Digital financial literacy and sustainable financial inclusion in metaverse ecosystems: the mediating roles of readiness and trust

Constructing networked, decentralised and immersive virtual worlds is disrupting the way the financial sector operates. One such virtual world, which includes blockchain technology, virtual reality (VR) and augmented reality (AR), has been identified as being the future digital business for investment, payments and finance (Krishnan et al., 2024). Metaverses have the capacity to disrupt financial inclusion by removing intermediation and barriers that hinder it in developing countries through participation in shared three-dimensional virtual worlds (Del Sarto and Ozili, 2025). With an emphasis on peer-to-peer management and user engagement of digital goods, this shift is from traditional paradigms of financial delivery to more decentralised, participative and real-time paradigms of delivery.

While the metaverse may offer avenues for crossing physical and institutional boundaries, the extent to which it can be an effective participant will largely depend on people’s Digital Financial Literacy (DFL), meaning the capacity to know, understand and effectively and securely use digital financial products (OECD, 2022). DFL is a set of skills that ranges from digital banking literacy, cybersecurity literacy, blockchain literacy and virtual finance platform literacy to effectively operate within a metaverse-based system (Uddin et al., 2024). Poorly managed DFL can exacerbate already prevalent disparities by entrenching a “literacy gap” between potential users and even create new digital exclusion mechanisms, thereby rendering the metaverse’s promise of inclusivity a hollow one (Al-Jabri and Roztocki, 2023).

Through initiatives like the Pradhan Mantri Jan Dhan Yojana (PMJDY), the Unified Payments Interface (UPI) and digital identification platforms like Aadhaar, India has made significant progress in expanding financial inclusion (Reserve Bank of India, 2023). Nevertheless, there is the issue of whether all socio-economic groups equally have access to these financial services, particularly when it concerns complex financial transactions in the form of cryptocurrencies, DeFi networks and virtual financial products that emerge from the metaverse (Yadav and Banerji, 2023). All of these issues pose numerous questions about how efficient the DFL will prove to be in providing equal opportunities in the virtual finance world.

This study aims to identify the key dimensions of Digital Financial Literacy (DFL) relevant to metaverse participation and examine its relationship with users’ readiness, trust and engagement in metaverse-based financial services. It also assesses the moderating role of security concerns, perceived complexity and technological infrastructure in influencing adoption. Further, the study provides policy and practical recommendations to encourage inclusive participation in the metaverse financial ecosystem.

DFL is thus described as a complex concept integrating the dimensions of finance, digitisation and technology literacy. In previous research where financial literacy was the focal point, mostly relating to numeracy and decision-making, recent literature underscores the importance of security literacy, platform digitisation and knowledge of blockchain for understanding the nature of DFL (OECD, 2022; Yadav and Banerji, 2023). Literature in relation to DFL provides contradictory evidence in regards to its impact on digital finance adoption. Some studies, such as Mustafa (2024), prove that DFL positively affects digital finance adoption since it reduces risk and enhances users’ confidence. On the other hand, existing literature suggests that literacy alone may not be enough since adoption is hampered by psychological, trust-related and infrastructure-related factors (Ogunola et al., 2024). Thus, DFL may be considered as necessary, yet not sufficient condition for digital finance adoption. Moreover, existing literature focuses primarily on traditional fintech applications, which include mobile banking and digital wallets, and ignores immersive ecosystems such as metaverse. However, DFL becomes particularly important in the case of decentralised financial environment where individuals control their transactions independently. Hence, this study investigates the importance of DFL in financial ecosystems of the metaverse.

The metaverse is now increasingly viewed as an immersive, permanent and decentralised digital space where various technologies such as virtual reality, blockchain and digital identities intersect (Krishnan et al., 2024). The metaverse has, over time, started to gain traction in various fields, including financial services such as decentralised finance (DeFi), trading of virtual assets and peer-to-peer financial interactions (Nguyen et al., 2023; Özdemir et al., 2024).

From the viewpoint of financial inclusion, it is clear that the metaverse has tremendous potential for changing the way in which financial services operate. Despite this, various authors in the field have expressed divergent views regarding the role played by DFL in ensuring financial inclusion in metaverse ecosystems (World Bank, 2023; Mashrur, 2024).

Most importantly, it is evident from an analysis of recent research in DFL and metaverse finance that very little research has been conducted in this field. Existing research has only explored either DFL in general settings or metaverse adoption in general settings, without linking both concepts (Mashrur, 2024). The gap in this study is thus very important in understanding how DFL plays a role in ensuring financial inclusion in metaverse ecosystems.

