The authors surveyed Bangladeshi manufacturing SMEs to evaluate the stated hypotheses. The selected SMEs function in a competitive environment where strategic approaches, such as technology adoption and dynamic TL, are crucial for boosting innovation and performance (Gao et al., 2023; Tan, Siddik, Sobhani, Hamayun & Masukujjaman, 2022). The data were collected from the three largest divisions of Bangladesh: Dhaka, Chittagong, and Rajshahi. These three regions include 71% of the total SME population of Bangladesh: Dhaka (38%), Rajshahi (18%), and Chittagong (15%) (Hossain, San, Ling & Said, 2020). Furthermore, Bangladesh was chosen for the empirical study due to several reasons (Holgersson, 2013). First, SMEs constitute an essential part of Bangladesh's economy, employing 7.8 million people directly and assisting 31.2 million others (LightCastle Analytics Wing, 2020). These SMEs account for approximately 25% of the nation's GDP and can contribute significantly more (Siddik et al., 2023).

As an emerging economy, Bangladesh is also making great strides toward attaining the United Nation's Sustainable Development Goals (Dai, Siddik & Tian, 2022; Guang-Wen & Siddik, 2022a; Zheng, Siddik, Masukujjaman, Fatema & Alam, 2021), which has fostered the creation of several businesses, especially SMEs. Nonetheless, the vulnerable SMEs in Bangladesh underwent a period of hardship during the COVID pandemic (Rahman, Azma, Masud & Ismail, 2020); moreover, numerous companies terminated business indefinitely and others sustained significant losses. Investigating how Bangladeshi SMEs may implement I4.0 technologies and sustainable business practices to maintain a vibrant business climate at home and abroad is critical. Furthermore, Bangladeshi SMEs resist adopting environmentally friendly practices and complying with environmental regulations; however, this situation is gradually improving as international organizations finance SMEs to follow environmental standards. Several international development organizations have financed investments in worker safety, green manufacturing practices, and environmental considerations in Bangladesh's large garment manufacturing industry (Islam, 2021). International organizations now assist Bangladeshi SMEs in pursuing green and inclusive business practices. For example, Agence Française de Développement has granted 50 million EUR for green SME investment in Bangladesh (Islam, 2021). The Sustainable Enterprise Project of the World Bank is directly aiding 40,000 Bangladeshi SMEs in promoting green growth initiatives and diversifying their portfolios to include environmental stewardship, waste and emissions control, and enhanced workplace safety (Yoshijima, Sharmin & Paul, 2020). These firms are constantly adopting technological advancements (The Financial Express, 2022) and developing green leadership (Muhammad & Al-Amin, 2022) to ensure environmental sustainability. Hence, assessing how FA and GTL can improve Bangladeshi manufacturing firms’ GI and performance is essential.

We devised a self-administered questionnaire to collect data on the influence of FA and GTL on the GI and ENP of Bangladeshi manufacturing SMEs. Two academics and ten manufacturing SME managers pre-tested the survey questionnaires to confirm their validity. The authors made minor modifications to the questionnaire items based on the findings of the pilot survey. The survey data were collected from SME managers with in-depth knowledge of their companies’ processes and performances. The survey was distributed to 450 SMEs along with a cover letter describing the study's objectives, emphasizing that participation was voluntary. Furthermore, we assured the participants that their replies would be kept anonymous and used only for academic research. Subsequently, 286 complete and valid surveys were returned, indicating a response rate of 63.5%. The data for this study were obtained between August 2021 and January 2022; moreover, 81.7% of the responders were men and 18.3% were women. Most respondents had at least one year of experience in their current/most recent managerial position (67%). Most managers (82%) were between the ages of 25 and 50 and possessed a postsecondary degree (73%). Furthermore, 52% of firms directly served customers, 21% directly served other businesses, and 27% directly served customers and enterprises. The inquiry covered four firm age categories: 3 years (14%), 3–5 years (31%), 6–10 years (37%), and >10 years (18%). Apart from these attributes, 28% of enterprises employ fewer than 50 workers, 59% employ between 51 and 100 workers, and 13% employ more than 100 employees.

