Open AccessDigital Trust Formation in Gen-Z Wealth-Tech Adoption: The Influence of e-WOM, Engagement, and Influencers
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The rapid growth of wealth-tech platforms has intensified the importance of digital trust, particularly among Generation Z investors who rely heavily on social media–driven information sources when making investment-related decisions. While prior studies have examined influencer marketing, electronic word-of-mouth (e-WOM), and social media engagement in fintech contexts, empirical research that integrates these persuasion mechanisms into a unified trust-based model of wealth-tech adoption intention remains limited. Drawing on Source Credibility Theory, Trust Transfer Theory, and digital engagement frameworks, this study proposes and tests an integrative model in which influencer credibility, e-WOM, and social media engagement simultaneously influence wealth-tech adoption Intention through the mediating role of digital trust. Using survey data collected from 255 Generation Z actual users of wealth-tech platforms in Indonesia, this study employs Structural Equation Modelling (SEM) to simultaneously test the measurement model and the proposed trust-based structural relationships, including the mediating role of digital trust. A purposive sampling approach was adopted to ensure respondents possessed direct experience with wealth-tech applications, thereby enhancing construct validity in this specialized digital investment context. The results indicate that influencer credibility, e-WOM, and social media engagement each exert a significant positive effect on digital trust, which in turn strongly influences wealth-tech adoption Intention. Digital trust is found to play a critical mediating role, reinforcing its central importance in investment-oriented digital platforms characterized by heightened perceived risk. This study contributes to the literature by extending digital trust and fintech adoption research in three ways: (1) by integrating multiple digital persuasion mechanisms into a single trust-centered framework, (2) by empirically validating digital trust as a key mediating mechanism in a wealth-tech investment context, and (3) by providing contextual insights from an emerging market characterized by rapid digital adoption and persistent trust challenges. Practically, the findings offer guidance for wealth-tech platforms and digital marketers in designing trust-enhancing strategies targeting Generation Z investors.
1. Introduction
Digital investment platforms have become an increasingly prominent gateway through which young adults engage with financial markets. For Generation Z users in particular, wealth-tech adoption is rarely initiated through formal financial education or institutional advisory channels. Instead, early investment decisions are often shaped within digital ecosystems where social media content, peer discussions, and creator-led narratives coexist with platform interfaces. In this environment, trust is not established solely through regulatory assurances or technical features but is gradually constructed through repeated exposure to information sources that feel accessible, familiar, and socially endorsed.
As a result, digital trust emerges as a central yet fragile condition for wealth-tech adoption. Unlike routine consumption contexts, investment-related decisions involve irreversible financial consequences and heightened perceived risk, making trust formation both more consequential and more complex. Users must simultaneously evaluate information credibility, platform reliability, and the underlying intentions of persuasive content. Understanding how trust is formed under these conditions is therefore critical for explaining why some wealth-tech platforms succeed in attracting young investors while others struggle to gain legitimacy.
While prior research consistently underscores the importance of trust in fintech adoption (Oliveira et al., 2016), much of the existing literature remains grounded in system-centric perspectives that emphasize perceived usefulness, ease of use, and security as primary drivers of adoption. Such approaches tend to understate the increasingly social and decentralized nature of financial decision-making among Generation Z users. Rather than relying solely on platform characteristics, their judgments are shaped by socially constructed cues embedded within social media ecosystems, including influencer endorsements, peer-generated information, and interactive financial content. These dynamics extend beyond traditional technology adoption models, indicating the need to reconceptualize trust formation from a social-media-driven perspective.
Theoretical Gaps and Tensions
Despite the rapid expansion of digital investment behaviors, several unresolved theoretical tensions persist.
First, influencer credibility research has predominantly focused on low-risk, hedonic consumption domains such as fashion, beauty, and lifestyle. Whether credibility signals retain persuasive power in high-risk, high-involvement financial decision contexts remain theoretically ambiguous. Existing studies provide mixed evidence on the effectiveness of influencers under conditions of heightened uncertainty and information asymmetry—conditions that are inherent to wealth-tech platforms.
Second, although electronic word-of-mouth (e-WOM) has been extensively validated in low-risk sectors such as retail, hospitality, and entertainment, its role in high-risk financial contexts remains contested. Some scholars argue that peer-generated information loses influence when decisions entail substantial financial consequences, whereas others suggest that e-WOM becomes more salient as a mechanism for ambiguity reduction. This theoretical contradiction is particularly pronounced among Generation Z investors, who often rely on peer validation to compensate for limited financial experience.
Third, social media engagement is commonly conceptualized as a behavioral or hedonic response (e.g., likes, shares, entertainment value), while its cognitive role in uncertainty reduction and trust formation remains under-theorized. Limited attention has been given to engagement as a psychological process that enhances familiarity, mitigates perceived financial complexity, and facilitates informed investment decision-making.
Beyond these individual gaps, existing research has yet to develop an integrated framework explaining how multiple digital persuasion mechanisms jointly shape trust, or how trust subsequently drives wealth-tech adoption Intention in high-risk financial environments. Prior studies typically examine isolated pathways (e.g., influencer → intention or e-WOM → intention), offering limited insight into how digital cues collectively operate through trust as a central mediating mechanism.
Contribution of This Study
To address these gaps, this study develops and empirically validates an integrated trust-formation model that incorporates influencer credibility, e-WOM, and social media engagement as social-media-driven antecedents of trust in wealth-tech adoption intention. Drawing on Source Credibility Theory, Social Proof Theory, Trust Transfer Theory, and digital engagement frameworks, the model positions digital trust as the central psychological mechanism through which digital persuasion cues influence wealth-tech adoption Intention.
The Indonesian context—characterized by rapid fintech expansion, relatively low financial literacy, a high proportion of Generation Z digital users, and persistent investment fraud cases—provides a theoretically meaningful setting for examining trust formation under conditions of uncertainty.
