1 Research Background

In the context of heightened global economic competition, technological innovation is crucial to the development of countries, regions, and enterprises, and this is especially true for small and medium-sized enterprises (SMEs). Traditional finance once played a positive role in promoting innovation, but now its drawbacks are becoming increasingly prominent: there is an imbalance between the supply and demand of financial resources, which often tilt towards large enterprises. SMEs, on the other hand, are faced with the predicament of difficult and expensive financing, which seriously restricts their innovation vitality. With the vigorous rise of digital technologies, digital inclusive finance has emerged as the times require. By virtue of advanced technologies such as big data and artificial intelligence, it has effectively made up for the shortcomings of traditional finance. It not only has stronger accessibility and a wider range of service coverage, but also has more prominent sustainability. It can provide more powerful financial support for vulnerable groups including SMEs and effectively improve their conditions for obtaining financial resources^[1].

Based on the above, this paper employs two-way fixed effects model, selecting SMEs listed on the SME Board from 2014 to 2024 as the research sample to thoroughly investigate the mechanisms through which digital inclusive finance influences SME innovation.

2 Literature Reviews

Digital inclusive finance, as an emerging financial service model, is gradually becoming a key driving force for promoting innovation among SMEs. Li and He (2025) pointed out that with the development of the digital economy, digital inclusive finance plays a crucial role in alleviating financing difficulties for SMEs, thereby effectively enhancing their innovation capabilities. Similarly, Tian (2024) emphasized that digital inclusive finance improves financing efficiency and reduces capital costs, creating more possibilities for SME innovation and development. Huang (2024) used data from companies listed on the New Third Board, finds a significant positive relationship between digital inclusive finance and SME innovation, suggesting that greater support for the development of digital inclusive finance can stimulate innovation vitality among SMEs. Additionally, Hu et al. (2024) found that digital inclusive finance not only promotes technological innovation but also enhances its effectiveness by reducing financing costs and information asymmetry, particularly in non-eastern regions and "specialized, refined, unique, and innovative" enterprises.

Therefore, based on the above analysis, the research hypothesis is proposed:

H1:Digital inclusive finance has a significantly positive impact on innovation of small and medium-sized enterprises^[2].

Mediating Effect of financing constraints

Chen and Miao (2021) demonstrated that digital inclusive finance significantly promotes both the quantity and quality of SME technological innovation by reducing debt financing costs. Mediation analysis confirms that this effect occurs indirectly through the reduction of financing costs. Teng and Ye (2022) supported this view, finding that digital inclusive finance promotes SME innovation by lowering debt financing costs and alleviating financing constraints. Furthermore, Huang (2024) revealed that the level of financing constraints acts as a mediator between digital inclusive finance and SME technological innovation, showing that financing constraints are not only a major obstacle to innovation but also a critical pathway through which digital inclusive finance facilitates innovation. Collectively, these studies highlight the mediating function of financing constraints in the impact of digital inclusive finance on SME innovation.

Therefore, based on the above analysis, the research hypothesis is proposed:

H2:Financing constraints plays a mediating role between digital inclusive finance and innovation of small and medium-sized enterprises.

Based on the above analysis, the research model of this paper is constructed, as shown in Figure 1:

3 Research Design

Sample Selection and Data Sources

This paper selects listed companies on China's Small and Medium Enterprise (SME) board from 2014 to 2024 as research samples. The data mainly comes from Tonghuashun iFinD, the Digital Inclusive Finance Index compiled by the Digital Finance Research Center of Peking University, and the National Statistical Yearbook. To ensure the standardization and accuracy of the samples, the following processing has been done: (1) Exclude ST and *ST enterprises; (2) Exclude samples with incomplete key enterprise data; (3) Exclude enterprises in the financial insurance industry; (4) Exclude enterprises with a debt-to-asset ratio greater than 1. After the above processing, a total of 11 years of data were obtained, consisting of 847 observations forming panel data^[3].

