretically and contextually justified, as the focus of the research is centered on Generation Z. Capturing their unique digital behaviors and value-driven consumption patterns provides a more accurate and comprehensive understanding of their role in shaping modern online consumer dynamics. This factor aligns with insights from Consumer Culture Theory and emerging generational behavior models, emphasizing that Gen Z places high value on authenticity, sustainability, and social engagement within the digital marketplace. In summary, the grouping of variables through EFA provides strong empirical support for the theoretical framework underpinning this study. Each factor represents a unique and essential component of online consumer behavior, offering insights into how individual traits, social dynamics, technological perceptions, branding elements, and generational identities collectively shape purchasing decisions. This factor structure not only enhances our theoretical understanding but also offers practical implications for targeted marketing, personalized content, and strategic brand positioning in digital environments.

Confirmatory Factor Analysis and Construct Validity

To validate the factor structure identified through exploratory factor analysis, a confirmatory factor analysis (CFA) was conducted. The CFA was performed using IBM SPSS AMOS (version 29). The model demonstrated a good overall fit, with fit indices satisfying conventional thresholds: χ²/df = 2.15, CFI = 0.935, TLI = 0.921, and RMSEA = 0.061. These results indicate an acceptable level of model-data fit, confirming the robustness of the proposed measurement model.

Table 5. Confirmatory Factor Analysis Results and Construct Validity.

Construct	CR	AVE	χ2/df	CFI	TLI	RMSEA
Behavioral and Attitudinal Factors	0.88	0.58	2.15	0.935	0.921	0.061
Social and Peer influences	0.86	0.55
Brand-related factors	0.91	0.63
Online experience	0.84	0.52
Marketing and advertising impact	0.80	0.51
Gen Z characteristics	0.87	0.57

presents the composite reliability (CR) and average variance extracted (AVE) for each latent construct. All CR values exceed the recommended minimum of 0.70, indicating strong internal consistency across the constructs. Additionally, AVE values for all constructs are above the threshold of 0.50, supporting adequate convergent validity. These findings confirm that the latent variables reliably reflect the theoretical dimensions they are intended to measure. Overall, the CFA results, along with the reliability and validity metrics, provide strong empirical support for the six-factor structure. This reinforces the appropriateness of the construct groupings used in subsequent structural analyses and highlights the psychometric soundness of the measurement model.

Finally, to statistically assess the risk of common method variance, Harman’s single-factor test was conducted. All items from the study were entered into an unrotated exploratory factor analysis. The results showed that the first factor accounted for 23.8% of the total variance, well below the 50% threshold commonly used to indicate serious common method bias. This suggests that common method variance is not a significant threat to the validity of the findings.

4.2. Correlation of Factors and Consumer Behavior

Consumer behavior, as a general construct encompassing pre-purchase attitudes, decision-making, and post-purchase perceptions, shows strong correlations with all influential factors (

Table 6. Correlations between influential factors and consumer behavior dimensions.

	CB	PI	AP	APCB	APLI
Behavioral and attitudinal factors	0.626 **	0.560 **	0.443 **	0.448 **	0.458 **
Social and peer influences	0.613 **	0.510 **	0.583 **	0.454 **	0.527 **
Marketing and advertising impact	0.444 **	0.215 **	0.323 **	0.208 *	0.296 **
Online experience	0.307 **	0.186 **	0.300 **	0.380 **	0.180 **
Brand-related factors	0.486 **	0.474 **	0.353 **	0.296 **	0.542 **
Gen Z characteristics	0.458 **	0.292 **	0.478 **	0.295 **	0.315 **

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). CB = Consumer behavior, PI = Purchase intention, AP = Actual purchase, APCB = After-purchase consumer behavior, APLI = After-purchase loyalty intentions.

). The most significant relationship is with behavioral and attitudinal factors (r = 0.626), highlighting that internal motivations, lifestyle choices, and openness toward online shopping play a pivotal role in shaping the overall behavior of Generation Z. This suggests that when Gen Z consumers hold favorable attitudes toward online shopping, believing it to be convenient, secure, and aligned with their values, they are more likely to demonstrate active engagement throughout the customer journey. Social and peer influences also show a strong association (r = 0.613), indicating that Gen Z is deeply embedded in digitally connected communities where opinions, reviews, and recommendations guide behavior. For marketers, this relationship suggests a need to design social commerce strategies that integrate user-generated content, influencer campaigns, and community-driven platforms. While marketing and advertising impact (r = 0.444) and online experience (r = 0.307) show lower correlations, their significance implies that seamless platform design, compelling digital content, and effective communication remain essential in encouraging broad online engagement. Brand-related factors (r = 0.486) and Gen Z characteristics (r = 0.458) round out the picture, affirming the importance of brand values and identity, and reinforcing that this generation behaves in ways that reflect their beliefs, lifestyle, and digital fluency.

