Rogers’ Diffusion of Innovation Theory Rogers’ Diffusion of Innovation Theory presents a powerful framework for understanding the adoption of fintech in sustainable agriculture (Keyling et al., 2015) points out that farmers adopt innovations when they consider them easy to use, with relative advantages, and compatible with existing practices (Chawla et al., 2024) applied this theory to assess the diffusion of digital payment systems among farmers and identified that perceived usefulness is strongly influencing the adoption rates (The Dynamics of a Degenerate Epidemic Model with Nonlocal Diffusion and Free Boundaries - ScienceDirect, 2024) stated that the diffusion of fintech solutions in rural regions highly depends on social networks and peer influence. This aspect of the theory, emphasizing early adopters and opinion leaders, is particularly relevant to the promotion of fintech in agriculture.

The revolutionary impact of fintech in sustainable agriculture is now increasingly acknowledged. It opens up loans, insurance, and market data for farmers, which are all important factors to increase sustainability and productivity. As Suri and Jack (Rao, 2025) have said, “Rural farmers who use mobile money are more financially resilient and have less poverty” (Mapanje et al., 2023) further elaborated on how fintech innovations enhance financial inclusion, which has a direct effect on agricultural productivity and reduces income inequality. Fintech enables sustainable practices by providing customized financial products, which lessens the environmental impact of farming operations, according to Anshari et al. (2019). Digital financial platforms, according to Mukhopadhyay (2020), “would make available real-time information that can help make effective decisions and thus largely reduce resource utilization inefficiencies.

In their 2022 study, Chatterjee and Singh (2022) probed how digital payment methods simplify transactions and ensure timely acquisition of seeds and fertilizers. Furthermore, (Jain and Gabor, 2020) examined the way fintech can reduce transaction costs and expand access of farmers to formal banking institutions. Fintech adoption promotes minimal environmental damage as it encourages climate-smart farming practices, as opined by Kumar and Parida (2013). Fintech platforms are instrumental in promoting sustainable land management techniques and crop diversification, according to Arora et al. (2018).

Sustainable agriculture largely benefits the rural areas because of its strong support toward economic development. Redesigning agricultural systems improves environmental results and strengthens rural livelihoods, observed (Kamilaris et al., 2019). In poverty-stricken areas with limited resources, sustainable agriculture methods implemented assure output while ensuring food security (found Jain and Gabor, 2020). The poverty rate drastically decreases in areas implementing climate-smart agriculture as the yield becomes stable, reports (Anshari et al., 2019).

According to Anifa et al. (2022), sustainable agriculture is that which enhances rural economies and creates employment opportunities in rural areas. The use of green techniques in agriculture increases both economic efficiency and environmental protection, according to Anshari et al. (2019). Green finance accelerates the adoption of environmentally friendly farming practices, thus enhancing long-term economic growth, according to Fintech Innovation for Smallholder Agriculture (2024).

In rural agriculture, financial inclusion is essential since it helps close the gap between farmers and official financial institutions. Formal bank account ownership improves farmers’ capacity to invest in productive assets, according to Sharma (2025). The adoption of novel agricultural technologies is substantially impacted by financial availability, as demonstrated by Sadia (2018) and Bilal Khan et al. (2021) assert that financial inclusion raises agricultural output and rural infrastructure. See Table 1 shows the important literature collections related to the present study. According to McIntosh and Mansini (2025), mobile-based financial services can promote agricultural productivity and reduce the cost of transactions. According to Arner et al. (2020), inclusive financial institutions enable smallholder farmers by offering cheap financing and insurance (Fintech Innovation for Smallholder Agriculture, 2024; Zhang, 2024) established that financial inclusion through fintech improves the resilience of rural communities to economic shocks.

Recent fintech evolution has brought a variety of tools to complement AI-based predictive systems. For example, blockchain technologies have been employed to enhance supply chain traceability and guarantee equitable market prices (Vignesh et al., 2024). Mobile money apps enable instant payments and enhance access to credit (Chatterjee and Singh, 2022). In the same way, digital ledger apps enable farmers to keep track of records, which makes them more qualified to receive formal credit and insurance products (Hao et al., 2024). Each of these innovations combined lowers information asymmetry, reduces transaction costs, and enhances inclusion in the agricultural finance system.

