Research on the enhancement mechanism of big data-driven government innovation subsidy policy on the multi-level financing model of science and technology-based enterprises

Technological innovation is the key driving force for the survival and development of science and technology-based enterprises, and it is only under the conditions of both technology and capital that an enterprise can smoothly transform its initial R&D ideas into R&D results when the technology-based enterprises can be widely used in the market of innovative technologies and R & D results of the relevant information to achieve a full and careful understanding and mastery of the enterprise through the R & D activities of their funds into long-term investment income, so as to complete the stage from R & D to financing.

The higher the return on investment of an R&D project, the higher the corresponding risk and investment. At this point, enterprise decision-makers should make a comprehensive consideration, that is to say, not only considering the return on investment but also considering the cost of investment, and at the same time, more need to have sufficient funds as the basis for investment. Therefore, how enterprises deal with risks and make investment decisions on R&D projects depends on the amount of capital they have. As an important source of capital for S&T enterprises, government subsidies can directly alleviate the financing constraints of enterprises and play the “financial support effect”, which makes S&T enterprises more willing to invest in risky projects, thus improving the risk-taking of S&T enterprises to a certain extent.

In addition, the decision-makers of technology-based enterprises tend to have more security and self-confidence after receiving government subsidies. And the confidence of management is crucial to the investment decisions of technology-based enterprises. If the management of technology-based enterprises is full of self-confidence, they will face up to risky projects and increase their enthusiasm for risk-taking, and eventually, technology-based enterprises will show a higher level of risk-taking.

Hypothesis 1: Government subsidies can enhance risk-taking in technology-based firms.

The likelihood of enterprises generating continuous innovation behavior will improve. The more financial subsidies an enterprise receives and the more risk-taking ability it has, the more likely it is to engage in innovative activities. In addition, the low cost of obtaining financial subsidies and the use of these funds for research and development reduces the cost of innovation for the enterprise. To a certain extent, it compensates for the loss of private benefits caused by technological spillovers, which makes the innovation activities of enterprises profitable and further improves the motivation and willingness of enterprises to continue innovation.

Hypothesis 2: Government subsidies have a positive impact on the innovation continuity of science and technology-based enterprises.

The financing constraints of innovative activities are mainly caused by information asymmetry. Innovation activities have positive externalities, and enterprises generally do not disclose too much information about innovation to the outside world in order to protect their interests, which makes it difficult for external investors to effectively assess the quality of innovation projects. In addition, the outputs of various stages of innovation activities mainly exist in the form of intangible assets, which further increases the difficulty of monitoring by external investors. The financing market for innovation activities is similar to a “lemon” market, in which external investors have to consider not only the high risk and uncertainty of the innovation itself but also the potential morally hazardous behaviors of the innovating firms. Compared to other investment activities, external investors tend to demand a higher risk premium for investment in innovation in order to meet a given rate of return. As a consequence, innovation activities are usually confronted with higher financial constraints.

Technology-based enterprises consider technological innovation to be their main business activity, and the existence of financing constraints seriously impedes the innovative development of technology-based enterprises.

Based on the above analysis, the following hypotheses are proposed:

Hypothesis 3: Financing constraints inhibit the innovative activities of technology-based enterprises.

From the perspective of resources, financial subsidies can be directly used as start-up funds for enterprises to carry out innovative activities, sharing some of the costs and risks, and incentivizing enterprises to increase R&D investments. However, financial subsidies as innovation funds only account for a small portion of R&D investment, which can only reduce the dependence on external financing in the short term, while it is difficult to realize a stable supply of funds for enterprises’ continuous innovation. When firms face high financing constraints, they tend to have higher levels of internal savings to avoid the possible adverse effects of funding shortages. The leverage effect of financial subsidies on increasing R&D investment is also weakened when firms themselves invest less in innovation activities. At the same time, fiscal subsidies are more likely to have a crowding-out effect on firms’ innovation when there is a shortage of funds. When firms face high financing constraints, they have more incentives to carry out short-cycle innovation projects and may even use fiscal subsidies for firms’ non-innovation projects. When firms are unable to obtain funds from other financing channels and are overly reliant on fiscal subsidies, they may also engage in rent-seeking behavior, whereby firms use these funds to establish good government-enterprise relations in order to obtain more fiscal subsidies and other policy benefits from the government.