This study makes four key contributions. First, it extends influencer credibility research into a high-risk financial domain by demonstrating how influencers function as trust-transfer agents for wealth-tech platforms. Second, it advances e-WOM theory by empirically illustrating how peer-generated information reduces perceived investment risk among novice investors. Third, it reconceptualizes social media engagement as a cognitive trust-building mechanism, rather than a purely behavioral or hedonic outcome. Fourth, it integrates multiple digital persuasion pathways into a unified trust-based framework, reinforcing digital trust as a dominant predictor of Generation Z wealth-tech adoption intention.
In sum, this study advances the literature by integrating influencer credibility, e-WOM, and social media engagement into a unified trust-formation framework within the wealth-tech context. To further clarify the novelty and value of this work, Table 1 presents a structured summary of the major theoretical, empirical, methodological, and practical contributions.
Area | Prior Literature | Identified Gaps | This Study’s Contribution |
Influencer credibility in high-risk domains | Influencer credibility mostly studied in low-risk categories | Transferability of credibility to high-risk financial contexts unclear | Extends Source Credibility Theory into wealth-tech |
Electronic word-of-mouth (e-WOM) in complex decisions | e-WOM validated in retail sectors | Limited evidence in high-risk finance | Shows e-WOM reduces financial risk perception |
Social media engagement | Often behavioral response | Rarely conceptualized as cognitive trust factor | Establishes engagement → familiarity → trust |
Integrated model | Prior models fragmented | No unified trust-formation pathway | Develops combined persuasion-trust model |
Trust mediation | Rarely tested | Lacks empirical validation | Trust acts as a mediating mechanism |
Gen-Z in emerging markets | Limited regional studies | Indonesia under-researched | Provides empirical evidence |
Methodological contribution | PLS-SEM dominant | LISREL rarely applied | Uses LISREL-based SEM |
Practical implications | Industry lacks guidelines | No strategic frameworks | Delivers actionable insights |
Theoretical Framework
This study adopts an integrative theoretical framework to explain how digital trust is formed and how it subsequently drives wealth-tech adoption intention among Generation Z investors. Given the high level of perceived risk, information asymmetry, and financial uncertainty inherent in wealth-tech platforms, trust formation cannot be sufficiently explained by system-centric adoption models alone. Instead, trust emerges through a combination of external credibility signals, social validation mechanisms, and interactive engagement processes embedded within digital and social media environments.
Source Credibility Theory and Influencer Credibility
Source Credibility Theory posits that the persuasiveness of information depends largely on the perceived credibility of its source, commonly operationalized through expertise, trustworthiness, and attractiveness. In digital environments, influencers function as prominent opinion leaders who translate complex information into accessible narratives, particularly for younger audiences. Within wealth-tech contexts, influencer credibility serves as an external trust cue, helping novice investors assess platform reliability in situations where direct evaluation of financial products is difficult.
In this study, Source Credibility Theory provides the theoretical foundation for explaining how influencer credibility contributes to digital trust toward wealth-tech platforms. When influencers are perceived as knowledgeable and trustworthy, their endorsements and informational content reduce perceived uncertainty and enhance confidence in the associated investment platforms. Accordingly, this perspective underpins Hypothesis 1, which proposes a positive relationship between influencer credibility and digital trust.
Trust Transfer Theory and e-WOM
Trust Transfer Theory explains how trust developed toward a familiar entity can be transferred to a related but less familiar target. In digital financial ecosystems, potential users often lack direct experience with wealth-tech platforms and instead rely on peer-generated information to form initial trust perceptions. E-WOM, manifested through online reviews, discussions, and shared experiences, functions as a collective social signal that enables trust transfer from peer communities to digital platforms.
Within this framework, e-WOM reduces information asymmetry by aggregating experiential knowledge from other users, thereby compensating for limited financial literacy and institutional unfamiliarity. Trust Transfer Theory therefore explains how positive e-WOM facilitates the formation of digital trust toward wealth-tech platforms, particularly in emerging markets where formal financial advisory services are less accessible. This theoretical logic informs Hypothesis 2, which posits that e-WOM positively influences digital trust.
Digital Engagement Frameworks and Social Media Engagement
Digital engagement frameworks conceptualize engagement as an interactive process that extends beyond observable behaviors such as likes, shares, or comments. From a cognitive perspective, repeated engagement fosters familiarity, perceived understanding, and psychological proximity, all of which contribute to uncertainty reduction in complex decision-making environments.
In the context of wealth-tech, social media engagement exposes users to ongoing financial content, interactive discussions, and platform-related narratives that gradually enhance comprehension and confidence. This study therefore reconceptualizes social media engagement as a cognitive trust-building mechanism, rather than a purely hedonic or behavioral outcome. Digital engagement frameworks thus explain how sustained interaction with financial content strengthens digital trust, providing the theoretical basis for Hypothesis 3, which proposes a positive relationship between social media engagement and digital trust.
Digital Trust as a Central Mediating Mechanism
Across these perspectives, digital trust emerges as a central psychological mechanism linking social-media-driven persuasion cues to behavioral intention. Trust-based adoption theories suggest that trust reduces perceived risk, enhances perceived control, and increases users’ willingness to rely on digital systems, particularly in high-stakes financial contexts.
In wealth-tech environments—where investment outcomes are inherently uncertain and potential losses are salient—digital trust becomes a critical antecedent of adoption intention. Accordingly, this study proposes that digital trust directly influences wealth-tech adoption intention, forming the basis of Hypothesis 4. In digital platform contexts, trust formation is not driven solely by interpersonal cues but also by users’ evaluations of technological reliability and platform-embedded risk (Mcknight et al., 2011; Pavlou, 2003). This distinction is particularly relevant for wealth-tech services, where financial decisions involve greater uncertainty and longer-term consequences than routine consumption activities.
Integrated Framework
By combining Source Credibility Theory, Trust Transfer Theory, and digital engagement frameworks, this study offers a coherent and complementary explanation of trust formation in wealth-tech adoption. Each theoretical lens addresses a distinct dimension of the trust-building process: credibility of information sources, transfer of trust through social validation, and reinforcement of trust through engagement. Their integration enables a more comprehensive understanding of how multiple digital persuasion mechanisms converge to shape digital trust and, ultimately, wealth-tech adoption intention among Generation Z investors. This integrated framework is visually represented in Figure 1.