We assessed the hypotheses using several questionnaire items, and all indicators were derived from previous studies except FA items; the research setting also required several adjustments. The variables in Table 1 were derived from prior research, comprising 23 items measuring FA, GTL, GI, and firms’ ENP. The questionnaire items reflect the FA, GTL, GI, and ENP constructs. The authors developed eight questionnaire items for measuring firms’ FA. A pre-test and standard construct reliability and validity metrics were used to assess reliability and validity (see Table 2). Even though numerous fintech services are available worldwide (i.e., digital wallets, blockchain, cryptocurrency, Robo-advisors, crowdfunding services, RegTechs, and InsurTechs), mobile financial service (MFS) providers, payment system operators, and payment service providers make up the majority of the fintech business in Bangladesh (Islam, 2022). MFS services are increasingly used for business-to-business (B2B) transactions, banking payments, salary payments, and utility payments. FA in Bangladesh is mainly related to payment mechanisms (Siddik et al., 2023). The GTL of managers was measured using six items from Chen and Chang (2013). We adopted six items from Singh et al. (2020) to measure the GI construct. Finally, we measured the ENP with four items adapted from Sajan, Shalij, Ramesh and Biju Augustine (2017). We asked the respondents to rate their company's ENP during the previous three years against that of their major competitor on a five-point scale from “much worse” (1) to “much better” (5). Excluding the demographic section, all survey items were scored on a 5-point Likert scale, with one representing “strongly disagree” and five representing “strongly agree.” Participants provided details about their gender, age, degree of education, and years of experience, among other demographic characteristics.

We used procedural adjustments during the data collection process to prevent common method bias (CMB) during the understanding phase of the survey questionnaire (Podsakoff, MacKenzie, Lee & Podsakoff, 2003). The survey comprised guidelines on how to respond to these questions. We guaranteed confidentiality, anonymity, and voluntary nature, and we urged the respondents to answer the questions as truthfully as possible. Furthermore, we advised them that there was no correct or incorrect response. Subsequently, we rigorously checked the composition of each item to verify that there were no obscure, misleading, or uncommon terms and that the wording was as straightforward as possible. Additionally, the sequence of the statements was adjusted to limit the likelihood that respondents would “guess” (Malhotra, Kim & Patil, 2006); thus, we ensured that our measuring items were concise and straightforward.

Furthermore, we conducted a post-hoc assessment using Harman's (1976) single-factor test and Lindell and Whitney's (2001) marker variable technique. Before examining the hypothesized research model, Harman's one-factor test determined if CMB was present. The contribution of a single component was 37.82% (less than 50%), indicating the absence of CMB. Next, Lindell and Whitney's (2001) method was used to determine if CMB affected the reliability of the findings using an item that was not conceptually connected to either of our constructs (marker variable). This research used a measuring item as the marker variable to gage participants’ level of agreement on their interpersonal style (“I am occasionally bothered by individuals who seek favors of me”). Lindell and Whitney's (2001) analysis revealed minimal and nonsignificant correlations (ρ = −0.01 to 0.04), implying that our data lack CMB.

We analyzed cross-sectional data of manufacturing SMEs in Bangladesh using partial least squares structural equation modeling (PLS-SEM). SmartPLS software is used to evaluate our model's hypotheses. Unlike basic regression, the SEM has multiple independent variables and predictors; thus, it is more comprehensive. Khan et al. (2022) argue that SEMs require at least 100 input data to provide accurate and reliable estimations; our data set contains 286 records. The SEM concurrently examines the mean, variance, and covariance characteristics for each item under consideration. PLS-SEM generates composite frameworks without sacrificing prediction accuracy (Khan & Yu, 2021). Using PLS-SEM, Astrachan, Patel and Wanzenried (2014) reported that it handles complex model distributions and numerous indicator variables. We assessed the validity of SEM-derived structural model parameters using divergent and convergent validity measures.

