4.1 Descriptive analysis
Descriptive statistics are helpful to better understand the basic characteristics of samples. The results are shown in Table 4 − 1:
Table 4-1
Descriptive statistics of variables
Variable name
|
Variable symbol
|
Average value
|
Standard deviation
|
Coefficient of standard deviation
|
Minimum value
|
Median
|
Maximum value
|
Innovation input
|
Rd
|
0.025
|
0.018
|
0.720
|
0.000
|
0.021
|
0.186
|
Reliability
|
X1
|
0.971
|
0.169
|
0.174
|
0.000
|
1.000
|
1.000
|
Timeliness
|
X2
|
102.879
|
18.577
|
0.181
|
17.000
|
110.000
|
182.000
|
Correlation
|
X3
|
0.566
|
0.708
|
1.251
|
0.000
|
0.000
|
2.000
|
Prudence
|
X4
|
0.063
|
0.116
|
1.841
|
0.003
|
0.033
|
3.496
|
Comparability
|
X5
|
−0.003
|
0.003
|
−1.000
|
−0.023
|
−0.002
|
−0.001
|
Financing constraint
|
Fc
|
−3.781
|
0.210
|
0.056
|
−4.802
|
−3.766
|
−3.098
|
Enterprise scale
|
Size
|
22.016
|
0.922
|
0.042
|
19.025
|
21.936
|
26.434
|
Asset-liability ratio
|
Lev
|
0.368
|
0.199
|
0.541
|
0.017
|
0.354
|
4.596
|
Net interest rate on total assets
|
Roa
|
0.037
|
0.092
|
2.486
|
−1.859
|
0.042
|
0.675
|
Revenue growth rate
|
Growth
|
0.276
|
1.988
|
7.203
|
−0.913
|
0.135
|
84.992
|
Ownership concentration
|
First
|
0.302
|
0.126
|
0.417
|
0.053
|
0.286
|
0.769
|
Through descriptive analysis statistics, we can know that the minimum value of innovation input is 0, the maximum value is 0.186, and the average value is 0.025, indicating that Chinese listed private enterprises have obvious differences in innovation input. For the reliability data of accounting information, the mean value is 0.971, the maximum value is 1, and the median value is 1, indicating that the accounting information of enterprises is relatively reliable during 2017–2021. In terms of the timeliness of accounting information, the mean value is 103.879 and the standard deviation is 0.181, indicating that there is a small difference in the timing of accounting information disclosure of listed private enterprises. In the correlation of the average value of 0.566 accounting information shows that the listed private enterprises in China are poor, have certain room for improvement; In the data of accounting information prudence and comparability, it shows that there is no unified standard in the enterprise innovation of Chinese listed private enterprises, and the importance of accounting information prudence is different. According to financing constraint data, there are corresponding problems in listed private enterprises in China.
For the control variables, according to the standard deviation of 0.042 of enterprise size data, the volume gap between Chinese listed private enterprises is small and the scale is close. In addition to some anomalies in the asset-liability ratio, the debt ratio of the sample companies is concentrated and high; The revenue growth rate and total assets data show that the revenue capacity of each enterprise varies, but the situation is all in a good state; In terms of ownership concentration, Chinese listed private enterprises have a high concentration of ownership, with the maximum value being 76.9%, indicating a large difference in distribution ratio.
4.2 Analysis of regression results
1. Analysis of main test results
The explained variable of this analysis is innovation input, and the explanatory variable is 5 accounting information quality requirements. The fixed-effect model is adopted to explore the influence of accounting information quality on enterprise innovation input. The results of the main test regression are shown in Table 4-2.1.
Table 4-2
Descriptive statistics of variables
Explaining Variable
|
Innovation input
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
Model 5
|
X1
|
0.0023**
(2.57)
|
|
|
|
|
X2
|
|
-0.0005
(-0.68)
|
|
|
|
X3
|
|
|
0.0008***
(2.62)
|
|
|
X4
|
|
|
|
-0.0006***
(-2.78)
|
|
X5
|
|
|
|
|
0.0047
(0.26)
|
Size
|
-0.0056***
(-11.59)
|
-0.0055***
(-11.32)
|
-0.0055***
(-11.34)
|
-0.0067***
(-10.63)
|
-0.0056***
(-11.46)
|
Lev
|
-0.0035***
(-2.83)
|
-0.0038***
(-3.05)
|
-0.0043***
(-3.41)
|
-0.0031**
(-2.40)
|
-0.0038***
(-3.04)
|
Roa
|
-0.0062***
(-3.29)
|
-0.0055***
(-2.95)
|
-0.0075***
(-3.71)
|
-0.0049***
(-2.60)
|
-0.0054***
(-2.89)
|
Growth
|
-0.0002***
(2.88)
|
0.0002***
(2.87)
|
0.0002***
(2.68)
|
0.0002**
(2.40)
|
0.0002***
(2.86)
|
First
|
-0.0092**
(-2.47)
|
-0.0091**
(-2.44)
|
-0.0089**
(-2.39)
|
-0.0099***
(-2.65)
|
-0.0090**
(-2.42)
|
cons
|
0.1480***
(13.84)
|
0.1504***
(13.77)
|
0.1474***
(13.78)
|
0.1735***
(12.50)
|
0.1490***
(13.92)
|
Year
|
Control
|
Control
|
Control
|
Control
|
Control
|
N
|
3940
|
3940
|
3940
|
3940
|
3940
|
R2
|
0.1289
|
0.1272
|
0.129
|
0.1292
|
0.1271
|
F值
|
26.41
|
26.32
|
25.73
|
26.44
|
26.2
|
Note: *** means relevant at 1% level, ** means relevant at 5% level, * means relevant at 10% level. |
Model 1 examines the impact of accounting information reliability on enterprise innovation input. According to the results, the reliability coefficient is positive at 5%, so it has a positive influence, and hypothesis H1 can be verified. The higher the reliability of accounting information, the healthier the enterprise finance, the better the enterprise will get investment and internal innovation input.
