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Sustainability 2023, 15, 736. https://doi.org/10.3390/su15010736 www.mdpi.com/journal/sustainability
Article
Internal Control, Environmental Uncertainty and Total Factor
Productivity of Firms—Evidence from Chinese Capital Market
Kun Wang
1
, Lichen Liu
1
, Mengyue Deng
2
and Yaxian Feng
1,
*
1
School of Management, Wuhan Institute of Technology, Wuhan 430205, China
2
School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
* Correspondence: pheonixfeng@163.com; Tel.: +86-131-1390-8652
Abstract: Based on the data of China’s A-share listed companies from 2009 to 2019, this paper em-
pirically examines the relationship between internal control and total factor productivity of enter-
prises in the presence of environmental uncertainty. The research shows that high quality internal
control can effectively improve the total factor productivity of enterprises. Environmental uncer-
tainty negatively moderates the relationship between internal control quality and total factor
productivity. Further research in this paper shows that there are heterogeneous effects on the ad-
justing effects of different life cycles, property rights and regional distribution of enterprises, that is,
when enterprises are in the growth stage, the quality of internal control has a significant effect on
the improvement of total factor productivity of enterprises. For enterprises and non-state-owned
enterprises located in the eastern region, the inhibition and adjustment effect of environmental un-
certainty is more significant. At the same time, the supplementary research finds that internal con-
trol directly affects the total factor productivity of enterprises through the intermediary role of pro-
moting enterprise development and innovation and easing financing constraints. The research con-
clusions enrich the literature on the mechanism of internal control affecting enterprises’ total factor
productivity and provide a new reference and basis for enterprises to effectively manage the inter-
nal environment, strengthen the risk control management mechanism and improve the enterprise
value.
Keywords: internal control; total factor productivity; environmental uncertainty; moderating effect
1. Introduction
In April 2010, the Ministry of Finance of China jointly issued internal control guide-
line documents such as the Guidelines for the Application of Internal Control in Enterprises,
which require that listed companies in China conduct self-evaluation of the effectiveness
of internal control and disclose annual self-evaluation reports, as well as engaging ac-
counting firms to audit the effectiveness of internal control over financial reporting and
issue audit reports. The report focuses on the effectiveness of internal control, and pro-
moting the internal control system has been improved in five aspects: internal manage-
ment, risk assessment, preventive control, information transmission and internal super-
vision, and the internal control index reflects the situation of enterprises more accurately.
This means that Chinese listed companies have officially entered a new period of stand-
ardized internal control management.
At present, the global epidemic is creating a long-term trend in development. In the
context of the transformation of the economic growth mode and the fluctuation of the
external environment, China is facing enormous pressure for economic development.
Many uncertain factors in the ecological, international and economic environment have
contributed to the uncertainty of the macro environment. For the Chinese economy, which
is in the critical period of development transformation, how to ensure the sustainable and
Citation: Wang, K.; Liu, L.; Deng,
M.; Feng, Y. Internal Control,
Environmental Uncertainty and
Total Factor Productivity of
Firms—Evidence from Chinese
Capital Market. Sustainability 2023,
15, 736.
https://doi.org/10.3390/
su15010736
Academic Editor: Gaetano della
Corte
Received: 7 November 2022
Revised: 20 December 2022
Accepted: 28 December 2022
Published: 31 December 2022
Copyright: © 2022 by the authors. Li-
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (https://cre-
ativecommons.org/licenses/by/4.0/).
Sustainability 2023, 15, 736 2 of 17
high-quality development of the economy is particularly urgent. However, the realization
of macroeconomic goals must be based on development at the micro level. How to further
optimize the structure of enterprises at the micro level and improve the management level
to improve the quality and efficiency of production is an important issue to be solved
urgently. Therefore, it is clearly pointed out in the report that we should accelerate the
construction of a modern economic system and focus on improving total factor produc-
tivity (TFP). Enterprise total factor productivity refers to the additional production effi-
ciency under the given factor level. It can comprehensively consider the contribution of
various input factors to enterprise output [1], so it can be used as an important indicator
representing the high-quality operation efficiency of enterprises. Shen Danhong (2022)
pointed out that the average growth of total factor productivity of the manufacturing in-
dustries in China’s six regions during 2003–2019 was only 3.46%, but the contribution rate
to the added value of the manufacturing industry was 33.18%, and after 2018, the growth
rate of total factor productivity of enterprises reached 11% [2], indicating that in the pro-
cess of China’s future development, the total factor productivity of enterprises will exert
greater economic contribution. Therefore, how enterprises optimize the internal environ-
ment, improve the total factor productivity of enterprises and maintain high quality de-
velopment has become a problem studied and discussed within enterprises and by many
scholars.
Current studies mainly discuss the influence of total factor productivity on the exter-
nal environment and internal enterprise. With regard to the external environment, Duan
and Li (2019) showed that economic policy uncertainty significantly inhibits the total fac-
tor productivity of firms [3]. Harrish and Trainor (2005) pointed out that compared with
other methods, government subsidies can effectively improve the total factor productivity
of subsidized enterprises [4]. Based on the internal environment, Zhao (2015) shows that
financing constraints reduce the efficiency of enterprise resource allocation and have a
negative impact on total factor productivity [5]; Sheng and Jiang (2019) find an inverted
“U-shaped” association between executive monetary compensation incentives and enter-
prise total factor productivity [6]. Qing Li et al. (2021) found that information management
ability can improve risk prediction, performance incentive, integrated innovation and, fi-
nally, effectively improve the total factor productivity of enterprises [7]. According to the
study of Guo Mengnan and Li Xiaohong (2020), internal control can effectively alleviate
resource mismatch, improve organizational efficiency and promote the growth of enter-
prise value [8].