This study integrates the Technology Acceptance Model (TAM) with the financial literacy–financial inclusion framework to explain user behaviour in metaverse-based financial ecosystems.

TAM suggests that perceived usefulness and perceived ease of use are the major factors influencing the adoption of technology (Davis, 1989; Venkatesh et al., 2016). Nevertheless, the model has been challenged for failing to provide sufficient information on the development of the two perceptions, particularly in the context of the metaverse’s complex and dynamic environment, where factors such as decentralisation, uncertainty and technological sophistication are inherent.

To overcome the limitations of the TAM model in the context of the current study, the concept of DFL has been incorporated as a fundamental antecedent in the formation of cognitive and emotional evaluations. In accordance with the financial literacy and financial inclusion model (OECD, 2022), DFL has been conceptualised as a multidimensional construct that incorporates various forms of digital, financial and technological competencies.

At a mechanism level, the framework operates through a sequential mediation process involving cognitive, affective and behavioural stages:

• Cognitive Mechanism (DFL → Readiness):DFL enhances knowledge, technical skills and self-efficacy, enabling individuals to navigate complex metaverse financial systems. This reduces uncertainty and cognitive resistance, thereby increasing readiness, defined as psychological and behavioural preparedness to adopt new technologies (Parasuraman, 2000; Shaikh and Sharif, 2024).

• Affective Mechanism (DFL → Trust): In decentralised environments with limited institutional safeguards, trust becomes critical. DFL improves users’ ability to assess risks, understand cybersecurity mechanisms and evaluate platform reliability, thereby reducing perceived uncertainty and strengthening confidence (Gefen et al., 2003; Mustafa, 2024).

• Behavioural Mechanism (Readiness and Trust → Engagement): Readiness and trust together drive engagement, reflected in active participation in financial activities such as transactions, investments and interactions within virtual ecosystems (Venkatesh et al., 2016; Hazarika and Rahmati, 2023).

The sequential model shows that the impact of DFL on engagement is not only direct. It also fills a gap in previous studies that mainly focused on direct relationships between factors.

Moreover, this framework can also explain the intention–behaviour gap in digital finance. In this context, individuals might have access to information but do not take part actively. Ogunola et al. (2024) explain this gap. By including readiness and trust as mediating factors, this study can also explain how DFL leads to actual participation.

User readiness has been described as the psychological, cognitive and behavioural state of preparedness for adopting new technologies (Parasuraman, 2000). In the context of digital finance, the construct has also been related to self-efficacy, innovativeness and control beliefs on technology adoption. Empirical studies in the conventional fintech domain have always revealed that the level of readiness in individuals is positively related to their literacy levels, as they are equipped with the necessary literacy skills to interpret, assess and use the technology (Shaikh and Sharif, 2024).

Although the relationship between literacy and readiness has been established in the conventional fintech domain, the relationship may not be linear in the context of technologically advanced environments, as some researchers have related the construct of readiness to psychological barriers such as the fear of technology failure, lack of trust and complexity (Bubou and Job, 2022).

The metaverse also brings in an added layer of complexity to the above equation. Financial systems based on the metaverse are not only different from other digital-based financial systems in terms of the usual digital skills required to use them, but also involve other higher-order skills, including the ability to understand risk in unregulated environments and new financial mechanisms.

In this regard, DFL plays a vital role in building readiness by providing individuals with the necessary skills to engage with metaverse-based financial services. Individuals with higher DFL are more likely to rate themselves higher in terms of competence, thus helping to break the psychological barriers to metaverse-based financial services. DFL, in essence, acts as an enabler to transform awareness into preparedness:

Trust has also been identified as a crucial factor in the adoption of digital finance, especially in uncertain environments where there are no physical interactions and where risks are considered (Gefen et al., 2003). However, in the case of metaverse-based finance, the environment may be considered uncertain due to the presence of decentralised, anonymous and unregulated environments.

According to the literature, DFL has a major influence on customers’ trust, as it can increase their knowledge of the digital environment (Mustafa, 2024; Kumar and Rani, 2025). However, there has also been debate about the influence of DFL on customers’ trust, as some authors have identified external factors that may shape trust (Ogunola et al., 2024).

Even with such divergent views, there is still a possibility of arguing that DFL plays an integral role in the development of trust, especially within decentralised systems. This is because DFL lowers information asymmetry levels and allows for informed decisions:

User engagement refers to the degree to which users participate in financial behaviours such as transactions, investment decisions and online interactions. Previous literature on fintech adoption reveals a significant positive association between financial literacy and usage behaviour, implying that financially literate users tend to exhibit greater participation in the use of digital finance technologies (Shaikh and Sharif, 2024).

