Green Banking as an Environmental Sustainability Instrument: Determinants of Green Banking Usage

Green banking has become an important environmental sustainability strategy by reducing paper consumption, supporting low-carbon financial services, and encouraging environmentally responsible consumer behavior. However, the adoption of green banking remains limited in many emerging economies. This study investigates the determinants of green banking usage among young consumers by integrating the Technology Acceptance Model with environmental concern, social influence, and green trust. Survey data were collected from 360 young banking customers in Vietnam and analyzed using partial least squares structural equation modeling (PLS-SEM). The measurement model demonstrated satisfactory reliability and validity, with composite reliability ranging from 0.840 to 0.886 and average variance extracted between 0.568 and 0.660. The results indicate that perceived usefulness, environmental concern, social influence, and green trust positively influence green banking usage, while green trust partially mediates these relationships. Environmental concern emerged as the strongest predictor of green banking adoption. The findings demonstrate that environmental awareness and trust are essential for promoting sustainable financial practices. This study contributes to environmental sustainability research by explaining environmentally responsible banking behavior and provides practical implications for advancing sustainable finance and resource-efficient banking systems.

MATERIALS AND METHODS

Research design and sample

A quantitative, cross-sectional survey design was adopted to test the hypothesised model. The target population comprised young retail banking customers in Vietnam who had been exposed to green banking services. A structured questionnaire was administered, and after data screening, 360 valid responses were retained for analysis. This sample size exceeds the recommended minimum of 300 for PLS-SEM and provides adequate statistical power to detect small-to-medium effects (Hair et al., 2019). The dataset analysed in this study comprises complete responses to the 20 measurement items, with no missing values.

Measures

All constructs were measured reflectively using multi-item scales adapted from prior validated studies and rated on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). Each construct was operationalised with four items. Perceived usefulness was adapted from the technology acceptance literature (Davis, 1989; Venkatesh et al., 2003); green trust from green-trust scales reflecting environmental credibility and benevolence (Chen, 2010); environmental concern from established environmental-attitude measures (Lee et al., 2025); social influence from the theory of planned behaviour and extended acceptance models (Ajzen, 1991); and green banking usage from green banking adoption studies (Aslam & Jawaid, 2023; Dobrzynski et al., 2024; Liedekerke et al., 2025). Table 1 summarises the constructs and their sources.

Table 1. Constructs, indicators, and sources

Analytical approach

Partial least squares structural equation modelling (PLS-SEM) was selected because it is well-suited to prediction-oriented research, models with mediating chains, and data that may depart from multivariate normality (Hair et al., 2019; Jannath et al., 2024; Elango & Govindaraju, 2025). Analysis followed the standard two-stage procedure. The measurement model was assessed for indicator reliability, internal consistency (Cronbach's alpha and composite reliability), convergent validity (average variance extracted, AVE) (Fornell & Larcker, 1981), and discriminant validity via the heterotrait-monotrait ratio (HTMT) (Henseler et al., 2015). The structural model was then evaluated through path coefficients and bootstrapped significance (5,000 subsamples, bias-corrected confidence intervals), the coefficient of determination (R²), effect sizes (f²), and predictive relevance (Q²) via blindfolding. Mediation was tested by examining the significance of indirect effects, with the significance of the direct effects distinguishing partial from full mediation.

Before hypothesis testing, common method bias was assessed using Harman's single-factor test, in which the first unrotated factor accounted for 38.2% of the variance, below the 50% threshold, indicating that common method bias is unlikely to be a serious concern. Collinearity was evaluated using variance inflation factors (VIF) for the inner model; all values ranged from 1.00 to 1.18, well below the threshold of 5, indicating no collinearity problems.

From an environmental science perspective, the study considers green banking usage as an environmentally responsible behavior that contributes indirectly to environmental protection through reduced resource consumption, paperless banking practices, and support for sustainable financial systems.

RESULTS AND DISCUSSION

Descriptive statistics

Construct means ranged from 2.81 to 3.12 on the five-point scale, indicating moderate ratings across all variables. Skewness and kurtosis values fell within the conventional ±1 range, supporting approximate normality of the composite distributions (Table 2).

