Digital Transformation, Absorptive Capacity and Enterprise ESG Performance: A Case Study of Strategic Emerging Industries PDF Free Download
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Citation: Li, W.; Zhang, M. Digital
Transformation, Absorptive Capacity
and Enterprise ESG Performance: A
Case Study of Strategic Emerging
Industries. Sustainability 2024,16, 5018.
https://doi.org/10.3390/su16125018
Received: 10 May 2024
Revised: 2 June 2024
Accepted: 5 June 2024
Published: 12 June 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Digital Transformation, Absorptive Capacity and Enterprise ESG
Performance: A Case Study of Strategic Emerging Industries
Wenjun Li 1,2 and Mu Zhang 2,*
1School of Applied Economics, Guizhou University of Finance and Economics, Guiyang 550025, China;
lwj7164944@163.com
2Guizhou Institution for Technology Innovation & Entrepreneurship Investment, Guizhou University of
Finance and Economics, Guiyang 550025, China
*Correspondence: zhangmu01@163.com; Tel.: +86-15285565731
Abstract: Digital transformation provides new drivers for economic performance growth in enter-
prises, but can it further improve ESG performance and support sustainable development? Based on
the perspective of resources and capabilities, this article uses the relevant data of 1588 listed compa-
nies in strategic emerging industries from 2011 to 2021 to study the impact of digital transformation
on enterprise ESG performance examines the intermediary role of absorptive capacity and the moder-
ating role of regional digitalization level, and further analyzes the heterogeneity of property rights
and industrial nature. The research results indicate that: firstly, digital transformation of enterprises
can positively promote the improvement of ESG performance; secondly, absorptive capacity serves
as a conduit through which digital transformation influences a company’s ESG performance; thirdly,
the regional digitalization level positively moderates the promotion effect of digital transformation
on enterprise ESG performance; fourthly, the impact of digital transformation on ESG performance of
enterprises is significantly differentiated in the nature of enterprise property rights and industrial
nature, and the ESG performance of state-owned enterprises and high-end equipment manufacturing
enterprises is more sensitive to digital transformation. The research conclusion is based on a digital
perspective, providing relevant insights for improving the ESG performance of strategic emerging
industry enterprises and expanding their ESG development paths.
Keywords: digital transformation; absorption capacity; enterprise ESG performance; strategic
emerging industries; regional digitalization level
1. Introduction
Strategic emerging industries are industries that have an essential function in the long-
term and holistic economic growth of the economy and society based on breakthroughs in
cutting-edge technologies and major development needs. Moreover, strategic emerging
industries, which are a deep integration of emerging technologies and industries, are
important spaces for fostering the development of new technologies, goods, and driving
forces as well as securing future competitive advantages in the global market [
1
]. The term
“Enterprise Environmental, Social, and Governance Performance” (ESG) describes how well
a corporation performs in these domains. The concept of ESG encourages organizations
to prioritize sustainable development by emphasizing environmental friendliness, social
responsibility, and corporate governance in their operations and development [
2
]. Strong
ESG performance can boost market value and corporate performance [
3
], give companies
a competitive edge in the marketplace, and assist companies in achieving sustainable
development [4].
Digital transformation refers to the change in the way businesses create value for
their customers by utilizing modern technology and communication methods [
5
]. Digital
transformation is defined by the T/AIITRE 10001-2020 standard [
6
] as the process of
Sustainability 2024,16, 5018. https://doi.org/10.3390/su16125018 https://www.mdpi.com/journal/sustainability
Sustainability 2024,16, 5018 2 of 18
adjusting to new industrial and technological developments and gradually incorporating IT
technologies such as blockchain and AI. This procedure is intended to unleash the creative
power of data, strengthen survival and advancement in the digital age, and accelerate
corporate improvement, transformation, and refinement. To facilitate the creation, transfer,
and acquisition of new value and to achieve both evolutionary and inventive advancement,
it also seeks to restructure conventional processes and foster innovative growth drivers [
6
].
Its essence is the transformational behavior of improving enterprise resource allocation
and reducing the impact of external uncertainty on enterprises through the efficient flow of
data [
7
–
9
], which will inevitably affect business operations [
10
,
11
]. Research shows that
digital transformation can maximize resource efficiency and promote the improvement
of corporate economic benefits [
12
–
14
]; meanwhile, digital transformation can empower
high-quality corporate development and enhance corporate market competitiveness [
15
,
16
].
Can businesses in strategic emerging industries (henceforth referred to as SEIEs) make the
most of their unique advantages in the current industrial digitalization wave to enhance
their ESG performance through digital transformation, acquire a sustained competitive
edge, and eventually achieve sustainable growth and development?
Current studies have largely centered on the economic effects of digital transforma-
tion [
9
,
17
], with few studies exploring the non-economic effects brought about by digital
transformation, such as the enhancement of enterprise ESG performance [
18
]. As societal
attention to corporate ESG performance gradually increases, some scholars have begun to in-
vestigate how corporate ESG performance and digital transformation are related. Research
indicates that digital transformation can enhance enterprise ESG performance
[19–22]
, with
mechanisms including promoting corporate green technology innovation [
23
], putting
more pressure on external legitimacy [
24
], and reducing information asymmetry [
24
]; mod-
erating effects include government subsidies and CEO professional experience [
18
]; hetero-
geneity includes property rights heterogeneity [
18
,
23
], heterogeneity of the technological
level [
18
,
23
,
25
], lifecycle heterogeneity [
23
], market competition intensity heterogeneity [
24
],
etc. In summary, the majority of the literature currently in publication examines how digital
transformation affects corporate ESG performance from the viewpoints of information
asymmetry and innovation capability within corporate dynamic capabilities [
26
]. Relatively
little research has included the absorption capability within corporate dynamic capabilities
into the research, and few studies have been conducted on the contextual mechanisms
through which digital transformation affects corporate ESG performance [27].
