Abstract
Purpose
Environmental threats are becoming severe in developing and undeveloped countries. It urges to know how green banking operations can foster sustainable development in these regions. This study aims to provide empirical evidence of the determinants of green banking operations in Bangladesh.
Design/methodology/approach
Based on the socially responsible investing (SRI) theory, this study examined the hypothesized relationships using a partial least square structural equation modeling (PLS-SEM) approach. The Bayesian SEM (BSEM) through a Markov Chain Monte Carlo (MCMC) approach was also used to validate the study's first-order model.
Findings
The findings show that sustainable innovativeness, green investment and green banking policy substantially and positively change green banking operations. Notably, green investment is the most influential predictor of green banking operations, driving banks to establish sustainable economic systems within the country.
Practical implications
The findings offer valuable guidance for scholars, financial institutions, policymakers and bank managers to develop and implement effective strategies for green banking operations. These strategies may significantly contribute to achieving the sustainable development goals (SDGs) in Bangladesh.
Originality/value
This study is ground-breaking in associating sustainable innovativeness and green banking operations from a developing country. It enriches our understanding of green banking, aligning with existing literature. Additionally, PLS-SEM and BSEM provide strong validation of the proposed theoretical model.
Keywords
Citation
Rahman, M.S., Moral, I.H., Akther, S., Hossain, G.M.S. and Islam, W. (2024), "What drives green banking operations in Bangladesh? Findings from PLS-SEM and BSEM", Asian Journal of Economics and Banking, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/AJEB-09-2023-0088
Publisher
:Emerald Publishing Limited
Copyright © 2024, Md. Shahinur Rahman, Iqbal Hossain Moral, Samia Akther, Gazi Md. Shakhawat Hossain and Waheda Islam
License
Published in Asian Journal of Economics and Banking. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Rapid population dynamics put increasing pressure on energy resources, leading to pollution, ozone depletion, rising sea levels, carbon emissions, and climate change (Mishra, 2023). A survey by Johnson et al. (2014) identified that 67% of 1,000 global CEOs doubted the economy could meet the demands of the growing population. This drives massive production, exacerbating environmental degradation (Mishra, 2023). Consequently, business and financial organizations are now scrutinized as ecological, social, and economic problems increasingly impact local communities (Porter and Kramer, 2014; Nath et al., 2014).
In recent decades, banks have progressively integrated SDGs and environmental, social and governance (ESG) standards into their operations (Bukhari et al., 2023). They are also encouraged to engage in the growing global climate change governance (Stauropoulou et al., 2023), which is crucial in supporting green banking initiatives (Elsner and Neumann, 2023). Previous studies have explored green banking from multiple lenses, such as institutional and corporate governance (Bose et al., 2018), green development (Feng et al., 2017), corporate social responsibility (CSR), and financial performance (Zhou et al., 2021). In Bangladesh, research has focused on green banking from diverse aspects, including regularity setting (Bose et al., 2018) and human resource perspective (Rubel et al., 2020). Nevertheless, these studies have rarely examined banks' green banking operations. Hence, delving into the factors affecting green banking in Bangladesh is essential for combating environmental hazards and promoting a sustainable financial system (Saha et al., 2020). In doing so, the current study examines and comprehends the factors driving green banking operations in Bangladesh.
The research examines the role of banks' green banking in addressing environmental challenges. Specifically, the study explores how banks integrate sustainable practices and green banking into their operations. The research also evaluates the impact of sustainable innovation, green investment, and green banking policies on green banking operations while analyzing the broader implications for local communities and the global economy.
Understanding the green banking operations from an emerging economy (i.e. Bangladesh), this research is significant for five key reasons. First, as primary financial sources, banks must help lessen environmental impacts (Bihari and Pradhan, 2011; Zhixia et al., 2018). Despite Bangladesh being the world's seventh most climate-vulnerable nation (United Nations Development Programme, 2023), there is limited research on how its banks can mitigate ecological imbalances. This study adds valuable insights into the role of the banking industry in response to environmental degradation, echoing calls by Saha et al. (2020) and Sengupta et al. (2023). Second, since green banking is still in its infancy in Bangladesh, with limited adoption by public and private banks (Masukujjaman and Aktar, 2014), this study offers noteworthy insights to practitioners and policymakers aiming to formulate sustainable policies centered on SDGs. In case of meeting several challenges (e.g. lack of customer awareness towards green banking and sustainable innovativeness and difficulty in assessing eco-friendly projects), this study can be ground-breaking. Third, this research offers insights into how banks can encourage staff to conserve energy in their branches and lend money to eco-friendly projects. Fourth, by leveraging this research's insights, banks can gain a competitive edge by promoting green banking over conventional banking (Nath et al., 2014). In doing so, Rashid (2010) argued that prioritizing loans to industries that support various environmental conservation initiatives can enhance customer retention and satisfaction, as well as corporate benefits. Fifth and finally, drawing the SRI theory, this study proposes a unique research framework (see Figure 1) incorporating three less explored variables, which would be interesting to examine in new context, like Bangladesh, for the first time.
The rest of the paper is organized as follows: The second section covers the literature review and hypothesis development. The third section presents the study's research methodology, followed by the results in the fourth section. The fifth, sixth, and seventh sections comprise discussions, implications, and conclusions respectively.
2. Literature review
2.1 Theoretical framework
This research leverages the SRI paradigm to explore the associations between the predictors (sustainable innovativeness, green investment, and green banking policy) and outcome (green banking operations). The SRI paradigm, rooted in ethical investment motivation, has a long history, though its exact definition remains debated (Renneboog et al., 2008). Evidence suggests that social, sustainable, ethical and environmental investments are the essence of SRI theory (Rehman et al., 2021a, b; Chatzitheodorou et al., 2019). Chatzitheodorou et al. (2019) argued that social and sustainable investments are often used interchangeably in SRI theory. The theory emphasizes individual values and the well-being of society as key factors in investment decisions (Ibe-enwo et al., 2019). Hernandez and Hugger (2016) noted that SRI considers the positive impact of social investments on community welfare and social benefits.
Existing research, grounded in SRI theory, shows that green banking operations accelerate diverse organizational outcomes, including a positive green image, trust in banks, and customer loyalty (Ibe-enwo et al., 2019). Rehman et al. (2021a, b) also found that green investments and policies are significantly related to sustainable operations. For policymakers and managers, focusing on sustainable innovativeness, investment, and policy development can be a collective strategy for enhancing green banking operations. Inadequate literature on emerging green banking drives the current research using the SRI theory (Chatzitheodorou et al., 2019; Rehman et al., 2021a, b), as it offers a comprehensive framework for understanding green banking practices from various socio-economic aspects (Ibe-enwo et al., 2019).
Scholars have shown that green banking is closely connected to societal well-being, driven by increasing environmental awareness (Islam, 2013; Zhixia et al., 2018). Similarly, Bihari and Pradhan (2011) affirmed that green banking promotes social responsibility by considering environmental impacts before financing, aligning with previous studies (Bhardwaj and Malhotra, 2013; Bihari and Pradhan, 2011). For instance, Jan et al. (2019) explored the relationship between sustainability practices and financial performances in Islamic banking, while Hummel et al. (2021) found that management practices are positively associated with environmental and sustainable operations, as the quality of management influences banks' performance. However, the extant literature (see Appendix 1) remains unclear on how banks' sustainable innovativeness, green investment, and green banking policy drive green banking operations leveraging the SRI theory, especially in a developing country. Scholars like Rahman et al. (2022) also urged further exploration of these drivers in developing countries. This research makes notable contributions to fill this research gap.