Nevertheless, recent literature provides evidence of complications with respect to this association. Financial literacy improves the user’s knowledge and skills; however, this does not automatically translate into participation. For example, studies like Ogunola et al. (2024) have revealed the existence of a disconnect between preparedness and actual participation.

Within the framework of metaverse-driven financial systems, this issue is further complicated owing to higher levels of complexity, decentralisation and risk perception. Nonetheless, given all the challenges, DFL still plays a crucial role in ensuring that users have the requisite skills to interact within the metaverse financial environment.

As such, although there may be various factors contributing to engagement, DFL will undoubtedly be instrumental in affecting user engagement:

Although DFL is expected to boost user engagement with metaverse-based financial service products, existing literature indicates that this relationship is not isolated. Rather, it is subject to various influences. In this context, security issues, complexity and technological infrastructure have been extensively cited in existing literature as critical factors that impact digital financial behaviour (Rogers, 2003; World Bank, 2023).

Although empirical evidence on the role of security issues, complexity and technological infrastructure in this context is mixed, existing literature indicates that security issues can have a substantial impact on the relationship between user capability and engagement. In this context, security issues encompass various concerns that might discourage users from engaging with digital financial service products. These might include issues related to data privacy, identity theft and financial fraud. Although existing literature indicates that users might be better able to cope with such risks if they are more financially literate (Mustafa, 2024), it is also true that such awareness might actually work against their engagement.

In a similar vein, research has revealed that perceived complexity has a negative relationship with technology adoption. Despite the fact that DFL is likely to mitigate complexity by improving user competency, the complex nature of metaverse environments may be a potential barrier.

In contrast, technological infrastructure is widely regarded as a facilitating factor that supports user interaction in digital environments:

Although previous literature has been centred mainly on the impact of DFL on consumer actions, newer findings highlight that such linkages tend to be more indirect and mechanism-based, mediated by internal factors like cognition and emotions (Davis, 1989; Venkatesh et al., 2016; Hazarika and Rahmati, 2023). In an intricate and unpredictable setting like the metaverse, the application of competence into action cannot happen directly, and thus mediation is required.

Theoretical studies on decision-making and behavioural processes along with technological adoption models indicate that individual capabilities exert influence on behaviour through intermediating psychological processes rather than being purely directly influential (Parasuraman, 2000). The concepts of readiness and trust have been considered as important intermediaries in understanding the relationship between DFL and participation.

Ready means that the individual possesses cognitive and behavioural readiness for the adoption of technological innovations. People with high levels of DFL have better feelings of efficacy, competence and proficiency in using digital financial tools, lowering the feeling of uncertainty and increasing their readiness to use the metaverse technology (Shaikh and Sharif, 2024; Bubou and Job, 2022).

In comparison, trust is an affective component of user behaviour, and it becomes important when the environment is decentralised, uncertain and lacks regulatory measures or intermediary organisations (Gefen et al., 2003; Krishnan et al., 2024). The DFL model facilitates trust-building through its ability to allow users to examine security systems, comprehend blockchain technologies and gauge platform credibility, thus mitigating perceived risks (Mustafa, 2024; Kumar and Rani, 2025).

Notably, current studies indicate that there exists a gap between intention and behaviour in which users can be knowledgeable yet not behave accordingly (Ogunola et al., 2024). Addressing this gap, readiness and trust form a sequential pathway through which DFL influences behaviour.

Compared to moderation-based explanations, mediation provides a stronger theoretical foundation, as it explicitly captures the internal processes through which literacy translates into action. By incorporating these mediators, the present study offers a more nuanced and mechanism-based understanding of engagement in metaverse financial ecosystems.

Accordingly, the following hypotheses are proposed:

The current research uses the cross-sectional research design in a quantitative manner to analyse the effect of DFL on metaverse financial services readiness, trust and use. This methodology best suits hypothesis-driven research studies (Hair et al., 2019). Generalisation could be done to larger populations using a combination of sampling techniques. The cross-sectional research design is used because it allows the researcher to follow up on responses at one point in time to get a snapshot of how users of the metaverse behave digitally in their financial transactions in the emerging metaverse world (Krishnan et al., 2024).

The target group of this study comprises salaried workers in Delhi NCR, a highly digitally advanced region in India characterised by extensive mobile internet access and strong financial service penetration. This group is particularly significant, as salaried workers tend to adopt formal financial services and thus serve as pioneers of digital financial innovations (Yadav and Banerji, 2024).