According to the dynamic capacities theory, businesses must constantly innovate,
adapt, and learn to deal with changes in the outside marketplace [
28
]. Dynamic skills are
essential to ensuring that businesses react to complicated and changing external contexts
in the unpredictable and complex world of digital transformation. WANG and AHMED
believe that dynamic capabilities include absorptive capacity, adaptive capacity, and in-
novative capacity [
29
]. Existing research has revealed the mediating role of innovative
capacity within dynamic capabilities between digital transformation and corporate ESG
performance [
26
]. However, there is still a lack of exploration into other sub-dimensions of
dynamic capabilities. Among them, absorptive capacity allows enterprises to quickly iden-
tify and seize opportunities; scan, create, learn, share, and interpret resources in the external
environment; and attempt to disperse organizational boundaries, absorb and integrate
external knowledge and resources, and ultimately apply them to business practice [
30
,
31
].
Research has found that digital transformation can promote real-time and continuous ex-
change of data and information between enterprises and customers, suppliers, and within
enterprises, expanding the scope of enterprise knowledge and improving the efficiency of
knowledge creation, sharing, and utilization [
32
]. It is conducive to enterprises identifying
business opportunities from a wide range of knowledge and perceiving changes in market
demand, promoting the improvement of enterprise absorptive capacity [
33
], and thus
helping enterprises better cope with the challenges brought about by social change, enhanc-
ing corporate performance in social responsibility, and obtaining long-term competitive
advantage [
34
]. Therefore, it is valuable to investigate how absorptive capacity mediates
Sustainability 2024,16, 5018 3 of 18
the relationship between enterprise ESG performance and the digital transition to provide
a thorough understanding of the company’s digitization process.
Secondly, drawing upon the resource dependence theory, the resources upon which an
organization relies are distributed within its environment, and there is an interdependence
between the organization and its environment [
35
]. The capacity of a firm to produce
digital technology is largely contingent upon the local digital infrastructure and level of
intelligence [
36
]. An enhanced level of regional digitization can provide firms with more
comprehensive infrastructure and communication platforms, facilitating the smooth im-
plementation of digital transformation [
37
]. Therefore, the level of regional digitization
provides strong support for a firm’s digital transformation and is a key contextual mecha-
nism by which the digital transformation of a firm influences its ESG performance. In light
of this, this article discusses the scenario mechanism of digital transformation affecting
the enterprise ESG performance from the perspective of the regional digitalization level,
which has practical significance for promoting the enterprise digitization of enterprises and
improving their ESG performance.
In summary, the questions to be explored in this article are:
1
Can digital transforma-
tion promote the improvement of corporate ESG performance, and is a differential impact?
2
What is the mechanism of digital transformation affecting corporate ESG performance?
3
What is the situational mechanism of digital transformation affecting corporate ESG per-
formance? To address the above issues, this article uses a sample of 1588 SEIEs in A-share to
conduct empirical research on the above issues using text analysis and panel two-way fixed
effect models. The potential contributions of this article are as follows: 1. By introducing
absorptive capacity, it broadens the research on the mechanisms through which digital
transformation affects corporate ESG performance. 2. It introduces the level of regional
digitization, deepening the contextual mechanisms by which digital transformation affects
corporate ESG performance. 3. It conducts a heterogeneity analysis of strategic emerging
industry sub-sectors, further enriching the research on the heterogeneity of the impact
of digital transformation on corporate ESG performance. The remaining structure of this
article is arranged as follows: Section 2proposes the research hypotheses and provides
theoretical analysis; Section 3introduces the research design of this article; Section 4is the
empirical study section of this article; Section 5is the discussion; Section 6is the conclusion.
2. Theoretical Analysis and Research Hypotheses
2.1. The Impact of Digital Transformation on ESG Performance of SEIEs
Firstly, modern production and lifestyle practices have been significantly impacted
by the digitization of the real economy. The new era’s core principles are balanced growth
and environmentally friendly, sustainable development. In addition to improving corpo-
rate economic performance, corporate digital transformation adds value to non-economic
performance areas like the environment, society, governance, and corporate culture [
38
].
Particularly against the backdrop of the digital economy, various stakeholders have higher
expectations for corporate social responsibility fulfillment capabilities and performance,
which, in turn, forces companies to innovate. Digital technology innovation can also im-
prove corporate social responsibility fulfillment capabilities and performance [
39
]. After
undergoing digital transformation, the support of digital technology can help enterprises
more efficiently refine and enhance their green image, customer reputation, and product
quality, thereby bringing growth in orders and profits for the enterprises [
40
]. Meanwhile,
digital transformation can also bring about changes in organizational structure and in-
ternal management [
41
], reducing the expected costs for enterprises to undertake green
transformations and activities, and even altering their profit models. While reducing
corporate costs, it can also create more employment opportunities for society, thus promot-
ing sustainable economic growth [
42
]. Therefore, this article proposes the first research
hypothesis H1:
H1: Digital transformation can positively enhance corporate ESG performance.