Even though previous studies focus on the benefits of fintech and sustainable agriculture, there are still several limitations. First, the empirical data do not support the precise effect of fintech on the environmental quality of agricultural practices (Ghosh and Kulkarni, 2024). Second, nothing is known about how fintech can help farmers lower their carbon footprint (Chatterjee, 2024). There is a scarcity of literature on the moderating effects of socioeconomic factors in determining the adoption of fintech solutions in agriculture (Feyen et al., 2025). More importantly, rather than integrated fintech ecosystems, most of the research focus on standalone technologies. Another gap is geographical context because most of the research focus on developed nations with less focus on rural areas in Tamil Nadu, India (Bakhshi et al., 2024).

While an increasing amount of literature discusses the potential of fintech in enhancing financial inclusion and economic development, there has been a paucity of empirical studies testing its multi-faceted influence on sustainable agriculture at the micro-level, especially in rural areas of developing nations. For example, while Yang et al. (2023) highlight the promise of digital finance in fostering ecological sustainability in Chinese villages, similar data-driven research on the Indian agricultural sector is lacking. In the same vein (Malik and Kapoor, 2023) discuss how AI-driven tools facilitate environmental decision-making but note that rural implementation is still hindered by infrastructure and literacy challenges (Joshi, 2023) also emphasize that while fintech innovations can boost sustainable exploitation of resources, their reach and sustainability in low-income agricultural environments need to be examined further. Most previous studies have taken national or macroeconomic measures into consideration or sectors other than agriculture. In addition, there is no integrative model that considers economic, environmental, and systemic sustainability outcomes simultaneously, and the moderating effect of digital literacy, which is an important factor in the uptake of technology by smallholder farmers. This work fills these gaps by employing primary field data from 670 Tamil Nadu farmers to examine the causal link between fintech uptake and farm-level sustainability. It includes digital literacy as a moderator, adding new evidence on the potential for digital tools to shape development outcomes in a regionally contextualized and rigorously empiric manner.

The objective of this study is to examine the role of financial technology (fintech) in promoting sustainable agriculture among smallholder farmers in Tamil Nadu, India. More specifically, the research aims at assessing how fintech innovations enhance economic efficiency, environmental sustainability, and systemic resilience in agriculture. By including digital literacy as a moderating variable, the study experiments with the impact of fintech adoption on the availability of credit, market integration, and the adoption of climate-smart agriculture. Drawing from empirical data from 670 farmers, the research seeks to provide actionable advice to policymakers, financial institutions, and stakeholders for enhancing digital financial inclusion and supporting sustainable rural development.

This section analyzes the potential impact of fintech adoption on several aspects of sustainable agriculture, such as system stability, economic efficiency, and environmental quality. The fintech solution is considered to have a revolutionary role in developing the practice of sustainable farming, as it is expected to provide farmers with better access to capital, risk management resources, and decision-making skills (Bharadwaj et al., 2025).

The following theories are developed to analyze these relationships:

This hypothesis, as reported by Bakhshi et al. (2024) and Zhang (2024), explains how the availability of financing for eco-friendly inputs and precision farming technologies enabled through fintech can reduce resource waste and deterioration of the environment.

According to Bilal Khan et al. (2021) and Zhao et al. (2022), this hypothesis examines how fintech tools might improve market access, minimize transaction costs, and optimize financial resources-all of which increase farmers’ economic production and profitability.

This theory further postulates that fintech can stabilize agricultural systems because it provides improved means for risk management, crop insurance, and financial planning that reduce susceptibility to climatic and market shocks (Omotilewa et al., 2019; Raj et al., 2021).

This section explores how fintech is motivating farmers in Tamil Nadu to adopt sustainable farming practices. Fintech solutions are believed to enhance resilience to external shocks, reduce environmental impact, and enhance resource management through digital innovation (Kumar, 2025a). The following theories are proposed to test these impacts:

The hypothesis explores how farmers can better make decisions and achieve greater financial inclusion through the use of digital financial platforms to improve access to credit, insurance, and timely market information (Demirguc-Kunt et al., 2018; Kumar, 2025b).

With resource monitoring systems and precision agriculture technologies, fintech tools will be able to help in the preservation of the environment by maximizing input utilization and minimizing waste and therefore reduce greenhouse gas emissions (Xing et al., 2022).