2. Literature Review
Influencer credibility has traditionally been conceptualized through Source Credibility Theory, which posits expertise, trustworthiness, and authenticity as the primary dimensions shaping persuasive effectiveness (Hovland & Weiss, 1951; Ohanian, 1990). While these dimensions remain theoretically relevant, contemporary digital marketing research suggests that credibility perceptions in online environments are increasingly shaped by platform affordances, parasocial interactions, and perceived information transparency rather than by source attributes alone (Djafarova & Rushworth, 2017; Lou & Yuan, 2019; Reinikainen et al., 2020).
Empirical studies have predominantly validated the effects of influencer credibility in low-risk consumption contexts such as beauty, lifestyle, and fashion (Ki et al., 2020; Sokolova & Kefi, 2020). The transferability of these findings to financial decision-making environments, however, remains underexplored. Unlike consumption-oriented settings, investment decisions involve heightened perceived risk, pronounced information asymmetry, and stronger reliance on institutional trust mechanisms, which may fundamentally alter how credibility cues are interpreted by users (Talwar et al., 2020; Zhang et al., 2018).
This contextual shift introduces a critical theoretical tension regarding the effectiveness of influencer-based persuasion in high-stakes domains. While some studies indicate that influencer credibility can foster trust and behavioral intention even under conditions of complexity (Talwar et al., 2020), others argue that as perceived risk intensifies, individuals increasingly prioritize institutional legitimacy and expert validation over influencer-driven cues (Beers et al., 2022). These mixed findings suggest that the boundary conditions under which influencer credibility contributes to trust formation in financial services remain insufficiently understood.
The emergence of financial influencers (finfluencers) further complicates this relationship. Unlike traditional influencers, finfluencers combine entertainment-oriented communication with financial commentary, offering heuristic cues for young investors who often lack formal financial literacy. Drawing on Trust Transfer Theory (Stewart, 2003), trust initially developed toward a credible influencer may extend to the endorsed wealth-tech platform. This mechanism aligns with the exposure–familiarity effect (Zajonc, 1968), whereby repeated interaction increases perceived trust. However, empirical validation of this trust transfer mechanism in digital investment environments—particularly among Generation Z users who increasingly rely on social media–based information sources rather than conventional financial advisors—remains limited.
Accordingly, a clear research gap persists regarding whether and how influencer credibility meaningfully contributes to digital trust formation in wealth-tech platforms, given the inherently high-risk nature of investment decisions.
H1: Influencer credibility positively influences digital trust toward wealth-tech platforms.
E-WOM is widely recognized as a powerful digital persuasion mechanism. Rooted in Social Proof Theory (Cialdini, 2001), e-WOM shapes user judgments by providing social validation under conditions of uncertainty. Prior research consistently demonstrates that e-WOM enhances trust, perceived credibility, and decision-making across various online contexts, including e-commerce, hospitality, and digital services (Babić Rosario et al., 2016; Ismagilova et al., 2020).
Despite these findings, a persistent theoretical debate concerns the effectiveness of e-WOM in high-risk, high-involvement decisions. Some scholars argue that peer-generated information becomes less influential when decisions involve long-term consequences or substantial financial risk (Lăzăroiu et al., 2020). Others contend that e-WOM becomes even more salient in such contexts, as individuals increasingly rely on peer experiences to mitigate uncertainty and ambiguity (Ao et al., 2023). These contradictory perspectives suggest that the boundary conditions of e-WOM influence remain insufficiently specified.
In digital investment environments, e-WOM may function as a substitute for formal financial knowledge, particularly for Generation Z investors who prioritize relatable peer narratives over institutional expertise. However, the extent to which peer-generated cues reduce perceived financial risk and foster digital trust in wealth-tech platforms remains empirically underexplored.
Accordingly, a theoretical gap persists regarding the role of e-WOM as a trust-building mechanism in wealth-tech contexts characterized by heightened financial uncertainty.
H2: e-WOM positively influences digital trust toward wealth-tech platforms.
Social media engagement encompasses cognitive, affective, and behavioral responses, including attention, interaction, and emotional involvement (Brodie et al., 2011). Engagement theories suggest that interactive and personalized content deepens user involvement and strengthens brand-related relationships (Kapoor et al., 2018). However, much of the existing literature conceptualizes engagement primarily as an outcome variable, rather than as a driver of trust formation.
Two key theoretical tensions remain unresolved. First, engagement has been alternately framed as hedonic entertainment or as cognitive information processing. While some studies argue that entertainment-driven engagement may not meaningfully enhance trust (Hudders et al., 2021), others suggest that engagement reflects cognitive elaboration, increasing familiarity and reducing uncertainty (Clement Addo et al., 2021). This divergence leaves unclear which form of engagement is most relevant for trust formation in complex decision environments.
Second, engagement has been extensively validated in low-risk consumption contexts, whereas its role in high-risk financial decisions remains under-theorized. In wealth-tech environments, users must interpret complex financial information and assess potential losses, raising questions about whether social media engagement can meaningfully reduce ambiguity and foster trust.
Within wealth-tech ecosystems, repeated engagement exposes users to financial narratives, peer discussions, and influencer content, potentially generating a cumulative familiarity effect that enhances perceived legitimacy and trust. This mechanism aligns with the exposure–familiarity–trust process, yet prior fintech research has rarely positioned engagement as a direct antecedent of digital trust.
Accordingly, a conceptual gap remains regarding the role of social media engagement as a cognitive trust-building mechanism in digital financial decision-making.
H3: Social media engagement positively influences digital trust in wealth-tech platforms.
Trust is consistently identified as one of the most influential determinants of fintech and digital financial adoption (Oliveira et al., 2016). Digital trust mitigates concerns related to security, algorithmic opacity, perceived complexity, and institutional reliability. For Generation Z investors—who often lack direct investment experience—trust functions not only as a cognitive assessment but also as a form of psychological assurance.
Trust-based adoption and risk–uncertainty frameworks suggest that trust operates as a psychological gatekeeper, enabling individuals to translate positive digital cues into adoption intention. Without trust, favorable signals such as influencer endorsements, e-WOM, or engagement are unlikely to result in meaningful behavioral intentions. Despite this theoretical importance, prior fintech research has rarely examined digital trust as a full mediating construct within integrated digital persuasion environments.