To sum up, at present, domestic researchers rarely explore the direct relationship be-
tween the concept of internal control within enterprises and the total factor productivity
of enterprises. Meanwhile, there are few studies that take environmental uncertainty as
the research angle of total factor productivity of enterprises. Under the current situation,
the operating efficiency of enterprises cannot ignore the impact of changes in the external
environment. Environmental uncertainty may lead to sharp changes in market demand,
intensify competition and information asymmetry, and seriously affect the development
of all aspects. Therefore, from the perspective of internal control quality, this paper ex-
plores the optimization mechanism of total factor productivity and the influence mecha-
nism of environmental uncertainties on the relationship between the two factors, which
has strong practical significance and application value.
In view of this, this paper takes China’s A-share listed companies from 2009 to 2019
as samples to explore the mechanism of internal control quality on the total factor produc-
tivity of enterprises in the presence of environmental uncertainty. The main innovations
and contributions of this paper are as follows: (1) Enrich the objective research on the fac-
tors that affect enterprise TFP. Most existing literature uses internal control as evidence
from the perspective of intermediary, and few studies demonstrate the direct relationship
between internal control quality and enterprise TFP. This paper expands the research hori-
zon and explores its direct effect on total factor productivity from the perspective of inter-
nal control quality. (2) Enrich the discussion about the economic consequences of internal
Sustainability 2023, 15, 736 3 of 17
control and make reasonable supplement to the existing literature. OLS analysis was
adopted in this paper to test the influence and mechanism of internal control on corporate
development. (3) Explore the environmental regulation effect between internal control
and total factor productivity of enterprises in depth and select enterprise innovation and
financing constraints as intermediate variables to explore the mediating effect, which
makes up for the deficiency of relevant literature research. (4) Certain practical signifi-
cance. Combined with the background of China’s economic focus shifting to high-quality
development, this paper provides a channel for how to improve the internal environment
of enterprises at the micro level so as to improve the total factor productivity of enterprises
and also provides a new idea for how to form an internal control system at the macro
level.
The rest of the basic structure of the paper is arranged as follows: The second part is
the literature review and research hypotheses. The third part is the study design. The
fourth part is the analysis of the empirical results. The fifth part is further research. The
sixth part is research conclusions and implications.
2. Literature Review and Research Hypotheses
2.1. Internal Control and Total Factor Productivity of Enterprises
The total factor productivity of enterprises is mainly affected by internal factors such
as technological innovation, resource allocation and information quality. As an effective
operating mechanism of enterprise environment, internal control, with value creation as
its main guidance, plays an important role in the implementation of internal management
decisions, resource allocation, process supervision and other major links [9], thus affecting
the total factor productivity of enterprises. In recent years, many studies have explored
the influence mechanism of internal control on the economic consequences of enterprises
from multiple perspectives, among which the influence of enterprise innovation and fi-
nancing constraints is very significant. In terms of enterprise innovation, high-level inter-
nal control means that enterprises can reasonably control risks at the innovation level, and
avoid unnecessary risks in a timely manner by establishing a sound internal control risk
assessment mechanism, supervision and evaluation mechanism [10,11], so as to improve
the possibility of innovation success. Meanwhile, the effective implementation of internal
control can alleviate the degree of information asymmetry among the members of the
company, restrain the agency conflicts between the board of directors and managers,
shareholders and minority shareholders, enhance the internal innovation willingness of
the company, help improve the driving force of innovation and promote the increase in
innovation investment of the company. Under the dual effects of increasing innovation
input and improving the possibility of innovation success, companies can fully transform
R&D resources into new technologies and products [12], enhance market competitiveness,
improve enterprise technical level [13] and thus improve total factor productivity of en-
terprises.
In terms of financing constraints, it refers to the inability of enterprises to obtain ex-
ternal financing due to capital market friction or the high cost of external financing, forc-
ing enterprises to give up favorable investment opportunities [14]. High-quality internal
management enterprises have lower capital cost and systematic risk [15] and better man-
agement of accounting information quality and liquidity, which can well reflect the prof-
itability and operation of enterprises. Therefore, it can effectively improve the possibility
of external investors to invest in the company and further improve the operation efficiency
and total factor productivity of enterprises.
In summary, internal controls can avoid innovation risks, alleviate the degree of in-
ternal information asymmetry and improve the driving force of innovation and the pos-
sibility of innovation success so as to achieve the goal of improving the overall business
performance of enterprises. Meanwhile, through reducing financing costs and improving
the quality of accounting information and working capital management, enterprises can
Sustainability 2023, 15, 736 4 of 17
enjoy a good investment environment to improve the total factor productivity of enter-
prises.
Based on the above analysis, the following hypothesis is proposed.
H1: Internal controls can effectively improve the total factor productivity of an enterprise.
H2: Internal control can improve the total factor productivity of enterprises by promoting enter-
prise innovation and easing financing constraints.
2.2. Moderating Effect of Environmental Uncertainty
Enterprises always operate in a specific external environment, and the changes in the
economic, international and political environment constitute environmental uncertainty.
Environmental uncertainty is defined as the inability of management to accurately ap-
praise the environment in which the firm operates, as well as its risk factors [16]. Specifi-
cally, it refers to the complexity of the environmental dynamics faced by the firm due to a
lack of information. Environmental uncertainty leads to unpredictability in the future
business risks and profitability of the firm [17], which will have an impact on the opera-
tional efficiency of enterprises.
In the case of high environmental uncertainty, external investors are less able to pre-
dict and monitor firm performance [18], and there are more information asymmetries be-
tween management and shareholders, which is detrimental to the functioning of the firm.