Purchase intention is most strongly influenced by behavioral and attitudinal factors (r = 0.560) and social and peer influences (r = 0.510), emphasizing the role of both individual perceptions and external social stimuli in motivating Gen Z to consider purchasing new tech products. These findings suggest that e-commerce platforms should focus on crafting personalized and value-driven digital experiences while also leveraging peer recommendations and credible voices within the community to reinforce consumers’ readiness to buy. Brand-related factors (r = 0.474) also play a critical role in forming intention, reinforcing the value of strategic brand positioning and consistent messaging that appeals to Gen Z’s desire for authenticity, innovation, and ethical alignment. Gen Z characteristics (r = 0.292) reflect how personal and generational values such as environmental consciousness, tech curiosity, and global-mindedness influence decision-making. Meanwhile, marketing and advertising impact (r = 0.215) and online experience (r = 0.186) are weaker predictors, yet still relevant, highlighting that creative campaigns and ease of navigation should not be overlooked in the strategy mix, even if they play a more supportive than primary role. Actual purchase behavior shows the strongest correlation with social and peer influences (r = 0.583), signifying that social proof and network endorsement are major determinants of Gen Z’s actual buying actions. This suggests that peer-generated content, influencer marketing, and transparent feedback mechanisms can convert intention into action. Gen Z characteristics (r = 0.478) and behavioral and attitudinal factors (r = 0.443) further contribute, revealing that personality traits such as digital savviness, desire for novelty, and openness to risk significantly shape purchase outcomes, especially when aligned with positive internal attitudes toward consumption. Brand-related factors (r = 0.353) confirm that trust and brand familiarity are important, particularly for newly launched technological products where uncertainty may be high. Online experience (r = 0.300) also plays a measurable role, highlighting the importance of providing a fluid and secure shopping environment. Interestingly, marketing and advertising impact (r = 0.323) is again on the lower end, indicating that for this generation, marketing communications alone may not be enough to drive purchase decisions unless reinforced by trust, peer support, and experiential value.

After-purchase consumer behavior refers to actions taken post-transaction, such as writing reviews, recommending products, or engaging with brand communities. It correlates most with social and peer influences (r = 0.454) and behavioral and attitudinal factors (r = 0.448), showing that even after a purchase, Gen Z remains socially motivated and attitudinally reflective. This means post-purchase engagement strategies, such as social sharing incentives, feedback invitations, and community forums, can significantly increase the likelihood of continued interaction and advocacy. Online experience (r = 0.380) also has a notable impact, suggesting that a positive digital shopping experience translates into ongoing consumer–brand relationships. Gen Z characteristics (r = 0.295) and brand-related factors (r = 0.296) contribute moderately, reflecting how lifestyle fit and brand identity guide consumers’ desire to engage with the brand after a transaction. Marketing and advertising impact (r = 0.208) is the weakest predictor here, reinforcing the notion that promotional efforts have less sway once the consumer enters the post-purchase phase, where satisfaction and social context become dominant forces. After-purchase loyalty intentions are most strongly tied to brand-related factors (r = 0.542), underscoring the critical role of brand trust, emotional engagement, and consistent delivery in earning long-term consumer commitment. When a brand resonates with Gen Z on a values-based and experiential level, the chances of loyalty increase dramatically. Social and peer influences (r = 0.527) follow closely, highlighting that even loyalty is a socially mediated construct for this generation; their allegiance to a brand can be affirmed or weakened based on peer feedback and broader digital discourse. Behavioral and attitudinal factors (r = 0.458) and Gen Z characteristics (r = 0.315) indicate that individual predispositions and generational norms play important supporting roles, pointing to the need for alignment between brand identity and consumer self-concept. Though less powerful, marketing and advertising impact (r = 0.296) still contributes, particularly when used to maintain ongoing awareness and reinforce brand narratives. Online experience (r = 0.180) has the weakest link, yet still suggests that functional satisfaction is necessary—though not sufficient—when it comes to fostering loyalty. Overall, these interpretations reveal the nuanced and multi-layered nature of Gen Z’s consumer behavior. For businesses targeting this demographic, strategies should prioritize building brand authenticity, leveraging peer networks, and creating meaningful, personalized experiences across the pre-purchase, purchase, and post-purchase journey. Understanding the relative weight of these influential factors can help firms tailor their digital presence and customer engagement tactics for maximum relevance and impact.

4.3. Location of E-Shop as Moderator

In the context of this study, moderation analysis was employed to explore whether the location of the e-shop, domestic (Greece) or international, modifies the strength and direction of the relationships between the influential factors and the key consumer behavior outcomes. While several models were tested, only a subset revealed statistically significant interaction effects, indicating that the impact of certain influential factors on consumer behavior differs depending on whether the purchase is made from a local or an international online store. The following three moderation analyses present the specific cases where e-shop location was found to play a moderating role, offering practical insights into how geographical context influences Generation Z’s digital consumption behavior, particularly in the domain of newly launched technological products.

4.3.1. Marketing and Advertising Impact/Purchase Intention

The moderation analysis presented in the results sheds important light on how e-shop location influences the relationship between marketing and advertising impact (MAI) and purchase intention (PI), particularly in the context of Generation Z’s online behavior for newly launched technological products. The overall model, as shown in

Table 7. Moderation Analysis Summary—Marketing and advertising impact/Purchase intention.