Snowball and convenience sampling techniques were used to collect data through LinkedIn groups, organisational email lists and professional networks. The sample included respondents from public and private sectors across diverse socioeconomic backgrounds within the Delhi NCR region. To improve representativeness, efforts were made to include participants with varied demographic characteristics, including age, gender, education, income and occupational sectors. Delhi NCR was selected due to its economically active and heterogeneous salaried population. Table 4 presents the demographic profile of respondents. Initially, 450 questionnaires were distributed, of which 400 valid responses were retained after screening for missing data and straight-lining responses, resulting in an effective response rate of 88.9%, which satisfies the recommended criteria for PLS-SEM analysis (Hair et al., 2019; Sarstedt et al., 2021).

To prove the adequacy of the sample size, statistical power analysis was conducted (Table 1). In PLS-SEM research, it has been suggested that a sample size of over 200 is enough for models with multiple constructs and moderating effects. The research has achieved this, as it has over 400 valid results.

The questionnaire was developed to ensure content validity and contextual relevance, using items adapted from validated scales within the metaverse context. A five-point Likert scale ranging from strongly disagree (1) to strongly agree (5) was used. A pilot study with 30 participants assessed clarity, format and reliability, resulting in minor revisions (Kline, 2023). Demographic variables included age, gender, education, income, employment sector and familiarity with metaverse or virtual reality technologies. DFL was measured through digital banking proficiency, cybersecurity knowledge, blockchain knowledge and virtual financial platform engagement (OECD, 2022; Kumar and Rani, 2025). Readiness, Trust and Engagement were adapted from established scales, whereas Security Concerns, Perceived Complexity and Technological Infrastructure were derived from prior research. DFL was modelled as a reflective-reflective second-order construct comprising four first-order dimensions, consistent with prior digital literacy studies (OECD, 2022; Al Doghan and Mirzaliev, 2024).

Table 2 presents the key constructs, dimensions and sources used in this study. Multiple-item measures were used to comprehensively capture all constructs. DFL includes digital banking skills, cybersecurity awareness, blockchain knowledge and virtual financial platform usage. Readiness, Trust, Engagement, Security Concerns, Perceived Complexity and Technological Infrastructure were included to assess users’ preparedness, participation, trust, perceived challenges and technological support in metaverse-based financial services.

This multi-dimensional measurement allowed robust testing of both direct and moderating relationships.

Data were analysed using PLS-SEM through SmartPLS 4.0 due to its suitability for complex models, non-normal data and predictive research (Hair et al., 2019). Table 3 outlines the stages of analysis, criteria and methods applied in the study. The analysis included measurement and structural model assessment. Reliability and validity were evaluated using Cronbach’s alpha, Composite Reliability (CR), Average Variance Extracted (AVE), Fornell–Larcker criterion and HTMT. The structural model was assessed through path coefficients, bootstrapping (5,000 subsamples), R², f², Q², moderation analysis and multi-group comparisons across demographic variables.

This two-step approach ensures that measurement error is minimised before evaluating structural relationships (Sarstedt et al., 2021).

Table 4 shows the demographic information of the 399 people who were interviewed for this research work. The gender ratio of the respondents revealed that most of the interviewees were males (56%), while females constituted 44%. In terms of the age distribution, the respondents ranged between 31 and 40 years (41.1%), 21 and 30 years (32.1%), 41 and 50 years (21.1%) and 50 years and above (5.8%).

In relation to their monthly income levels, 40.1% of the respondents had an income of between INR 40,000 and INR 70,000, 34.8% were earning more than INR 70,000 and 25.1% were earning less than INR 40,000. Their educational qualifications were impressive since 55% were postgraduates while 45% were graduates. In respect of the type of employment, 59.9% were from the private sector while 40.1% belonged to the public sector.

The interesting thing about the respondents was that 47.9% of them had previous exposure to the metaverse, while 52.1% of them lacked such experience.

The screening process involved checking the validity and normality of the data. Only very few missing data points existed (less than 2%), and they were replaced using the series mean technique. The skewness and kurtosis statistics for the combined variables were acceptable for PLS (approximately ± 2.0) (Kline, 2023).

Table 5, The level of DFL of respondents was relatively high (M = 3.68), and also respondents have a moderate level of preparedness and trust towards financial services of metaverse. Their level of engagement seems to be relatively low (M = 3.38). This shows that respondents have a moderate level of literacy and preparedness but still their engagement seems to be at a moderate level.

However, since all the data for these constructs were collected using a single instrument and a similar Likert scale from a group of respondents, a test for CMB was conducted. In line with best practice in PLS-SEM, Harman’s single-factor test and the full collinearity variance inflation factor (VIF) test were conducted.