Sustainability 2024,16, 5018 4 of 18
2.2. The Impact Channels of Digital Transformation on ESG Performance of SEIEs
In the current environment of rapid development of the digital economy, SEIEs need to
facilitate a comprehensive incorporation of cutting-edge digital tech with their operations to
accelerate their digitalization process. Therefore, how to use digital advances to ameliorate
the quality of internal operations and innovation capability has become the key to enter-
prises carrying out digital transformation. Using digital technology enables companies to
efficiently reduce the threshold for obtaining innovative resources [
43
], providing them
with more knowledge and resources, thereby enhancing the enterprise’s ability to identify
and absorb knowledge and opportunities, that is, absorptive capacity, ultimately helping
enterprises create their value and achieve better development [
44
]. Meanwhile, industry
practices and empirical research indicate that corporate R&D innovation is a necessary
technical prerequisite for enterprises to implement production transformation [
45
], and the
enhancement of absorptive capacity can reduce the cost of corporate digital transforma-
tion and exert a stable positive impact on corporate ESG responsibility performance [
46
].
Therefore, this article proposes the second research hypothesis H2:
H2: Digital transformation promotes the improvement of ESG performance by enhancing the
absorption capacity of enterprises.
2.3. The Impact Mechanism of Digital Transformation on ESG Performance of SEIEs
2.3.1. The Regulatory Role of Regional Digitalization Level
Enterprises are embedded in regional environments, and regional digitalization repre-
sents a new external environment. This implies that the effectiveness of enterprise digital
transformation will be constrained by the level of regional digitalization [
47
]. On the one
hand, the better the level of regional digitalization is, the more sound the regional digital
infrastructure will be, laying a better foundation for enterprise digitalization. This accel-
erates the dissemination and sharing of information and knowledge among enterprises,
facilitates interconnectivity between enterprises, reduces the cost of obtaining information,
and is conducive to enterprise digital transformation [
48
]. On the other hand, as regional
digitalization accelerates, the region’s digital technology becomes relatively advanced,
and with strong government support for digital transformation, enterprises are better
able to efficiently utilize the new knowledge they have absorbed and convert it into new
knowledge and capabilities, thus better conducting ESG practices [
49
]. Given this, this
article proposes the following sub-hypothesis H3a within the third research hypothesis:
H3a: The level of regional digitalization plays a positive moderating role between digital transfor-
mation and corporate ESG performance.
2.3.2. The Heterogeneous Impact of Digital Transformation on ESG Performance of SEIEs
Digital transformation can impact corporate ESG performance through channels such
as enhancing corporate information transparency. Existing literature indicates that this
impact exhibits a certain degree of heterogeneity, varying across different industries [
50
],
regions [
51
], and organizational sizes [
52
]. This article further analyzes whether there
is a heterogeneous effect of digital transformation on corporate ESG performance under
different ownership properties and industrial properties.
1. Ownership Characteristics
State-owned enterprises (SOEs) occupy a significant position in China’s economic
development and play a crucial role in supporting national sustainable development
strategy goals. Therefore, SOEs have an external motivation to further enhance their ESG
performance to comply with ESG-related policies issued by the government and regulatory
agencies [
53
]. Moreover, compared to private enterprises, SOEs possess more abundant
financial resources and more stable human and material resources, which facilitate digital
transformation and ensure corporate ESG practices [
54
,
55
]. Through digital transformation,
Sustainability 2024,16, 5018 5 of 18
SOEs can improve operational efficiency, reduce resource consumption, better manage
supply chains, and enhance the monitoring and control of environmental impacts, thereby
achieving higher levels of corporate social responsibility. Therefore, this article proposes
the subdivision hypothesis H3b of the third research hypothesis:
H3b: There are property rights differences in the driving effect of digital transformation on
enterprise ESG performance.
2. Industrial Nature
High-end equipment manufacturing, also known as advanced manufacturing, refers
to industries that produce high-tech, high-value-added advanced industrial facilities and
equipment [
56
]. This industry is characterized by high product value, high technology
intensity, and strong growth potential, offering significant competitive advantages and
development prospects. Under the guidance of relevant policies, high-end equipment
manufacturing has become a key sector for achieving the “dual carbon” goals. Compared
to other industries, high-end equipment manufacturing is more willing to respond to
national calls and actively fulfill corporate social responsibility [57].
Additionally, due to the special nature of their products and technologies, high-
end equipment manufacturing enterprises typically possess high technical research and
development capabilities and a strong sense of innovation. They play an active role in
energy conservation, and green manufacturing. These companies are more inclined to
acknowledge the significance of environmental conservation and social accountability for
their enduring growth. Consequently, they exhibit a greater propensity to allocate resources
and exert efforts towards a range of initiatives aimed at mitigating environmental effects
and elevating the extent to which they fulfill their social duties [
58
]. Therefore, this article
proposes the subdivision hypothesis H3c in the third research hypothesis:
H3c: The promotion of corporate ESG performance by digital transformation exhibits heterogeneity
across sub-industries in strategic emerging industries.
3. Research Design
3.1. Model Setting
To examine the influence of digital transformation on ESG performance and the impact
mechanism, this article sets a benchmark regression model as shown in Equation (1):
ESGit =β0+β1DTit +∑
j
βjControls +λi+µt+εit (1)
where ESG is the explained variable—enterprise ESG performance, DT is the explana-
tory variable—enterprise digital transformation, Controls is the control variable,
λi
is the
individual fixed effect, µtis the time fixed effect, and εit is the random error term.