Green has emerged as a global symbol of environmental consciousness, promoting all economic sectors to adopt green initiatives (Zhang et al., 2008). Unlike traditional banks, green banks prioritize social and ecological factors to protect the environment and conserve resources (Zhang et al., 2022). Conventional banks can turn into green banks by aligning their core operations with environment conservation (Julia and Kassim, 2020). Mir and Bhat (2022) defined green banking as environmentally conscientious banking, or sustainable banking (Rehman et al., 2021a, b). Green banking creates a win-win situation for everyone, enhancing operational efficiency, reducing fraud and errors, and achieving cost savings in banking operations (Zhang et al., 2008; Rehman et al., 2021a, b).
Green banking operations encompass sustainable practices with long-term socio-economic and environmental impacts (Bhardwaj and Malhotra, 2013). These include offering financial products that support environmentally friendly businesses, implementing sustainable practices within banks, and investing in renewable energy (Bihari and Pradhan, 2011; Zhixia et al., 2018; Julia and Kassim, 2020). Given the significant environmental impact of banks due to resource consumption like paper and power (Zhixia et al., 2018), there is a global expectation for banks to integrate sustainable banking into their regular financing and investment plans.
2.2 Hypothesis development
2.2.1 Sustainable innovativeness → green banking operations
An innovation's novelty is measured by its innovativeness (Longoni and Cagliano, 2018), which includes two main types: radical innovations (bring fundamental changes and structurally transform the existing system); progressive innovations (slightly modify the existing operations) (Longoni and Cagliano, 2018). Longoni and Cagliano (2018) identified that technological advancements allow companies to develop new strategies, products, and operations. As Kolk and Pinkse (2010) highlighted, technical innovations are increasingly emphasized in the global response to climate change, particularly in the banking sector. For instance, enhancing banks' innovativeness is key to attaining a competitive edge, enabling to adopt cost-effective, risk-reducing, and product-improving ideas (Frame and White, 2004). Sustainable innovativeness strengthens banks’ capacity to embrace these ideas, aligning with environmental needs. Research shows that innovativeness significantly influences organizations' operations and performances (Menguc and Auh, 2006). Thus, investigating the relationship between sustainable innovativeness and green operations in banking is a worthwhile endeavor. Based on this argument, we can propose the following hypothesis:
Sustainable innovativeness positively impacts green banking operations.
2.2.2 Green investment → green banking operations
Green investment comprises using resources from the public and private sectors to invest in two key areas: (1) providing environmental products and services, such as water filtration systems and (2) addressing environmental causes, for instance, energy conservation initiatives or renewable energy adoption (Inderst et al., 2016). It closely relates to other investment approaches, including socially responsible and environmentally sustainable investing (Finance, 2012).
Research indicates that access to financial resources is crucial for promoting green investments and embracing environmental performance (Hernandez and Hugger, 2016; Falcone, 2020). Rehman et al. (2021a, b) found a positive relationship between green investment and both businesses' financial and environmental performance. According to van Dooren and Galema (2018), individual and social well-being are crucial to evaluating investment choices. Since green investment is considered a social investment, banks often face ethical and social pressure (Masukujjaman and Aktar, 2014). While the relationship has received scholarly attention, more empirical evidence is needed to determine its significance. Therefore, we propose:
Green investment positively impacts green banking operations.
2.2.3 Green banking policy → green banking operations
Banks, driven by environmental policies, increasingly incorporate green operations (Rehman et al., 2021a, b) and strict ecological measures for companies and enterprises (Shaumya and Arulrajah, 2017; Akter et al., 2017). As key a stakeholder in addressing carbon emissions and environmental degradation, bank’s significant economic influence and close societal connections highlight its role (Shaumya and Arulrajah, 2017). Depositors, borrowers, and regulators urge to prioritize sustainability over profit maximization (Khan and Kadir, 2011; Rehman et al., 2021a, b; Shaumya and Arulrajah, 2017). Consequently, banks implement environmental policies and voluntary carbon disclosures to enhance their competitive advantage (Persakis et al., 2024). Although research on the relationship between green policy and banking operations is limited (Rehman et al., 2021a, b), this study seeks to explore this relationship through the following hypothesis:
Green banking policy has a positive impact on green banking operations.
3. Research methodology
3.1 Research design
This study uses a structured questionnaire to examine the factors influencing green banking operations in Bangladeshi banks by collecting primary data from bank employees. The study's research design is descriptive, which is well-suited for explaining the characteristics, functionality, and behavior of a phenomenon and identifying relationships among selected variables (Dulock, 1993).
3.2 Measures
The questionnaire comprises demographic questions and 18 measurement items (see Appendix 2) for each variable. The demographic part covers the respondent's age, gender, education level, income, and working experience in the banking industry. The measurement items were selected from previous studies: three items of sustainable innovativeness from Song et al. (2020), five items of green investment from Igbudu et al. (2018), four items of green banking policy from Rehman et al. (2021a, b), and six items of green banking operation from Chen et al. (2013). Later, the questionnaire was reviewed and adjusted based on feedback from two experts to ensure the validity and reliability of the selected items in the study context. All the survey questions were formatted using a five-point Likert scale: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree.
3.3 Participants and sampling
We collected data from bank employees of the local branches under the Khulna division. This division was chosen because it is the second-largest division in Bangladesh, hosting many banks. Convenience sampling was employed through face-to-face interviews, selected for its ease, cost-effectiveness, and simplicity. This sampling technique is widely recognized in the field, with previous scholars utilizing it in green banking (Bouteraa et al., 2022; Zhang et al., 2022). The interview began with a formal greeting and a comprehensive explanation of the research objectives, which facilitated valuable data collection and adherence to ethical standards. No gifts or incentives were offered to the participants to avoid response biases.
The data collection occurred from April 10 to July 25, 2023, during which 270 questionnaires were distributed. After an initial screening process and excluding incomplete or outlier responses, 249 valid responses were retained for further analysis. For rigorous regression analysis, such as structural equation modeling (SEM), Lee (2011) suggested a sample size of 150–400, while Nunnally (1978) noted that a sample size of 100–150 is sufficient for achieving statistically significant results. Hence, the sample size of this study falls within the acceptable range as outlined by both Lee (2011) and Nunnally (1978).
3.4 Data analysis
We employed the partial least squares SEM (PLS-SEM) to assess the hypothesized relationships, given its well-acceptability for evaluating the reliability and validity of multiple instruments across various demographic groups (Raines-Eudy, 2000). In doing that, we utilized a two-step technique (Anderson and Gerbing, 1988). First, we conducted confirmatory factor analysis (CFA) using the maximum-likelihood estimation (MLE) to evaluate the measurement model. Then, we tested the structural model to examine model fit and hypothesized associations among the constructs (Hair et al., 2010).
To further refine our analysis, we applied the Markov Chain Monte Carlo (MCMC) method to estimate the Bayesian SEM (BSEM), allowing us to gain the posterior distributions of model parameters. This approach considers model parameters as unknown and random, presented by a prior distribution that reflects our initial knowledge (Ong et al., 2018). Using Bayes' theorem, we derived the posterior distribution (Arbuckle, 2009). Research supports Bayesian methods as offering a more flexible approach to data analysis (Ong et al., 2018; Muthén and Asparouhov, 2012; Kanapathy et al., 2014). Besides, BSEM should be used alongside frequentist SEM to address limitations inherent in the frequentist approach (Ong et al., 2018). Mainly, BSEM provides more accurate parameter estimates and credible intervals, offering a robust complement to the frequentist method (Muthén and Asparouhov, 2012; Kanapathy et al., 2014). By integrating prior information, BSEM can improve the precision of estimates and handle parameter uncertainty more effectively, improving overall robustness and leading to more nuanced and reliable results (Muthén and Asparouhov, 2012).