The Harman’s single factor test was conducted to check if a single factor explained most of the variance in all variables. However, the results showed that a single factor explained less than 50% of all variance in the data; therefore, common method bias is not a major concern (Table 6).

The full collinearity VIF test was also conducted to check for any potential method bias. All VIF values are below 3.3; therefore, common method bias is not a major concern (Table 7).

We assessed internal consistency (Cronbach’s α), composite reliability (CR) and convergent validity (AVE). Results are summarised in Table 8.

Table 8. All Constructs are Reliable and Converge Adequately Against Common Benchmarks: α > 0.80 (acceptable); CR > 0.70; AVE > 0.50 (Fornell-Larcker Criterion). Average item loadings are very high (=0.77–0.90), suggesting that the measurement indicators do indeed capture their respective constructs (Hair et al., 2019).

Methodological aside: The loadings presented above are based on the absolute correlation coefficients between each item and the overall composite for its construct.

• Fornell–Larcker: the square root of each construct’s AVE (placed on the diagonal) exceeded its correlations with other constructs (no violations).

• HTMT: all HTMT ratios were below 0.85, indicating good discriminant validity according to (Henseler et al., 2015).

The measurement model demonstrates adequate convergent and discriminant validity, supporting treating the constructs as distinct reflective latent variables for further structural analysis.

The Fornell–Larcker criterion and HTMT ratio test results are presented in Table 9. Diagonal values correspond to the square root of AVE for respective constructs, whereas off-diagonal values present inter-construct correlations below the diagonal and HTMT ratios above the diagonal.

It can be seen in Table 9 that the square root of the AVE of each variable (DFL = 0.84; Readiness = 0.81; Trust = 0.83; Engagement = 0.85) exceeds their respective correlation with other variables, thus fulfilling the Fornell–Larcker criterion. Additionally, all HTMT ratios among the variables (values between 0.63 and 0.73) lie considerably below the threshold of 0.85 proposed by Henseler et al. (2015).

Overall, these findings clearly suggest discriminant validity among all the latent constructs of the measurement model. It clearly indicates that DFL, Readiness, Trust and Engagement are indeed unique constructs that can be included in further analysis using structural equation modelling.

Item-level loadings analysis yields compelling evidence of convergent and discriminant validity. All items loaded highly on their intended constructs above the recommended cut-off point of 0.70, while loading less than 0.45 on other constructs. None of the items reported unsatisfactory cross-loadings to merit rejection. These findings confirm that the items have strong convergent validity with their respective constructs, DFL, Readiness, Trust, Engagement, Security Concerns, Perceived Complexity and Technological Infrastructure and sufficient discriminant validity against other constructs. The items retained are therefore valid and appropriate for use in future structural equation modelling.

The structural model was evaluated to test the relationships between the constructs. Model fit was also evaluated using the Standardised Root Mean Square Residual (SRMR), which compares the observed correlation matrices to the correlation matrices obtained by the proposed model. The value obtained for the SRMR was 0.0419, which is below the acceptable limit of 0.08 (Henseler et al., 2015).

However, it is essential to note that in the PLS-SEM approach, the fit indices obtained for the structural model are not definitive measures of model fit. Therefore, the evaluation of the structural model was further reinforced by the significance of the relationships between the constructs.

The results obtained for the direct effects show that DFL has significant positive effects on the three endogenous constructs: Readiness (β = 0.47, p < 0.001), Trust (β = 0.46, p < 0.001) and Engagement (β = 0.43, p < 0.001). Therefore, the results indicate that individuals who have high DFL are more prepared to use metaverse-enabled financial services.

Table 10 presents the direct effects of DFL on key constructs of Readiness, Trust and Engagement. In structural equation modelling (SEM), direct paths represent the hypothesised causal relationships between an independent variable (here, DFL) and dependent variables (Readiness, Trust, Engagement). The bootstrapping technique was adopted for estimating standard error, confidence interval and significance level using 5,000 subsamples, which is essential in case the sampling distribution is not normally distributed. Standardised path coefficient (β), t-values, p  value and 95% confidence interval (CI) are presented in Table 10, reflecting the significance of the direct relationships among variables.

The statistical significance and positivity of all three proposed direct effects indicate that DFL has an important positive relation with Readiness, Trust and Engagement in financial services in the metaverse. This finding is aligned with the TAM framework and financial literacy studies that predict the impact of competence on perceived risks, usefulness and usage (OECD, 2022; Davis, 1989).

Table 11 displays the amount of explained variance in the endogenous variables in the structural model, denoted by R², which measures the proportion of variance explained by the predictor variables.