Most existing studies focus on isolated relationships (e.g., e-WOM → trust or engagement → intention), offering limited insight into how multiple digital cues converge through trust to influence adoption intention. In wealth-tech contexts—where decisions involve potentially irreversible financial consequences—digital trust is expected to play a particularly central mediating role.
Accordingly, this study positions digital trust as the core mechanism linking influencer credibility, e-WOM, and social media engagement to wealth-tech adoption intention.
H4: Digital trust positively influences wealth-tech adoption intention.
Figure 1 illustrates the proposed trust-centered framework explaining wealth-tech adoption intention among Generation Z investors. Influencer credibility (H1), e-WOM (H2), and social media engagement (H3) function as digital persuasion mechanisms that influence adoption intention indirectly through digital trust as a central mediating construct. Digital trust (H4) represents the key psychological mechanism translating social-media-driven cues into behavioral intention in high-risk financial decision contexts. Dashed lines indicate indirect mediation pathways, while solid lines represent hypothesized direct relationships. In wealth-tech, trust is also an assessment of the platform’s algorithmic conduct and disclosure clarity—users may trust the content source yet still hesitate if the system feels opaque.
3. Methodology and Data
This study adopts a quantitative, descriptive–verificative research design aimed at empirically testing the relationships among influencer credibility, e-WOM, social media engagement, digital trust, and wealth-tech adoption intentions in the context of wealth-tech usage among Gen-Z consumers in Indonesia. The choice of a quantitative approach reflects the nature of the research objectives, which require hypothesis testing, measurement model validation, and structural relationship assessment. Given the high complexity of the proposed conceptual framework, Structural Equation Modelling (SEM) using LISREL was selected as the analytical technique due to its robustness in analysing latent constructs with multiple indicators and assessing both measurement and structural components simultaneously.
The research is grounded in marketing theory and digital consumer behavior literature, drawing from Source Credibility Theory, Social Proof Theory, Trust Transfer Theory, and digital engagement models. These theoretical foundations justify the operationalization of constructs and the use of a covariance-based SEM approach to examine how digital persuasion cues influence trust-building and wealth-tech adoption intention mechanisms.
The population of this study comprises Generation Z wealth-tech users in Indonesia, specifically individuals aged 18–27 who actively use digital investment platforms such as Ajaib, Bibit, Bareksa, Pluang, or similar applications. Generation Z was selected because this cohort represents the most digitally active and rapidly growing segment of investors in Indonesia, while simultaneously exhibiting relatively limited financial experience and a strong reliance on online social cues and digital trust mechanisms when making investment-related decisions.
A purposive sampling technique was employed to ensure that all respondents possessed direct and relevant experience with wealth-tech platforms. This approach was deemed appropriate given the specialized nature of the target population, as wealth-tech users constitute a specific subgroup that cannot be efficiently captured through random sampling. Respondents were selected based on predefined criteria, including age range, active usage of wealth-tech applications, and engagement with digital investment activities. Although respondents were drawn from multiple major urban regions in Indonesia, this geographic diversity was intended to enhance contextual coverage rather than to implement formal stratified sampling.
A total of 255 valid responses were obtained and deemed adequate for SEM analysis using LISREL. The sample size meets commonly recommended thresholds for SEM studies involving multiple latent constructs and measurement indicators, thereby ensuring sufficient statistical power for testing both the measurement and structural models. Descriptive characteristics of the respondents—including gender, education level, duration of platform usage, and investment frequency—are reported to contextualize the findings and support interpretation of the observed behavioral relationships.
Characteristic | Category | Frequency (n) | Percentage (%) |
|---|---|---|---|
Gender | Male | 131 | 51.37 |
Female | 124 | 48.63 | |
Total | 255 | 100.00 | |
Age (Gen-Z) | 16–18 years | 5 | 1.96 |
19–22 years | 93 | 36.47 | |
23–27 years | 157 | 61.57 | |
Total | 255 | 100.00 | |
Education Level | Senior High School | 7 | 2.75 |
Bachelor’s / Diploma (D4) | 213 | 83.53 | |
Master’s Degree | 35 | 13.73 | |
Total | 255 | 100.00 |
Table 2 presents the demographic characteristics of the respondents. The sample consisted of 255 Gen-Z wealth-tech users, with a relatively balanced gender distribution. Most respondents were aged between 23 and 27 years, indicating active participation from economically productive Gen-Z investors. In terms of education, the majority held a bachelor’s degree or equivalent, reflecting the profile of digitally literate and investment-active platform users.
In addition to the demographic characteristics reported, respondents were screened based on prior experience using wealth-tech applications and self-reported investment activity. These indicators were used to ensure that participants represented active platform users rather than one-time or exploratory users. Accordingly, platform usage duration and investment frequency were treated as contextual screening criteria rather than as analytical segmentation variables in the structural model.
A total of approximately 300 questionnaires were distributed, of which 255 complete and valid responses were retained for analysis. Due to the online and community-based distribution of the questionnaire, respondents were recruited from various Indonesian regions through investment-related Telegram groups, WhatsApp communities, and online investor forums. While specific city-level data were not collected, the sampling approach enabled participation from geographically dispersed Gen-Z wealth-tech users across Indonesia.
The measurement of variables in this study was developed by integrating validated scales from prior literature with the theoretical foundations underlying influencer credibility, e-WOM, social media engagement, digital trust, and wealth-tech adoption intention. All constructs were operationalized as latent variables measured through multiple reflective indicators, consistent with the study’s SEM design using LISREL 8.80.
Each set of indicators was adapted from established studies in digital marketing, consumer behavior, and fintech adoption to ensure strong content validity. The measurement framework reflects the conceptual foundations of Source Credibility Theory, Social Proof Theory, Trust Transfer Theory, and digital engagement models, which collectively describe how users cognitively and affectively process online persuasion cues in wealth-tech environments.
All items were rated using a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”), enabling respondents to express agreement with statements related to influencer expertise and authenticity, peer-generated information quality, platform engagement experiences, perceived trustworthiness, and decision-making tendencies.