Most of the existing literature has used the step-by-step method proposed by Baron
and Kenny (1986) to test the mediating effect [
59
]. However, Jiang (2022) pointed out that
the main problem of the current mediating effect analysis is the abuse of the stepwise
test of mediating effect grafted from psychology, which leads to errors in the mediating
effect test [
60
]. Meanwhile, we observed that most of the literature has used the same
control variables as the benchmark regression in the mediating effect test process, resulting
in logical defects in the mediating effect test. Thus, we refer to the suggestion proposed
by Jiang (2022) [
60
], set different control variables for different mediating variables, and
constructed the mediating effect test model as shown in Equation (2):
ACit =β0+β1DTit +∑
j
βjControls +λi+µt+εit (2)
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Similarly, the moderating mechanism test method proposed by Jiang Ting (2022) [
60
]
was adopted to test the moderating effect of regional digitalization level between digi-
tal transformation and enterprise ESG performance. The regression model is shown in
Equation (3):
ESGit =β0+β1DTit +β2RDLit +β3DTit ×RDLit +∑
j
βjControls +λi+µt+εit (3)
Among them, the mediating variable is the absorptive capacity (AC), and the moderat-
ing variable is the regional digitalization level (RDL).
3.2. Variable Setting
3.2.1. Explained Variable: ESG Performance (ESG)
In this article, ESG score data from the selected companies spanning the years
2011–2021
were utilized for the analysis. The ESG scores were categorized into nine tiers, ranging
from C to AAA, with each tier assigned a score from 1 to 9 and assessed quarterly. The
mean score from the four quarterly evaluations was taken as the dependent variable, with
higher scores indicating superior ESG performance for the respective companies.
3.2.2. Explanatory Variable: Digital Transformation (DT)
In this article, referring to the method of Wu Fei et al. (2021), the ratio of word fre-
quency of six categories of digital transformation, AI, big data, cloud computing, blockchain,
and digital application to the total number of words in the annual report [
61
] were adopted
as the original data of digital transformation. Additionally, this article refers to the method
of Lu Ming and Chen Zhao (2004), adding 0.00000001 to the word frequency ratio and then
taking the natural logarithm to serve as the explanatory variable. Among these, the word
frequency ratio of DT was used as the core explanatory variable [62].
3.2.3. Mechanism Variable: Absorptive Capacity (AC)
Inspired by scholar Xiao Jing et al. (2023), this article introduces absorptive capacity as
a mechanism variable between digital transformation and enterprise ESG performance [
34
].
3.2.4. Moderating Variable: Regional Digitization Level (RDL)
Based on the practice of Xiao Jing et al. (2023), this article introduces the regional digi-
talization level as the moderating variable between digital transformation and enterprise
ESG performance [
34
]. Among them, the regional digitization level was measured by the
comprehensive index of regional digitization level, which was calculated by weighting
five indicators, including the digital output, fixed rate of telephone penetration, mobile
telephone penetration rate, internet broadband penetration rate, and number of web pages
per capita.
3.2.5. Control Variable
To guarantee the consistency of the research results, we selected enterprise size (Size),
age (Age), operating income growth rate (Growth), asset-liability ratio (Lev), cash flow
ratio (Cash Flow), profitability (ROA), ownership concentration index (1%) (TOP1), board
size (Board), independent director ratio (Indep), dual (Dual), executive shareholding ratio
(M Share), and executive team size (TMT Size) as control variables. The main variable
definitions in this article are shown in Table 1.
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Table 1. Variable definition.
Variable Type Name Symbol Definition
Explained Variable ESG Performance ESG Sino-Securities ESG rating data
Explanatory
Variable
Digital Transformation DT
The proportion of digital transformation word frequency in
the total word number of the annual report
Artificial Intelligence
Technology AI The proportion of word frequency of artificial intelligence
technology in total word number of annual report
Big Data Technology BD
The proportion of word frequency of big data technology in
total word number of annual report
Cloud Computing
Technology CC The proportion of cloud computing technology word
frequency in total annual report word number
Blockchain Technology BC
The proportion of blockchain technology word frequency in
the total number of annual reports
Digital Technology
Application ADT The proportion of word frequency of digital technology
application in total word number of annual report
Mechanism
Variable Absorptive capacity AC Annual R&D expenditure/operating income
Regulating
Variable
Regional Digitization
Level RDL
Using the entropy weight method, the digital output, fixed
telephone penetration rate, mobile telephone penetration
rate, Internet broadband penetration rate, and number of
web pages per capita were weighted to calculate the
comprehensive index of regional digitalization level
Control Variable
Enterprise Scale Size The natural log of total assets
Enterprise Age Age Ln (Year—year of listing + 1)
Revenue Growth Rate Growth (Revenue growth/total revenue of last year) ×100%
Asset-liability Ratio Lev Total liabilities/total assets ×100%
Cash Flow Ratio Cash Flow Net cash flow from operating activities/ending current
liabilities
Profitability ROA (Net profit/average total assets) ×100%
Ownership Concentration
Index 1(%) TOP1 The proportion of the largest shareholder
Board Size Board Ln (Number of Directors)
Proportion of Independent
Directors Indep
(Number of independent directors/Number of directors)
×
100%
Dual Function Dual The combination of chairman and general manager is 1,
otherwise, it is 0
Executive Ownership
Ratio M Share Number of shares held by executives/total shares
Executive Team Size TMT Size Ln (Number of executives)
3.3. Sample and Data Source
For the research sample, this article uses pertinent data from 1588 representative
A-share companies that are part of the China Strategic Emerging Industries Comprehensive
Index (000891) for the years 2011–2021. The following is how the data were handled:
Initially, ST samples and delisted samples were eliminated; thereafter, all continuous
variables were winsorized at the top and bottom 1% to lessen the extreme values’ impact on
the results. With STATA 17.0, data processing and regression analysis were carried out. In
the end, 11,682 valid data entries encompassing nine industries, including new-generation
information technology, were acquired from the 1588 strategic emerging industry-listed
enterprises. The CSMAR database provided the financial information utilized in this
article, the data related to the business digital transition from the Mark Data Network,
the enterprise ESG rating data from the WIND database, and other original data from the
annual reports of listed companies, the National Bureau of Statistics of China’s official
website, and the CSMAR database.