Model 2 examines the influence of timeliness of accounting information on innovation input. Time delay coefficient is -0.0005, and timeliness is positively correlated with innovation input, but the relationship is not obvious. There is little correlation between the timeliness of accounting information and innovation input because there is little difference between accounting information disclosure time of each enterprise.
Model 3 examines the influence of accounting information relevance on enterprise innovation input. According to the results, the correlation coefficient is positive at 1%, so it has a positive influence, and hypothesis H3 can be verified. The higher the relevance of accounting information is, the higher the possibility of investment will be, and the smaller the financing constraints will be.
Model 4 examines the influence of accounting information prudence on innovation input. According to the results, the caution coefficient is negative at 1%, so it has a negative effect. Caution prevents enterprises from further investment in innovation, and hypothesis H4 can be verified.
Model 5 examines the impact of accounting information comparability on firms' innovation input. As shown by the regression coefficient of 0.0047, there is a positive correlation between comparability and innovation input, but the relationship is not obvious. The higher the comparability of the accounting information of enterprises, the external investors can compare the business performance of enterprises with other areas of external enterprises. However, the short-term situation of some enterprise managers will lead to the increase of performance competition pressure faced by enterprises. If investors pay more attention to tangible asset investment behavior, enterprise managers may pay more attention to such investment behavior, which will have a negative impact on enterprise innovation input.
2. Analysis of the test results of the moderating effect of financing constraints
Since the comparability and timeliness of accounting information may not have an obvious relationship with innovation input of enterprises, based on this, this analysis further studies the moderating effects of financing constraints on reliability, prudence and relevance and innovation input.
Table 4-3
Test results of the moderating effect of financing constraints
Explaining Variable
|
Innovation input
|
Model 6
|
Model 7
|
Model 8
|
X1
|
0.0027***
(2.96)
|
|
|
X1*Fc
|
-0.0010**
(-2.41)
|
|
|
X3
|
|
0.0008***
(2.87)
|
|
X3*Fc
|
|
-0.0004***
(-3.74)
|
|
X4
|
|
|
-0.0058***
(-2.68)
|
X4*Fc
|
|
|
0.0013**
(2.35)
|
Fc
|
0.0010**
(2.38)
|
0.0002**
(2.19)
|
-0.0001
(-1.02)
|
Size
|
-0.0057***
(-11.65)
|
-0.0055***
(-11.35)
|
-0.0067***
(-10.60)
|
Lev
|
-0.0035***
(-2.79)
|
-0.0044***
(-3.48)
|
-0.0031**
(-2.43)
|
Roa
|
-0.0063***
(-3.33)
|
-0.0075***
(-3.71)
|
-0.0049***
(-2.59)
|
Growth
|
0.0002***
(2.93)
|
0.0002***
(2.65)
|
0.0002**
(2.38)
|
First
|
-0.0092**
(-2.48)
|
-0.0087**
(-2.33)
|
-0.0103***
(-2.76)
|
cons
|
0.1483***
(13.88)
|
0.1472***
(13.79)
|
0.1732***
(12.48)
|
Year
|
Control
|
Control
|
Control
|
N
|
3940
|
3940
|
3940
|
R2
|
0.1305
|
0.1328
|
0.1307
|
F值
|
26.45
|
25.83
|
26.48
|
Note: *** means relevant at 1% level, ** means relevant at 5% level, * means relevant at 10% level. |
Model 6 examines the moderating effect of financing constraints on accounting information reliability and firm innovation input. As shown in the data results, X1*Fc coefficient is negative, indicating that the smaller the constraint of financing constraints on enterprises, the higher the innovation input of enterprises, which has a negative correlation promoting effect, and H6 can be verified.
Model 7 examines the moderating effect of financing constraints on accounting information relevance and firm innovation input. As shown in the data results, X3*Fc coefficient is negative, indicating that the stronger the constraint of financing constraints on enterprises, the weaker the innovation input of enterprises, with negative correlation and negative effect, H8 can be verified.