From a signaling perspective, financial subsidies can effectively shorten the information gap between firms and external investors, making it easier for firms to obtain financial support from external investors. When firms are generally affected by financing constraints, the external financing incentive effect, or signaling effect, generated by fiscal subsidies has a greater impact on firms’ innovation than the incentive effect of fiscal subsidies itself. When firms face high financing constraints, the degree of information asymmetry is greater, and the endorsement of fiscal subsidies is not sufficient to attract sufficient external funding. Thus, the incentive effect on the sustainability of firm innovation will be reduced. Since the right to grant financial subsidies is in the hands of the government, the government is able to guide firms to carry out high-quality innovation activities through financial subsidies. When enterprises face high financing constraints, the government’s role in guiding enterprises to innovate becomes stronger, but it reduces the initiative of enterprises to make innovation decisions and discourages enterprises from carrying out innovation activities.

Based on the above analysis, the following hypothesis is proposed:

Hypothesis 4: Financing constraints play a mediating effect in the impact of government subsidies on innovation of science and technology-based enterprises.

Hypothesis 5: Government subsidies can alleviate the financing constraints of science and technology-based enterprises and have a positive impact on their innovative activities.

Hypothesis 6: Compared with the high financing constraint background, the positive effect of financial subsidies on the sustainability of innovation of science and technology-based enterprises is greater in the low financing constraint background.

There are significant differences in the financing capabilities and operating levels of enterprises across different life cycles, which will also have a heterogeneous mediating effect on financing constraints.

In terms of government subsidies, enterprises in the start-up stage will face very serious financing constraints because of their large funding gap, high uncertainty, long R&D cycle, low market visibility, and average level of operation and management. At this point, government subsidies are not sufficient to reverse this unfavorable situation. Growth-period firms face similar problems, and it is difficult to alleviate financing constraints through signalling methods due to low product awareness and other reasons, so the mediating effect of financing constraints may not be significant in either period. In the maturity period, the enterprise’s market reputation and market share have reached a high level, and the positive signals of government subsidies can be communicated to the market more smoothly, which can better alleviate the financing constraints. Entering the recession period, the enterprise’s operation level is not good, and the capital demand is large, so it will tend to use the government subsidies received in short-cycle, low-risk projects, and the promotion effect on innovation may not be significant.

Hypothesis 7: When examining the impact of government subsidies on firms’ innovation, the mediating effect of financing constraints mainly exists in the maturity period and is not significant in the start-up, growth and recession periods.

Corporate Risk Taking (Risk). There are several measures of risk taking, mainly including cash holding level, financial leverage, earnings volatility, and stock return volatility. And due to the high volatility of the stock market, existing studies generally use the earnings volatility indicator to measure risk taking. Therefore, this paper selects the indicator of earnings volatility to measure corporate risk taking. The method for calculating this indicator is as follows:

First, firms’ annual ROA is adjusted using industry averages and annual averages with a view to mitigating industry and cyclical effects. Then, the standard deviation Risk1 and extreme deviation Risk2 of ROA adjusted by industry average and annual average are calculated every three years on a rolling basis.