Sustainability 2024,16, 5018 8 of 18
4. Empirical Results and Analysis
4.1. Descriptive Statistics
This article utilizes Stata 17 software to conduct descriptive statistics on variables. The
results, as shown in Table 2, indicate that ESG had a mean of 4.03, with a maximum of
7.75 and a minimum of 0.5, suggesting significant variations in ESG performance among
different companies, with an overall tendency towards the lower end. The core explanatory
variable had a mean of
−
12.3 after natural logarithm transformation, indicating that a
considerable portion of companies have yet to engage in digital transformation, with a
maximum of
−
6.03966 and a minimum of
−
18.42068, suggesting a generally low level of
digital transformation across companies. Additionally, descriptive statistics for control
variables show a high level of consistency with relevant literature [23].
Table 2. Descriptive statistics for variables.
Variable Obs Mean Std. Dev. Min Max
ESG 11,682 4.0316 1.0973 0.5000 7.7500
DT 11,682 −12.3013 4.0074 −18.4207 −6.0397
AC 11,682 0.0689 0.0630 0.0000 0.3643
RDL 11,682 0.3687 0.1397 0.0933 0.6528
Size 11,682 21.9823 1.1605 19.8575 25.6465
Age 11,682 2.8346 0.3333 1.7918 3.4657
Growth 11,682 0.2102 0.3871 −0.4782 2.3539
Lev 11,682 0.3689 0.1888 0.0441 0.8157
CashFlow 11,682 0.0449 0.0647 −0.1362 0.2384
Roa 11,682 0.0513 0.0673 −0.2298 0.2497
Top1 11,682 0.3138 0.1373 0.0794 0.6873
Board 11,682 2.1064 0.1887 1.6094 2.5649
Indep 11,682 37.8100 5.3249 33.3300 57.1400
Dual 11,682 0.3355 0.4722 0.0000 1.0000
MShare 11,682 0.1791 0.2084 0.0000 0.7049
TMTSize 11,682 2.8115 0.1944 2.3979 3.3322
4.2. Benchmark Regression
This article utilizes Stata 17 software and employs the OLS method for coefficient
estimation of Equation (1) (baseline regression), looking into how the digital transition
affects corporate ESG performance. Table 3presents the baseline regression results of this
article, with columns (1) and (2) indicating the baseline regression and two-way fixed
effects regression results after only incorporating the primary variable used to explain
digital transformation. Columns (3) and (4) represent the baseline regression and two-
way fixed effects regression results after incorporating control variables. The empirical
results, above all, indicate, at a 10% confidence level, a positive and significant function of
digital transformation in enhancing corporate ESG performance. This validates the research
hypothesis H1.
Additionally, this study further refines the explanatory variables into five sub-indicators
such as artificial intelligence technology. These five sub-indicators were introduced into
the regression analysis, which is presented in Table 4, indicating that among the five sub-
indicators, the effects of big data technology and cloud computing technology on enhancing
corporate ESG performance were more pronounced.
Sustainability 2024,16, 5018 9 of 18
Table 3. Benchmark regression results (core explanatory variables).
Variable (1) (2) (3) (4)
ESG ESG ESG ESG
DT 0.0201 *** 0.0160 *** 0.0180 *** 0.0133 ***
(7.9729) (3.8532) (7.2885) (3.3056)
Size 0.2644 *** 0.3053 ***
(24.7852) (9.3811)
Age 0.0296 −0.2475
(0.9623) (−1.4990)
Growth −0.1073 *** 0.0287
(−4.0747) (1.1640)
Lev −0.3582 *** −0.2765 **
(−5.5434) (−2.2707)
CashFlow 0.4510 *** 0.2048
(2.7045) (1.1489)
Roa 1.2754 *** −0.5636 ***
(7.1594) (−2.8428)
Top1 0.2707 *** 0.7857 ***
(3.7502) (3.3929)
Board 0.0107 −0.3782 **
(0.1379) (−2.5029)
Indep 0.0127 *** 0.0047
(5.5940) (1.2369)
Dual −0.0930 *** −0.0587
(−4.3241) (−1.5552)
MShare 0.3244 *** 0.7547 ***
(6.1248) (5.1429)
TMTSize 0.3095 *** 0.2939 **
(4.5873) (2.2992)
Constant 4.2794 *** 4.0319 *** −3.0586 *** −2.3772 ***
(130.9129) (54.0189) (−10.7682) (−3.0019)
Observations 11,682 11,682 11,682 11,682
R-squared 0.0054 0.0284 0.0966 0.0610
code fe no yes no yes
year fe no yes no yes
Number of id 1588 1588 1588 1588
Standard errors are in parenthesis. *** p< 0.01, ** p< 0.05.
Table 4. Benchmark regression results (subdivided explanatory variable).
Variable (1) (2) (3) (4) (5)
ESG ESG ESG ESG ESG
AI 0.0032
(0.0044)
BD 0.0099 *
(0.0040)
CC 0.0150 ***
(0.0044)
BC 0.0081
(0.0074)
ADT 0.0059
(0.0040)
_cons −2.5282 ** −2.3658 ** −2.2894 ** −2.4490 ** −2.5115 **
(0.8020) (0.7976) (0.7943) (0.8032) (0.7934)
N11682 11682 11682 11682 11682
r20.0597 0.0604 0.0612 0.0598 0.0599
F 23.2358 23.3905 23.3942 23.2236 23.3029
p0.0000 0.0000 0.0000 0.0000 0.0000
Standard errors are in parenthesis. *** p< 0.01, ** p< 0.05, * p< 0.1.