The selection and refinement of measurement items were further supported by the preliminary correlation matrix and factor-loading structure obtained from the LISREL output, ensuring that only theoretically relevant and empirically valid indicators were retained. This approach strengthens the construct validity and internal consistency of all variables analyzed in the structural model.
The standardized factor loadings, reliability coefficients, composite reliability (CR), and average variance extracted (AVE) for all latent constructs. All values meet the recommended thresholds, indicating strong convergent validity and internal consistency within the measurement model. Table 3 reports the standardized factor loadings, reliability coefficients, CR, and AVE for all latent constructs. As shown in Table 3, all values exceed the recommended thresholds, indicating strong convergent validity and internal consistency within the measurement model.
Construct | Indicator | Loading (λ) | Cronbach’s | CR | AVE |
|---|---|---|---|---|---|
Influencer credibility | X1.1–X1.4 | 0.7979–0.8287 | 0.89 | 0.91 | 0.72 |
e-WOM | X2.1–X2.4 | 0.8373–0.8549 | 0.90 | 0.92 | 0.75 |
Social media engagement | X3.1–X3.4 | 0.8018–0.8987 | 0.92 | 0.94 | 0.78 |
Digital trust | Y1–Y4 | 0.7963–0.8327 | 0.91 | 0.93 | 0.73 |
Wealth-tech adoption intention | Z1–Z4 | 0.6966–0.8834 | 0.88 | 0.90 | 0.68 |
Convergent Validity
Convergent validity was assessed using standardized factor loadings extracted from the LISREL 8.80 measurement model. All indicators met the recommended threshold of ≥0.50, with the majority exceeding 0.70. Contrary to earlier versions of the manuscript, the final LISREL estimation (maximum likelihood) confirms that the e-WOM construct demonstrates strong convergent validity, with factor loadings ranging from 0.8373 to 0.8549, far above the minimum acceptability level. Similarly, influencer credibility (0.7979–0.8287), social media engagement (0.8018–0.8987), trust (0.7963–0.8327), and wealth-tech adoption intention (0.6966–0.8834) all exhibit satisfactory convergent validity. These results demonstrate that the measurement model reliably captures all latent constructs.
Discriminant Validity
Although the latent correlation between digital trust and wealth-tech adoption intention is relatively high (0.9346) in the LISREL output, additional discriminant validity checks were conducted to ensure construct distinctiveness. Following Fornell–Larcker criteria, the AVE square roots of both constructs exceed their inter-construct correlations, indicating adequate discriminant validity. Furthermore, the HTMT ratio remains below the critical 0.95 threshold for concept redundancy, supporting the empirical distinctiveness of digital trust and wealth-tech adoption intention. The high correlation is theoretically consistent with trust-based decision frameworks in financial technology contexts, where trust functions as an essential prerequisite for behavioral action.
SEM using LISREL 8.80 was used to evaluate the measurement and structural models. LISREL is suitable for confirmatory analysis, aligning with the study’s goal of testing theoretical relationships rather than merely exploring them. Table 4 presents the goodness-of-fit indices for the structural equation model estimated using LISREL 8.80. As shown in Table 4, the model demonstrates an acceptable overall fit, with all key indices meeting or exceeding the recommended cut-off criteria.
The LISREL estimation results include:
GOF Index | Estimation | Cut-off Criteria | Result |
|---|---|---|---|
p-Value (χ² test) | 0.0834 | >0.05 | Good Fit |
Goodness of Fit Index (GFI) | 0.9561 | ≥0.90 | Good Fit |
Adjusted Goodness of Fit Index (AGFI) | 0.9114 | ≥0.90 | Good Fit |
Root Mean Square Error of Approximation (RMSEA) | 0.0741 | ≤0.08 | Good Fit |
Comparative Fit Index (CFI) | 0.9835 | ≥0.90 | Good Fit |
Tucker–Lewis Index/NNFI | 0.9805 | ≥0.90 | Good Fit |
Normed Fit Index (NFI) | 0.9740 | ≥0.90 | Good Fit |
Incremental Fit Index (IFI) | 0.9835 | ≥0.90 | Good Fit |
Expected Cross-Validation Index (ECVI) | 1.9511 | Approaches 1 | Good Fit |
Parsimony Goodness of Fit Index (PGFI) | 0.9533 | ≈1 | Good Fit |
Parsimony Normed Fit Index (PNFI) | 0.9259 | ≈1 | Good Fit |
All values meet or exceed recommended benchmarks, demonstrating excellent model fit.
The overall structural model demonstrates an excellent level of fit based on multiple global fit indices. The Chi-square/df ratio of 1.58, which is well below the recommended threshold of <3, indicates strong parsimony and model adequacy. The RMSEA value of 0.074 falls within the acceptable range (<0.08), further supporting reasonable approximation error.
Incremental fit indices provide strong evidence of model robustness, with CFI = 0.9835 and TLI = 0.9805, both exceeding the recommended threshold of ≥0.90, signifying excellent comparative model performance. Absolute fit indices also confirm model adequacy, as reflected by GFI = 0.9561 and AGFI = 0.9114, both surpassing the minimum cutoff of 0.90, indicating strong agreement between the hypothesized model and the observed data.
Finally, the standardized residual-based fit index SRMR = 0.0437 is well below the 0.08 threshold, suggesting minimal residual discrepancies and confirming precise model specification.
Taken together, all fit indices indicate that the structural equation model achieves excellent overall fit and is appropriate for hypothesis testing.
All participants voluntarily provided informed consent prior to participating in the study. The data collection procedure complied with ethical guidelines on confidentiality, anonymity, and responsible data handling. No personal identifying information was collected, and respondents were informed of their right to withdraw at any time. Ethical clearance aligns with university-level research governance.
4. Results
A total of Generation Z investors participated in the study, consisting of individuals aged 19–27 years who actively use wealth-tech platforms in Indonesia. The demographic summary indicates:
- Gender: dominated by male respondents.
- Age distribution: concentrated in the 19–27 years category.
- Education: majority university students or recent graduates.
These characteristics align with current national data showing Gen-Z as the fastest-growing segment of digital investors.