As mentioned above, the internal information asymmetry of enterprises will reduce the
willingness of enterprises to innovate, so the intensification of environmental uncertainty
will inhibit the innovation of enterprises [19] and then negatively affect the development
of enterprises’ total factor production. Meanwhile, environmental uncertainty can signif-
icantly increase the difficulty and cost of assessing risks, reduce the effectiveness of firms’
preventive control strategies and increase the risk premiums demanded by external in-
vestors, all of which impair firm value [20]. As mentioned above, the increase in invest-
ment cost will aggravate the problem of corporate financing constraints, so environmental
uncertainty will inhibit the total factor productivity of enterprises by improving the de-
gree of financing constraints.
Based on the above viewpoints, the following hypothesis is proposed.
H3: Environmental uncertainty inhibits the degree to which internal controls can contribute to a
firm’s total factor productivity.
3. Study Design
3.1. Data Sources and Descriptive Statistics
Considering the importance and standardization of internal control theory in China
in recent years and the remarkable results of high-quality economic development in China,
the actual data of Chinese listed companies are representative. Therefore, this paper is
targeted to select Chinese A-share listed companies from 2009 to 2019 as the research sam-
ple, and data of the years that correspond to the financial crisis and Covid-19 pandemic
are excluded to reduce the impact of extreme values. The data related to internal control
are mainly from the DIB database and supplemented by the CSMAR database. The data
are further processed: (1) excluding ST and *ST listed companies; (2) excluding financial
and insurance listed companies; (3) removing some listed companies with serious data
omission. A total of 22,058 sample observations were obtained after processing, and ±1%
tailing was applied to all continuous variables.
Sustainability 2023, 15, 736 5 of 17
3.2. Variable Selection and Description
3.2.1. Explained Variable: Total Factor Productivity
Referring to related studies [21], this paper adopts the LP method to calculate enter-
prise total factor productivity and uses the results obtained from the OP method as a basis
for robustness testing to alleviate the endogeneity problem and selectivity bias. The LP
method is a combination of the Cobb-Douglas (C-D) production function and semipara-
metric estimation to estimate the values of variables by establishing a simple linear regres-
sion of the production function, but in order to correct the simultaneity bias and sample
selectivity bias problem, the LP method introduces intermediate inputs as proxy variables
to reduce the loss of sample size, and it can more accurately represent the production
surplus from inputs other than the capital and labor factors.
3.2.2. Explanatory Variables: Internal Control
This paper selects the “DIB Internal Control Index” from the influential DIB Internal
Control and Risk Management Database as a reference indicator. The Internal Control
Index is designed by DIB based on the degree of achievement of internal control objectives
and consideration of the five elements of internal control. The internal control index score
is designed to measure the efficiency and effectiveness of corporate internal control prac-
tices. In this study, it can describe the quality of internal control of enterprises scientifically.
It is also standardized considering the appropriateness of the regression coefficients as a
specific indicator of the final measure of internal control quality, and the larger the index
is, the higher the quality of internal control of the enterprise.
3.2.3. Moderating Variable: Environmental Uncertainty
Environmental uncertainty represents the unpredictability and complexity of the ex-
ternal environment, and changes in the external comprehensive environment will have an
impact on the firm’s operations. Therefore, this paper refers to the calculation method of
Shen (2012) [18] and adopts the least squares method (OLS) to construct model (1), which
uses the firm’s performance indicators to measure environmental uncertainty; specifi-
cally, the standard deviation of the company’s abnormal sales revenue for the last five
years is forecast.
Sale = φ0 + φ1Year + ε (1)
In this model, Sale is sales revenue, Year is the year and ε is abnormal sales revenue.
The model is calculated by dividing the standard deviation of abnormal sales revenue for
the last five years by the corresponding average for the same period, reflecting the unad-
justed environmental uncertainty of the last five years. In order to more accurately meas-
ure the indicator, the model excludes sales revenue due to stable company growth and
finally divides each company’s unadjusted environmental uncertainty by the industry en-
vironmental uncertainty (the median of unadjusted environmental uncertainty of all com-
panies in the same industry in the same year) to reflect the industry-adjusted environmen-
tal uncertainty. A larger value indicates greater environmental uncertainty.
3.2.4. Main Control Variables
In order to explore the causal relationships between the main variables more accu-
rately, this article refers to related studies, and the asset–liability ratio (Det), firm age
(Age), proportion of independent directors (Lnd_r), firm size (Size), growth rate (Growth),
board size (Board) and whether the CEO is also the chairman (Duality) were selected as
control variables among the firm factors that may affect the total factor productivity of the
firm. All are shown in Table 1.
Table 1. Definition of variables.
Sustainability 2023, 15, 736 6 of 17
Variable Type Variable Name
Variable
Symbol Definition
Explained varia-
bles
Total Factor
Productivity TFP_LP Measurement used by the LP method
Explanatory var-
iables Internal Control IC DIB Internal Control Index/100
Intermediate
variables
Environmental Uncer-
tainty EU Environmental uncertainty without industry restructur-
ing/industry environmental Uncertainty as a whole
Control variables
Asset-liability ratio Det Total liabilities/ total assets
Corporate Life Age Years of establishment
Percentage of
independ-
ent directors Lnd_r Number of independent directors/ Total board members
Enterprise size Size Ln (Total assets + 1)
Business Growth Growth (Operating income—L. Operating income)/L. Operat-
ing income
Board Size Board Ln (number of board members + 1 )
Dual Roles Duality 1 if the chairman is also the CEO; 0 otherwise
Year Year Year dummy variable
Industry Ind Industry dummy variables
3.3. Model Construction
To explore the relationship between total factor productivity and internal control
(H1), the following model (2) is constructed in this paper.
TFP_LP = α0 + α1IC + α2Det + α3Age + α4Lnd_r + α5Size + α6Growth + α7Board + ∑Year + ∑Ind + ε (2)
TFP_LP is the explanatory variable, representing total factor productivity and IC is
the explanatory variable, representing internal controls. α1 -α7 denotes the coefficient of
each control variable, Year denotes the year, Ind is the industry fixed effect and ε is the
estimated residual. If the coefficient α1 in Model (2) is positive, it reflects that internal con-
trol promotes total factor productivity of firms.