Model 8 examines the moderating effect of financing constraints on accounting information prudence and firm innovation input. According to the data results, X4*Fc coefficient is positive, which indicates that financing constraints inhibit enterprise innovation input, contrary to hypothesis H9. The reasons may lie in: The lower the financing constraint, the higher the degree of capital control of enterprises, and will reduce unnecessary expenditure, improve the utilization rate of innovation resources, reduce long-term R&D innovation, transfer limited innovation resources to short-term advantageous projects, reduce the investment risk and internal pressure of enterprise managers, reduce their short-term behavior, so as to reduce the inhibition effect of accounting information prudence on enterprise innovation input.
4.3 Robustness test
In order to prove the correctness of the experimental results, the sample data are further expanded to the A-share private enterprises in Shanghai and Shenzhen stock markets, and the results are shown in Table <link rid="tb6">4</link>–4.
Table 4-4
Robustness test of main effect
Explaining Variable
|
Innovation input
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
Model 5
|
X1
|
0.0024***
(3.12)
|
|
|
|
|
X2
|
|
-0.0006
(-0.97)
|
|
|
|
X3
|
|
|
0.0009***
(3.72)
|
|
|
X4
|
|
|
|
-0.0049***
(-2.74)
|
|
X5
|
|
|
|
|
0.0020
(0.12)
|
Size
|
-0.0052***
(-12.02)
|
-0.0050***
(-11.68)
|
-0.0050***
(-11.79)
|
-0.0061***
(-10.76)
|
-0.0050***
(-11.45)
|
Lev
|
-0.0035***
(-2.99)
|
-0.0037***
(-3.16)
|
-0.0043***
(-3.63)
|
-0.0030**
(-2.52)
|
-0.0037***
(-3.09)
|
Roa
|
-0.0075***
(-4.22)
|
-0.0068***
(-3.86)
|
-0.0095***
(-4.98)
|
-0.0062***
(-3.56)
|
-0.0071***
(-3.97)
|
Growth
|
0.0000
(1.31)
|
0.0000
(1.25)
|
0.0000
(1.11)
|
0.0000
(0.97)
|
0.0000
(1.26)
|
First
|
-0.0051
(-1.55)
|
-0.0050
(-1.54)
|
-0.0049
(-1.51)
|
-0.0060*
(-1.83)
|
-0.0060*
(-1.75)
|
cons
|
0.1361***
(14.38)
|
0.1385***
(14.31)
|
0.1357***
(14.35)
|
0.1591***
(12.68)
|
0.1365***
(14.05)
|
Year
|
Control
|
Control
|
Control
|
Control
|
Control
|
N
|
4980
|
4980
|
4980
|
4980
|
4980
|
R2
|
0.1229
|
0.1209
|
0.1238
|
0.1224
|
0.122
|
F值
|
34.61
|
34.42
|
33.68
|
34.59
|
34.6
|
Note: *** means relevant at 1% level, ** means relevant at 5% level, * means relevant at 10% level. |
According to the data in Table <link rid="tb6">4</link>–4, the reliability, relevance and caution of accounting information are still consistent with the coefficient values in Table 4 − 2 after the expansion of sample data. Therefore, the timeliness and comparability of accounting information are not correlated with innovation input, which is consistent with the conclusion of the coefficient values in Table 4 − 2. This analysis passes the test.
Table 4-5
Robustness test of the moderating effect of financing constraints
Explaining Variable
|
Innovation input
|
Model 6
|
Model 7
|
Model 8
|
X1
|
0.0028***
(3.57)
|
|
|
X1*Fc
|
0.0009**
(-2.55)
|
|
|
X3
|
|
0.0010***
(3.95)
|
|
X3*Fc
|
|
0.0003***
(-3.11)
|
|
X4
|
|
|
0.0055***
(-3.03)
|
X4*Fc
|
|
|
0.0010*
(1.94)
|
Fc
|
0.0009***
(2.64)
|
0.0002**
(2.37)
|
0.0000
(-0.23)
|
Size
|
0.0052***
(-12.07)
|
0.0050***
(-11.79)
|
0.0061
(-10.85)
|
Lev
|
0.0034***
(-2.92)
|
0.0043***
(-3.67)
|
0.0030***
(-2.50)
|
Roa
|
0.0075***
(-4.23)
|
0.0095***
(-4.97)
|
0.0062***
(-3.52)
|
Growth
|
0.0000
(1.33)
|
0.0000
(1.09)
|
0.0000
(0.97)
|
First
|
0.0049
(-1.51)
|
0.0047
(-1.45)
|
0.0061*
(-1.85)
|
cons
|
0.1360***
(14.38)
|
0.1355***
(14.35)
|
0.1604***
(12.76)
|
Year
|
Control
|
Control
|
Control
|
N
|
4980
|
4980
|
4980
|
R2
|
0.1244
|
0.126
|
0.1233
|
F值
|
34.64
|
33.72
|
34.59
|
Note: *** means relevant at 1% level, ** means relevant at 5% level, * means relevant at 10% level. |
As shown in the data in Table <link rid="tb6">4</link>–4, the reliability, relevance and caution of accounting information are still consistent with the coefficients in Table 4 − 3 after the expansion of sample data. Therefore, the reliability, relevance and caution of accounting information are correlated with innovation input, which is consistent with the conclusions of the coefficients in Table 4 − 3. This analysis passes the test.