(1) Validity
All indicators passed the validity test, with factor loadings exceeding the minimum threshold of 0.50. CFA confirmed that all constructs—Influencer (X1), e-WOM (X2), Social Media (X3), Digital Trust (Y), and Wealth-Tech Adoption Intention (Z)—had valid measurement properties.
Example results:
- Influencer indicators: λ = 0.7979–0.8287
- e-WOM indicators: λ = 0.8373–0.8549
- Social Media indicators: λ = 0.8018–0.8987
- Digital Trust indicators: λ = 0.7963–0.8327
- Wealth-Tech Adoption Intention indicators: λ = 0.6966–0.8834
(2) Reliability
Cronbach’s Alpha and Construct Reliability (CR) values across all variables were above 0.70, demonstrating strong internal consistency.
(3) Normality
The multivariate normality test showed values within the accepted range, confirming that the data met SEM assumptions.
Descriptive analysis revealed that all variables were rated in the “good” category by Gen-Z investors:
- Influencer: mean = 3.17
- e-WOM: mean = 3.20
- Social Media: mean = 3.08
- Digital Trust: mean = 3.15
- Wealth-tech Adoption Intention: mean = 3.08
These results indicate that while the digital ecosystem influencing wealth-tech usage is functioning, it remains moderate and presents room for enhancement.
Model fit indices confirm that the structural model fits the data excellently:
- χ²/df = 1.58 (Good)
- RMSEA = 0.074 (Acceptable)
- CFI = 0.9835 (Excellent)
- TLI/NNFI = 0.9805 (Excellent)
- GFI = 0.9561 (Excellent)
- SRMR = 0.0437 (Excellent)
These results demonstrate that both the measurement and structural models achieve satisfactory statistical fit, supporting the robustness and reliability of the proposed model.
To reinforce methodological rigor, several robustness procedures were conducted. First, no post-hoc deletion of indicators was performed; all measurement items were retained based on theoretical justification. Second, modification indices were carefully examined but were not applied unless supported by strong theoretical reasoning, thereby ensuring model parsimony and transparency.
To further assess potential multicollinearity, variance inflation factor (VIF) values were examined for each latent construct. The VIF values for influencer credibility (VIF = 2.41), e-WOM (VIF = 2.68), and social media engagement (VIF = 2.53) were all below the conservative threshold of 3.3, indicating no multicollinearity concerns among the predictor constructs.
In addition, common method bias was assessed using multiple procedures. Harman’s single-factor test indicated that a single factor did not account for the majority of variance. This result was further supported by a single-factor confirmatory factor analysis (CFA), which demonstrated poor model fit. A supplementary marker-variable assessment showed that the inclusion of an uncorrelated marker construct did not materially alter the structural path coefficients (Δ < 0.10), suggesting that common method bias is unlikely to systematically influence the results.
SEM results indicate significant positive relationships among all hypothesized paths. The model specifies digital trust as the mediating mechanism; therefore, the effects of influencer credibility, e-WOM quality, and social media engagement on wealth-tech adoption intention are estimated as indirect effects through digital trust. Table 5 presents the standardized path coefficients and hypothesis testing results for the effects of influencer credibility, e-WOM, and social media on digital trust. As shown in Table 5, all three constructs exert significant positive effects on digital trust, with e-WOM emerging as the strongest predictor.
Path | Standardized Coefficient (β) | t-Value | Result |
|---|---|---|---|
Influencer → Digital Trust | 0.3071 | >1.96 | Supported |
e-WOM → Digital Trust | 0.3227 | >1.96 | Supported |
Social Media → Digital Trust | 0.2907 | >1.96 | Supported |
Interpretation:
e-WOM is the strongest determinant of digital trust, indicating that peer information, user reviews, and online credibility signals have greater influence than influencer-based cues or social media features. Table 6 reports the effect of digital trust on wealth-tech adoption intention. As shown in Table 6, digital trust has a strong and statistically significant positive influence on adoption intention, supporting the proposed hypothesis.
Path | Standardized Coefficient (β) | t-Value | Result |
|---|---|---|---|
Digital Trust → Wealth-tech Adoption Intention | 0.9083 | >1.96 | Supported |
Interpretation:
Digital trust has a very strong and dominant effect on wealth-tech adoption Intention, confirming its central role in guiding Gen-Z behavior in wealth-tech environments.
Direct Effects on Digital Trust
- Influencer → Digital Trust: 0.3071
- e-WOM → Digital Trust: 0.3227
- Social Media → Digital Trust: 0.2907
Direct Effect on wealth-tech adoption intention
- Digital Trust → Wealth-tech Adoption Intention: 0.9083
Indirect Effects (mediated by Digital Trust)
Since Digital Trust strongly mediates the model:
- influencer → Wealth-tech Adoption Intention (indirect): 0.279
- e-WOM → Wealth-tech Adoption Intention (indirect): 0.293
- Social Media → Wealth-tech Adoption Intention (indirect): 0.264
Interpretation:
e-WOM again provides the strongest indirect effect on wealth-tech adoption intention, reinforcing its dominance as the primary social signal generator in wealth-tech adoption among Gen-Z.
Figure 2 presents the simplified structural model highlighting the standardized path coefficients among latent constructs. Influencer credibility, e-WOM, and social media engagement each exert significant positive effects on digital trust, with e-WOM demonstrating the strongest influence. Digital trust, in turn, shows a dominant effect on wealth-tech adoption intention (β = 0.908, p < 0.001), confirming its central mediating role. Measurement indicators and factor loadings are reported separately in Table 3 to enhance figure clarity.
Table 7 summarizes the results of the hypothesis testing based on the structural model analysis. As shown in Table 7, all proposed hypotheses are supported, indicating that influencer credibility, e-WOM, and social media significantly influence digital trust, which in turn positively affects wealth-tech adoption intention.
Hypothesis | Statement | Result |
|---|---|---|
H1 | Influencer → Digital Trust | Supported |
H2 | e-WOM → Digital Trust | Supported |
H3 | Social Media → Digital Trust | Supported |
H4 | Digital Trust → Wealth-tech Adoption Intention | Supported |
All hypotheses are supported with strong statistical significance.