The study employed Normal priors for the model parameters, specifying a mean of 0 and a standard deviation of 1, denoted as N (0,1). This choice effectively balances flexibility and computational efficiency in Bayesian SEM, representing a neutral prior belief about the direction and magnitude of relationships between variables (Murrar et al., 2024). The standard deviation of 1 reflects moderate uncertainty, allowing the data to influence the posterior estimates substantially. Additionally, we assigned non-informative Inverse Gamma (0.01,0.01) to the overall variance in Bayesian model. Thach (2023, 2024) recommends to assign non-informative Inverse Gamma (0.01, 0.01) priors for variance parameters.
Normal priors are particularly suitable for BSEM because they correspond with the likelihood function of conventional regression models, leading to Normal posterior distributions. This correspondence simplifies the computation of posterior distributions, facilitating analytical solutions and efficient numerical approximations. Furthermore, with priors centered at zero, significant deviations of posterior estimates from zero can be interpreted as evidence of meaningful relationships between variables. This capability is especially beneficial in policy analysis and decision-making, where understanding the direction and strength of these relationships is essential (Murrar et al., 2024).
4. Results
4.1 Demographics analysis and frequency distribution
Table 1 summarizes the demographic characteristics of the respondents. The majority were male (70.28%), while 29.72% were female. The age distribution shows that 4.02% were aged 20-24, 18.07% were aged 25–29, 44.18% were 30–34, 25.70% were 35–39, and 8.03% were 40 or older. Regarding education, 44.18% held a master's degree, followed by 36.95% with a bachelor's degree. Experience varied, with 4.02% having less than 3 years, 8.03% having 3–4, 27.71% with 5–6 years, 48.19% with 7–8 years, and 12.05% having 9 years or more of experience. Income distribution showed that 49.80% earned between 20,001–40,000 Tk, and 30.12% earned between 40,001–60,000 Tk.
4.2 Validity and reliability test
Bartlett's test of sphericity and the Kaiser–Meyer–Olkin (KMO) were used to assess sampling adequacy and confirm the sufficiency of the sample. The KMO value of this study was 0.962, well above the acceptable threshold of 0.6, indicating adequate sampling (Tabachnick and Fidell, 2019). Additionally, Bartlett's test of sphericity was significant (p = 0.000).
Pearson's Skewness and Kurtosis benchmark values were used to determine the normality of the collected data. According to Kline (2015), acceptable threshold values for data normality range from less than 3 to not more than 10. As shown in Table 2, the descriptive statistics indicate that both Skewness and Kurtosis values fall within this range, suggesting that the data were normality distributed.
Table 3 represents the Cronbach's alpha values used to assess the reliability of the data (Cronbach, 1951). All four constructs' Cronbach's alpha values−0.972 for sustainable innovativeness, 0.9888 for green investment, 0.987 for green banking policy, and 0.986 for green banking operations− were within acceptable ranges, indicating strong internal consistency (Cortina, 1993; Fornell and Larcker, 1981).
We conducted CFA to the reliability and validity of the measurement model. To improve model fit and satisfy overall requirements, we removed measurement items with low factor loadings and excessive cross-loading, following the recommendation of Hair et al. (2010). We assessed the study's convergent validity by examining standardized factor loading, composite reliability (CR), average variance extracted (AVE), and model fit indices (see Table 3). Discriminant validity was confirmed by comparing the AVE with the squared inter-construct correlations (Table 4), and the results were satisfactory.
Table 3 shows convergence validity, which was acceptable. For instance, all the factor loadings for the observed items are higher than 0.6, based on Bagozzi and Yi (1988) and Fornell and Larcker (1981). The reported CR values were also higher than the recommended value of 0.7 suggested by Hair et al. (2010). The AVE values were higher than 0.50, showing sufficient convergence validity.
The measurement model fit indices were consistent with the recommended values (Bentler, 1990): χ2 = 309.260, χ2/df = 2.397, goodness of fit index (GFI) = 0.881, Tucker-Lewis index (TLI) = 0.97, comparative fit index (CFI) = 0.981, and root mean square error of approximation (RMSEA) = 0.075.
The structural model was then tested with the MLE to evaluate the hypotheses. The fit statistics (χ2 = 309.260, χ2/df = 2.397; RMSEA = 0.075; CFI = 0.981; GFI = 0.881; TLI = 0.977) indicated that the model fits the observed data well (Bentler, 1990). Consequently, the fit indices for both the measurement and structural models suggest considerable validity and reliability.
4.3 Hypothesis testing
This research examined the factors influencing green banking operations in a developing economy. Figure 2 and Table 5 show that all three connections − sustainable innovativeness, green investment, and green policy leading to green banking operations − were positively and statistically significant. Specifically, the results demonstrate that sustainable innovativeness (β = 0.20; t = 3.39; p = 0.000), green investment (β = 0.50; t = 9.239; p = 0.000), and green policy (β = 0.27; t = 5.108; p = 0.000) all had a substantial impact on green banking operations. Consequently, the hypotheses H1, H2, and H3 were supported.
4.4 BSEM
This research applied the BSEM approach following the methodologies of Ong et al. (2018), Muthén and Asparouhov (2012), and Kalia (2024). The Bayesian estimation method is beneficial when SEM is estimated through MLE and involves a latent measurement model (Muthén and Asparouhov, 2012). Moreover, this approach is well-suited to deal with small sample sizes. The BSEM model in this study is grounded in a robust theoretical framework and supported by rigorous MLE procedures and estimation techniques (Muthén and Asparouhov, 2012).
This study generated the posterior distributions using MCMC via SPSS and AMOS. The estimation process required approximately 81,500 samples (excluding 500 burn-in samples) to achieve convergence. The results confirmed acceptable convergence of the BSEM model, in line with Arbuckle's (2009) recommendations. The potential scale reduction (PSR) value was 1.0001, indicating statistical convergence. The posterior predictive value of the model was 0.50, suggesting that the model effectively captures the underlying data patterns. Additionally, the credible intervals (Table 6) confirmed a 95% certainty that the true parameter values are above 0, indicating that the direct impacts on the latent dimensions are positive and statistically significant.
Figure 3 presents the trace plots and autocorrelation curves. The trace plots demonstrate the stability of the posterior mean values, while the autocorrelation curves show a rapid asymptotic decline during the MCMC sampling process. This decline stabilized at lag 15, after which the correlation of newly drawn samples with the previous samples was close to zero (Ong et al., 2018; Kanapathy et al., 2014). These figures indicate that the BSEM results were consistent and that convergence was successfully achieved. Moreover, the trace plots show that the sensitivity analysis using the non-informative Inverse Gamma (0.01,0.01) prior to overall variance yields acceptable results.
Additionally, we compared the results from the PLS-SEM and the BSEM estimation (see Table 7) to highlight similarities and differences. In SEM, the importance of predictors was assessed through the standardized effects of each independent construct on the dependent construct. A positive association is observed when examining the relationship between sustainable innovativeness and green banking operations, with nearly identical coefficients across both methods (0.198 for PLS-SEM and 0.176 for BSEM). Additionally, both estimations reveal a positive and close relationship between green investment and green banking operations (0.501 for PLS-SEM and 0.444 for BSEM). This close relationship is also seen between green banking policy and green banking operations (0.268 for PLS-SEM and 0.225 for BSEM). Both methods identified green investment as the most influential predictor of green banking operations, with sustainable innovativeness being the least influential. The similarity in results between these two approaches confirms robustness and strong associations between the constructs.