This hypothesis focuses on how farmers could employ fintech services, including weather forecasting, crop insurance, and risk management, in reducing the adverse impacts of climate variability and the sustenance of sustainable agricultural practices (Kumar, 2025a; Iftikhar et al., 2024).

To analyze how fintech may enhance sustainable farming practices in the state of Tamil Nadu, the study develops a model. The diffusion of innovation theory, which focuses on the factors influencing the adaptation of new technologies, developed by Diffusion of Innovations by Rogers (2003), is integrated into the model. It takes into account how the adaptation of fintech may benefit the farmers in terms of money, the environment, and society. The model framework considers both the direct and indirect effects of fintech on agriculture.

According to Anshari et al. (2019), it outlines the key forces behind adoption, which include socioeconomic characteristics, financial literacy, and technology infrastructure. Moreover, it depicts how fintech services such as crop insurance, digital payments, and microloan availability can improve agricultural sustainability. According to the model, the use of fintech leads to increased income generation, reduced transaction costs, improved market access, and environmentally friendly practices (Lavrinenko et al., 2023). Both mediating and moderating elements are included in the framework, such as government regulations and digital literacy (Wheeler, 2025). This enables the possibility of seeing variables and their connections in a broad context of rural agricultural growth. How these relationships are measured with actual data will be explained in the next sections.

The dependent variables in this study are the results of using fintech in agriculture sustainability. Sustainable social, environmental, and economic practices are some of them. According to Jiang et al. (2019), factors like income levels, cost reduction, and market access are used to measure economic sustainability. Water use, carbon footprint reduction, and environmental-friendly practices are some examples of the metrics that will measure environmental sustainability (Hashemizadeh et al., 2023). Access to social services, risk resilience, and financial inclusion are metrics used in measuring social sustainability (Finger, 2023).

Based on the studies of Anifa et al. (2022) and Rogers1985-libre.pdf (2024), independent variables include fintech adoption criteria such as awareness, usability, and the perceived benefits of fintech instruments to farmers. These elements would affect how much farmers use fintech services. The moderating element that affects how well fintech adoption works is digital literacy, as identified by Jiang et al. (2019). Sustainability outcomes are anticipated to be amplified by higher levels of digital literacy as a result of the impact of fintech technologies (Guo et al., 2022).

Age, income, farm size, and education can be used as control variables to ensure the controlling factors capture outside influences that can possibly influence the results (Arora et al., 2018). These controlling factors help one determine the exact impact which the adaptation of fintech has on sustainability (Lavrinenko et al., 2023).

The Variance Inflation Factor (Table 4) results indicate that independent variables do not demonstrate serious multicollinearity at a significant level. Most importantly, all of the VIF values, specifically, are much below 10, which indicates very minor correlation between the predictors. Although it carries a minimal relationship with other factors, the Green Fintech Access has the greatest VIF (VIF = 2.12), but this is still not of concern. There are values of tolerance well above 0.1 for all variables, that also makes further evidence which proves no significant multicollinearity. Thus the regression coefficient estimates can safely be used as reliable since the multicollinearity in this regression model cannot cause much adjustment. Typically include the test statistic, the p-value and critical values at several different significance levels when demonstrating a stationarity test -like the Augmented Dickey-Fuller test- results. In the study of time series, the stationarity concept plays an important role as this relates to the maintaining the stability of statistical features of your data (its variance and mean) with time.

The results of the Augmented Dickey-Fuller (ADF) test (Table 5) indicate that, at 5% significance level, p < 0.05, Financial Inclusion, Green Fintech Access, and Age of Farmers are stationary. The null hypothesis of a unit root can be rejected since the test statistic for these variables is lower than the crucial value at 5%, which is an indication of non-stationarity. However, Environmental Policy may be having a unit root and, therefore, may require some further processing such as differencing to make it stationary because it is not stationary at the 5% significance level with a p = 0.185. The 10% level of stationarity for Education Level with a p = 0.040 means that, although marginal, it might be considered stationary for modeling purposes.