The specific calculation formula is as follows: (1)AdjRoai,t=EBITi,tASSETi,t−1X∑k=1χEBITi,tASSETi,t$Ad{j_R}o{a_{i,t}} = \frac{{EBI{T_{i,t}}}}{{ASSE{T_{i,t}}}} - \frac{1}{X}\sum\limits_{k = 1}^\chi {\frac{{EBI{T_{i,t}}}}{{ASSE{T_{i,t}}}}}$ (2)Risk1i,t=1T−1∑i=1T(AdjRoai,t−1T∑i=1TAdjRoai,t)2|T=3$Risk{1_{i,t}} = \sqrt {\frac{1}{{T - 1}}\sum\limits_{i = 1}^T {{{\left( {Adj{\:_R}o{a_{i,t}} - \frac{1}{T}\sum\limits_{i = 1}^T A dj{\:_R}o{a_{i,t}}} \right)}^2}} } |T = 3$ (3)Risk2i,t=Max(AdjRoai,t)−Min(AdjRoai,t)$Risk\:{2_{i,t}} = Max(Ad{j_R}o{a_{i,t}}) - Min(Ad{j_R}o{a_{i,t}})$

Government Grants (GOV). In this paper, the ratio of the total amount of government grants received by a firm to its operating revenue is used as a measure of the GOV variable.

Firm innovation (RD): the natural logarithm of the amount of firms’ R&D investment.

Financing constraints (SA). Since financing constraints cannot be directly determined, previous studies have measured them in two main ways: univariate and multivariate measurements. The former is somewhat one-sided, using dividend payout ratio, interest coverage multiple, etc., as a measure, which is too univariate. The latter, on the other hand, is more comprehensive, extracting two or more financial indicators into a composite indicator for measurement. The SA index is the most robust among the comprehensive indicators and does not include endogenous financing variables [29]. Therefore, the SA index is selected to measure financing constraints in this paper. Since the calculation results of this index are all negative, then when the absolute value is larger, the financing constraint is more serious. The coefficient of this index is positive, which represents a negative correlation and vice versa. The formula of SA index is as follows: (4)SA=0.043*Size∧2−0.737*Size−0.04*Age$SA = {0.043^*}Siz{e^ \wedge }2 - {0.737^*}Size - {0.04^*}Age$

Where Size is the size of the business and Age is the number of years the business has been listed.

In this paper, firm size is measured by the “natural logarithm of total firm assets”.

Turnover is equal to the ratio of operating income to total assets.

Tobin’s Q measures the value of the company.

In this paper, we use the ratio of net inventory and net fixed assets of a firm to total assets to measure the tangibility of the firm’s assets (PPE).

This paper uses the ratio of the firm’s total liabilities to net assets to measure the firm’s equity ratio (DER) and the ratio of the ending balance of cash and cash equivalents to total assets to measure the firm’s level of cash holdings (Cash).

The ratio of selling expenses to operating income is used to measure the product market competitiveness of the firm (Sfin). At the same time, higher product market competitiveness can bring more sales and profits to the firm. Then, the firm will invest more capital in science and technology innovation. The opposite is also true. Accordingly, the return on total assets (ROA) and the growth rate of operating income (Growth) are chosen to represent the product demand.

The shareholding ratio of the first largest shareholder is used to measure the firm’s equity concentration (LHR), and the natural logarithm of the firm’s listing time is used to measure its age (Age).

In the process of government subsidies (X) affecting innovation (Y) of science and technology-based firms, it may act directly on firm innovation or through financing constraints (M). To wit: (5)Y=cX+e1$Y = cX + {e_1}$ (6)M=aX+e2Y=c′$M = aX + {e_2}Y = c'$ (7)X+bM+e3$X + bM + {e_3}$

This paper proposes the mediation effect test procedure to test the mediation effect of financing constraints. The mediation effect test procedure is shown in Figure 1, and the main steps are as follows:

STEP 1:Test the coefficient of total effect c. Through Poisson regression to derive the total effect of government subsidies on innovation of science and technology-based enterprises c. If the total effect c is significant, then proceed to step STEP2. On the contrary, it is determined that there is no significant correlation between government subsidies and innovation of science and technology-based enterprises and stop the analysis.