To further study the role that the environmental uncertainty plays to moderate the
internal control and total factor productivity of firms (H2), model (3) was created based
on model (2).
TFP_LP = β0 + β1 EU + β2EU×IC + β3IC + β4Det + β5Age + β6Lnd_r + β7Size + β8Growth + β9Board + ∑Year + ∑Ind + ε (3)
β2, the coefficient of between internal control and environmental uncertainty, is
mainly considered in this model. If it is negative, it means that environmental uncertainty
inhibits the interaction between internal control and total factor productivity of the firm.
Sustainability 2023, 15, 736 7 of 17
4. Analysis of the Empirical Results
4.1. Descriptive Statistics
The results of descriptive statistics of the variables are shown in Table 2. The mini-
mum value of TFP_LP is 3.751, the maximum value is 10.432, the mean value is 6.909 and
the standard deviation is 2.143, indicating that the total factor productivity values of the
sample companies differ greatly, and at the same time, it means that the residual efficiency
of output other than capital and labor factor inputs in the sample companies is generally
moderate; the minimum value of IC is 0, the maximum value is 8.866, the mean value is
6.438 and the standard deviation of 1.409, indicating that the extreme values of the internal
control index of the sample companies differ greatly; the minimum value of enterprise
size (Size) is 19.538, indicating that the sample companies are all large; the minimum value
of gearing (Det) is 0.052, the maximum value is 0.94 and the mean value is 0.431, indicating
that the values of gearing of the sample companies vary widely, but the overall leverage
level of Chinese companies is moderate; the mean value of enterprise life (Age) is 17, in-
dicating that the enterprise life of the sample companies is generally large; the minimum
value of enterprise growth (Growth) is −0.57 and the maximum value is 3.48, indicating
that the extreme values of growth of the sample companies vary greatly; the minimum
value of board size (Board) is 1.792 and the maximum value is 2.77, with a mean value of
2.555, indicating that the board size of the sample companies is generally moderate; the
minimum value of independent director ratio (Lnd_r) is 0.333 and the mean value is 0.374,
which is in line with the requirement of the China Securities Regulatory Commission that
the proportion of independent directors should not be less than one-third, indicating that
the proportion of independent directors is not less than one-third and the corporate gov-
ernance is relatively sound. The mean value of two-job integration (Duality) is 0.26, which
indicates that the proportion of two-job integration in Chinese sample companies is low,
at only 26%.
Table 2. Descriptive statistics of variables.
Variables Sample Size Average
Standard Devi-
ation Median Minimum
Value Maximum Value
IC 22,058 6.438 1.409 6.728 0 8.86
TFP_LP 22,058 6.909 2.143 6.248 3.751 10.43
Size 22,058 22.117 1.296 21.948 19.538 26.07
Det 22,058 0.438 0.212 0.431 0.052 0.94
Age 22,058 17.292 5.195 17 7 32
Growth 22,058 0.206 0.501 0.119 −0.57 3.48
Board 22,058 2.255 0.178 2.303 1.792 2.77
Lnd_r 22,058 0.374 0.053 0.333 0.333 0.57
Duality 22,058 0.26 0.439 0 0 1
4.2. Univariate Test
The results of the univariate test are shown in Table 3. The mean of total factor
productivity in the sample group with higher-quality internal controls is 7.537, which is
higher than those with lower-quality internal controls (6.281) and significant at the 1%
level. This indicates that internal controls can increase firms’ total factor productivity
when the effects of the other variables are controlled for and tentatively supports hypoth-
esis H1.
Sustainability 2023, 15, 736 8 of 17
Table 3. Univariate test results.
Variables
Higher Internal Controls Lower Internal Controls Differences
Sample Size Average Standard
Deviation Sample Size Average Standard
Deviation Average T-Value
IC 11,030 7.537 2.074 11,028 6.281 2.024 −1.256 *** −45.507
Note: ***, ** indicate significance at the 1%, 5% levels, respectively.
4.3. Correlation Analysis
Total factor productivity is significantly and positively related to internal controls at
the 1% level, which is consistent with hypothesis H1 (see Table 4). Size (Size), enterprise
growth (Growth) and board size (Board) are significantly positively correlated with total
factor productivity at the 1% level, while asset–liability ratio (
D
et Det), firm age (Age)
and dual management roles (Duality) show a significant negative correlation with total
factor productivity. However, the correlation analysis only studies the numerical relation-
ship between the main variables and ignores the influence of time, region and industry. It
also only reflects the correlation between variables. Thus, a multiple regression analysis
is needed to draw more accurate conclusions.
Table 4. Correlation matrix.
IC TFP_LP Size Det Age Growth Board
IC 1
TFP_LP 0.302 *** 1
Size 0.176 *** 0.661 *** 1
Det −0.112 *** 0.115 *** 0.463 *** 1
Age −0.114 *** 0.101 *** 0.146 *** 0.144 *** 1
Growth 0.076 *** 0.146 *** 0.041 *** 0.044 *** −0.008 1
Board 0.076 *** 0.176 *** 0.261 *** 0.146 *** 0.003 −0.022 *** 1
Lnd_r −0.01 0.017 ** 0.015 ** −0.005 −0.026 *** 0.005 −0.512 ***
Duality −0.014** 0.097 *** −0.162 *** −0.135 *** −0.061 *** 0.031 *** −0.178 ***
Note: ***, ** indicate significance at the 1%, 5% levels, respectively.
In this paper, we also use the variance inflation factor (VIF) to test whether there is
multicollinearity in the model; the larger the value, the more serious the multicollinearity
problem. If the maximum value does not exceed 10, it is empirically proven that the model
does not have multicollinearity problem. The results in Table 5 show that the values of
model (2) and model (3) are all less than 10, and the maximum values are 1.53 and 1.49
respectively, so model (2) and model (3) do not have multicollinearity.