As seen in Table 11, a significant proportion of variance in Readiness (R² = 0.224) and Trust (R² = 0.216) was explained by DFL, while a fair proportion of variance was explained in Engagement (R² = 0.189) in the base model. However, upon the addition of the moderating variables and the interaction terms, the proportion of variance in Engagement was seen to increase to 0.258.

Apart from R², effect size was also used to assess the relative contribution of the predictor variables to the endogenous variables. As seen in Table 11, the results revealed that DFL has a significant effect on Readiness (f² = 0.289) and Trust (f² = 0.275) and a fair effect on Engagement (f² = 0.233), indicating that DFL was a major driver of metaverse-based financial engagement.

Nevertheless, it was revealed that the overall effect size of all interaction terms, calculated as f², was equal to 0.008, which is lower than the threshold for a small effect, i.e. 0.02. This demonstrates that the overall contribution of all moderating variables is negligible. This is in line with the moderation analysis, where it was revealed that none of the interaction effects were statistically significant.

The negligible effect of interaction terms implies that, theoretically, DFL is a dominant predictor of user engagement, negating the relative importance of contextual constraints such as security concerns, complexity and technological infrastructure.

Effect sizes (f²) (Cohen benchmarks: small = 0.02, medium = 0.15, large = 0.35):

• DFL → Readiness: 0.289 (medium-to-large)

• DFL → Trust: 0.275 (medium-to-large)

• DFL → Engagement (base): 0.233 (medium)

• Interactions on Engagement (collectively): f² = 0.008 (negligible)

The predictive relevance (Q²) of the endogenous variables is shown in Table 12, where the predictive relevance values have been estimated using a 10-fold cross-validation method under the Stone–Geisser approach. The predictive relevance values reveal the predictive power of the structural model concerning the ability to forecast data points for each construct, and the values more significant than zero indicate sufficient predictive relevance.

To assess the mediating effect of readiness and trust in the relationship between DFL and engagement in metaverse-based financial services, mediation analysis was conducted by applying the bootstrapping technique in PLS-SEM.

Table 13, The results indicate that DFL has a significant effect on readiness (β = 0.473, t = 10.709, p < 0.001) and trust (β = 0.465, t = 10.832, p < 0.001). Furthermore, DFL also has a significant direct effect on engagement (β = 0.434, t = 10.235, p < 0.001). To assess the mediating effect of readiness and trust in the relationship between DFL and engagement in metaverse-based financial services, the indirect effect was also assessed by applying the bootstrapping technique.

The results indicate that the mediating effect of readiness in the relationship between DFL and engagement in metaverse-based financial services was found to be significant (β = 0.205, p < 0.001). Similarly, the mediating effect of trust in the relationship between DFL and engagement in metaverse-based financial services was also found to be significant (β = 0.198, p < 0.001).

This indicates that individuals who possess higher levels of Digital Financial Literacy are more likely to build readiness and trust for metaverse-enabled financial services, thus enhancing their engagement. Furthermore, it is evident that the relationship between DFL and engagement is statistically significant, thus confirming partial mediation. This implies that DFL has a direct and indirect relationship with engagement, were readiness and trust act as mediators.

The moderating role of Security Concerns, Perceived Complexity and Technological Infrastructure in the relationship between DFL and Engagement was tested using the bootstrapping technique in PLS-SEM. The interaction effects were tested using standardised path coefficients (β), t-values, p-values and 95% confidence intervals.

As shown in Table 14, the interaction effects were not statistically significant. Specifically, the interaction effects of DFL × Security Concerns (β = 0.035, p = 0.448), DFL × Perceived Complexity (β = 0.007, p = 0.881) and DFL × Technological Infrastructure (β = 0.076, p = 0.125) were not statistically significant as the confidence intervals contain zero.

Moreover, the effect size (f²) of the interaction effects was negligible, which implies the weak moderating role of the variables Security Concerns, Perceived Complexity and Technological Infrastructure in the relationship between DFL and Engagement.

The findings suggest that the positive association between DFL and the use of metaverse-enabled financial services is resilient and not impacted by several limitations associated with contextual factors including security problems, complexity concerns and technological constraints. Hence, people with high DFL would be more inclined to use metaverse-enabled financial services regardless of several limitations.

From a theoretical standpoint, the absence of moderating effects suggests that DFL is a key determinant of metaverse-enabled financial services use. Therefore, the impact of some of the limitations concerning technology and security would be mitigated. The research adds to the existing literature on digital finance by considering the influence of capabilities in engaging with new financial environments.