Model	R	R2	MSE	F	df1	df2	p
1	0.2570	0.0660	0.7172	3.3937	3	144	<0.05

, is statistically significant with R² = 0.066 (F_(3,144) = 3.3937, p < 0.05), meaning that approximately 6.6% of the variance in purchase intention can be explained by the combined influence of MAI, e-shop location, and their interaction. While the explanatory power of the model is modest, it is still meaningful in behavioral sciences, especially when considering the multitude of psychological and environmental variables that simultaneously influence consumer decision-making.

Looking further into the regression coefficients in

Table 8. Regression Coefficients—Marketing and advertising impact/Purchase intention.

Predictor Variables	β	SE	t	p	LLCI	ULCI
Constant	5.0669	1.5065	3.3633	0.0010	2.0891	8.0447
Marketing and Advertising Impact (MAI)	−0.5573	0.4154	−1.3415	0.1819	−1.3783	0.2638
Location	−2.4812	1.3407	−1.8507	0.0663	−5.1312	0.1688
Interaction (MAI x Location)	0.7457	0.3696	2.0178	0.0455	0.0152	1.4762

, we observe that neither Marketing and Advertising Impact (β = −0.5573, p = 0.1819) nor Location (β = −2.4812, p = 0.0663) alone has a statistically significant effect on purchase intention. However, the interaction between them (β = 0.7457, p = 0.0455) is statistically significant, which means that the impact of marketing and advertising on Gen Z’s purchase intention depends on whether the e-shop is domestic or international.

This finding is further clarified in

Table 9. Conditional Effects of MAI at Values of Location on Purchase Intention.

Location (Moderator Level)	Effect (B)	SE	t	p	LLCI	ULCI
Greece	0.1885	0.1095	1.7208	0.0874	−0.0280	0.4049
Abroad	0.9342	0.3530	2.6466	0.0090	0.2365	1.6318

, which provides the conditional effects of MAI on purchase intention across the two levels of the moderator (i.e., e-shop location). When the e-shop is located in Greece, the relationship between MAI and purchase intention is not significant (b = 0.1885, p = 0.0874). However, when the e-shop is located outside Greece, the effect becomes strong and significant (b = 0.9342, p = 0.0090). This implies that Gen Z consumers place greater weight on marketing and advertising when purchasing from international online stores, perhaps because they rely more on persuasive messages to reduce uncertainty, perceive higher value, or are more drawn to the prestige or exclusivity of foreign offerings.

The figure below offers a visual representation of the moderation effect of e-shop location on the relationship between Marketing and Advertising Impact (MAI) and Purchase Intention (PI). As illustrated in Figure 2, two regression lines are plotted, one for purchases made from e-shops located in Greece (blue line) and the other for those made from international e-shops (red line). The results visually confirm the statistical findings from the moderation analysis. The slope of the red line (Abroad: y = 0.1 + 0.93x) indicates a much stronger positive relationship between MAI and PI when the e-shop is located abroad, with an R² value of 0.730—implying that 73% of the variation in purchase intention is explained by marketing and advertising when consumers shop from international e-shops. In contrast, the blue line (Greece: y = 2.59 + 0.19x) demonstrates a flatter slope and a much weaker fit (R² = 0.021), suggesting that marketing and advertising have minimal influence on purchase intention for domestic online stores. These findings highlight the practical importance of tailoring digital marketing efforts based on e-shop geography. Specifically, brands targeting Gen Z through international e-commerce platforms should heavily invest in high-impact marketing strategies, such as creative digital ads, influencer endorsements, and interactive social media campaigns, as these efforts significantly shape buying intentions in this context. On the other hand, for domestic e-shops, the focus may need to shift toward enhancing user experience, trust building, and community engagement, as traditional advertising seems to carry less weight in influencing Gen Z’s intentions to purchase.

The practical implications of these findings are substantial. For businesses operating international e-shops, strategic investments in creative and compelling marketing communications, ranging from digital advertising and influencer collaborations to interactive campaigns, can be particularly effective in increasing Gen Z’s intention to purchase. These consumers may see international brands as more innovative or aspirational and may require higher levels of promotional engagement to feel confident in their decision-making. On the other hand, for domestic e-shops, traditional marketing and advertising efforts may play a more supportive or background role, with Gen Z consumers potentially relying more on familiarity, ease of access, or personal recommendations. This suggests that local e-commerce platforms might benefit more from strategies focused on community-building, customer service, and trust-based initiatives, rather than solely increasing marketing spend. In conclusion, this analysis highlights how e-shop location serves as a meaningful contextual factor, altering the strength and nature of marketing’s influence on Gen Z’s purchasing intentions. It emphasizes the importance of adapting promotional strategies based on geographic positioning and consumer perception, reinforcing the need for nuanced, data-driven approaches in cross-border digital commerce.

4.3.2. Gen Z Characteristics and Purchase Intention

The following moderation analysis explores the effect of Gen Z characteristics (GZC) on Purchase Intention (PI), with the e-shop location acting as a moderating variable. As shown in the results of the overall model, presented in

Table 10. Moderation Analysis Summary—Gen Z characteristics/Purchase intention.