Table 2. Descriptive statistics for the latent constructs

Measurement model

The measurement model demonstrated satisfactory reliability and validity (Table 3). All indicator loadings exceeded 0.70. Cronbach's alpha values ranged from 0.745 to 0.829, and composite reliability from 0.840 to 0.886, all above the 0.70 threshold. AVE values ranged from 0.568 to 0.660, exceeding 0.50 for every construct and supporting convergent validity (Fornell & Larcker, 1981; Hair et al., 2019).

Table 3. Reliability and convergent validity

Note. CR = composite reliability; AVE = average variance extracted. All loadings significant at p < 0.001.

Discriminant validity was examined using the HTMT criterion (Table 4). All ratios fell below the conservative 0.85 threshold, with the highest being 0.438 (green trust and usage), indicating that the constructs are empirically distinct (Henseler et al., 2015).

Table 4. Discriminant validity (HTMT ratios)

Note. All HTMT values < 0.85, supporting discriminant validity. PU = perceived usefulness; EC = environmental concern; SI = social influence.

Structural model and hypothesis testing

The structural model was assessed using bootstrapping with 5,000 subsamples. As reported in Table 5, all seven direct paths were positive and statistically significant, supporting H1 through H7. Environmental concern was the strongest direct predictor of usage (β = 0.289, t = 6.18), followed by perceived usefulness (β = 0.222), green trust (β = 0.190), and social influence (β = 0.181). Among the antecedents of green trust, perceived usefulness exerted the strongest effect (β = 0.250).

Table 5. Structural model results (direct effects)

Note. Bootstrapping with 5,000 subsamples, bias-corrected 95% confidence intervals. f² = effect size.

Mediation analysis (Table 6) showed that all three indirect effects through green trust were significant, with bootstrap confidence intervals excluding zero. Because the corresponding direct effects (H1, H4, H6) also remained significant, green trust partially mediates the relationships between each antecedent and usage, supporting H8, H9, and H10.

Table 6. Mediation results (indirect effects)

The model explained 24.3% of the variance in green banking usage (R² = 0.243) and 15.0% in green trust (R² = 0.150). Blindfolding produced positive Q² values for both endogenous constructs (usage = 0.122; trust = 0.078), indicating predictive relevance. The standardised root mean square residual (SRMR = 0.064) fell below the 0.08 threshold, suggesting acceptable model fit.

This study integrated TAM with sustainability and trust perspectives to explain green banking usage among young consumers, and tested green trust as a mediating mechanism. All 10 hypotheses are supported by the data, which show that adoption is jointly shaped by utilitarian, environmental, and social factors and that some of their influence is transmitted through green trust.

The finding that environmental concern is the strongest direct predictor of usage (β = 0.289) is noteworthy. It suggests that, for younger Vietnamese customers, the decision to use green banking is anchored at least as much in environmental values as in perceived utility. This echoes evidence that environmental values motivate engagement with green financial products (Merli et al., 2024) and extends it to an emerging-market, youth-focused context. Perceived usefulness remained a substantial driver (β = 0.222), reaffirming TAM's central claim that perceived utility drives adoption (Davis, 1989), while social influence contributed more modestly (β = 0.181), consistent with evidence that social endorsement supports green adoption among younger consumers.

Green trust significantly drove usage (β = 0.190) and partially mediated the effects of all three antecedents, consistent with research positioning trust as the bridge between perceptions, values, and adoption in green banking (Nguyen et al., 2023; Hasan et al., 2025; Rocha et al., 2025). The partial nature of the mediation indicates that, while trust is an important conduit, each antecedent also retains a direct pathway to usage. The relatively modest R² values (0.243 for usage; 0.150 for trust) indicate that the model captures a meaningful but bounded portion of the variance, leaving room for additional determinants.

The results indicate that environmental concern is the strongest predictor of green banking usage, suggesting that environmentally aware consumers are more willing to adopt financial services that contribute to sustainable resource management. This finding reinforces the role of environmental awareness in promoting environmentally responsible consumption behaviors.

From an environmental management perspective, wider adoption of green banking can reduce paper consumption, minimize unnecessary transportation, lower operational resource use, and encourage environmentally sustainable financial ecosystems.