4 Variable Selection

The explained variables are innovation output (Patent), innovation input (R&D), and innovation efficiency (IEs). The number of patents applied for by an enterprise can reflect the utilization efficiency of invested resources and is widely recognized as the most direct measure of innovation. Since there are annual patent application numbers of zero for some sample enterprises, the natural logarithm after adding 1 to the total number of patent applications is used as the measurement index for innovation output (Li et al. , 2020). The natural logarithm of R&D expenditure is used as the measurement index for innovation input. By analyzing the changes in the amount of innovation input each year, it reflects the economic contribution made by the enterprise in improving its innovation level (Sun et al. ,2016). Some scholars believe that the number of patents has a certain functional relationship with the current and historical R&D expenditures of enterprises. Therefore, referring to Yao and Zhou (2018), the ratio of the number of patent applications to the sum of the current and previous year’s R&D expenditures is used as the measurement index for enterprise innovation efficiency. Since the value of innovation efficiency is relatively small, it is multiplied by 107 to facilitate observation.

The explanatory variable is the level of digital inclusive finance development (Index). This paper uses the Digital Inclusive Finance Index released by the Digital Finance Research Center of Peking University as a proxy variable, which covers all provinces in China and can comprehensively and systematically reflect the development level of digital inclusive finance in China.Due to its larger value compared to other variables, the natural logarithm is taken^[4].

The mediating variable is financing constraints (SA). The calculation formula is: SA = -0.737Size + 0.043Size^2 - 0.04Age. Where Size represents enterprise scale, Age represents enterprise age.

In this paper, enterprise age, scale, equity concentration, growth, liability levels, government subsidies, and regional economic development levels are used as control variables. The difference between the current observation year and the company establishment year is taken as the natural logarithm to represent enterprise age. The natural logarithm of the enterprise's total assets for the current year is used to represent enterprise scale. The proportion of shares held by the top ten shareholders represents equity concentration. Revenue growth rate represents enterprise growth. Debt-to-asset ratio represents enterprise liability level. Government subsidies are taken as the natural logarithm to represent government subsidies, and per capita GDP is taken as the natural logarithm to represent regional economic development levels^[5].

5 . Research results

The descriptive statistical analysis of each variable is shown in Table 1.

<Table 1> Descriptive Statistics of Variables

From the above descriptive statistical results, it can be seen that for innovation output , the minimum value is 0, the maximum value is 7.09, the mean is 1.9018, and the standard deviation is 1.62266, indicating that many enterprises have no patents and there is a significant difference in the level of R&D innovation among SMEs; for R&D input , the minimum value is 10.59, the maximum value is 22.08, the mean is 18.2655, and the standard deviation is 1.33575, indicating that SMEs have different levels of willingness to invest in R&D; for innovation efficiency, the minimum value is 0, the maximum value is 6.33, the mean is 0.8325, and the standard deviation is 1.18399, indicating that the overall innovation efficiency of SMEs is relatively weak. The three innovation indicators prove that for SMEs, their own innovation level cannot meet consistent requirements. The development capacity of digital inclusive finance ranges from 4.33 to 6.13, proving that the degree of development varies by region. Financing constraints have a minimum value of -8.30, a maximum value of -2.73, and a standard deviation of 1.02752, indicating that SMEs generally face financing constraints, and the degree of financing constraints varies among enterprises.

Based on the descriptive statistics, Pearson correlation analysis was conducted on each variable, and the results are shown in Table 2.

<Table 2> Correlation Coefficients

Note: ** denotes p<0.01, * denotes p<0.05.*

From the above correlation analysis results, it can be seen that the correlation coefficients between the explanatory variable digital inclusive financial development level and the explained variables innovation output, R&D input, and innovation efficiency are 0.084, 0.316, and 0.273 respectively, all positive and p<0.05, indicating that digital inclusive finance helps to improve the innovation level of SMEs. The absolute values of the correlation coefficients of control variables prove that each variable meets the independence requirement and does not need to consider the existence of multiple collinear relationships, which will not adversely affect the research.