Moreover, the PLS literature strongly advises using G*power analysis to determine the appropriate sample size (Hair, Hult, Ringle & Sarstedt, 2016); therefore, the sample size was analyzed using the G*Power 3.1.9.7 software. Our sample size of 286 exceeded the minimal sample size of 80 required by G*power, with a power level of 0.80, three predictors, an alpha value of 0.05, and an effect size of 0.15. The investigation of non-response bias followed the approaches outlined by past research (Rogelberg & Stanton, 2007). We used Pearson's chi-square test for discrete variables to compare the early and late responders’ firm age, firm size, education, and experience (Greenwood & Nikulin, 1996). The results show no difference between early and late responders; hence, non-response bias was not an issue in this research. ANN is also used to supplement PLS-SEM findings because it captures non-linear linkages in our model, which is helpful in decision-making (Lee, Hew, Leong, Tan & Ooi, 2020; Wong, Tan, Ooi, Lin & Dwivedi, 2022) due to the limitations of PLS-SEM, which can only identify corrective and linear investigations (Lim, Lee, Foo, Ooi & Wei–Han Tan, 2021). Based on the results of the SEM analysis, we used the significant predictors to rank the normalized importance of their significant predictors (Yan et al., 2022). The graphical representation of the research methodology is illustrated in Fig. 2.

Fig. 2.

Flowchart of research materials and methods.

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The descriptive analysis of the latent constructs presented in Table 2 demonstrates that the mean scores for FA, GTL, GI, and ENP were 3.497, 3.435, 3.667, and 3.793, respectively. Following previous research (Kline, 2011), the skewness and kurtosis values were lower than the thresholds of ±3 and ±10, respectively. The correlation analysis revealed a maximum value of 0.74 between the latent constructs, indicating the lack of multicollinearity (Table 1). Consequently, the absence of multicollinearity implies that the model is appropriate for further statistical analysis.

Four tests were used to establish convergent validity, discriminant validity, item-level reliability, and internal consistency reliability. Table 1 demonstrates that the minimum factor loading is 0.611 and the maximum is 0.881, exceeding the suggested cutoff value of 0.50 (Hair et al., 2016). This result implies that this research has no issues with the reliability of individual items. If the value of the outer loading is more than 0.40 and less than 0.50, researchers may maintain the item if it does not affect the retrieved composite reliability (CR) and average variance (AVE). The CR and Cronbach's alpha for each variable should be used to determine the internal consistency reliability. Following Nunnally and Bernstein's (1994) suggestion, the derived Cronbach's alpha values, ranging from 0.766 to 0.884, surpassed the threshold value of 0.70, indicating strong internal consistency (Rahman et al., 2020). According to Hair et al. (2016), the CR value should be more than 0.60. In exploratory investigations, CR values between 0.60 and 0.70 are acceptable; values between 0.70 and 0.95 are deemed adequate to good, while values beyond 0.95 are considered troublesome. Table 1 shows that the CR value of all constructions greater than 0.60 indicates uniformity, internal consistency, and the reliability of all factors (Bagozzi, Yi & Phillips, 1991). This research demonstrates that the internal consistency criteria are met. Convergent validity refers to the extent to which elements of variables evaluate an identical construct. The lowest AVE value in Table 1 is 0.507 and the highest is 0.712; therefore, this study meets the convergent validity requirement established by Hair et al. (2016), which states that the AVE value must be at least 0.50.