Table 5. Results of correlation analysis.
Model 2 Model 3
VIF 1/VIF VIF 1/VIF
Size 1.4 0.7142 1.40 0.7138
Det 1.29 0.7750 1.25 0.8026
Age 1.04 0.9649 1.03 0.9747
Growth 1.01 0.9945 1.38 0.7235
Board 1.53 0.6541 1.49 0.6725
Lnd_r 1.41 0.7101 1.37 0.7304
Duality 1.06 0.9461 1.04 0.9597
IC 1.17 0.8577
EU×IC 1.43 0.6725
Sustainability 2023, 15, 736 9 of 17
4.4. Benchmark Regression Analysis
The results of the regression between internal controls and total factor productivity
are shown in Table 6. Column (2) of Table 6 describes the regression results of model (2),
in which the coefficient between internal control and total factor productivity of the firm
is significantly positive at the 1% level, indicating that internal control can effectively im-
prove total factor productivity of the firm with sufficient control variables as well as the
year industry, fully validating H1; column (3) describes the regression results of model
(3), which shows that the coefficient between internal control and environmental uncer-
tainty is significantly negative at the 1% level, indicating that the greater the uncertainty
of the environment in which the firm is located, the more significantly inhibited the posi-
tive effect of internal control on total factor productivity of the firm will be, validating H3.
Overall, the coefficients of the control variables firm size (Size) and firm age (Age)
are positively significant at the 1% level, thus indicating that total factor productivity is
positively influenced by firm size and age. The regression coefficient of the asset–liability
ratio (Det) is significantly negative, thus indicating that a higher asset–liability ratio re-
duces innovation investment, which in turn affects enterprise performance. The coeffi-
cient of enterprise growth (Growth) is significantly positive at the 1% level, thus indicating
that as the growth rate of operating revenue increases, the total factor productivity of en-
terprises will also increase.
Table 6. Base model regression results.
Explanatory
Variables
Explained Variables TFP_LP
(1) (2) (3)
IC 0.460 ***
(40.449)
0.204 ***
(21.33)
0.246 ***
(17.74)
Size
1.218 ***
(111.731)
1.197 ***
(96.812)
Det
−2.406 ***
(−38.192)
−2.462 ***
(−33.556)
Age
0.019 ***
(8.807)
0.026 ***
(9.495)
Growth
0.471 ***
(19.593)
0.570 ***
(18.224)
Board
−0.188 ***
(−2.590)
−0.182 **
(−2.214)
Lnd_r
−1.306 ***
(−5.885)
−1.279 ***
(−4.995)
Duality
0.015
(0.654)
0.052 *
(1.797)
EU
0.111 ***
(3.65)
EU×IC
−0.030 ***
(−6.338)
_cons 3.948 ***
(52.333)
−20.046 ***
(−74.581)
−19.883 ***
(−64.479)
Year No Yes Yes
Ind No Yes Yes
N 22,058 22,058 17,323
Note: *** and * indicate significance at the 1% and 10% levels, respectively.
4.5. Robustness Tests
Sustainability 2023, 15, 736 10 of 17
To confirm the reliability of the results, the following tests were made in this paper.
4.5.1. Change the Measurement of the Total Factor Productivity
The measurement of core variables can affect the reliability of the study results. To
control this impact, we adopt the Olley–Pakes method [22] to measure the value expressed
by TFP_OP. The results are shown in column (1), Table 7.
Table 7. Robustness tests.
Explanatory Varia-
bles
Explained Variables TFP_LP
(1) (2) (3)
IC 0.251 ***
(17.935)
0.172 ***
(10.516)
0.202 ***
(17.353)
Size 1.198 ***
(96.555)
1.060 ***
(24.062)
1.237 ***
(82.793)
Det −2.468 ***
(−33.572)
−2.408 ***
(−14.949)
−2.518 ***
(−31.463)
Age 0.026 ***
(9.634)
0.012
(0.215)
0.019 ***
(6.971)
Growth 0.561 ***
(18.128)
0.424 ***
(13.869)
0.563 ***
(16.165)
Board −0.156 *
(−1.905)
−0.007
(−0.037)
−0.241 **
(−2.543)
Lnd_r −1.313 ***
(−5.093)
0.082
(0.18)
−1.526 ***
(−5.380)
Duality 0.057 **
(1.964)
0.004
(0.085)
0.033
(1.219)
EU 0.123 ***
(4.052)
0.054
(1.591)
0.118 ***
(2.908)
EU×IC −0.032 ***
(−6.649)
−0.012 **
(−2.418)
−0.034 ***
(−5.289)
_cons −19.980 ***
(−64.897)
−17.027 ***
(−12.975)
−19.547 ***
(−54.124)
Year Yes Yes Yes
Ind Yes Yes Yes
N 17,323 17,323 10,554
Note: ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
4.5.2. Replacement of Sample Range
As the core industry of China’s economic development, the sample data are more
representative, so this paper replaces the sample of Chinese listed manufacturing compa-
nies from 2009 to 2019 for the study. The results in column (2) of Table 7 find that the
coefficient of internal control is still positively significant at the 1% level and that the co-
efficient of EU×IC is significantly negative at the 1% level, which is basically consistent
with the previous regression results, testing the previous base regression results.
Sustainability 2023, 15, 736 11 of 17
4.5.3. Endogeneity Test
The endogeneity problem mainly arises from measurement error, omitted variable
error, reverse causality and selection error. In this paper, in order to mitigate the possible
endogeneity problems, a fixed-effects model is used to mitigate the endogeneity problems
caused by omitted variable errors, and a two-stage least squares (2SLS) method is used to
mitigate the endogeneity problems caused by reverse causality.
Based on the basic regression model, we further control for individual fixed effects.