Sustainability 2024,16, 5018 10 of 18
4.3. Endogeneity Problem and Robustness Test
4.3.1. Endogeneity Problem
To tackle endogeneity concerns, this research utilizes PSM alongside multi-period
DID methods. Initially, companies were categorized into two cohorts: those that had
undergone digital transformation and those that had not. For PSM, control variables served
as matching criteria. Various matching techniques, including nearest neighbor, radius,
and kernel methods, were applied to identify suitable control group counterparts for the
treatment group. Post balance checks, regression analysis was conducted on the matched
samples, with findings detailed in columns (1) to (3) of Table 5. The regression results
indicate a significant positive effect of digital transformation on corporate ESG performance,
supporting the robustness and credibility of the core conclusion of this article.
Table 5. Endogeneity test results.
Variable PSM DID
(1) (2) (3) (4)
ESG ESG ESG ESG
DT 0.0138 ** 0.0133 ** 0.0138 **
(0.0043) (0.0040) (0.0043)
DID 0.0742 **
(2.5400)
controls YES YES YES YES
code fe YES YES YES YES
year fe YES YES YES YES
_cons −2.1427 * −2.3757 ** −2.1427 * −2.6264 ***
(0.8353) (0.7924) (0.8353) (−3.3422)
N9276 11,672 9276 11,682
r20.0609 0.0611 0.0609 0.0604
Standard errors are in parenthesis. *** p< 0.01, ** p< 0.05, * p< 0.1.
Furthermore, the study treats the temporal progression of digital transformation within
the sample as a quasi-experimental setup and applies the multi-period DID approach for
analysis. The pertinent regression models are delineated by Equations (4) and (5):
ESGit =β0+β1duit +dtit +∑
j
βjControls +λi+µt+εit (4)
ESGit =β0+β1duit +dtit ×DTit +∑
j
βjControls +λi+µt+εit (5)
where
du
is the virtual variable of the processing group,
du
1 indicates that the enterprise
has carried out digital transformation during the sample period, and
du
= 0 indicates that
the enterprise did not do it. dt is a time dummy variable,
dt
= 1 when the processing group
enterprises implement digital transformation, and
dt
= 0 when the control group enterprises
and the processing group enterprises do not implement digital transformation. The empiri-
cal test results of multi-stage DID are shown in column (4) of Table 5. The key parameters
to be estimated in the model are significantly positive, that is, the core conclusions of this
article are still robust and credible after multi-phase DID model identification.
4.3.2. Robustness Test
In this article, in the robustness test, four methods were adopted, such as the hysteresis
of the explained variable and the elimination of zero-value samples: Column (1) indicates
that the explained variable was introduced into the model for regression with a lag of
two periods as explanatory variable, and the model was changed to a dynamic model;
column (2) indicates that the explained variable was still used as explanatory variable for
regression with a lag of one period; column (3) represents truncation of the core explanatory
variable; column (4) indicates that zero samples of DT were eliminated. The results of the
Sustainability 2024,16, 5018 11 of 18
above four regressions are shown in Table 6. All four regressions passed the significance
test, indicating that the above results that prove that digital transformation can positively
promote enterprise ESG performance are robust and credible.
Table 6. Robustness test results.
Variable (1) (2) (3) (4)
ESG L.ESG ESG ESG
L2.ESG 0.0532 ***
(0.0129)
DT 0.0123 ** 0.0144 *** 0.0474 **
(0.0039) (0.0042) (0.0171)
DT_w 0.0133 ***
(0.0040)
controls YES YES YES YES
code fe YES YES YES YES
year fe YES YES YES YES
_cons −1.1017 −1.2026 −2.3780 ** −2.3018 *
(0.8266) (0.8131) (0.7919) (0.9685)
N8612 10,094 11,682 8429
r20.0617 0.0550 0.0610 0.0591
F 10.8219 17.1297 23.6177 15.6250
p0.0000 0.0000 0.0000 0.0000
Standard errors are in parenthesis. *** p< 0.01, ** p< 0.05, * p< 0.1.
4.4. Mechanism Test
According to Equation (2), the mediating effect was examined. Table 7displays the
mechanism test results. In the mechanism test, the estimated parameter of DT was sig-
nificantly positive at the 1% confidence level, suggesting that digital transformation can
positively enhance corporate ESG performance by improving corporate absorptive capacity.
In conclusion, research hypothesis H2 is validated and confirmed.
Table 7. Mechanism and regulatory effect test results.
Variable Mechanism Verification Moderating Effect Test
(1) (2) (3)
AC ESG ESG
DT 0.0004 * 0.0127 ** 0.0151 ***
(0.0002) (0.0040) (3.7286)
AC 1.4934 **
(0.4658)
RDL 0.5136
(1.4443)
DT ×RDL 0.0535 **
(2.1521)
controls YES YES YES
code fe YES YES YES
year fe YES YES YES
_cons 0.0900 * −2.5117 ** −2.4950 ***
(0.0369) (0.7931) (−3.1732)
N11,682 11,682 11,682
r20.1184 0.0636 0.0620
p0.0000 0.0000 0.0000
Standard errors are in parenthesis. *** p< 0.01, ** p< 0.05, * p< 0.1.
4.5. Moderation Effect Test
According to Equation (3), the moderating effect was examined. Table 7displays the
results. In the moderation effect test, the estimated parameter of the interaction term was
Sustainability 2024,16, 5018 12 of 18
significantly positive at the 5% confidence level, which means that the regional digitalization
level positively moderates the relationship between digital transformation and corporate
ESG performance. In conclusion, research hypothesis H3a is validated and confirmed.