STEP 2 Test the coefficients a, b. Regress the equation (6) and equation (7). If the coefficient a of government subsidies on financing constraints and coefficient b of the effect of financing constraints on innovation of science and technology-based enterprises are significant, it indicates that the mediation test passes. However, if one of the coefficients of a and b is not significant, the mediation effect needs to be further tested in step STEP4.

STEP 3:Test the coefficient direct effect c’. Whether the direct effect c’ of government subsidies on innovation of science and technology-based enterprises is significant reflects the effectiveness of the mediating effect. If c’ is not significant, it means that government subsidies do not have a direct effect. It needs to be mediated by the mediating variable (financial constraints) in order to affect the innovation of science and technology-based firms, and the mediating variable plays the role of a full mediator here. If c’ is significant, it indicates that government subsidies can impact the innovation of science and technology-based enterprises directly and indirectly, which can be used to calculate the mediating effect.

STEP4:Sobel test is conducted. If the test result is significant, it indicates that the mediation effect is significant. On the contrary, there is no mediation effect.

Figure 1.

Intermediate effect test procedure

Based on the testing procedure of the mediation effect, this paper constructs the following model.

Model 1: Government subsidies and innovation of science and technology-based enterprises

This paper uses patent data to measure the explanatory variables, considering that patent data is a non-negative count variable, which does not satisfy the assumption of normal distribution, and even after logarithmic transformation, the regression results may still have bias.

This paper adopts the great likelihood estimation method to establish a regression model to study the relationship between government subsidies and innovation capacity and different types of innovation of science and technology-based enterprises, and the model is constructed as follows: (8)E(Yit|Xit,ηind,τt=exp(α×GOVit+∑i=1kλi×Controlit+ηind+τt))$E({{Y}_{it}}\vert{{X}_{it}},{{\eta }_{ind}},{{\tau }_{t}}=exp(\alpha \times GO{{V}_{it}}+\sum\limits_{i=1}^{k}{{{\lambda }_{i}}}\times Contro{{l}_{it}}+{{\eta }_{ind}}+{{\tau }_{t}}))$

In Eq. (8), i represents individual firms, t represents annual identifiers, E(Y_it | X_it) represents the explanatory variable innovation of science and technology-based firms, which mainly includes innovation ability (NOI), substantial innovation (HQI) and strategic innovation (LQI), and GOV denotes the company’s government subsidies, Control_it represents control variables, η_ind represents industry fixed effects, and τ_t represents year fixed effects.

Model 2: Government subsidies and financing constraints

In this paper, the following model is constructed to investigate the impact of government subsidies on financing constraints: (9)XitE(FCit|Xit,ηind,τt=exp(β×GOVit+∑i=1kλi×Controlit+ηind+τt))${X_{it}}E(F{C_{it}}|{X_{it}},{\eta _{ind}},{\tau _t} = exp(\beta \times GO{V_{it}} + \sum\limits_{i = 1}^k {{\lambda _i}} \times Contro{l_{it}} + {\eta _{ind}} + {\tau _t}))$

In equation (9), Control denotes other control variables.

Model 3: Mediating role of financing constraints (10)E(Yit|Xit,ηind,τt=exp(α×GOVit+β×FCit+∑i=1kλi×Controlit+ηind+τt))$E({Y_{it}}|{X_{it}},{\eta _{ind}},{\tau _t} = exp(\alpha \times GO{V_{it}} + \beta \times F{C_{it}} + \sum\limits_{i = 1}^k {{\lambda _i}} \times Contro{l_{it}} + {\eta _{ind}} + {\tau _t}))$

The variables in equation (10) are consistent with the definitions in the models above.