The results in column (3) of Table 7 show that the coefficient of IC is significantly positive
at the 1% level, the coefficient of the environmental uncertainty and internal control
EU×IC (β2) is significantly negative at the 5% level and the sign of the control variables
does not change. It indicates that changing the model settings does not change the exper-
imental results, which verifies the corresponding conclusions in the previous section.
The 2SLS model continues to be used for regression testing. In this paper, first-order
lags L.IC are chosen as the instrumental variables. There is a significant correlation be-
tween the instrumental variables and the endogenous variables in the first stage regres-
sion, and, in addition, the results of the underidentification test and the weak identifica-
tion test significantly reject the original hypothesis, indicating that the instrumental vari-
ables are selected effectively. The results of the second stage using instrumental variables
to correct the bias of the endogenous variables are shown in Table 8, and IC still has a
significant positive correlation with TFP_LP, which is consistent with the previous find-
ings.
Table 8. Endogeneity test (2SLS).
Variables First Stage 2SLS
IC TFP_LP
L.IC 0.456 ***
(0.014)
IC 0.239 ***
(0.022)
Det −1.314 *** −2.464 ***
(0.075) (0.079)
Size 0.221 *** 1.216 ***
(0.012) (0.014)
Growth 0.319 *** 0.451 ***
(0.027) (0.026)
Board 0.029 −0.228 ***
(0.065) (0.080)
Lnd_r −0.100 −1.400 ***
(0.204) (0.246)
Age −0.003 0.022 ***
(0.002) (0.002)
_cons −0.943 ***
(0.252)
Underidentification test 588.421***
Weak identification test 4271.963***
Observation 18,836 18,836
R-squared 0.300 0.481
Ind YES YES
Year YES YES
F 330.6 2217
Robust standard errors in parentheses, *** p < 0.01.
Sustainability 2023, 15, 736 12 of 17
5. Further Research
After exploring the moderating effect of environmental uncertainty on internal con-
trol and firms’ total factor productivity, this paper further explores the heterogeneous in-
fluences of this effect in different environments. It selects firm innovation and financing
constraints, which are related to internal factors of the company as mediating variables to
investigate in depth the direct mechanism of action between internal control and firm total
factor productivity and to elaborate more clearly the mechanism of action between the
main variables.
5.1. Heterogeneity Test
Enterprise Innovation Mechanism Test
The nature of enterprise ownership, the region in which the enterprise is located and
the life cycle usually affect total factor productivity. The heterogeneity test in this paper
analyzes the results from each of these three perspectives, and the results are shown in
Tables 9 and 10.
Table 9. Heterogeneity analysis between the ownership structure and the region where the enter-
prise is located.
Explanatory Var-
iables
Explained Variables TFP_LP
(1) (2) (3) (4)
State-Owned Non-State-Owned East Midwest
IC 0.150 ***
(6.353)
0.209 ***
(10.575)
0.193 ***
(8.665)
0.155 ***
(6.597)
Size 1.001 ***
(13.068)
1.138 ***
(22.261)
1.076 ***
(19.713)
1.046 ***
(14.467)
Det −3.049 ***
(−11.301)
−2.065 ***
(−10.766)
−2.424 ***
(−11.572)
−2.455 ***
(−9.782)
Age −0.003
(−0.043)
0.091
(1.494)
−0.001
(−0.015)
0.124
(0.786)
Growth 0.466 ***
(10.742)
0.371 ***
(9.063)
0.448 ***
(10.754)
0.367 ***
(8.54)
Board −0.098
(−0.397)
0.065
(0.254)
0.158
(0.706)
−0.289
(−1.018)
Lnd_r 0.385
(0.65)
−0.333
(−0.509)
0.241
(0.404)
−0.149
(−0.212)
Duality −0.054
(−0.683)
0.044
(0.738)
−0.011
(−0.205)
0.026
(0.304)
EU 0.025
(0.523)
0.101 ***
(3.098)
0.100 *
(1.91)
0.042
(1.023)
EU×IC −0.011
(−1.548)
−0.017 ***
(−3.395)
−0.022 ***
(−2.740)
−0.006
(−0.956)
_cons −14.818 ***
(−7.324)
−19.680 ***
(−11.885)
−17.634 ***
(−11.093)
−17.345 ***
(−6.675)
Year Yes Yes Yes Yes
Ind Yes Yes Yes Yes
N 8463 8777 10,955 6368
Note: *** and * indicate significance at the 1% and 10% levels, respectively.
Table 10. Heterogeneity analysis between different business life cycles.
Explanatory Varia-
bles
Explained Variables
TFP_LP
(1) (2) (3)
Sustainability 2023, 15, 736 13 of 17
Growth Maturity Decline
IC 0.259 ***
(10.09)
0.203 ***
(10.45)
0.105 ***
(7.803)
Size 1.237 ***
(37.856)
1.205 ***
(46.561)
1.208 ***
(45.204)
Det −2.690 ***
(−16.258)
−2.680 ***
(−17.565)
−2.365 ***
(−16.852)
Age 0.027 ***
(3.696)
0.043 ***
(7.793)
0.035 ***
(6.843)
Growth 0.283 ***
(7.133)
0.302 ***
(6.718)
0.264 ***
(6.591)
Board −0.138
(−0.646)
−0.112
(−0.712)
0.066
(0.488)
Lnd_r −0.2
(−0.333)
−1.415 ***
(−3.053)
−0.245
(−0.613)
Duality −0.021
(−0.392)
−0.056
(−1.112)
−0.012
(−0.312)
_cons −20.943 ***
(−25.478)
−19.869 ***
(−30.700)
−20.743 ***
(−35.504)
Year Yes Yes Yes
Ind Yes Yes Yes
N 5023 6935 9810
Note: *** indicate significance at the 1% levels.