4.6. Heterogeneity Analysis
This article separates the sample companies into SOES and non-state-owned busi-
nesses to perform group regression based on property rights for the heterogeneity analysis.
The sample enterprises were then further separated into nine sub-industries, such as the
high-end equipment manufacturing industry, for group regression, by the categorization
norms of strategic developing industries. Tables 8and 9display the regression results.
The empirical findings show that, at least at the 5% confidence level, the positive impact
of digital transformation on corporate ESG performance was obvious for both SOEs and
non-state-owned businesses, with a more emphatic effect seen in state-owned businesses.
Among the nine industries, the promotion effect was more prominent in the high-end
equipment manufacturing industry. Conversely, this promotion effect was negative in the
new-generation information technology industry. Analysis suggests that the higher digi-
talization in this industry, coupled with smaller enterprise size and lower social visibility,
results in poorer performance in social responsibility fulfillment. In conclusion, research
hypotheses H3b and H3c are validated and confirmed.
Table 8. Heterogeneity analysis regression results (property rights).
Variable ESG (State-Owned) ESG (Private)
DT 0.0164 *** 0.0105 **
(3.0864) (2.4165)
controls YES YES
code fe YES YES
year fe YES YES
_cons −1.5428 −3.1860 ***
(−1.4689) (−4.7082)
N2926 8379
r20.0869 0.0721
Standard errors are in parenthesis. *** p< 0.01, ** p< 0.05.
Table 9. Heterogeneity analysis regression results (industry).
Variable (1) (2) (3) (4) (5)
The new
generation of the
information
technology
industry
High-end
equipment
manufacturing
industry
New materials
industry
Biological industry
New energy
vehicle industry
ESG ESG ESG ESG ESG
DT −0.0051 0.0240 ** 0.0104 0.0160 * 0.0750 *
(0.0075) (0.0089) (0.0081) (0.0074) (0.0301)
controls YES YES YES YES YES
code fe YES YES YES YES YES
year fe YES YES YES YES YES
_cons −1.9721 * −2.7688 −2.2656 −4.4945 ** −16.3140
(0.8917) (1.5179) (1.6031) (1.4076) (8.5966)
N3817 1688 1568 1842 136
r20.0753 0.0671 0.0891 0.0896 0.4395
Sustainability 2024,16, 5018 13 of 18
Table 9. Cont.
Variable (6) (7) (8) (9)
New energy
industry
Energy conservation and environmental
protection industry
Digital creative
industry
Related service
industry
ESG ESG ESG ESG
DT 0.0130 0.0252 * 0.0325 0.5608
(0.0102) (0.0118) (0.0220) (0.6900)
controls YES YES YES YES
code fe YES YES YES YES
year fe YES YES YES YES
_cons −2.7086 −5.6682 * −1.4455 27.9815
(1.7140) (2.3335) (2.7648) (24.2249)
N1276 853 461 41
r20.0762 0.1427 0.1447 0.8442
Standard errors are in parenthesis. ** p< 0.05, * p< 0.1.
5. Discussion
This article chooses a sample of 1588 A-share listed companies in strategic emerging
industries, selecting ESG rating data, digital transformation data, and relevant financial
data from 2011 to 2021 as the sample data. Through employing a two-way fixed effects
model, the research empirically investigates how corporate ESG performance is affected
by digital transformation and how it works. It examines the moderating effect of regional
digitalization level on the relationship between digital transformation and corporate ESG
performance and further conducts heterogeneity analysis by property rights and different
industries.
Empirical results indicate:
1
Digital transformation positively promotes corporate ESG performance. After
a series of endogeneity and robustness tests, this conclusion remains valid, confirming
research hypothesis H1. Comparisons with relevant literature reveal high consistency with
previous studies [18–27].
2
Digital transformation positively enhances corporate ESG performance by improv-
ing absorptive capacity, suggesting that enhancing absorptive capacity is a key pathway
through which digital transformation drives improvements in corporate ESG performance,
contributing to long-term competitive advantages and sustainable development. Research
hypothesis H2 is supported. However, comparisons with existing research show that
previous studies have affirmed the mediating roles of green technology innovation [
18
],
internal information transparency [
23
], external legitimacy pressure [
24
], and corporate
sustainable development reputation [
27
] in the link between digital transformation and
corporate ESG performance. Some studies also confirm that innovation capability within
dynamic capabilities is one of the main channels through which digital transformation
affects corporate ESG performance [
26
]. This study enriches the research on the impact
pathways between digital transformation and corporate ESG performance.
3
The regional digitalization level serves as a positive moderator in the interplay
between digital transformation and corporate ESG performance, suggesting that a robust
regional digital infrastructure significantly bolsters enterprises’ digital evolution and boosts
their ESG ratings. Our findings validate research hypothesis H3a. While previous studies
have underscored the positive moderating role of regional digitalization between digital
transformation and enterprise financial performance [
34
], our research highlights its pivotal
role in influencing the non-economic benefits of digital transformation. Furthermore, while
government subsidies and CEO’s career experience are noted as important contextual
factors shaping the impact of digital transformation on corporate ESG performance [
18
],
this article augments the understanding of such situational mechanisms, thereby enriching
the research landscape.
Sustainability 2024,16, 5018 14 of 18
4
Heterogeneity analysis reveals that the impact of digital transformation on corporate
ESG performance varies across different entities. The effectiveness of digital transformation
in boosting corporate ESG performance is particularly evident in SOEs and high-end
equipment manufacturing industry enterprises. Research hypotheses H3b and H3c are
validated. Comparisons with relevant literature show that most scholars believe that digital
transformation has differential effects on corporate ESG performance [
18
,
23
–
26
], but the
reasons for these differential effects vary. Specifically, this study finds that the reasons for
these differential effects lie in the differences in property rights and sub-industries within
strategic emerging industries. The research results on the differential effects caused by
the nature of property rights are consistent with the findings of scholars such as Yang
Peng [
18
] and Hu Jie [
23
]. However, the research results on the differential effects caused
by the nature of sub-industries within strategic emerging industries have not been found
in the current literature. Additionally, some researchers have found that differences in
regional distribution [
19
,
26
] and corporate technological levels [
25
] also lead to these
differential effects.