5. Discussion
This study investigated green banking operations within both private and public banks in Bangladesh. Using SEM and BSEM, the study assessed the validity, reliability, and structural relationships of these variables. The results indicated significant impacts of sustainable innovativeness, green investment, and green banking policy on green banking operations, aligning with previous studies (Rehman et al., 2021a, b; Bukhari et al., 2020).
5.1 Sustainable innovativeness and green banking operations
Sustainable innovativeness significantly influenced green banking operations, consistent with the findings by Khaer and Anwar (2022). Banks' green operations heavily depend on adopting new and eco-friendly technologies, as customers prefer banks that offer innovative and sustainable financial products (Sharma and Choubey, 2022). We contend that banks' initiatives of reducing paperwork and adopting digital transactions are crucial to enhancing their overall green performance. Additionally, sustainable innovations can play a pivotal role in risk management and provide a competitive edge by offering eco-friendly products and services. Increased stakeholder interest in environmentally friendly policies (Julia and Kassim, 2020) underscores the need to shift toward green banking, particularly in developing countries (Bukhari et al., 2020).
5.2 Green investment and green banking operations
The study also found a positive association between green investment and banking operations, aligning with Rehman et al. (2021a, b). This relationship suggests that allocating funds to eco-friendly projects facilitates the implementation of green initiatives and enhances the banks' social image. Huang and Huang (2023) identified that traditional banks are transitioning to green banking due to the demand for “carbon neutrality,” integrating green finance knowledge and practices. Incorporating green investments into a bank's core activities diversifies its portfolio, can reduce risk exposure, and enhances operational resilience, helping to build a solid reputation and attract investors, customers, and partners.
5.3 Green banking policy and green banking operations
The research showed a positive and significant relationship between green policy and banking operations, echoing the findings from Sharmeen and Yeaman (2020). Green banking policies demonstrate a bank's commitment to environmental sustainability, which enhances reputation, accountability, and profitability. Additionally, green policies can improve environmental performance, shielding banks from regulatory penalties. Availing various incentives, subsidies, and technical support from government and agencies is possible when banks welcome sustainable policies.
6. Implications
6.1 Theoretical implications
The study provides a significant theoretical and practical contribution, exploring the drivers of green banking operations. While previous research has examined green banking from various angles, such as green intellectual capital and human resource management (Bose et al., 2018; Zhou et al., 2021), this study distinguishes itself by focusing on contemporary, less explored linkages through the lens of SRI theory of an emerging economy. More specifically, it addresses a gap by investigating the association between sustainable innovativeness and green banking operations, a previously less or unexplored relationship. This study shows the substantial influence of sustainable innovativeness on banks' green operations and provides empirical evidence and a conceptual framework specific to an emerging economy. By examining the driving forces of green banking operations, this study enhances our theoretical comprehension of how stakeholders influence and incentivize green banking. This deeper theoretical insight can help researchers uncover the fundamental mechanisms and determinants of green banking operations in similar contexts.
6.2 Practical implications
Besides the theoretical contributions, the current study offers practical implications for practitioners and policymakers. Limited research on green banking operations in developing countries was found, and the findings provide crucial guidance for policy formulation and implementation in other countries (e.g. Nepal, Pakistan, and Bhutan). The study highlights the significant relationship between sustainable innovativeness and green banking operations, underscoring the need for financial institutions and decision-makers to invest in eco-friendly technologies. This aligns with Ren et al. (2023), who identified the interconnection between green innovation and sustainable development. Emphasizing sustainable innovativeness can aid banks in deploying sustainable technologies to achieve SDGs.
Furthermore, this study suggests that banks should implement a robust verification process to assess the environmental impact of potential investments. Failing to measure the ecological impact can indirectly lead to environmental harm. Policymakers may consider holding banks accountable for environmentally harmful enterprises (Masukujjaman and Aktar, 2014). By exclusively supporting businesses with strong environmental practices, banks can uphold their ethical responsibilities and positively impact the environment and quality of life.
The research's practical significance extends to its benefits for various stakeholders. The findings can help Bangladeshi banks understand how green banking practices affect their financial performance compared to traditional banking. It offers insights into the nature of green banking banks should take to leverage opportunities and overcome challenges, particularly considering that green banking services are still in their infancy in the country.
6.3 Limitations and directions for future research
This empirical study acknowledges several limitations and suggests directions for future research from multiple aspects. First, the focus on green banking operations excludes those not utilizing the existing green banking services. Second, the study uses convenience sampling from Khulna only, which may limit the generalizability of the results to other regions or populations. Expanding the sample size beyond 249 bank employees could yield more generalized findings. Third, this study examines only three constructs− sustainable innovativeness, green investment, and green banking policy. Future research could comprehensively explore additional constructs like green loans, sustainable competitiveness, sustainable performance, and environmental well-being to understand their impact on green banking operations. Finally, the study's cross-sectional design could be supplemented with a longitudinal research design for more robust validation. Besides, incorporating mixed methods−both qualitative and quantitative− could offer more profound insights.
7. Conclusion
Bangladeshi banks lag behind their counterparts in developed countries in terms of green banking. This study highlights the need for Bangladeshi banks to integrate green banking into their core operations. It examines the influence of sustainable innovativeness, green investment, and green policy in enhancing green banking practices in Bangladesh. The findings show that these factors significantly promote sustainable banking practices, resulting in the country's environmental conservation and sustainable development. The research reveals that sustainable innovativeness enables banks to offer environmentally friendly products and services, enhancing environmental performance and customer satisfaction. On the other hand, green investments in sectors like renewable energy, energy efficiency, and waste management foster sustainable banking operations, generating long-term financial returns. Additionally, supportive regulatory guidelines facilitate transparency and accountability, ensuring adherence to sustainable standards. Thus, by prioritizing these factors, banks can play a vital role in driving sustainable development and supporting an environmentally viable economy.