For most variables of interest, such as Financial Inclusion, Green Fintech Access, Age of Farmers, and Education Level, the outcome for (Table 6) the Pedroni panel cointegration test, the Johansen cointegration test, and the Kao panel cointegration test taken together indicates that a long-term cointegration relationship at different significance levels does indeed exist. The Pedroni and Kao tests provide further evidence for these results with statistically significant negative t-statistics and ADF values, while the Johansen Trace and Max-Eigen statistics both validate cointegration for these variables. However, as Environmental Policy’s p-values are higher than the significance level, it does not show evidence of cointegration across all tests. The findings of these studies reveal that most of the variables seem to have a long-term relationship, but, on the other hand, Environmental Policy seems to need more research or modification to be incorporated into the model.

Through a statistical significance from Table 7, that has values below 0.05 for variables like Financial Inclusion, Green Fintech Access, Age of Farmers, Education Level, and Sustainable Economic Growth, the test result of Harris–Tzavalis is observed to be fulfilled by fulfilling conditions under this panel data test of stationarity. Environmental Policy, with a p-value of 0.122, suggests that this is not stationary and thus may require transformation or differencing prior to further analysis. The results here generally indicate that most variables are sufficiently robust for application in long-run econometric models.

As shown in Table 8, the regression output of models (1), (4), and (7) indicates that green finance improves economic effectiveness significantly (α = 0.034***, p < 0.05), environmental quality significantly (α = 0.009**, p < 0.01), and the economic system significantly (α = 0.014***, p < 0.05), thereby supporting hypotheses one, two, and three. Concurrent to this insight is that green finance promotes a tridimensional sustainable economic development; therefore, it reveals that the integration of green finance in practice and policymaking can support long-lasting sustainability of the economy. Indeed, as an additional evidence supporting the synergetic positive influence of financial technology upon green economic development, model (3) shows that the mediating effect of fintech adoption is significant for regulating the relationship between green finance and protection of environmental quality, as shown in Table 8.

Tables 8, 9 provide solid proof of the beneficial contribution of fintech adoption and green finance to sustainable economic growth in the agricultural sector. Regression analysis of Table 8 shows that the adoption of fintech and green finance significantly impacts system stability, economic efficacy, and environmental quality. It also points out that fintech solutions can enhance the optimization of financial resources, increase market access, and reduce the waste of resources. Likewise, in Table 9, by using the Two-Step GMM estimators, it shows that green finance and fintech adoption are both positively correlated with sustainable economic outcomes. This supports the conclusions from Table 8 with more trustworthy causal relationships, and control variables like GDP and energy consumption are also important in the economic context. Fintech adoption increases farmers’ access to financial services and climate change resilience, while green finance promotes economic efficacy and environmental protection, according to Table 10, which verifies the study’s hypotheses. However, since there is no observable effect, the hypothesis regarding fintech’s contribution to carbon footprint reduction is rejected. In summary, these tables illustrate how green finance and fintech adoption are crucial for promoting sustainable farming practices and driving Tamil Nadu’s economy, while also indicating areas that need further research to fully understand their combined effects.

One of the most important problems in econometric modeling is endogeneity, which arises when an independent variable and the error term are correlated and may offer biased and inconsistent estimates. Some of the possible reasons why endogeneity might appear in this study on the function of fintech in sustainable agriculture in Tamil Nadu include omitted variable bias, measurement errors, or reverse causality. For example, fintech may lead to better economic efficiency and environmental practices. When the quality of the environment or economic efficiency improves, however, farmers are probably more likely to make greater use of digital financial services because they have greater surpluses or more developed financial capacity. The Two-Stage Least Squares (2SLS) method is one of the instrumental variable methodologies we use to perform a robustness check in order to isolate the causal relationship between the adoption of fintech and sustainable agricultural results. Variables that are correlated with the potentially endogenous independent variables but not directly connected with the dependent variable—aside from the endogenous regressor—are considered valid instruments, which is a fundamental premise of the 2SLS technique.

We utilize the regional internet penetration rates in Tamil Nadu and mobile network coverage as tools for an endogeneity check since they are likely to affect the adoption of fintech but not the sustainability outcomes themselves (such as economic effectiveness, environmental quality, etc.) measured by the study. When the endogeneity issue is resolved, the main conclusions about the beneficial effects of fintech adoption on economic efficiency and environmental quality are still strong, according to the results of the robustness check (Table 9). This suggests that endogeneity did not substantially skew the initial estimates. Summing up, this robustness test (Table 10) ensures the validity of the findings of the study and confirms that the relations found between sustainable agriculture and fintech adoption are probably causal, thus making recommendations derived from these findings more convincing for policymakers.