Simple slopes (standardised values): I computed the conditional slope of DFL on Engagement at low (−1 SD), mean (0) and high (+1 SD) levels of each moderator:

• Security: DFL slope on Engagement = 0.324 (low), 0.359 (mean), 0.394 (high)

• Complexity: DFL slope = 0.353 (low), 0.359 (mean), 0.365 (high)

• Infrastructure: DFL slope = 0.282 (low), 0.359 (mean), 0.436 (high)

According to the findings from the moderation analysis, there was no statistical significance for the three interaction terms: DFL × Security (β = 0.035, p = 0.448), DFL × Complexity (β = 0.007, p = 0.881) and DFL × Technological Infrastructure (β = 0.076, p = 0.125). This finding means that the effect of DFL on Engagement remained strong regardless of the differences in security concerns among the users, perceived complexity of the metaverse platform and technological infrastructure available. The effect of the interaction term of DFL × Infra appeared weak but insignificant, suggesting that infrastructure had an insignificant effect on engagement.

This study aims to investigate the role played by DFL in forming the readiness, trust and engagement levels of salaried individuals in the Delhi NCR region for metaverse-based financial services. The findings of this research establish DFL as an essential and strong factor in shaping user behaviour in immersive financial environments. More importantly, this research extends beyond the general nature of DFL by identifying it as an essential factor in shaping user behaviour through structured mechanisms. In this regard, this research provides an in-depth understanding of financial inclusion in immersive environments. From an overarching viewpoint, this research contributes to the emerging body of knowledge on sustainable financial inclusion, where access is not enough without corresponding capabilities, trust and engagement (OECD, 2022; World Bank, 2023). In this regard, this research locates DFL not just as an attribute but as an essential capability for sustainable financial inclusion in environments such as decentralised financial systems in the metaverse.

The results lend robust empirical support to H1, H2 and H3, thereby confirming that DFL has a significant and positive influence on readiness, trust and engagement.

The robust influence of DFL on readiness (H1) lends further support to the underlying assumptions of the TAM, which places great emphasis on the role of user competence in shaping perceptions of ease of use and readiness (Davis, 1989; Venkatesh et al., 2016). However, the present study extends the TAM framework by showing that in the metaverse environment, literacy actually precedes the underlying assumptions of the technology acceptance model.

Furthermore, the significant positive relationship between DFL and trust (H2) supports the underlying assumptions of the Technology Acceptance Model (TAM), which suggests that literacy precedes these assumptions; specifically, the more literate a user is, the less likely they are to experience information asymmetry and uncertainty in the metaverse-based financial system (Gefen et al., 2003; Mustafa, 2024). In this context, trust cannot be institutionally facilitated and must instead be cognitively constructed by the user in a pseudonymous and decentralised environment.

The result of DFL on engagement (H3) also reiterates that literacy is associated with actual behavioural participation. However, the relatively weaker explanatory power for engagement indicates that engagement is a multifaceted construct that is influenced by various behavioural and environmental factors. This also reiterates earlier studies (Ogunola et al., 2024; Hazarika and Rahmati, 2023) that highlight the gap between capability and actual usage in digital financial systems.

One important contribution of this research is in confirming the mediating effects of readiness (H7) and trust (H8), which were found to be significant in transmitting DFL’s effect on engagement.

The presence of partial mediation implies that DFL affects engagement through both direct and indirect routes. Theoretically, this is important because it changes the conceptualisation of financial literacy from being static to being dynamic and process-oriented. More concretely, it implies that there is a sequential process in which DFL affects engagement:

• Cognitive pathway (DFL → Readiness): Literacy enhances user competence and preparedness

• Affective pathway (DFL → Trust): Literacy reduces perceived risk and builds confidence

• Behavioural outcome (Readiness and Trust → Engagement): Prepared and confident users actively participate

This provides a further explanation for both the TAM and financial literacy models. The former model is based on perceived usefulness and ease of use; however, it does not explain how these factors are actually perceived. The research fills this gap by considering DFL as an essential antecedent for both cognitive and affective states.

Moreover, the research findings meet one of the limitations identified in previous research, where only direct relationships were explored. The inclusion of mediation in this research provides an extensive understanding of how digital competence leads to actual financial behaviour in immersive environments.

Unlike what is expected here, H4, H5 and H6 were not confirmed as their moderation was found statistically insignificant and had an effect size that is minimal. From a theoretical point of view, it is a remarkable outcome, and in addition to that, it serves to make one aspect from the literature clear. Even though previous studies (Nguyen et al., 2023; Özdemir et al., 2024) suggested that context could impact the association between user capabilities and engagement, in reality, DFL remains a prevalent predictor of user engagement.