Figures
Figure 1
Conceptual framework
Figure 2
Results of the structural model
Figure 3
BSEM autocorrelation and trace of associations between constructs
Item loadings of the constructs with CR, AVE, and Cronbach's alpha values
| Constructs | Items | Estimate | CR | AVE | Cronbach's alpha |
|---|---|---|---|---|---|
| Sustainable innovativeness (SI) | SI1 | 0.957 | 0.973 | 0.924 | 0.972 |
| SI2 | 0.973 | ||||
| SI3 | 0.953 | ||||
| Green investment (GI) | GI1 | 0.963 | 0.988 | 0.942 | 0.988 |
| GI2 | 0.974 | ||||
| GI3 | 0.973 | ||||
| GI4 | 0.979 | ||||
| GI5 | 0.964 | ||||
| Green banking policy (GBP) | GBP1 | 0.962 | 0.987 | 0.949 | 0.987 |
| GBP2 | 0.981 | ||||
| GBP3 | 0.977 | ||||
| GBP4 | 0.976 | ||||
| Green banking operation (GBO) | GBO1 | 0.948 | 0.987 | 0.924 | 0.986 |
| GBO2 | 0.967 | ||||
| GBO3 | 0.975 | ||||
| GBO4 | 0.979 | ||||
| GBO5 | 0.971 | ||||
| GBO6 | 0.928 |
Source(s): Authors' own work
Discriminant validity
| CR | AVE | GI | SI | GBP | GBO | |
|---|---|---|---|---|---|---|
| GI | 0.988 | 0.942 | 0.971 | |||
| SI | 0.973 | 0.924 | 0.795 | 0.961 | ||
| GBP | 0.987 | 0.949 | 0.749 | 0.793 | 0.974 | |
| GBO | 0.987 | 0.924 | 0.860 | 0.809 | 0.801 | 0.961 |
Note(s): Sustainable Innovativeness (SI); Green Investment (GI); Green Banking Policy (GBP); Green Banking Operation (GBO)
Source(s): Authors' own work
Results of the hypotheses
| Hypothesis | Estimate (standardized) | Estimate (unstandardized) | S.E. | t-values | p-values | Decision | |||
|---|---|---|---|---|---|---|---|---|---|
| H1 | SI | → | GBO | 0.198 | 0.176 | 0.052 | 3.39 | 0.000 | Supported |
| H2 | GI | → | GBO | 0.501 | 0.445 | 0.048 | 9.239 | 0.000 | Supported |
| H3 | GBP | → | GBO | 0.268 | 0.225 | 0.044 | 5.108 | 0.000 | Supported |
Note(s): Sustainable Innovativeness (SI); Green Investment (GI); Green Banking Policy (GBP); Green Banking Operation (GBO); Standard Error (SE)
Source(s): Authors' own work
Summary of the direct relationships
| Relationships | Estimates | S.D. | 95% lower bound | 95% upper bound | Min | Max |
|---|---|---|---|---|---|---|
| SI → GBO | 0.176 | 0.053 | 0.074 | 0.280 | −0.049 | 0.393 |
| GI → GBO | 0.444 | 0.049 | 0.350 | 0.542 | 0.251 | 0.682 |
| GBP → GBO | 0.225 | 0.045 | 0.137 | 0.314 | 0.032 | 0.416 |
Note(s): Sustainable Innovativeness (SI); Green Investment (GI); Green Banking Policy (GBP); Green Banking Operation (GBO); Standard Deviation (SD)
Source(s): Authors' own work
Comparison of estimated parameters between PLS-SEM and BSEM
| Predictors | PLS-SEM | BSEM | ||
|---|---|---|---|---|
| Estimates | Ranking | Estimates | Ranking | |
| SI | 0.198 | 3 | 0.176 | 3 |
| GI | 0.501 | 1 | 0.444 | 1 |
| GBP | 0.268 | 2 | 0.225 | 2 |
Note(s): Sustainable Innovativeness (SI); Green Investment (GI); Green Banking Policy (GBP); Green Banking Operation (GBO)
Source(s): Authors' own work
Summary of the recent literature
| Authors | Contexts | Findings |
|---|---|---|
| Gulzar et al. (2024) | Banks' green banking practices and environmental performances | The findings highlight that green banking practices, including employee engagement, operational procedures, customer interactions, and policy adherence, significantly promote green finance and yield substantial positive outcomes |
| Khan et al. (2024) | Green banking practices, bank reputation, and environmental awareness of Islamic banks | Employees-related practices (ERPs), daily operations-related practices (DORPs), customer-related practices (CRPs), and policy-related practices (PRPs) within banks all have a significant positive impact on the bank's reputation |
| Rahman et al. (2023) | Comparative analysis of sustainability and green banking between India and Bangladesh | State Bank of India (SBI) has adopted more green initiatives and invested more in green projects than Bangladesh Bank (BB), yet both banks, along with their respective governments, are committed to achieving SDGs 7 and 13 by increasing funding for green projects, supporting the clean energy transition, redesigning banking practices, and developing new products aligned with green finance |
| Stauropoulou et al. (2023) | Banks' economic, environmental, and social SDG strategies and consumer behavior | The results indicate that the SDGs about economic and social aspects have influenced the level of trust, fair pricing, image, and loyalty among bank clients |
| Ellahi et al. (2023) | Customer awareness on green banking | The findings reveal that customers hold positive attitudes towards implementing banks' green initiatives and are willing to embrace and incorporate them |
| Elsner and Neumann (2023) | Adoption of green banking practices in South Africa | The study provides insight into the phenomenon of greenwashing in the context of disclosure procedures and the broader issue of the absence of effective corporate regulations |
| Khaer and Anwar (2022) | Sustainability and innovation through green banking | Green banking practices are crucial in fostering an environmentally friendly and sustainable financial system |
| Mir and Bhat (2022) | Green banking and sustainability | Multiple stakeholders, including governments, companies, and individuals, collectively contribute to mitigating global warming and establishing a more sustainable global environment |
| Rahman et al. (2022) | Green finance in banking industry | This paper examines the significance of green securities, green investments, climate financing, green insurance, green credit, green bonds, and green infrastructure in green banking operations |
| Bouteraa et al. (2022) | Adoption challenges of green banking technology | The study revealed that customer awareness, personal innovativeness, system quality, and bank reputation substantially influenced customers' intention to use green banking technology |
| Bukhari et al. (2020) | Adoption of green banking | The study examines Pakistan's banking industry's adoption of green banking, including challenges, milestones, and insights for other developing nations facing environmental deterioration |
| Falcone (2020) | Role of green finance in environmental regulations and green investments | The transition towards sustainability is widely acknowledged to possess significant intricacy and unpredictability in securing the necessary funding for investment projects |
Source(s): Authors' own work
Measurement items
| Sustainable innovativeness Song et al. (2020) | |
| 1 | My bank is pioneer among other banks to buy new sustainable products |
| 2 | Compared with other banks, my bank has a lot of sustainable products |
| 3 | My bank likes to buy sustainable products before other banks do |
| Green investments Igbudu et al. (2018) | |
| 1 | My bank provides loans to environmental protection and energy saving related projects |
| 2 | My bank implements certain independent and unique green initiatives, projects, and etc. (e.g. tree planting) |
| 3 | My bank promotes and facilitates environmental oriented enterprises through special grants, loans, and guidance |
| 4 | My bank promotes and facilitates environmental enterprises through special sachems, loans, and guidance |
| 5 | My bank uses social and environmental management system or any other mechanisms to evaluate all project proposals |
| Green banking policy Rehman et al. (2021a, b) | |
| 1 | My bank involves in setting up green branches (energy-efficient buildings/green buildings) |
| 2 | My bank has an environmental (green) policy |
| 3 | My bank has environmental-related agreements with relevant parties/stakeholders (suppliers, customers, and etc.) |
| 4 | My bank promote an environmental friendly policy at corporate level |
| Green banking operations Chen et al. (2013) | |
| 1 | My bank has initiatives to reduce paper usage and other wastage of materials |
| 2 | My bank has introduced energy-efficient equipment's, system solutions and practices |
| 3 | My bank uses e-waste management practices |
| 4 | My bank has environmental friendly banking practices (e-mail, intranet, e-statements, online approval system, and etc.) |
| 5 | My bank encourage customers to use environmental friendly banking practices (e-statements, online transfer etc.) |
| 6 | My bank regularly arranges seminar and workshop to promote environment friendly practices |
Conflict of interest: The authors declare no potential conflict of interest.
This research was not sponsored by any person or organization.