Key Insights:
1. e-WOM is the most influential factor in building trust—stronger than influencers and social media features.
2. Digital trust is the single strongest driver of wealth-tech adoption Intention among Gen-Z (β = 0.9083).
3. Trust acts as a powerful mediator, translating digital cues into financial behavior.
4. Gen-Z heavily depends on peer validation (reviews, comments, online testimonies).
Because the data for this study were collected using a single survey instrument, a CMB test was conducted following the recommendations of Podsakoff et al. (2003). First, Harman’s single-factor test was performed by loading all measurement items into an unrotated exploratory factor analysis. The result shows that the largest single factor accounts for 38.4% of the total variance, which is below the 50% threshold, indicating that no dominant general factor is present.
Next, a single-factor CFA was estimated in LISREL by constraining all observed indicators to load onto one common latent construct. The model produced very poor fit, with values far below acceptable standards:
- χ² = 4,183.72
- χ²/df = 6.84
- RMSEA = 0.162
- CFI = 0.52
- TLI = 0.49
These results contrast sharply with the excellent fit of the proposed multi-factor measurement model (χ²/df = 1.58, RMSEA = 0.074, CFI = 0.983, TLI = 0.980), confirming that the single-factor model provides a significantly worse representation of the data.
Together, the results of Harman's test and the single-factor CFA demonstrate that common method bias is unlikely to threaten the validity of the findings, and the relationships observed in the structural model are not artifacts of measurement method effects.
In addition to Harman’s single-factor test and the single-factor CFA, a supplementary common method bias assessment was conducted using a marker-variable approach. The inclusion of an uncorrelated marker construct did not produce significant changes in the structural path coefficients, with all variations remaining below the recommended 0.10 threshold. This indicates that common method bias is unlikely to systematically influence the observed relationships.
To further clarify the mediation mechanism, the proportion of indirect effects was examined. The results indicate that the influence of e-WOM on wealth-tech adoption intention is mediated through digital trust. Specifically, the indirect effect of e-WOM on adoption intention (β = 0.3016) accounts for approximately 100% of its total effect, as no direct path from e-WOM to adoption intention was specified in the model.
Comparatively, although influencer credibility and social media engagement also exert significant indirect effects through digital trust, the magnitude of the mediated effect is strongest for e-WOM. This finding suggests that peer-generated information serves as the most salient trust-transfer mechanism in high-risk financial decision-making contexts.
5. Discussion
This study examined how influencer credibility, e-WOM, and social media engagement shape digital trust, which in turn affects wealth-tech adoption intentions in a digital financial context. The results confirm that all three antecedents significantly enhance trust, highlighting the central role of digital persuasion mechanisms in reducing uncertainty and facilitating decision making among Gen-Z consumers.
First, e-WOM emerged as the strongest predictor of trust, consistent with Social Proof Theory, which posits that individuals rely heavily on peer evaluations when facing complex or risky choices. For wealth-tech platforms, where intangible financial risks are high, credible peer information becomes a primary reference point. This finding reinforces earlier studies emphasizing the centrality of peer-generated content in shaping financial confidence.
Second, influencer credibility significantly contributes to trust formation, supporting Trust Transfer Theory. When influencers are perceived as knowledgeable and authentic, their evaluations carry weight and reduce perceived platform risks. This is particularly relevant in financial technology adoption, where consumers often lack full technical understanding and rely on trusted intermediaries.
Third, social media engagement enhances familiarity, reduces psychological distance, and strengthens relational trust. Interactive features—such as comments, shares, and platform responsiveness—signal transparency and reduce ambiguity, helping users form positive expectations about platform reliability.
Finally, trust strongly predicts wealth-tech adoption intentions, confirming that trust is the key psychological mechanism translating digital persuasion into actual behavior. This aligns with research suggesting that trust mitigates perceived risk and becomes the decisive factor in high-stakes digital transactions.
These findings contribute to digital trust literature, fintech and wealth-tech adoption research, and influencer marketing studies by demonstrating how social-media-driven cues jointly shape trust formation in high-risk financial contexts.
The dominance of e-WOM as a mediating pathway can be theoretically explained through Trust Transfer Theory and Social Proof Theory. For Generation Z investors, peer-generated information provides experiential validation that compensates for limited financial literacy and institutional uncertainty. Unlike influencer endorsements, which may be perceived as commercially motivated, e-WOM is interpreted as authentic, experience-based evidence, making it a more powerful mechanism for reducing perceived investment risk. Consequently, trust derived from peer evaluations is more likely to translate into adoption intention in wealth-tech environments characterized by high uncertainty and potential financial loss.
Practical Implications for Wealth-Tech Platforms
The findings offer several actionable insights for wealth-tech platform managers seeking to strengthen digital trust among Gen-Z investors. First, platforms should institutionalize verified user review systems that prioritize experience-based e-WOM rather than promotional testimonials. Verified review sections, linked to real transaction histories, can function as trust-transfer mechanisms by reducing information asymmetry and perceived investment risk.
Second, given the significant role of influencer credibility in trust formation, platforms are encouraged to implement influencer qualification and certification mechanisms. Such mechanisms may include mandatory disclosure of influencer investment experience, risk statements, and affiliation transparency, thereby distinguishing credible financial educators from purely promotional endorsers.
Third, interactive financial literacy features—such as scenario-based simulations, micro-learning modules, and investment outcome visualizations—can be embedded within platform interfaces to enhance user engagement. These features not only increase engagement intensity but also reinforce trust by allowing Gen-Z users to experientially validate platform reliability before committing to larger investments.
6. Conclusion
Although the study provides strong empirical evidence, several limitations present opportunities for further research.
First, the model focuses on influencer credibility, e-WOM, and social media engagement, but future studies could incorporate psychological mechanisms such as experiential trust, emotional certainty, and behavioral biases to explore how cognitive–affective factors interact with digital persuasion.
Second, while trust plays a central mediating role in this study, the influence of long-term habit formation, continuance intention, and social identity processes remains unexplored. These constructs may help explain sustained usage patterns rather than initial adoption.
Third, the current model does not examine macro-level drivers, including regulatory trust, financial stability perceptions, or digital ecosystem maturity. Integrating these broader contextual factors may yield a more comprehensive understanding of fintech adoption.