Model	R	R2	MSE	F	df1	df2	p
1	0.3878	0.1504	0.6774	17.5838	3	298	<0.001

, the interaction between GZC and e-shop location is statistically significant. The model explains 15.04% of the variance in purchase intention (R² = 0.1504, F_{(3, 298)} = 17.5838, p < 0.001), indicating a notably stronger explanatory power compared to the previous moderation model on marketing impact.

The regression coefficients from

Table 11. Regression Coefficients—Gen Z characteristics/Purchase intention.

Predictor Variables	β	SE	t	p	LLCI	ULCI
Constant	4.7647	1.1143	4.2758	0.0000	2.5717	6.9577
Gen Z characteristics (GZC)	−0.7127	0.3500	−2.0365	0.0426	−1.4015	−0.0240
Location	−3.1342	1.0213	−3.0687	0.0023	−5.1441	−1.1242
Interaction (GZC x Location)	1.1126	0.3254	3.4195	0.0007	0.4723	1.7529

show that Gen Z characteristics, when considered alone, have a slightly negative effect on purchase intention (β = −0.7127, p < 0.05). Similarly, e-shop location has a significant negative effect (β = −3.1342, p < 0.01), suggesting that international shops are generally linked with lower purchase intention, perhaps due to trust concerns or unfamiliarity. However, the key finding here is the significant positive interaction between GZC and location (β = 1.1126, p < 0.001), meaning that Gen Z characteristics influence purchase intention differently depending on the shop’s location.

Further analysis of the conditional effects (

Table 12. Conditional Effects of GZC at Values of Location on Purchase intention.

Location (Moderator Level)	Effect (B)	SE	t	p	LLCI	ULCI
Greece	0.3999	0.0744	5.3724	0.0000	0.2534	0.5463
Abroad	1.5124	0.3167	4.7750	0.0000	0.8891	2.1358

) offers important insights. When the e-shop is based in Greece, Gen Z characteristics have a moderately positive and statistically significant effect on purchase intention (b = 0.3999, p < 0.001). However, this influence becomes much stronger when the e-shop is located abroad (b = 1.5124, p < 0.001). This dramatic increase highlights the fact that Gen Z values, such as tech-savviness, digital immersion, sustainability awareness, and demand for authenticity, are even more activated and impactful when interacting with international online platforms.

Figure 3 below visually illustrates the moderating role of e-shop location on the relationship between Gen Z characteristics (GZC) and Purchase Intention (PI). The scatter plot presents two regression lines: one for participants who made purchases from Greek e-shops (blue line) and another for those who bought from international e-shops (red line). The regression lines show a notable difference in slope between the two groups. For consumers shopping on Greek e-shops, the slope is gentler (y = 1.63 + 0.4x), and the R² value is 0.092, indicating a modest influence of Gen Z traits on their purchase intention. In contrast, when purchases are made from international e-shops, the relationship becomes much stronger (y = −1.5 + 1.51x), with an R² value of 0.665, meaning that 66.5% of the variance in purchase intention can be explained by Gen Z characteristics in this context. This graphical evidence reinforces the quantitative findings. It suggests that the impact of Gen Z’s values, expectations, and digital behaviors is significantly more pronounced when the shopping context is global. Brands operating internationally need to recognize and leverage this connection—tailoring campaigns to resonate with Gen Z’s desire for innovation, personalization, sustainability, and authenticity. On the other hand, domestic e-shops in Greece may need to adopt more nuanced and targeted approaches to activate Gen Z traits effectively, as their influence, while still present, is comparatively less dominant. This differentiation underscores the importance of localization and strategic adaptation in digital marketing, especially when appealing to a demographic as complex and value-driven as Generation Z.

These findings suggest several practical implications. For international e-commerce platforms, it is essential to align marketing strategies with the core identity traits of Gen Z, such as global consciousness, environmental awareness, digital fluency, and social inclusivity. Messaging and branding that reinforce these characteristics are more likely to resonate and boost purchase intentions. Conversely, domestic e-shops, while still influenced by Gen Z traits, may need to work harder to contextualize these values in familiar, locally relevant narratives and build trust through authenticity and community engagement.

4.3.3. Gen Z Characteristics and After-Purchase Consumer Behavior

The overall model was statistically significant (R² = 0.0750, F_{(3, 298)} = 8.0583, p < 0.001), explaining 7.5% of the variance in APCB. While the percentage of explained variance is modest, the influence of the examined variables is statistically meaningful (

Table 13. Moderation Analysis Summary—Gen Z characteristics/After-purchase consumer behavior.

Model	R	R2	MSE	F	df1	df2	p
1	0.2739	0.0750	0.7130	8.0583	3	298	<0.001

).

Gen Z characteristics, when examined in isolation (at a fixed location), were not significantly associated with APCB (β = −0.3860, p > 0.05). However, e-shop location had a significant negative effect (β = −2.1614, p = 0.04), indicating that certain locations (e.g., domestic e-shops) may be associated with lower levels of post-purchase behavior. The interaction between Gen Z characteristics and location was statistically significant (β = 0.6751, p = 0.044), meaning that the effect of GZC on APCB depends on the e-shop’s location (

Table 14. Regression Coefficients—Gen Z characteristics/After-purchase consumer behavior.