Respondents' demographic details (N = 249)
| Variables/dimensions | Frequency | Percentages |
|---|---|---|
| Gender | ||
| Male | 175 | 70.28 |
| Female | 74 | 29.72 |
| Age | ||
| 20–24 | 10 | 4.02 |
| 25–29 | 45 | 18.07 |
| 30–34 | 110 | 44.18 |
| 35–39 | 64 | 25.70 |
| 40– or Above | 20 | 8.03 |
| Education level | ||
| HSC | 40 | 16.06 |
| Bachelor | 92 | 36.95 |
| Master | 110 | 44.18 |
| M-Phil | 02 | 0.80 |
| PhD | 05 | 2.01 |
| Experience | ||
| 00–02 years | 10 | 4.02 |
| 03–04 years | 20 | 8.03 |
| 05–06 years | 69 | 27.71 |
| 07–08 years | 120 | 48.19 |
| 09– or above | 30 | 12.05 |
| Income | ||
| 0001–20,000 tk | 20 | 8.03 |
| 20,001–40,000 tk | 124 | 49.80 |
| 40,001–60,000 tk | 75 | 30.12 |
| 60,001– or above | 30 | 12.05 |
Source(s): Authors' own work
Mean, standard deviation, Skewness, and Kurtosis values
| Items | Mean | Std. Deviation | Skewness | Kurtosis | ||
|---|---|---|---|---|---|---|
| Statistic | Statistic | Statistic | Std. Error | Statistic | Std. Error | |
| SI1 | 3.8675 | 1.11555 | −0.965 | 0.154 | 0.126 | 0.307 |
| SI2 | 3.9357 | 1.05680 | −0.925 | 0.154 | 0.057 | 0.307 |
| SI3 | 3.9076 | 1.07177 | −0.904 | 0.154 | 0.017 | 0.307 |
| GBO1 | 3.8635 | 1.02658 | −0.850 | 0.154 | 0.062 | 0.307 |
| GBO2 | 3.8394 | 1.05795 | −0.787 | 0.154 | −0.195 | 0.307 |
| GBO3 | 3.8394 | 1.08057 | −0.797 | 0.154 | −0.285 | 0.307 |
| GBO4 | 3.8514 | 1.03857 | −0.787 | 0.154 | −0.180 | 0.307 |
| GBO5 | 3.8635 | 1.05371 | −0.871 | 0.154 | −0.047 | 0.307 |
| GBO6 | 3.8635 | 0.97831 | −0.791 | 0.154 | −0.079 | 0.307 |
| GI1 | 3.8233 | 1.11844 | −0.936 | 0.154 | 0.089 | 0.307 |
| GI2 | 3.8233 | 1.10392 | −0.914 | 0.154 | 0.144 | 0.307 |
| GI3 | 3.7791 | 1.13032 | −0.924 | 0.154 | 0.075 | 0.307 |
| GI4 | 3.7871 | 1.13897 | −0.928 | 0.154 | 0.042 | 0.307 |
| GI5 | 3.8594 | 1.06271 | −0.996 | 0.154 | 0.391 | 0.307 |
| GBP1 | 3.7108 | 1.09098 | −0.908 | 0.154 | 0.249 | 0.307 |
| GBP2 | 3.7108 | 1.11656 | −0.881 | 0.154 | 0.132 | 0.307 |
| GBP3 | 3.7349 | 1.09711 | −0.843 | 0.154 | 0.061 | 0.307 |
| GBP4 | 3.7028 | 1.11081 | −0.850 | 0.154 | 0.065 | 0.307 |
Note(s): Sustainable Innovativeness (SI); Green Investment (GI); Green Banking Policy (GBP); Green Banking Operation (GBO)
Source(s): Authors' own work
References
Akter, N., Siddik, A.B. and Mondal, M.S.A. (2017), “Sustainability reporting on green financing: a study of listed private sustainability”, Journal of Business and Technology, Vol. 12 No. 2, pp. 14-27.
Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modeling in practice: a review and recommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-423, doi: 10.1037//0033-2909.103.3.411.
Arbuckle, J. (2009), AMOS 18 User's Guide, AMOS Development Corporation, New York, NY.
Bagozzi, R.P. and Yi, Y. (1988), “On the evaluation of structural equation models”, Journal of the Academy of Marketing Science, Vol. 16 No. 1, pp. 74-94, doi: 10.1177/009207038801600107.
Bentler, P.M. (1990), “Comparative fit indexes in structural models”, Psychological Bulletin, Vol. 107 No. 2, pp. 238-246, doi: 10.1037/0033-2909.107.2.238, available at: http://www.ncbi.nlm.nih.gov/pubmed/2320703
Bhardwaj, B.R. and Malhotra, A. (2013), “Green banking strategies: sustainability through corporate entrepreneurship”, Greener Journal of Business and Management Studies, Vol. 3 No. 4, pp. 180-193, doi: 10.15580/gjbms.2013.4.122412343.
Bihari, S.C. and Pradhan, S. (2011), “CSR and performance: the story of banks in India”, Journal of Transnational Management, Vol. 16 No. 1, pp. 20-35, doi: 10.1080/15475778.2011.549807.
Bose, S., Khan, H.Z., Rashid, A. and Islam, S. (2018), “What drives green banking disclosure? An institutional and corporate governance perspective”, Asia Pacific Journal of Management, Vol. 35 No. 2, pp. 501-527, doi: 10.1007/s10490-017-9528-x.
Bouteraa, M., Raja Hisham, R.R.I. and Zainol, Z. (2022), “Challenges affecting bank consumers' intention to adopt green banking technology in the UAE: a UTAUT-based mixed-methods approach”, Journal of Islamic Marketing, Vol. 14 No. 10, pp. 2466-2501, doi: 10.1108/jima-02-2022-0039.
Bukhari, S.A.A., Hashim, F. and Amran, A. (2020), “The journey of Pakistan's banking industry towards green banking adoption”, South Asian Journal of Business and Management Cases, Vol. 9 No. 2, pp. 208-218, doi: 10.1177/2277977920905306.
Bukhari, S.A.A., Hashim, F. and Amran, A. (2023), “Green banking: a strategy for attainment of UN-Sustainable Development Goals 2030”, International Journal of Environment and Sustainable Development, Vol. 22 No. 1, pp. 13-31, doi: 10.1504/ijesd.2021.10038708.
Chatzitheodorou, K., Skouloudis, A., Evangelinos, K. and Nikolaou, I. (2019), “Exploring socially responsible investment perspectives: a literature mapping and an investor classification”, Sustainable Production and Consumption, Vol. 19, pp. 117-129, doi: 10.1016/j.spc.2019.03.006.
Chen, H., Chen, Q. and Gerlach, S. (2013), “The implementation of monetary policy in China: the interbank market and bank lending”, International Finance Review, No. 14, pp. 31-69, doi: 10.1108/s1569-3767(2013)0000014005.
Cortina, J.M. (1993), “What is coefficient alpha? An examination of theory and applications”, Journal of Applied Psychology, Vol. 78 No. 1, pp. 98-104, doi: 10.1037/0021-9010.78.1.98.
Cronbach, L.J. (1951), “Coefficient alpha and the internal structure of tests”, Psychometrika, Vol. 16 No. 3, pp. 297-334, doi: 10.1007/bf02310555.
Dulock, H.L. (1993), “Research design: descriptive research”, Journal of Pediatric Oncology Nursing, Vol. 10 No. 4, pp. 154-157, doi: 10.1177/104345429301000406.
Ellahi, A., Jillani, H. and Zahid, H. (2023), “Customer awareness on Green banking practices”, Journal of Sustainable Finance and Investment, Vol. 13 No. 3, pp. 1377-1393, doi: 10.1080/20430795.2021.1977576.
Elsner, C. and Neumann, M. (2023), “Caught between path-dependence and green opportunities–Assessing the impetus for green banking in South Africa”, Earth System Governance, Vol. 18, 100194, doi: 10.1016/j.esg.2023.100194.
Falcone, P.M. (2020), “Environmental regulation and green investments: the role of green finance”, International Journal of Green Economics, Vol. 14 No. 2, pp. 159-173, doi: 10.1504/ijge.2020.109735.
Feng, C., Wang, M., Liu, G.C. and Huang, J.B. (2017), “Green development performance and its influencing factors: a global perspective”, Journal of Cleaner Production, Vol. 144, pp. 323-333, doi: 10.1016/j.jclepro.2017.01.005.