Discriminant validity (DV) refers to a condition where researchers discover that two indicators must not be statistically identical (Islam et al., 2019). Fornell and Larcker (1981) provided a traditional metric for calculating DV in two distinct approaches. The first procedure is to compare the value of the AVE square root to correlational statistics; the AVE values can be compared to square correlation values. Recently, researchers have developed an innovation to compute DV, concluding that the old metric is inappropriate. Henseler, Ringle and Sarstedt (2016) introduced the heterotrait–monotrait ratio (HTMT) of correlation as a novel approach to computing DV. This paper applied both the standard Fornell–Larcker and the HTMT criterion. The conventional metric indicates that the square root of AVE for each construct is greater than the correlation coefficients for each row (Table 3); thus, the DV of the structures has been established. As Henseler et al. (2016) suggested, the threshold value of HTMT is 0.90 for theoretically identical constructs and 0.85 for conceptually distinct variables. Table 4 illustrates that the HTMT of all the structures is less than 0.85. Hair et al. (2016) indicated that the variance inflation factor (VIF) is used to determine multicollinearity and its value should be less than 5. This analysis revealed that the VIF value is less than 5, satisfying the DV criterion (see Table 4).

Table 5 presents the predictive relevance of the constructs, indicating the predictive ability of our model's predictors. R2 and Q2 are two predictive power measures; per Cohen (1988), R2 must be more than 0.26 to be substantial. The R2 values of ENP and GI are 0.595 and 0.599, respectively, indicating that both constructs have excellent predictive capacity. Furthermore, the Q2 value reveals the predictive relevance of the endogenous components, with a value larger than 0 indicating their predictive significance. The results also indicated that the factors in this study had predictive relevance (ENP Q2 = 0.413 and GI Q2 = 0.376). Moreover, the PLS-SEM SRMR was used to evaluate the model's fit. The model fits quite well, as indicated by the SRMR coefficient of 0.065, below the upper limit of 0.10.

After executing the measurement model, we discuss how hypotheses can be validated. Using SmartPLS 3.3.3, hypotheses concerning the research model were tested. Following the measurement model, this part presents the structural or inner model. The inner model used to evaluate the presented hypotheses calculates the p-value and t-value. The proposed hypothesis is accepted if the t-value is more than 1.96 or the p-value is less than 0.05, and vice versa. This study utilized PLS-SEM, which included a bootstrapping resampling technique with 5000 subsamples. Fig. 3 and Table 6 depict the results of the hypothesis testing.

Fig. 3.

Structural model.

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The results indicate that our model's seven hypothesized relationships are significant. Table 6 illustrates that the FA significantly impacts SMEs’ ENP (β = 0.163, t = 2.865, p  = 0.004); thus, H1a is supported. The coefficients indicate that a 1% change in FA results in a 0.163% increase in ENP. Next, we note that FA substantially affects firms’ GI (β =  0.540, t = 13.387, and p  = 0.000). The linkage between fintech and GI demonstrates the most substantial impact among all the hypothesized linkages, confirming that a 1% rise in FA would enhance the firms’ GI by 0.540%; thus, H2 is accepted.

Furthermore, the findings report that GTL positively influences firms’ ENP and GI. GTL strongly affects corporate environmental performance (β =  0.195, t = 3.513, and p  = 0.000), indicating that a 1% change in GTL would lead to a 0.195% improvement in corporate ENP. Thus, H3 is confirmed. Moreover, GTL has a substantial positive effect on firms’ GI (β =  0.333, t = 7.755, and p = 0.000); hence, H4 is also supported. Next, firms’ GI substantially impacts their environmental performance (β =  0.505, t = 8.281, and p  = 0.000), as a 1% improvement in GI can enhance firms’ ENP by 0.505%; therefore, H5 is confirmed.

A mediation analysis was conducted to assess the mediating effect of GI between FA, ENP, GTL, and ENP, revealing that firms’ GI strongly mediates the FA–ENP linkage (β =  0.273, t = 6.915, and p  = 0.000), supporting H6. Next, we observe that firms’ GI also mediates the linkage between GTL and ENP (β =  0.168, t = 5.453, and p  = 0.000); therefore, H7 is also confirmed.