However, the significant main effects of these variables indicate that they remain important as independent determinants of engagement. Specifically:

• Security concerns and perceived complexity act as barriers, reducing engagement.

• Technological infrastructure acts as an enabler, facilitating participation.

The distinction is a vital one. It implies that contextual factors may not necessarily “moderate” behaviour; rather, they may directly influence it, and this is a more precise concept.

The implication is particularly important since it provides a better justification for the idea of mediation than moderation. While in the idea of moderation it is taken that there are some external influences on behaviour, it becomes obvious that it is the internal factors, like readiness and trust, that are much more important in triggering behaviour.

Firstly, this study enhances the technology acceptance model (TAM) by incorporating the construct of digital financial literacy (DFL) as a crucial prerequisite that influences the development of readiness and trust. Hence, this study addresses the shortcomings of the theory by highlighting that a lack of readiness and trust significantly hinders its ability to explain how user perceptions are shaped in the modern technological era. Secondly, this research makes a contribution to the fields of financial literacy and financial inclusion by illustrating that financial literacy is not enough for financial inclusion. The third contribution of this study is its ability to fill a notable gap in the literature since the integration of DFL and the use of the metaverse in financial systems are underdeveloped topics despite increased focus on fintech and immersive financial systems. Fourthly, the study makes a conceptual contribution by refuting the moderation effect while supporting the mediation effect. Future studies should therefore focus on the mechanisms that lead to certain outcomes in digital environments, rather than on interaction-based theories. Fifthly, the study makes another contribution to the literature on the theme of financial sustainability and financial inclusion through digital technologies by focusing on the role played by DFL in participation within metaverse systems.

The present study aimed to explore the role played by DFL in facilitating metaverse-enabled financial inclusion among salaried employees in the Delhi NCR region. This research contributes to the existing body of literature on digital finance and inclusive financial systems by exploring the interface between DFL and metaverse-enabled financial systems. By using PLS-SEM, this study found that DFL has a strong and positive impact on readiness, trust and engagement, which are critical antecedents to metaverse-enabled financial inclusion.

The results also showed that readiness and trust have a significant mediating role between DFL and engagement. This indicates that DFL is not only a direct driver of engagement but also an indirect driver of engagement. While security issues, complexity and technology have been found to have a significant impact on metaverse-enabled financial inclusion, the moderating role of these factors is negligible. This indicates that DFL is a primary driver of engagement that can reduce the impact of limiting factors.

The research findings also offer significant practice-related implications. As a result of this salient effect of readiness and trustworthiness, stakeholders in both the public and private sectors are able to develop goal-oriented training programs that are beyond financial literacy, including aspects of virtual asset management, blockchain and simulated scam detection. Metaverse financial platforms, as well as financial service organisations, must ensure that they adopt this research in developing user interface models of low perceived complexity so that new users may easily enter. As a result of this salient effect of readiness and trustworthiness, policymakers must ensure that they emphasise security-related concerns and technological underpinnings, given that these are the primary consequences. By prioritising safety measures such as procedural safety, third-party assurance and privacy, consumers can overcome their risk perception, and infrastructure development will facilitate their transition from literacy and readiness to actual engagement. Finally, outreach strategies, including segmentation, such as creating unique user experiences for new consumers versus seasoned digital investors, may be able to bridge this engagement gap identified in this research.

This research also contributes to the broader discourse on Sustainable Finance by emphasising the importance of DFL in promoting sustainable financial inclusion in digital environments. Sustainable financial inclusion is not only limited to having access to financial services; it is also about ensuring long-term financial well-being, responsible financial behaviour and inclusive participation in financial systems.

In addition, in the context of climate change and sustainable development, digitally enabled financial systems, such as those enabled by metaverse technologies, can be effective in ensuring “low-cost, scalable, and geographically independent access to financial services”. The use of such means of digital finance could be essential when it comes to contributing to the creation of sustainable financial inclusion mechanisms in the economies of developing countries. The DFL system can also contribute significantly to the development of an inclusive and flexible financial system. Finally, the role of trust and readiness, which are key intermediaries in this process, is critical to guaranteeing individuals’ willingness to participate.

This study offers valuable insights, but further research must address certain limitations. Firstly, by constraining the target population only to salaried employees of Delhi NCR, this study is unable to produce any useful results for rural settings, the informal workforce and limited internet connectivity. Moreover, the lack of a longitudinal research design within this study prevents it from determining any changes in the levels of preparedness, trust and participation in the metaverse experience over time. Further longitudinal research designs will be essential in establishing causal relationships between different variables. Some null hypotheses in moderation analysis suggest potential measurement issues that may require addressing through broadband efficiency and complexity perception.