Predictor Variables	β	SE	t	p	LLCI	ULCI
Constant	4.8159	1.1432	4.2125	0.0000	2.5660	7.0657
Gen Z characteristics (GZC)	−0.3860	0.3590	−1.0751	0.2832	−1.0926	0.3206
Location	−2.1614	1.0478	−2.0627	0.0400	−4.2234	−0.0993
Interaction (GZC x Location)	0.6751	0.3338	2.0224	0.0440	0.0182	1.3320

).

The conditional effects analysis shows that for Greek e-shops, the relationship between GZC and APCB is positive and statistically significant (b = 0.2891, p < 0.001) (

Table 15. Conditional Effects of Gen Z characteristics at Values of Location in After-purchase consumer behavior.

Location (Moderator Level)	Effect (B)	SE	t	p	LLCI	ULCI
Greece	0.2891	0.0764	3.7858	0.0002	0.1388	0.4393
Abroad	0.9642	0.3250	2.9671	0.0032	0.3247	1.6037

). That is, the more Gen Z consumers express typical generational traits (technological savviness, values of sustainability, digital interaction), the more likely they are to engage positively after the purchase from local stores. For international e-shops, the effect is even stronger (b = 0.9642, p = 0.0032), suggesting that Gen Z consumers are more inclined to exhibit post-purchase engagement when the purchase is made from an international online store.

Figure 4 below offers a visual representation of the interaction between Gen Z characteristics (GZC) and after-purchase consumer behavior (APCB), moderated by the location of the e-shop. Two separate regression lines are plotted: one for consumers who purchased from Greek e-shops (blue line) and one for those who made purchases from international e-shops (red line). From the figure, it becomes evident that the relationship between Gen Z traits and after-purchase behavior is stronger for international e-shops. The red line, corresponding to foreign e-shops, has a steeper slope (y = 0.49 + 0.96x) and a higher R² of 0.406, compared to the blue line representing Greek e-shops (y = 2.65 + 0.29x, R² = 0.048). This indicates that over 40% of the variation in after-purchase behavior in the foreign context can be explained by Gen Z characteristics, while the predictive power of those same characteristics in the Greek context is minimal. These findings underscore the necessity for brands operating abroad to recognize the post-purchase expectations of Gen Z, who tend to value follow-up communication, personalized experiences, and continued brand engagement more strongly when interacting with non-local retailers. Companies targeting Gen Z internationally may need to invest in strategic post-purchase processes, such as feedback collection, loyalty programs, or tailored communications that resonate with the values of this digitally native generation. On the contrary, although Gen Z characteristics still positively influence post-purchase behavior in Greek e-shops, the influence is noticeably weaker. This implies that local retailers may need to amplify their efforts to keep Gen Z engaged after the point of sale, especially in fostering satisfaction, building trust, and encouraging loyalty. The findings highlight how strategic adjustments depending on e-shop location can improve long-term consumer-brand relationships and enhance customer retention among Gen Z consumers.

The above differentiation highlights the need to adjust marketing strategies based on the location of the e-shop. For international e-shops, leveraging Gen Z traits through personalized, technologically advanced, and authentic experiences can significantly strengthen post-purchase engagement. This includes leaving reviews, repurchasing, making recommendations, and sharing on social media. On the other hand, Greek e-shops, while still benefiting from a positive relationship, must invest more effort into enhancing the post-purchase experience. This could include loyalty programs, responsive customer service, and environmentally responsible practices, in order to build trust and long-term commitment among this demanding consumer group. In summary, location functions as a critical factor that shapes the effectiveness of Gen Z characteristics in influencing after-purchase consumer behavior, making it an essential consideration when designing targeted digital marketing strategies. These results provide clear evidence that consumer engagement does not end at checkout, and brands that operate internationally may have a competitive advantage in cultivating ongoing relationships with Gen Z. The enhanced influence of GZC on APCB in foreign e-shops suggests that Gen Z consumers trust international platforms more, or that these platforms provide more compelling post-purchase experiences aligned with their values.

From a strategic marketing perspective, this calls for Greek e-shops to reconsider how they handle the post-purchase journey. While Gen Z consumers are willing to remain engaged even with local brands, the magnitude of engagement is considerably lower, indicating potential weaknesses in areas such as follow-up communication and thank-you messages; post-purchase satisfaction surveys; prompt customer service responses; access to return/refund services; gamified or community-based loyalty programs; opportunities to leave reviews, earn digital badges, or participate in advocacy. International e-shops may already offer these services, thereby aligning better with Gen Z’s need for interaction, recognition, and convenience. Moreover, this finding confirms the broader literature that portrays Gen Z as a value-driven generation, for whom post-purchase experiences, not just product satisfaction, contribute to brand trust and loyalty. This generation is more likely to remain loyal to brands that acknowledge their opinions, offer transparency, and build interactive communities around consumption. This evidence reinforces the notion that post-purchase behavior is not merely a byproduct of satisfaction but an active dimension of consumer-brand interaction. For Gen Z, especially, post-purchase behavior reflects a deeper psychological investment in the brand, and this investment is enhanced when the brand operates with global visibility, accessibility, and cultural sensitivity. In conclusion, businesses aiming to appeal to Gen Z, especially in tech-oriented markets, must not only focus on getting the purchase but also craft holistic and location-sensitive strategies that keep this generation engaged long after the transaction is completed. This approach could be the key to turning one-time buyers into loyal advocates.