Benchmark Regression Results Analysis

The benchmark regression results are presented in Table 3:

<Table 3> Benchmark Regression

Note: *** denotes p<0.01, ** denotes p<0.05, * denotes p<0.1, values in parentheses are t-values.

According to the regression results, it can be seen that the digital inclusive financial development level has a significant regression coefficient relationship with SME innovation output, with a coefficient value of 1.100617 and a p-value within 0.05, thus proving that digital inclusive finance is a positive factor affecting SME innovation output; the regression coefficient between the digital inclusive financial development level and SME R&D input is 1.516688, p<0.01, indicating that digital inclusive finance promotes SME R&D input; the regression coefficient between the digital inclusive financial development level and SME innovation efficiency is 2.985111, p<0.01, indicating that digital inclusive finance promotes SME innovation efficiency. Therefore, Hypothesis 1 holds true.

Mediation Effect

The mediation effect test results are presented in Table 4:

<Table 4>Mediation Effect Test

Note: *** denotes p<0.01, ** denotes p<0.05, * denotes p<0.1, values in parentheses are t-values.

From the regression results, the regression coefficient between the digital inclusive financial development level and financing constraints is -0.033906, and p<0.05, indicating that digital inclusive finance can alleviate the financing constraints of SMEs. The regression coefficients of the digital inclusive financial development level with innovation output and financing constraints, R&D input and financing constraints, innovation efficiency and financing constraints are 1.128387, 1.475568, and 3.045431 respectively, with the regression coefficients of financing constraints being -0.81903, -1.212766, and -1.779035 respectively, p<0.05, indicating that financing constraints play a partial mediating role. Therefore, Hypothesis 2 hold true.

5. Robustness Check

Following the approach of Liu et al.(2022), the ordinary least squares method is employed, and the explanatory variables are lagged by one period to address the endogeneity issue of the model. The results are shown in Table 5.

<Table 5>Robustness Check

Note: *** denotes p<0.01, ** denotes p<0.05, * denotes p<0.1, values in parentheses are t-values.

From the regression results, it can be seen that the regression coefficients of the development level of digital inclusive finance with innovation output, R&D input, and innovation efficiency are 1.090209, 1.079251, and 1.101403 respectively, all positive and p<0.05, consistent with previous conclusions, indicating the robustness of the model results.

Ⅴ. Conclusions

Based on detailed data from SME board-listed companies between 2014 and 2024, this study systematically explores the actual effects of digital inclusive finance on the technological innovation of SMEs. The findings are as follows:

Firstly, digital inclusive finance significantly drives the innovative development of SMEs. Leveraging the advantages of digital technology, digital inclusive finance effectively addresses the issue of information asymmetry faced by SMEs. By enhancing corporate financing capabilities, it alleviates financing bottlenecks and cost pressures during the innovation process, thereby directly promoting comprehensive improvements in innovation output, R&D investment, and innovation efficiency.

Secondly,digital inclusive finance stimulates corporate innovation by alleviating financing constraints. As digital inclusive finance continues to develop, information transparency has steadily increased, significantly lowering the financing thresholds for SMEs and greatly enhancing financing efficiency. This effectively alleviates financing pressures, creating a sustained driving effect on the enhancement of corporate innovation capabilities.

References

[1]Anderson, C. (2004). The long tail. Wired Magazine, 12(10), 170–177.

[2]Chen, Y., & Miao, L. (2021). Digital inclusive finance, debt financing cost and technological innovation of SMEs. Zhejiang Finance, (09), 10–22.

[3]Hu, W., Zhang, K., Xu, Z., & Yu, L. (2024). Research on the impact of digital inclusive finance on technology innovation of SMEs and its transmission path. Forum on Science and Technology in China, (01), 27–37.

[4]Huang, B.(2024). Digital inclusive finance and SME innovation. Industrial Innovation Research, (01), 131–133.

[5]Huang, W. (2024). Digital inclusive finance and technological innovation of SMEs. China Trade Fair Economy, (07), 97–100.