Finance (2012), Defining and Measuring Green Investments, OECD, available at: https://www.google.com/search?q=OECD+defines+green+investment+broadly+that+is+closely+related+to+other+investment+approaches+like+%22socially+responsible+investing%22+%28SRI%29%2C+%22environmental%2C+social%2C+and+governance+investing%22+%28ESG%29%2C+%22s
Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, p. 39, doi: 10.2307/3151312.
Frame, W.S. and White, L.J. (2004), “Empirical studies of financial innovation: lots of talk, little action?”, Journal of Economic Literature, Vol. 42 No. 1, pp. 116-144, doi: 10.1257/.42.1.116.
Gulzar, R., Bhat, A.A., Mir, A.A., Athari, S.A. and Al-Adwan, A.S. (2024), “Green banking practices and environmental performance: navigating sustainability in banks”, Environmental Science and Pollution Research, Vol. 31 No. 15, pp. 23211-23226, doi: 10.1007/s11356-024-32418-7.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2010), Multivariate Data Analysis, New Jersey.
Hernandez, D. and Hugger, C. (2016), “Creating social impact through responsible investing”, Benefits Magazine, Vol. 53 No. 2, pp. 14-22.
Huang, S. and Huang, X. (2023), “How green bankers promote behavioral integration of green investment and financing teams—evidence from Chinese commercial banks”, Sustainability, Vol. 15 No. 9, 7350, doi: 10.3390/su15097350.
Hummel, K., Laun, U. and Krauss, A. (2021), “Management of environmental and social risks and topics in the banking sector – an empirical investigation”, The British Accounting Review, Vol. 53 No. 1, 100921, doi: 10.1016/j.bar.2020.100921.
Ibe-enwo, G., Igbudu, N., Garanti, Z. and Popoola, T. (2019), “Assessing the relevance of green banking practice on bank loyalty: the mediating effect of green image and bank trust”, Sustainability, Vol. 11 No. 17, 4651, doi: 10.3390/su11174651.
Igbudu, N., Garanti, Z. and Popoola, T. (2018), “Enhancing bank loyalty through sustainable banking practices: the mediating effect of corporate image”, Sustainability, Vol. 10 No. 11, 4050, doi: 10.3390/su10114050.
Inderst, G., Kaminker, C. and Stewart, F. (2016), “Defining and measuring green investments”, SSRN Electronic Journal, available at: https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=2742085
Islam, M.S. (2013), “Green banking practices in Bangladesh”, IOSR Journal of Business and Management, Vol. 8 No. 3, pp. 39-44, doi: 10.9790/487x-0833944.
Jan, A., Marimuthu, M. and Mat Isa, M. P.M. (2019), “The nexus of sustainability practices and financial performance: from the perspective of Islamic banking”, Journal of Cleaner Production, Vol. 228, pp. 703-717, doi: 10.1016/j.jclepro.2019.04.208.
Johnson, S., Lacy, P., Hayward, R., McLEan, E. and Jhanji, A. (2014), “The consumer study: from marketing to mattering: the UN global compact-accenture CEO study on sustainability”, UN Global Compact Reports, available at: http://www.fairtrade.travel/source/websites/fairtrade/documents/Accenture-Consumer-Study-Marketing-Mattering_2014.pdf
Julia, T. and Kassim, S. (2020), “Exploring green banking performance of Islamic banks vs conventional banks in Bangladesh based on Maqasid Shariah framework”, Journal of Islamic Marketing, Vol. 11 No. 3, pp. 729-744, doi: 10.1108/jima-10-2017-0105.
Kalia, A. (2024), “Promoter share pledging and dividend payouts in India: does family involvement matters?”, Asian Journal of Economics and Banking, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/ajeb-01-2024-0009.
Kanapathy, K., Khong, K.W. and Dekkers, R. (2014), “New product development in an emerging economy: analyzing the role of supplier involvement practices by using Bayesian Markov chain Monte Carlo technique”, Journal of Applied Mathematics, Vol. 2014, 542606, pp. 1-12, doi: 10.1155/2014/542606.
Khaer, M. and Anwar, S. (2022), “Encouraging sustainability and innovation: green banking practices growing in Indonesia”, EKSYAR: Jurnal Ekonomi Syari’ah and Bisnis Islam, Vol. 9 No. 2, pp. 173-182, doi: 10.54956/eksyar.v9i2.422.
Khan, N. and Kadir, S.L.S.A. (2011), “The impact of perceived value dimension on satisfaction and behavior intention : young-adult consumers in banking industry”, African Journal of Business Management, Vol. 5 No. 11, pp. 4087-4099.
Khan, I.U., Hameed, Z., Khan, S.U. and Khan, M.A. (2024), “Green banking practices, bank reputation, and environmental awareness: evidence from Islamic banks in a developing economy”, Environment, Development and Sustainability, Vol. 26 No. 6, pp. 16073-16093, doi: 10.1007/s10668-023-03288-9.
Kline, R.B. (2015), Principles and Practice of Structural Equation Modeling, 4th ed., Guilford publications, New York.
Kolk, A. and Pinkse, J. (2010), “The integration of corporate governance in corporate social responsibility disclosures”, Corporate Social Responsibility and Environmental Management, Vol. 17 No. 1, pp. 15-26, doi: 10.1002/csr.196.
Lee, H.W. (2011), “An application of latent variable structural equation modeling for experimental research in educational technology”, Turkish Online Journal of Educational Technology, Vol. 10 No. 1, pp. 15-23.
Longoni, A. and Cagliano, R. (2018), “Sustainable innovativeness and the triple bottom line: the role of organizational time perspective”, Journal of Business Ethics, Vol. 151 No. 4, pp. 1097-1120, doi: 10.1007/s10551-016-3239-y.
Masukujjaman, M. and Aktar, S. (2014), “Green banking in Bangladesh: a commitment towards the global initiatives”, Journal of Business and Technology (Dhaka), Vol. 8 Nos 1-2, pp. 17-40, doi: 10.3329/jbt.v8i1-2.18284.
Menguc, B. and Auh, S. (2006), “Creating a firm-level dynamic capability through capitalizing on market orientation and innovativeness”, Journal of the Academy of Marketing Science, Vol. 34 No. 1, pp. 63-73, doi: 10.1177/0092070305281090.
Mir, A.A. and Bhat, A.A. (2022), “Green banking and sustainability–a review”, Arab Gulf Journal of Scientific Research, Vol. 40 No. 3, pp. 247-263, doi: 10.1108/agjsr-04-2022-0017.
Mishra, R.K. (2023), “Fresh Water availability and It's Global challenge”, Journal of Marine Science and Research, Vol. 2 No. 1, pp. 01-03, doi: 10.58489/2836-5933/004.
Murrar, A., Asfour, B. and Paz, V. (2024), “Banking sector and economic growth in the digital transformation era: insights from maximum likelihood and Bayesian structural equation modeling”, Asian Journal of Economics and Banking, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/ajeb-12-2023-0122.
Muthén, B. and Asparouhov, T. (2012), “Bayesian structural equation modeling: a more flexible representation of substantive theory”, Psychological Methods, Vol. 17 No. 3, pp. 313-335, doi: 10.1037/a0026802.
Nath, B., Chaudhuri, P. and Birch, G. (2014), “Assessment of biotic response to heavy metal contamination in Avicennia marina mangrove ecosystems in Sydney Estuary, Australia”, Ecotoxicology and Environmental Safety, Vol. 107, pp. 284-290, doi: 10.1016/j.ecoenv.2014.06.019.
Nunnally, J.C. (1978), “An overview of psychological measurement”, Clinical Diagnosis of Mental Disorders, pp. 97-146, doi: 10.1007/978-1-4684-2490-4_4.