5. Discussion

This study examined the moderating effect of e-shop location, defined as domestic (Greece) versus international, on the relationship between influential factors and consumer behavior among Generation Z in the context of newly launched technological products. The findings demonstrated that e-shop location significantly shapes how various psychological, attitudinal, and behavioral drivers influence both purchase-related decisions and post-purchase actions. From a theoretical standpoint, the study contributes to the digital consumer behavior literature by demonstrating how context-sensitive variables—particularly geographic and cultural cues—moderate key relationships within established behavioral models. This study addresses a notable gap by integrating the spatial dimension—i.e., the geographic origin of e-commerce platforms—into established models of digital consumer behavior. This integration adds an important boundary condition to widely used theories such as the Theory of Planned Behavior, the Technology Acceptance Model, and Relationship Marketing Theory, helping to evolve their application within globalized digital markets. Importantly, these results expand current understanding in digital marketing and the consumer behavior literature by revealing that the geographic and cultural context of an e-shop can alter the strength and even the direction of key consumer relationships. The first moderation analysis highlighted that the influence of marketing and advertising impact (MAI) on purchase intention (PI) is significantly stronger when Gen Z consumers shop from international e-shops. This aligns with prior research that portrays Gen Z as highly responsive to dynamic, visually appealing, and digitally native marketing strategies [15]. However, the present study adds nuance by showing that this responsiveness is context-sensitive: marketing has a weaker effect in domestic contexts. This may be explained by factors such as cultural familiarity, trust in local institutions, or differing expectations in promotional sophistication. Compared to international platforms, which are often associated with innovation, exclusivity, and global appeal, local e-shops may be perceived by Gen Z as less differentiated or inspiring in their branding strategies. This finding echoes previous insights by Muhammad, Adeshola, & Isiaku [17], who argue that international brands, due to their broader reach and more complex market positioning, are better equipped to deploy high-impact marketing. The practical implication here is that international platforms can significantly enhance purchase intention through targeted and emotionally resonant campaigns. Conversely, Greek e-shops may need to place less emphasis on advertising and more on experiential marketing, community trust, or customer relationship management to drive the same behavioral outcome.

The second moderation analysis examined how Gen Z characteristics (GZC), such as digital nativeness, environmental values, and authenticity orientation, affect purchase intention under varying e-shop locations. The analysis revealed that GZC positively influence PI in both settings, but the effect is significantly stronger in international e-shops. This supports the growing body of literature emphasizing that Gen Z exhibits stronger behavioral alignment with brands that reflect their values [191,192]. The enhanced impact observed in the international context may be due to the perception that foreign platforms better embody Gen Z’s preferred brand traits, such as sustainability, tech innovation, and inclusivity. This contrasts with earlier findings by Sharma and Kanchwala [14], who argued that Gen Z is more influenced by local brand narratives that emphasize cultural relevance and social connection. While this may be true in some product categories, the current findings suggest that for tech-related purchases, where innovation and perceived quality are critical, international e-shops may better match Gen Z’s aspirations. Therefore, domestic e-shops could benefit by adapting not only their product offerings but also the symbolic language of their branding to resonate more deeply with Gen Z’s evolving identity and digital consciousness.

The third and perhaps most significant analysis explored the interaction between Gen Z characteristics and after-purchase consumer behavior (APCB), again moderated by e-shop location. The results showed that while GZC had a positive and significant influence on APCB in both domestic and international contexts, the strength of this relationship was considerably higher for international e-shops. This indicates that post-purchase engagement, such as providing feedback, sharing brand experiences online, or demonstrating loyalty, is more likely when the initial purchase is made through a foreign platform. These findings are consistent with previous studies suggesting that Gen Z places high value on post-transactional experiences [181,183]. Theoretically, this adds to the literature by showing that the strength of post-purchase behavior predictors is not only context-dependent but also moderated by perceived brand congruence with generational values, extending current conceptualizations of loyalty formation among digital-native consumers. International brands may offer more refined post-purchase processes, ranging from responsive customer service to gamified loyalty programs, that meet the heightened expectations of Gen Z. The steep slope observed in the regression line for foreign e-shops reinforces the notion that these brands are more adept at creating emotionally and functionally satisfying follow-up experiences. For Greek e-shops, this poses a strategic imperative. Although GZC still positively influence APCB in the domestic context, the relatively weaker effect suggests gaps in the post-purchase journey. These gaps may include limited personalization, absence of community engagement, or insufficient mechanisms for consumers to feel heard and valued after the transaction. This echoes insights by Francis and Hoefel [56], who noted that Gen Z loyalty is largely driven by shared values and interactive brand experiences, not just product quality.