ANN is a “massively parallel distributed processor made up of simple processing units, which have a neural propensity for storing experimental knowledge and making it available for use” (Haykin, 2001, p. 2) and has been reported to outperform traditional regression techniques (Lo et al., 2022). We utilized the SPSS software to perform the ANN analysis. The significant variables from the PLS-SEM were included in the ANN analysis; thus, the FA, GTL, GI, and ENP variables are considered. Fig. 4 shows that the ANN model comprises one output neuron (e.g., ENP) and three input neurons (i.e., FA, GTL, and GI). We employed a deep ANN with one hidden layer for each output neuron node (Lee et al., 2020). The sigmoid function in our study stimulates both output and hidden neurons. Furthermore, we established the interval between input and output neurons as [0, 1] (Alhumaid, Habes & Salloum, 2021). To minimize over-fitting in the ANN, we employed cross-validation methods (ratio 90:10) for testing and training the collected data (Sharma & Sharma, 2019). The ANN model of the data yields mean-RMSE values of 0.116 and 0.105, respectively (see Table 7). The RMSE metric was used to evaluate the accuracy of neural network models (Liébana-Cabanillas, Marinković & Kalinić, 2017). RMSE is calculated by applying this formula: RMSE=√SSEN; where SSE is the sum of the square error of the training or testing data, and N is the sample size of the training or testing data. The RMSE values of testing and training data reported in the ANN analysis are relatively small; thus, we infer that our ANN model demonstrates high predictive accuracy (Lau et al., 2021; Leong, Hew, Ooi & Wei, 2020).

Fig. 4.

Artificial neural network diagram.

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Next, we conducted a sensitivity analysis to rank the exogenous components based on their normalized relative significance to the endogenous construct, as shown in Table 8. We calculate the normalized importance of each neuron by dividing its relative importance by its maximal significance and reporting the outcome as a percentage (Leong, Hew, Tan & Ooi, 2013). GI is the most significant predictor of corporate ENP, followed by FA, which has a normalized value of 77.94%, and GTL (51.88%).

Emerging environmental concerns need firms to consistently strengthen their GTL within their HRM and adopt novel financial technology to enhance their sustainability performance. This research has produced several theoretical implications. First, this paper expands the scope of two crucial theories: EMT and the AMO hypothesis. The EMT approach proposes that the environmental impacts of business operations may be mitigated by implementing new and integrated technologies. Our SEM–ANN findings demonstrate that businesses may improve their GI and ENP by adopting financial technology. As postulated by the AMO hypothesis, we also present evidence that GTL produces greater capacity, incentive, and opportunity for employees to drive GI and corporate ENP.

Second, our study suggests a framework for future studies on environmental management, especially those examining the role of FA, GTL, and GI in the manufacturing sector. This framework is based on the interaction between FA, GTL, GI, and organizational ENP. This research also offered empirical evidence for the indirect impacts of fintech and GTL on enhancing the ENP of organizations and established the mediating function of GI in these linkages. We expand the literature on green innovation by addressing the mediating effect of GI on the interaction between FA and ENP, as well as GTL and ENP. Existing research has primarily centered on the GI as an outcome of I4.0 technology adoption (Liu & Chen, 2022; Rao et al., 2022) and a facilitator of organizations’ ENP (Kraus et al., 2020; Yan et al., 2022), neglecting its mediating role. Our research concentrates on the role of GI as a mediator between FA and ENP and GTL and ENP. The empirical findings suggest that GI positively mediates the FA–ENP and GTL–ENP linkages. Thus, the findings imply that adopting fintech and developing GTL enable manufacturing SMEs to engage in green products and process innovation, which may substantially boost businesses’ ENP. Given the dearth of research examining the crucial mediating role of GI in the FA–ENP and GTL–ENP associations, our findings enrich the existing body of knowledge.