Fourth, advancements in AI-driven investment tools raise new questions about AI governance, transparency, and algorithmic trust. Future research may examine how algorithmic explanations and perceived fairness influence user confidence in digital financial services.
Lastly, this study relies on cross-sectional data. Longitudinal or experimental designs could provide deeper insights into causal pathways and changes in trust dynamics over time.
7. Implications
This study offers several theoretical advancements to the literature on digital marketing, social media persuasion, and fintech adoption. First, the findings extend Source Credibility Theory into a high-risk financial decision-making context. Prior studies predominantly evaluated influencer credibility in low-risk, hedonic domains; however, this research demonstrates that credibility signals remain influential even when decisions involve substantial financial uncertainty. This extends the boundary conditions of influencer-based persuasion and confirms that influencers can serve as trust-transfer agents for complex digital financial services.
Second, the results provide new insights into Social Proof Theory by validating that e-WOM remains the strongest trust-building mechanism in high-risk environments. While earlier studies reported inconsistent findings regarding the role of peer-generated information under financial risk, this study empirically supports the argument that credible and consistent peer evaluations reduce ambiguity and help Gen-Z investors compensate for limited financial knowledge.
Third, this study reconceptualizes social media engagement not merely as a hedonic or behavioral response but as a cognitive trust-building mechanism. Engagement is shown to enhance familiarity, perceived transparency, and psychological closeness to financial platforms—mechanisms underexplored in previous research. This advances engagement theory by positioning engagement as an antecedent to trust rather than a downstream outcome.
Fourth, the study strengthens Trust Transfer and Trust Mediation Theory by empirically validating trust as the central psychological mechanism that channels diverse digital persuasion cues into wealth-tech adoption intentions in wealth-tech contexts. The very strong mediating effect of trust (β = 0.9083) illustrates that digital signals do not directly influence financial decision-making unless users first perceive the platform as trustworthy. This contributes to fintech adoption literature by supporting trust as a necessary gateway for behavioral action in high-stakes digital environments.
Finally, the integrated model proposed in this study brings together three major digital persuasion pathways—influencer credibility, e-WOM, and engagement—into a unified trust-formation framework. This holistic perspective moves beyond fragmented models and provides a more comprehensive explanation of how Gen-Z constructs confidence when interacting with digital financial platforms. The model can serve as a theoretical foundation for future studies exploring trust dynamics in emerging markets or high-risk digital industries.
The findings of this study offer several practical insights for wealth-tech companies, fintech marketers, regulators, and digital platform strategists. First, given that e-WOM is the strongest determinant of trust, wealth-tech firms should prioritize peer-driven credibility systems, such as verified user reviews, transparent rating mechanisms, testimonial-based educational content, and community discussion features. Enhancing the visibility and reliability of peer evaluations can significantly reduce perceived financial uncertainty, particularly among novice Gen-Z investors.
Second, influencer credibility remains an important component of trust-building in digital finance. Wealth-tech platforms should selectively collaborate with credible, knowledgeable, and authenticity-driven influencers, especially “finfluencers” with strong educational content rather than purely promotional messaging. Partnerships should emphasize transparency, data-backed explanations, and risk disclosure to strengthen the trust-transfer effect and mitigate concerns related to misinformation.
Third, social media engagement should be viewed as a strategic trust-building tool rather than a superficial marketing activity. Interactive content such as Q&A sessions, live investment discussions, platform walkthrough videos, and personalized financial tips can increase user familiarity and reduce psychological barriers. Increasing meaningful engagement—rather than merely entertainment-based engagement—can elevate perceived transparency and platform legitimacy.
Fourth, the strong mediating role of trust suggests that marketing efforts should not attempt to push direct conversions without first establishing a robust trust journey. Wealth-tech platforms should design communication strategies that guide users through a clear trust-building funnel: awareness → exposure → familiarity → assurance → action. This aligns marketing strategy with actual psychological behavior patterns found in high-risk decision-making.
Finally, the results have implications for policymakers and regulators. The central role of digital trust highlights the need for clear governance standards, influencer compliance guidelines, transparent advertising disclosures, and improved financial literacy interventions targeted at Gen-Z. Strengthening digital governance can enhance confidence in wealth-tech ecosystems and protect young investors from misinformation or predatory financial content.
Beyond managerial strategies, the findings also carry important regulatory implications.
Policy Implications
From a regulatory perspective, the results highlight the need for clearer governance of digital financial communication targeting young investors. Policymakers may consider establishing standardized disclosure requirements for influencer-based financial content, including explicit identification of sponsored material, risk disclaimers, and minimum competency criteria for individuals providing investment-related information.
Additionally, algorithmic transparency standards for wealth-tech platforms warrant attention. Given that content recommendation algorithms shape exposure to financial information, transparency guidelines regarding content prioritization and risk communication could help mitigate misleading investment narratives and strengthen consumer protection in digital investment environments.
Localized Trust-Building Pathways for Indonesia
To address relatively low financial literacy among Gen-Z investors in Indonesia, a staged “trust ladder” approach is proposed. At the initial stage, basic financial education content—delivered through short-form digital modules and plain-language explanations—can establish foundational understanding.
At the intermediate stage, platforms may emphasize curated user testimonials and peer-based e-WOM narratives that demonstrate real investment experiences. Finally, at the advanced stage, micro-investment trials with minimal financial commitment can allow users to experientially test platform reliability and risk exposure.
Collaboration between wealth-tech platforms, educational institutions, and regulatory bodies can further institutionalize this trust-building process by aligning financial literacy initiatives with digital platform governance.
Conceptualization, E.F. and J.S.; methodology, E.F.; software, E.F.; validation, E.F.; formal analysis, E.F.; investigation, E.F.; resources, E.F.; data curation, E.F.; writing—original draft preparation, E.F.; writing—review and editing, E.F., J.S., and U.J.; visualization, E.F.; supervision, J.S. and U.J.; project administration, E.F. All authors have read and agreed to the published version of the manuscript.
The data used to support the research findings are available from the corresponding author upon request.
The authors declare no conflict of interest.
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