Taken together, the moderation analyses provide compelling evidence that e-shop location plays a pivotal role in shaping the effectiveness of consumer behavior drivers. This represents a valuable theoretical contribution, especially since previous research has largely treated consumer behavior determinants as context independent. By introducing e-shop location as a boundary condition to established behavioral theories (e.g., TPB, TAM, and Relationship Marketing Theory), the study provides a novel synthesis that advances our understanding of how structural and cultural factors influence digital consumption. By incorporating e-shop location as a moderator, the current study shows that even well-established consumer psychology variables can behave differently depending on cultural, logistical, and brand-related perceptions tied to geographic origin. Practically, these findings suggest that global and local e-commerce platforms must adopt differentiated strategies. For international e-shops, aligning with Gen Z’s value system, offering rich marketing experiences, and maintaining high standards for post-purchase engagement may offer a competitive edge, particularly when these strategies are carefully tailored to the values and expectations of this demographic. Given the modest effect sizes observed, such efforts should be implemented incrementally and tested for effectiveness within specific market segments rather than assumed to deliver universal gains. For domestic e-shops, the focus should shift toward building trust, delivering consistent service quality, and adopting locally resonant narratives that can evoke the same level of emotional and behavioral commitment. In conclusion, the present study underscores the importance of strategic localization in e-commerce. While Gen Z is globally connected, their behavior remains highly sensitive to contextual cues such as brand origin, platform aesthetics, and post-purchase experiences. The moderation effect of e-shop location reveals that digital consumer behavior cannot be fully understood without considering geographic and cultural framing. This is in line with findings from studies conducted in markets such as the U.S., South Korea, and Germany, which also highlight how Gen Z’s purchasing behavior is influenced by culturally specific expectations around service speed, communication tone, and brand authenticity [193,194]. By comparing these findings, a more comprehensive picture emerges of how Gen Z adapts its digital engagement across borders, balancing global values with local preferences. However, it is important to note that the observed effect sizes were modest, indicating that e-shop location alone accounts for a limited portion of behavioral variance. In theoretical terms, the study advances the conceptual understanding of Generation Z’s digital consumer behavior by showing how traditional behavioral models interact with spatial context. The study proposes an enriched conceptual framework where generational values, technology-oriented behaviors, and location-sensitive cues interact to shape the digital consumer journey. This multidimensional synthesis moves beyond conventional linear models by explicitly accounting for both internal drivers (e.g., attitudes, norms, behavioral control) and external moderators (e.g., brand origin and platform geography). This multidimensional approach moves beyond linear consumer models, offering a dynamic and context-aware lens for future research. For both academics and practitioners, this points to the necessity of embracing a multidimensional approach to consumer research, one that accounts not only for who the consumer is, but also for where and how the consumer chooses to engage. Strategic decisions based on these findings should be made with caution, and marketers are encouraged to test location-sensitive strategies on a pilot basis before broader implementation.

Beyond the moderating role of e-shop location, the study’s bivariate correlation analysis reveals a rich and layered structure of influential relationships shaping Generation Z’s consumer behavior across all phases of the customer journey. Consumer behavior as a general construct, encompassing pre-purchase attitudes, decision-making, and post-purchase actions, was most strongly correlated with behavioral and attitudinal factors. This reinforces prior findings [e.g., 181] that internal motivators such as openness to innovation, comfort with digital environments, and lifestyle compatibility are central to how Gen Z approaches consumption. These consumers are not simply reactive to stimuli; rather, their behavior reflects a deeply integrated perception of what shopping means within their broader value system. The strong correlation with social and peer influences further affirms the importance of digitally mediated communities, where peer recommendations, social media discourse, and online reviews significantly guide behavioral patterns. In practice, this suggests that brands must ensure not only functional excellence but also social relevance, engaging in two-way dialogues, investing in community platforms, and curating authentic user-generated content that mirrors the voice and ethos of Gen Z. When broken down into specific behavioral dimensions, such as purchase intention, actual purchase, and post-purchase loyalty, distinct patterns emerge that deepen our understanding of this demographic’s motivations. Purchase intention is most strongly influenced by attitudinal and behavioral predispositions and social influences, supporting the notion that both individual readiness and external validation must align before Gen Z consumers act. Actual purchase behavior, on the other hand, is most closely tied to social and peer influences, indicating that trust and authenticity gained through social proof are essential for transaction completion. This reflects the broader literature around Gen Z’s digital consumer habits, which suggests that loyalty and conversion are increasingly linked to the credibility of peer voices rather than brand authority alone [174]. Post-purchase behavior, including continued engagement and brand advocacy, is also driven by social motivations and prior experience, pointing to the necessity of seamless, satisfying interactions not just up to the point of sale, but beyond it. The strongest predictor of after-purchase loyalty intentions was brand-related factors, echoing previous findings (e.g., [179]) that trust, emotional attachment, and consistent brand values are fundamental for long-term consumer allegiance. These findings collectively support a more holistic view of the consumer–brand relationship, one where influence is cumulative, multifaceted, and often socially negotiated. Businesses aiming to resonate with Gen Z must therefore embrace an ecosystemic approach, combining personalized, value-driven messaging with robust social integration and strong brand identity, across both domestic and international e-commerce environments.