To explore the impact of different ownership structures on the environmental uncer-
tainty moderating effect of internal controls and total factor productivity, this paper cate-
gorizes firms into state-owned and non-state-owned firms. It is found that the coefficient
of EU×IC is negatively significant at the 1% level for non-state-owned enterprises, thus
indicating that environmental uncertainty has a significant inhibitory effect on the posi-
tive effect of internal controls on total factor productivity in non-state-owned enterprises.
For state-owned enterprises, the moderating effect of environmental uncertainty is not
significant, which may be due to the fact that non-state-owned enterprises do not receive
financial support from the government. Furthermore, non-state-owned enterprises are
also subject to “credit discrimination” by banks and other financial institutions [23], which
leads to increased financing risks and financial constraints. Both factors are further exac-
erbated in the presence of uncertainty, which significantly affects the total factor produc-
tivity of enterprises.
To study the influence of different locations on the moderating effect of environmen-
tal uncertainty on the internal controls and total factor productivity of firms, this paper
distinguishes between the eastern and central-western regions. The results show that en-
vironmental uncertainty significantly suppresses the positive effect of internal controls on
total factor productivity in the eastern region, while the moderating effect is not signifi-
cant for firms in the central and western regions. The reason for this finding may be that
firms in the eastern region are more internationalized and locate in a concentrated indus-
trial area [24], so macroeconomic uncertainty will affect their total factor productivity by
affecting the degree to which they engage in international trade as well as the prices of
production factors.
In this paper, we distinguish three groups of enterprises according to their life span,
which we divide into the growth period, maturity period and decline period, and empir-
ically analyze the impact of different enterprise life cycles on the total factor productivity
of enterprises. The results show that improving the quality of internal controls can signif-
icantly improve the total factor productivity of enterprises in the growth stage group pro-
vided that the influence of other factors is effectively controlled for. The reason for this
Sustainability 2023, 15, 736 14 of 17
finding may be that for enterprises in the growth stage, the advantages of investment and
financing are relatively obvious. Improving the quality of internal controls can effectively
alleviate capital constraints, reduce corporate financing risks, improve corporate invest-
ment efficiency and thus increase total factor productivity.
5.2. Analysis of Mediating Mechanisms
In this paper, firm innovation and financing constraints are selected as mediating
variables to study the mediating effect between internal control and firm total factor
productivity.
5.2.1. Enterprise Innovation Mechanism Test
Enterprises promote innovation and improve their performance by improving the
internal environment and its management, thus improving corporate total factor produc-
tivity. In this paper, we construct a model by referring to the mediating effect of Wen [25].
For the mediating variable of firm innovation, this paper adopts the index
(number of applications) as a specific measure of enterprise innovation by referring to
previous studies. As shown in model (4), the larger the value of this indicator, the greater
the enterprise’s innovation ability is.
Patent1 = Ln (utility model + design patent +invention patent + 1) (4)
To explore the mediating relationship between enterprise innovation on internal con-
trol and total factor productivity of firms, the following models (5) and (6) are constructed
in this paper.
Patent1 = α0 + α1IC + α2Det + α3Age + α4Lnd_r + α5Size + α6Growth + α7Board + ∑Year + ∑Ind + ε (5)
TFP_LP = α0 + α1IC + α2patent1+ α3Det + α4Age + α5Lnd_r + α6Size + α7Growth + α8Board + ∑Year + ∑Ind + ε (6)
The results are shown in Table 11. Column (1) describes the regression results of
model (5), and the value of α1 is 0.093 and is significant at the 1% level, indicating that the
improvement of corporate internal control can effectively promote corporate innovation;
column (2) describes the regression results of model (6), and the values of α1 and α2 are
both significantly positive at the 1% level, verifying that internal control can improve the
residual efficiency of corporate production by promoting corporate innovation. The result
passes the test of mediating effect, validating H2.
Table 11. Tests for the mediating effect.
Explanatory
variables
(1) (2) (3) (4)
patent1 TFP_LP KZ TFP_LP
IC 0.093 ***
(12.609)
0.195 ***
(19.827)
−0.120 ***
(−14.358)
0.264 ***
(22.838)
Size 0.543 ***
(52.915)
1.198 ***
(103.23)
−0.385 ***
(−38.553)
1.167 ***
(101.304)
Det −0.363 ***
(−6.752)
−2.397 ***
(−37.744)
5.789 ***
(98.139)
−1.320 ***
(−17.006)
Age −0.005 ***
(−2.610)
0.018 ***
(8.349)
0.014 ***
(7.202)
0.019 ***
(8.778)
Growth −0.025
(−1.303)
0.476 ***
(19.675)
−0.595 ***
(−15.502)
0.330 ***
(12.409)
Board 0.085
(1.273)
−0.203 ***
(−2.793)
−0.075
(−1.261)
−0.235 ***
(−3.276)
Lnd_r 0.134 −1.283 *** 1.090 *** −1.272 ***
1patent
Sustainability 2023, 15, 736 15 of 17
(0.638) (−5.755) (5.883) (−5.819)
Duality 0.104 ***
(4.931)
0.011
(0.473)
−0.104 ***
(−4.906)
−0.03
(−1.345)
patent1 0.048 ***
(6.095)
KZ
−0.249 ***
(−30.664)
_cons −11.802 ***
(−46.299)
−19.556 ***
(−69.080)
7.213 ***
(30.384)
−19.533 ***
(−71.048)
Year Yes Yes Yes Yes
Ind Yes Yes Yes Yes
N 21,808 21,808 20,784 20,784
Note: *** indicate significance at the 1% levels.
5.2.2. Financing Constraint Mechanism Test
To verify whether firms reduce their financing risk by strengthening their internal
controls to enhance total factor productivity, Models (7) and (8) are constructed by select-
ing financing constraints as mediating variables.