These findings support the growing environmental sustainability literature by demonstrating that financial technologies can contribute indirectly to environmental protection through changes in consumer behavior. Consequently, green banking should be viewed not merely as a digital financial innovation but also as an environmental sustainability strategy that supports resource conservation and low-carbon development.

Theoretical implications

Theoretically, this study contributes to environmental sustainability research by extending the application of the Technology Acceptance Model (TAM) to explain environmentally responsible financial behavior. By integrating technology acceptance with environmental concern, social influence, and green trust, the proposed framework demonstrates how environmental and behavioral factors jointly encourage the adoption of green banking as a sustainable financial practice. The findings highlight that environmental concern is not only an important antecedent of green banking usage but also a key mechanism for promoting sustainable consumption and resource-efficient financial behavior. Furthermore, the mediating role of green trust advances current understanding of how environmental values are translated into environmentally responsible banking practices. This integrative framework responds to calls for multidisciplinary approaches that combine technological, environmental, and social dimensions to explain pro-environmental behavior and support environmental sustainability (Aslam & Jawaid, 2023).

Practical implications

Practically, the findings provide important implications for financial institutions, environmental policymakers, and sustainability practitioners seeking to promote environmentally responsible financial behavior. Banks should pursue a coordinated strategy by enhancing the usefulness and accessibility of green banking services, strengthening green trust through transparent and verifiable environmental commitments, communicating the environmental benefits of green banking, and leveraging social influence to encourage wider adoption, particularly among Generation Z and Millennial consumers. Increasing the adoption of green banking can contribute to reducing paper consumption, supporting paperless transactions, improving resource efficiency, and encouraging sustainable consumption patterns. These outcomes align with broader environmental sustainability objectives by promoting environmentally responsible financial practices and supporting the transition toward low-carbon and resource-efficient banking systems. Because these strategies collectively strengthen green trust, an integrated approach is likely to be more effective than isolated initiatives in advancing both sustainable finance and environmental management.

CONCLUSION

This study examined the determinants of green banking usage and the mediating role of green trust among young banking customers in Vietnam by integrating the Technology Acceptance Model with sustainability and trust perspectives. Based on survey data from 360 customers and PLS-SEM analysis, the findings indicate that perceived usefulness, environmental concern, and social influence significantly influence green banking usage, both directly and indirectly through green trust, with environmental concern emerging as the strongest predictor. These findings suggest that environmental awareness plays an important role in encouraging environmentally responsible financial behavior and supporting the adoption of sustainable banking practices. By demonstrating how green trust translates environmental values and technology acceptance into green banking usage, this study extends the understanding of environmentally responsible consumer behavior within the context of sustainable finance. The findings also provide practical implications for financial institutions and policymakers seeking to promote green banking as a strategy for reducing paper consumption, improving resource efficiency, encouraging sustainable consumption, and supporting broader environmental sustainability objectives. Overall, the study contributes to environmental sustainability research by positioning green banking as an important mechanism for advancing environmentally responsible financial systems and facilitating the transition toward a more sustainable economy.

Limitations and future research

Several limitations should be noted. The cross-sectional design cannot establish causal ordering, so longitudinal or experimental designs would more convincingly test the temporal logic of the antecedents–trust–usage chain. Reliance on single-source, self-reported measures invites common-method concerns; although procedural and statistical checks suggested these were not severe, future studies could incorporate additional remedies. The focus on a single country and on younger cohorts limits generalisability; future work could examine other segments and markets, and test moderators such as gender, income, and digital literacy. Finally, the model could be extended by incorporating constructs such as perceived ease of use, green awareness, ESG perceptions, green FinTech features, and emerging technologies to capture the evolving landscape of sustainable finance.

ACKNOWLEDGMENTS: The authors would like to thank Industrial University of Ho Chi Minh City (IUH) and Ho Chi Minh City University of Industry and Trade (HUIT) for their support of this research.

CONFLICT OF INTEREST: None

FINANCIAL SUPPORT: None

ETHICS STATEMENT: Participation in this study was voluntary. Informed consent was obtained from all participants, and the

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