Ong, F.S., Khong, K.W., Yeoh, K.K., Syuhaily, O. and Nor, O.M. (2018), “A comparison between structural equation modelling (SEM) and Bayesian SEM approaches on in-store behaviour”, Industrial Management and Data Systems, Vol. 118 No. 1, pp. 41-64, doi: 10.1108/imds-10-2016-0423.
Persakis, A., Fassas, A. and Koutoupis, A. (2024), “How does world economic policy uncertainty influence the carbon dioxide emission reporting and performance? Study of Global Fortune 500 firms”, Environmental Science and Pollution Research, pp. 1-28.
Porter, M.E. and Kramer, M.R. (2014), “A response to andrew Crane et al”, California Management Review, Vol. 56 No. 2, pp. 149-151.
Rahman, S., Moral, I.H., Hassan, M., Hossain, G.S. and Perveen, R. (2022), “A systematic review of green finance in the banking industry: perspectives from a developing country”, Green Finance, Vol. 4 No. 3, pp. 347-363, doi: 10.3934/gf.2022017.
Rahman, M.H., Rahman, J., Tanchangya, T. and Esquivias, M.A. (2023), “Green banking initiatives and sustainability: a comparative analysis between Bangladesh and India”, Research in Globalization, Vol. 7, 100184, doi: 10.1016/j.resglo.2023.100184.
Raines-Eudy, R. (2000), “Using structural equation modeling to test for differential reliability and validity: an empirical demonstration”, Structural Equation Modeling, Vol. 7 No. 1, pp. 124-141, doi: 10.1207/s15328007sem0701_07.
Rashid, M. (2010), “Green banking comes to focus”, The Daily Star, available at: https://www.thedailystar.net/news-detail-154690
Rehman, A., Ullah, I., Afridi, F.E.A., Ullah, Z., Zeeshan, M., Hussain, A. and Rahman, H.U. (2021a), “Adoption of green banking practices and environmental performance in Pakistan: a demonstration of structural equation modelling”, Environment, Development and Sustainability, Vol. 23 No. 9, pp. 13200-13220, doi: 10.1007/s10668-020-01206-x.
Rehman, S.U., Kraus, S., Shah, S.A., Khanin, D. and Mahto, R.V. (2021b), “Analyzing the relationship between green innovation and environmental performance in large manufacturing firms”, Technological Forecasting and Social Change, Vol. 163, 120481, doi: 10.1016/j.techfore.2020.120481.
Ren, S., Huang, M., Liu, D. and Yan, J. (2023), “Understanding the impact of mandatory CSR disclosure on green innovation: evidence from Chinese listed firms”, British Journal of Management, Vol. 34 No. 2, pp. 576-594, doi: 10.1111/1467-8551.12609.
Renneboog, L., Ter Horst, J. and Zhang, C. (2008), “Socially responsible investments: institutional aspects, performance, and investor behavior”, Journal of Banking and Finance, Vol. 32 No. 9, pp. 1723-1742, doi: 10.1016/j.jbankfin.2007.12.039.
Rubel, M.R.B., Kee, D.M.H. and Rimi, N.N. (2020), “The influence of green HRM practices on green service behaviors: the mediating effect of green knowledge sharing”, Employee Relations, Vol. 43 No. 5, pp. 996-1015, doi: 10.1108/er-04-2020-0163.
Saha, S., Sarker, R. and Ahmed, S. (2020), “Impact of green human resource management (GHRM) practices in garment industry: Bangladesh perspective”, International Journal of Management and Accounting, Vol. 2 No. 2, pp. 22-30.
Sengupta, U., Pramanik, H.S., Datta, S., Dutta, S., Dasgupta, S. and Kirtania, M. (2023), “Assessing sustainability focus across global banks”, Development Engineering, Vol. 8, 100114, doi: 10.1016/j.deveng.2023.100114.
Sharma, M. and Choubey, A. (2022), “Green banking initiatives: a qualitative study on Indian banking sector”, Environment, Development and Sustainability, Vol. 24 No. 1, pp. 293-319, doi: 10.1007/s10668-021-01426-9.
Sharmeen, K. and Yeaman, A.M. (2020), “Benefits that islamic and conventional banks can attain by implementing green banking”, Journal of Islamic Monetary Economics and Finance, Vol. 6 No. 4, pp. 833-860, doi: 10.21098/jimf.v6i4.1134.
Shaumya, K. and Arulrajah, A. (2017), “The impact of green banking practices on bank's environmental performance: evidence from Sri Lanka”, Journal of Finance and Bank Management, Vol. 5 No. 1, pp. 77-90, doi: 10.15640/jfbm.v5n1a7.
Song, M., Zhang, H. and Heng, J. (2020), “Creating sustainable innovativeness through big data and big data analytics capability: from the perspective of the information processing theory”, Sustainability, Vol. 12 No. 5, p. 1984, doi: 10.3390/su12051984.
Stauropoulou, A., Sardianou, E., Malindretos, G., Evangelinos, K. and Nikolaou, I. (2023), “The effects of economic, environmentally and socially related SDGs strategies of banking institutions on their customers' behavior”, World Development Sustainability, Vol. 2, 100051, doi: 10.1016/j.wds.2023.100051.
Tabachnick, B. and Fidell, L. (2019), “Using multivariate statistics title: using multivariate statistics”, Pearson Education, Vol. 5 No. 7, available at: https://www.pearsonhighered.com/assets/preface/0/1/3/4/0134790545.pdf
Thach, N.N. (2023), “Applying Monte Carlo simulations to a small data analysis of a case of economic growth in COVID-19 times”, Sage Open, Vol. 13 No. 2, 21582440231181540, doi: 10.1177/21582440231181540.
Thach, N.N. (2024), “Bayesian hierarchical modeling of individual effects: renewables and non-renewables on global economic growth”, Sage Open, Vol. 14 No. 3, 21582440241268739, doi: 10.1177/21582440241268739.
United Nations Development Programme (2023), “Climate vulnerability index (draft)”, available at: https://www.undp.org/bangladesh/publications/climate-vulnerability-index-draft
van Dooren, B. and Galema, R. (2018), “Socially responsible investors and the disposition effect”, Journal of Behavioral and Experimental Finance, Vol. 17, pp. 42-52, doi: 10.1016/j.jbef.2017.12.006.
Zhang, B., Bi, J., Yuan, Z., Ge, J., Liu, B. and Bu, M. (2008), “Why do firms engage in environmental management? An empirical study in China”, Journal of Cleaner Production, Vol. 16 No. 10, pp. 1036-1045, doi: 10.1016/j.jclepro.2007.06.016.
Zhang, X., Wang, Z., Zhong, X., Yang, S. and Siddik, A.B. (2022), “Do green banking activities improve the banks' environmental performance? The mediating effect of green financing”, Sustainability, Vol. 14 No. 2, 989, doi: 10.3390/su14020989.
Zhixia, C., Hossen, M.M., Muzafary, S.S. and Begum, M. (2018), “Green banking for environmental sustainability-present status and future agenda: experience from Bangladesh”, Asian Economic and Financial Review, Vol. 8 No. 5, pp. 571-585, doi: 10.18488/journal.aefr.2018.85.571.585.
Zhou, G., Sun, Y., Luo, S. and Liao, J. (2021), “Corporate social responsibility and bank financial performance in China: the moderating role of green credit”, Energy Economics, p. 97, 105190.
Acknowledgements
The authors express their gratitude to the editor-in-chief and anonymous reviewers for their invaluable feedback, which has greatly enhanced the quality of the manuscript.