Third, a plethora of studies focus on consumers’ FA and continuation behavior; however, there is a scarcity of research on the influence of FA on organizational performance. Few studies have been undertaken regarding the use of fintech in businesses and its role in increasing sustainability performance; however, most of these investigations are grounded in case studies and literature reviews. In addition, most studies have concentrated on the function of digital finance in organizational sustainability rather than financial technology in general (Rao et al., 2022). Thus, recent studies have advocated for empirical studies on adopting fintech at the enterprise level and its relationship to corporate sustainability performance (Pizzi et al., 2021). Our research fills this gap by demonstrating the substantial beneficial impact of FA on enterprises’ GI and ENP.

Finally, this article expands the current knowledge base on fintech and GTL in the context of Bangladesh, which can symbolize emerging economies. As previously stated, research on fintech and GTL in emerging regions is limited, which prompted this study to evaluate the adoption of fintech and GTL in Bangladesh and the role of GI and its mediating impacts. Furthermore, there is a lack of evidence on the setting of manufacturing SMEs, which strengthens the value of our study to the extant research; therefore, this study expanded the exploration of fintech, GTL, GI, and ENP in Bangladesh's SME manufacturing industry.

This study has several policy implications for both the government and organizations. First, the laws and regulations should prioritize the localization of fintech and GI following the capabilities and objectives of particular countries. As each country's capability to prepare for and adopt fintech and GI practices varies, the national government must understand these notions and integrate them with national and local economic objectives. With a defined direction and guidelines, regulations may be designed and executed with greater flexibility, improving the possibility of successfully adopting fintech in the manufacturing industry and shifting to the nation's sustainability goals.

Second, to effectively leverage the advantages of technological innovation in minimizing negative environmental consequences, the government should prioritize the acceptability and participation of all stakeholders in adopting financial technology and GI practices. As an emerging economy, Bangladesh must establish national policies for FA and GI, and it must support these plans with legislation that stimulates these executions.

Firms have a crucial role in adopting fintech and GIs since they are accountable for integrating and advancing innovation and building an efficient business model according to government objectives. Firms should emphasize harmonizing innovation and industrial policy with the objectives and endeavors of other stakeholders. Therefore, with proper government–industry interactions, technological advancement and structural change produced by fintech solutions and GI may favorably affect sustainable development.

Moreover, manufacturing SME managers from emerging nations must develop GTL to motivate the staff to attain environmental goals and inspire them to perform beyond the expectations of standard ecological requirements. SME managers must motivate employees to acquire, exchange, and implement the knowledge and technological capabilities required to introduce GI and enhance businesses’ ENP.

This research contains several shortcomings; however, other untapped avenues remain to be investigated. Our study provided preliminary evidence that should only be used as a reference point for additional research into the link between FA, GTL, GI, and organizational ENP. Nonetheless, future research should investigate the various categories of GI, including product, process, and management innovation. Although this study seeks to incorporate the potential effects of fintech and GTL, further research is required to provide a deeper understanding of the different dimensions of fintech and GTL. Furthermore, this study focused primarily on Bangladeshi manufacturing SMEs; however, enterprises in other emerging economies and industries may have varied adoption patterns and technological capabilities, which must be investigated. Future research may employ a global comparison to expand the application of these results. In addition, this is one of the few empirical studies to explore the linkage between firm-level FA and the ENP of SMEs. To confirm our proposition, we call for further empirical investigation of the implications of FA on corporate green behavior and sustainability performance. Apart from FA and GTL, external factors, such as R&D expenditure, government support, environmental legislation, access to finance, and internal factors, such as managers’ dynamic capabilities, sustainability orientation, and green value co-creation, may influence GI and ENP. Future researchers might explore these determinants of GI and ENP in different contexts. Finally, since we adopted a cross-sectional research design, there is no information on longitudinal linkages between the constructs under investigation. Therefore, future research should employ different longitudinal studies to assess the long-term effects of fintech and GTL on GI and firms’ ENP. (Appendix A).