KZ = α0 + α1IC + α2Det + α3Age + α4Lnd_r + α5Size + α6Growth + α7Board + ∑Year + ∑Ind + ε (7)
TFP_LP = α0 + α1IC + α2KZ+ α3Det + α4Age + α5Lnd_r + α6Size + α7Growth + α8Board + ∑Year + ∑Ind + ε (8)
In Model (7), the KZ index represents financing constraints, which are calculated us-
ing the method of Kaplan and Zingales (1997) and set as a mediating variable which is
shown as model (9). OCF, Dividends and Cash represent net cash flow from operations,
dividends and cash holding levels, respectively, and are normalized. Lev and Tobin’sQ
represent the asset-liability ratio and Tobin’sQ, respectively. The higher the value, the
higher the degree of financing constraints faced by the firm.
KZ = -1.001909×OCF/Asset + 3.139193×Lev - 39.3678×Dividends/Asset - 1.314759×Cash/Asset + 0.2826389×Tobin’sQ (9)
The results show that Column (3) in Table 11 describes the regression results of model
(7), and the value of α1 in column (3) is significantly negative at the 1% level, which indi-
cates that the strengthening of internal controls can effectively reduce financing con-
straints. Column (4) describes the regression results of model (8), the value of α1 is signif-
icantly positive and the value of α2 is significantly negative, which verifies that internal
control can provide financial support for business operations by alleviating the financing
constraint of the business and ultimately increase the return from total output other than
factor inputs. All variables pass the mediating effect test, validating H2.
6. Research Conclusions and Implications
6.1. Research Conclusions
How to effectively improve the total factor productivity of enterprises has been the
focus of academic discussion in recent years. This paper takes China’s A-share listed com-
panies from 2009 to 2019 as experimental samples and is based on the perspective of the
uncertainty of the enterprise external environment to deeply analyze the influence of in-
ternal control on the total factor productivity of enterprises and the regulating mechanism
and further test the mediating effect of enterprise innovation and financing constraints.
The empirical study on the relationship between enterprise internal control and TFP
and its mechanism of action shows the following conclusions: (1) High-quality internal
control is beneficial to the improvement of enterprise total factor productivity, and the
influence is more significant in growing enterprises. (2) Environmental uncertainty plays
a negative role in moderation, that is, environmental uncertainty significantly inhibits the
Sustainability 2023, 15, 736 16 of 17
promoting effect of internal control on total factor productivity. At the same time, the
moderating effect of environmental uncertainty is different, which is more obvious in non-
state-owned enterprises and enterprises in the eastern region. (3) Internal control can ef-
fectively stimulate enterprise innovation, alleviate financing constraints, and thus im-
prove the total factor productivity of enterprises.
The research conclusions of this paper deepen the understanding of the influence of
internal control on the total factor productivity of enterprises and enrich the theoretical
achievements of the influencing factors of the total factor productivity of enterprises. The
test from the angle of the moderating effects of environmental uncertainty also provides
a new perspective for the study of internal control and total factor productivity of enter-
prises. In the practical operation level, this paper provides certain enlightenment for en-
terprises on how to optimize the internal control system, achieve high efficiency operation
and enhance enterprise value.
6.2. Implications
Firstly, we must pay attention to internal control systems, establish a sound enter-
prise governance structure and improve the effectiveness of internal control operation and
the efficiency of organization management to promote the growth of enterprise total fac-
tor productivity. We can adopt the principle of combining qualitative and quantitative
methods, identify and locate internal control defects through the supervision mechanism
in time and take targeted measures to solve them, so as to avoid causing major financial
defects and thus affecting the company’s business performance.
Secondly, fully consider the negative impact of environmental uncertainties on en-
terprises, establish dynamic and effective adjustment and communication mechanisms,
reserve appropriate financial flexibility and improve enterprises’ ability to withstand
risks. Realize real-time supervision and regulation of enterprise investment activities, im-
prove the rationality of capital use, avoid information asymmetry and blind or excessive
investment behavior, and realize innovation investment and operation efficiency. We will
effectively reduce financing costs, ease financing constraints for enterprises, and address
various problems caused by environmental uncertainties in a timely manner, with partic-
ular emphasis on enterprises and non-state-owned enterprises in the eastern region.
Thirdly, we should strengthen innovation and research and development, encourage
enterprises to make continuous innovation at multiple levels of technology and system
and reduce substitutability within the market scope, so as to promote the growth of en-
terprise value. In the process of enhancing enterprise value, the principal–agent problem
in the enterprise should be fully considered, and the conflicts between investors and man-
agers should be balanced by means of executive compensation incentive mechanism and
investor supervision and management mechanism, so as to achieve a good investment
environment within the enterprise.
In addition, this study also has certain limitations: (1) Only some representative var-
iables are selected as control variables for the study, which may ignore some factors that
also affect the explained variables and fail to completely solve the endogeneity problem;
(2) China’s A-share listed companies from 2009 to 2019 were selected for the study, and
the sample data of other countries with different economic status were not taken into ac-
count, so the research results are limited.
Therefore, in the follow-up study, to make the model more reliable, control variables
can be expanded, and the role of different variables between internal control and total
factor productivity of enterprises can be further explored. In addition to the environmen-
tal uncertainty as a moderating variable studied in this paper, the effects of other moder-
ating variables on the two can be further studied, or the mediating path between the two
can be further explored. The sample range can also be expanded to improve the accuracy
of the conclusions and supplement the differences.
Sustainability 2023, 15, 736 17 of 17
Author Contributions: Conceptualization, K.W.; investigation, M.D.; writing—original draft prep-
aration, L.L.; writing—review and editing, Y.F. All authors have read and agreed to the published
version of the manuscript.
Funding: This research was funded by [The Open Fund Project of the Research Center for the
Coordinated Development of Enterprises and Environment "Research on Hubei Business Environ-
ment Assessment Index System"] grant number [2019QHY004]
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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