6. Conclusions

This study set out to explore the complex dynamics of Generation Z’s online consumer behavior toward newly launched technological products, with a specific emphasis on the moderating role of e-shop location. The findings confirmed that while various influential factors, ranging from behavioral attitudes to social influences and brand perceptions, are generally predictive of Gen Z behavior, the geographical context of the e-shop adds a critical layer of complexity. In particular, the location of the online store, whether domestic or international, significantly shaped the strength of relationships between influential variables and consumer outcomes such as purchase intention and post-purchase behavior. These moderating effects highlight the importance of recognizing that Gen Z’s engagement with brands is not only value-driven but also context-sensitive. Among the key findings, the marketing and advertising impact was found to significantly influence purchase intention only in the case of international e-shops. This implies that Gen Z consumers are more responsive to promotional messaging and brand storytelling when interacting with foreign platforms, likely due to perceived exclusivity or higher brand prestige. Similarly, Gen Z characteristics, such as digital fluency, preference for innovation, and emphasis on sustainability, were shown to exert a stronger influence on both purchase intention and after-purchase behavior when the shopping occurred through international online channels. These insights underscore the need for businesses operating in cross-border digital markets to align their strategies with Gen Z’s generational traits, offering tailored, culturally aware, and technologically advanced experiences. Additionally, correlation analyses revealed a clear pattern: Gen Z consumer behavior is strongly rooted in internal attitudes, peer validation, and brand identity. From the pre-purchase stage to post-purchase engagement and loyalty, social and behavioral drivers consistently outperformed more traditional predictors such as advertising exposure or general platform usability. This not only reinforces the literature positioning Gen Z as a socially embedded, value-conscious generation but also calls on brands to recalibrate their digital presence by focusing on authenticity, social integration, and post-sale relational marketing. In closing, the study contributes to the growing body of research on digital consumer behavior by introducing e-shop location as a moderating variable, a factor previously underexplored. It emphasizes that understanding Gen Z’s purchasing decisions requires a multifaceted approach that incorporates both psychological traits and contextual cues. For businesses, the practical implication is clear: success in the Gen Z market depends not only on what is being sold or how, but also on where the interaction takes place. Tailoring strategies to local and international contexts, while staying attuned to the generational values of this digitally native cohort, will be critical for long-term brand relevance and customer retention.

Research Limitations and Future Directions

Based on the methodological approach and key findings of this study, several limitations should be acknowledged, along with suggestions for future research. First, the use of a cross-sectional design, while effective for capturing behavioral trends at a single point in time, limits the ability to make causal inferences or observe changes in consumer behavior over time. Given the rapid evolution of both Gen Z’s preferences and technological advancements, future studies could employ longitudinal designs to track behavioral shifts and better understand how digital habits develop or change with exposure, experience, or life stage progression. Secondly, the sampling strategy, which combined convenience and systematic sampling, enabled practical data collection from a relevant and accessible Gen Z population, primarily university students. Notably, 85% of the sample consisted of students, which further narrows the representativeness of the data. This approach may limit the generalizability of the findings to the broader Gen Z demographic, particularly those not enrolled in higher education or from rural and underrepresented regions. Future research should consider employing more diverse or probabilistic sampling techniques, such as stratified or cluster sampling, to ensure a broader demographic representation and to validate the general patterns observed across different segments of Generation Z. Moreover, future studies could specifically compare student and non-student subsets within Gen Z to explore potential differences in attitudes, behaviors, or experiences, thereby offering more targeted insights into this generational cohort.

Additionally, while the study explored the moderating role of e-shop location, offering new insights into how geographic context shapes consumer responses, it focused specifically on the binary distinction between domestic (Greek) and international platforms. This dichotomy, while analytically useful, may oversimplify the nuanced consumer perceptions of different international markets. Future research could explore a more detailed categorization of international e-shops (e.g., Western Europe, North America, East Asia) to assess whether Gen Z’s behavior varies based on specific cultural, logistical, or branding cues associated with particular global regions. Moreover, future studies could place a stronger emphasis on cross-cultural comparisons to examine how digital consumer behavior manifests in differing economic, regulatory, and technological contexts. Investigating these behaviors across varied market environments would contribute to a more comprehensive and globally relevant understanding of Gen Z’s digital engagement patterns.

Another important limitation of the study concerns the relatively low explanatory power of the moderation models. Specifically, the R² values for the moderation effects of marketing and advertising impact on purchase intention (R² = 0.066) and Gen Z characteristics on post-purchase behavior (R² = 0.075) indicate that only a small proportion of the variance in these outcomes is explained by the models. While these effects are statistically significant and provide useful directional insights, they suggest that additional variables, such as personality traits, platform-specific design factors, or psychological drivers, may also play a meaningful role. Future research should therefore consider expanding the model to include further contextual, behavioral, or affective constructs to enhance explanatory power and build a more comprehensive understanding of digital consumer decision-making. Lastly, the data collection tool, though grounded in a robust theoretical framework and validated scales, relied solely on self-reported responses, which can be subject to social desirability and recall bias. Triangulating quantitative results with qualitative methods, such as in-depth interviews, behavioral tracking, or experimental designs, would enrich our understanding of the mechanisms driving Gen Z consumer behavior. This multi-method approach could uncover deeper motivations and unconscious drivers that are not easily captured through structured surveys. Such culturally sensitive and contextually diverse research efforts would further contribute to refining e-commerce strategies tailored to the complex and dynamic nature of this digitally native generation.