Model IV: Threshold model

In order to further explore the threshold effect of financing constraints in the impact of government subsidies on the innovation of science and technology-based enterprises, a panel threshold model is proposed for empirical analysis.Firstly, a single threshold econometric model is established as follows: (11)LNYit=β0+I(qit≤γ)β1×GOVit+I(qit>γ)β1×GOVit +β2×FCit+∑βkXki,t+vi+vt+ϵit$\begin{align}{l} LN{{Y}_{it}}={{\beta }_{0}}+I({{q}_{it}}\le \gamma ){{\beta }_{1}}\times GO{{V}_{it}}+I({{q}_{it}}>\gamma ){{\beta }_{1}}\times GO{{V}_{it}} \\ +{{\beta }_{2}}\times F{{C}_{it}}+\sum{{{\beta }_{k}}}{{X}_{ki,t}}+{{v}_{i}}+{{v}_{t}}+{{\epsilon }_{it}} \\ \end{align}$

Where LNYit is the number of patent applications (NOI), the number of invention patent applications (HQI), and the number of non-invention patent applications (LQI) of the enterprise plus 1 to take the natural logarithm, q_it is the threshold variable, γ is the threshold value, and I(g) is the schematic function: (12)I(qit≤γ)={ 1 qit≤γ 0 qit>γ$I({q_{it}} \leq \gamma ) = \left\{ {\begin{array}{*{20}{l}} 1&{{q_{it}} \leq \gamma } \\ 0&{{q_{it}} > \gamma } \end{array}} \right.$ (13)I(qit>γ)={ 1 qit>γ 0 qit≤γ$I({q_{it}} > \gamma ) = \left\{ {\begin{array}{*{20}{l}} 1&{{q_{it}} > \gamma } \\ 0&{{q_{it}} \leq \gamma } \end{array}} \right.$

First, a threshold effect test is performed by ① obtaining the residual sum of squares RSS* of model (β1=β1′)$({\beta _1} = \beta _1^\prime )$ when there is no threshold effect. ② Obtain the residual sum of squares of model (β1≠β1′)$({\beta _1} \ne \beta _1^\prime )$ when there is a threshold effect RSS(γ). Construct the LR statistic as follows: (14)LR=RSS*−RSS(γ^)RSS(γ^)/N(T−1)$LR = \frac{{RS{S^*} - RSS(\hat \gamma )}}{{RSS(\hat \gamma )/N(T - 1)}}$

If a threshold effect exists, the threshold needs to be tested. Construct the following LR statistic: (15)LR=RSS(γ)−RSS(γ^)RSS(γ^)/N(T−1)$LR = \frac{{RSS(\gamma ) - RSS(\hat \gamma )}}{{RSS(\hat \gamma )/N(T - 1)}}$

In the above equation, RSS(γ) is the sum of squares of the residuals of the threshold model when the original assumptions hold.

If two thresholds exist, it needs to be expanded into a two-threshold panel model as follows: (16)LNYit=β0+I(qit≤γ1)β1×GOVit +I(γ1<qit≤γ2)β1×GOVit+I(qit>γ2)β1×GOVit +β2×FCit+∑βkXki,t+vi+vt+ϵit$\begin{array}{l} LN{{Y}_{it}}={{\beta }_{0}}+I({{q}_{it}}\le {{\gamma }_{1}}){{\beta }_{1}}\times GO{{V}_{it}} \\ +I({{\gamma }_{1}}< {{q}_{it}}\le {{\gamma }_{2}}){{\beta }_{1}}\times GO{{V}_{it}}+I({{q}_{it}}>{{\gamma }_{2}}){{\beta }_{1}}\times GO{{V}_{it}} \\ +{{\beta }_{2}}\times F{{C}_{it}}+\sum{{{\beta }_{k}}}{{X}_{ki,t}}+{{v}_{i}}+{{v}_{t}}+{{\epsilon }_{it}} \\ \end{array}$

In order to analyse the impact of government grants on firms’ innovation financing constraints, government grants (GOV) and the product term of government grants and financing constraints (GOV×FC) are added to the model.

This paper is based on using a two-step system GMM and other estimation techniques for regression. The results of the impact of government grants on firms’ innovation activities are summarised in Table 4. The coefficient of government grants (GOV) is significantly positive at the 5% level, indicating that the impact of government grants on the innovative activities of enterprises is a promotional effect, i.e., it belongs to the incentive effect, which verifies the hypothesis proposed in this paper.2 The coefficient of the product term of government grants and financing constraints (GOV×FC) is significantly positive at the 1% level, indicating that government grants will alleviate the inhibitory effect of financing constraints on the innovative activities of enterprises. The government subsidy has a direct effect on the innovative activities of enterprises in two ways. Government subsidies increase the source of funds for innovative activities of SMEs in science and technology, which provides a basis for enterprises to carry out innovative activities. The second is the indirect effect. Government support indicates that the innovation projects of S&T SMEs have development potential and that the innovations generated in the future will be competitive in the market. Through the signalling mechanism, external investors will increase their financial support for science and technology-based SMEs, thus alleviating the financing constraints faced by science and technology-based SMEs. Verify the hypotheses 4 and 5 proposed in this paper.

In order to analyse the impact of financing constraints on the innovation capacity of enterprises, the variable of financing constraints (FC) is added on the basis of the model. The effect of financing constraints on innovation capacity is shown in Table 5. The sign of the coefficient of FC and its significance can be observed to indicate the effect of financing constraints on firms’ innovation capacity.

The significance of the coefficient of financing constraint (FC) is negative at the 1% level, which means that financing constraints significantly inhibit firms’ ability to innovate, suggesting that Hypothesis 3 of this paper is valid. The possible explanation for this is that financing constraints may have a more pronounced dampening effect on firms’ innovative activities compared to their ordinary projects. This is because the innovative activities of enterprises originate from a new idea, then carry out product development and testing, and only after repeated trial production and market testing can they produce and sell on a large scale. The demand for capital at each stage of the innovative activities of enterprises is huge, and if there is a break in the capital chain at a certain stage, the innovative activities of the enterprise will be terminated. Relying on internal financing alone is difficult to meet the capital gap for innovation activities, and enterprises need external financing to be able to solve the large amount of capital required for innovation activities. Therefore, the degree of difficulty in obtaining external financing will, to some extent, determine the level of the firm’s innovation activities. However, since enterprise innovation activity is a high-risk project, coupled with the lack of a highly developed market to evaluate enterprise innovation projects and the low information transparency of enterprises, most financing institutions tend to avoid such high-risk projects and are reluctant to provide loans, resulting in enterprise innovation activities facing a higher degree of financing constraints, which in turn restricts the innovation capability of enterprises. Thus, it can be verified that the research hypotheses 4, 5 and 6 of this paper are valid.

Enterprises in different life cycles have different profitability, risk tolerance and growth, and therefore face different financing constraints. This paper analyses the differences in the mediating effects of financing constraints in different life cycles under the perspective of government subsidies by dividing the sample enterprises into four groups: start-up, growth, maturity and decline through the net cash flow from operating activities, net cash flow from investing activities and the earned cash flow from financing activities.

First, the heterogeneity of the mediating effect of different life cycle financing constraints under the government subsidy approach is analysed using the stepwise regression method. The results of the intermediation effects of different life cycle financing constraints under the government subsidy perspective are shown in Table 6.

The coefficients of government subsidies in II and V are -0.003 and -0.006, respectively, which are not significant, indicating that there is no intermediary effect of financing constraints during the start-up and growth periods.

The coefficients of 0.125 and -0.052 for government grants in VII and VIII, respectively, are significant at the 1% significance level. The coefficient of financing constraint in IX is -0.786, and the coefficient of government subsidy is 0.135, which are both significant at the 1% level, indicating that the mediating effect of financing constraint for firms in the maturity period exists and is partially mediated. The coefficient of government subsidy in Ⅹ is 0.056 and insignificant, indicating that the mediating effect of financing constraints in the decline period does not exist. It can be seen that under the perspective of government subsidies, the mediating effect of financing constraints mainly arises in the maturity period and is not significant in the start-up period, the growth period and the recession period, which verifies hypotheses 1 and 7.