6. Conclusions
This article analyzes the impact and mechanisms of digital transformation on corporate
ESG performance using 1588 SEIEs as the research sample. The findings are as follows.
Firstly, digital transformation, to some extent, promotes improvements in corporate
ESG performance. Digital transformation enhances not only operational efficiency and
management capabilities but also fosters absorptive capacity, enabling better adaptation
and response to external environmental changes, thereby improving corporate ESG perfor-
mance.
Secondly, digital transformation can improve corporate ESG performance by en-
hancing absorptive capacity. Specifically, the improvement of information technology
infrastructure, data analysis, and knowledge-sharing efficiency brought about by digi-
tal transformation can promote the enhancement of absorptive capacity, facilitating the
effective utilization of knowledge and technology and comprehensive improvement of
corporate ESG performance.
Thirdly, it is observed that regional digitalization levels positively moderate the pro-
motion effect of digital transformation on corporate ESG performance. This implies that in
regions with higher levels of digitalization, the improvement is more significant. This may
be because regions with high digitalization levels have more complete digital infrastructure,
abundant digital talents, and more open digital ecosystems, providing better environments
and conditions for corporate digital transformation.
Finally, it is noted that this promotion effect is more significant in SOES and high-
end equipment manufacturing companies. This may be attributed to SOES having richer
resources and policy support during the digital transformation process, enabling faster
realization of digital transformation and translation of its effects into improvements in
ESG performance. Moreover, because of the traits of the sector, digital transformation in
high-end equipment manufacturing companies often brings more significant benefits and
competitive advantages, thereby promoting improvements in their ESG performance.
The following suggestions are put out in light of these empirical findings:
For enterprises:
1
Embrace digital transformation as a strategic imperative for en-
during growth. Amidst the dynamic landscape of the digital economy, characterized by
swift and continuous technological advancements, businesses must view digital transfor-
mation as a cornerstone of their long-term strategic planning. This approach is essential
for securing sustainable growth and maintaining a competitive edge in the current cut-
throat market environment. The study shows that digital transformation can significantly
improve corporate ESG performance, especially for SOEs and high-end equipment manu-
facturing companies. Therefore, enterprises should formulate differentiated development
strategies based on their situations.
2
Strengthen the cultivation of absorptive capacity.
According to the research findings, enhancements in company ESG performance can be
Sustainability 2024,16, 5018 15 of 18
fostered by digitalizing by strengthening absorptive capacity. Enterprises should enhance
absorptive capacity while improving digital technology, actively seeking and absorbing
knowledge in the environment, applying it to business practices, and thereby improving
corporate ESG performance.
3
Strengthen friendly interactions with the government.
Regional digitalization levels profoundly affect the process of corporate digital transforma-
tion. Enterprises should strengthen interactions with relevant departments, establish good
government–business relations, and work together to create a conducive environment for
digital transformation.
For the government:
1
Improve regional digital infrastructure construction to reduce
regional disparities in digitalization levels. Regulatory authorities should continuously
improve digital infrastructure construction, enhance regional digitalization levels, and
minimize regional disparities in digitalization levels as much as possible, providing more
comprehensive support services for corporate digital transformation.
2
Enhance support
for digital transformation in non-state-owned enterprises. Relevant authorities should en-
hance policy and financial support for digital transformation in privately owned enterprises,
implement tailored policies for enterprises across different sectors, and comprehensively
drive corporate digitalization.
The marginal contributions of this study are as follows:
1
By introducing absorptive capacity, the study broadens the research on the impact
pathways of digital transformation on corporate ESG performance, thereby promoting the
development of corporate ESG.
2
By introducing regional digitalization levels, the study deepens the research on
the situational mechanisms of digital transformation affecting corporate ESG performance,
considering the differences in digital development in different regions, thereby facilitating
a broader and more nuanced perspective on the impacts of digital transformation across
diverse settings.
3
By conducting heterogeneity analysis of sub-industries within strategic emerging
industries, the study further enriches the research on the heterogeneity analysis of the influ-
ence of digital transformation on corporate ESG performance, providing a theoretical basis
and enlightenment for relevant departments and enterprises to promote ESG development
and improve ESG performance.
However, this study has the following limitations:
1
Theoretical limitations: In terms of impact pathway research, this study only
includes absorptive capacity in dynamic capabilities. Future research can simultaneously
include innovation capacity, absorptive capacity, and adaptability in dynamic capabilities
for more detailed exploration. In terms of moderating effect research, this study only
considers the moderating effect of regional digitalization levels, while future research can
further consider the moderating influence of elements like the performance of regional ESG.
Regarding heterogeneity analysis research, this study only considers the heterogeneity
of property rights and sub-industries within strategic emerging industries, while future
research can further consider heterogeneities such as data assets and knowledge intensity.
2
Sample region limitations: The sample of this study is limited to A-share listed
companies in China’s strategic emerging industries. This means that the research results
mainly reflect the situation of the Chinese market and specific industries, making it difficult
to comprehensively represent the changes in corporate ESG performance during the digital
transformation process in other regions and markets globally. Therefore, the universality of
the research results across regions or globally is limited.
3
Sample industry limitations: Strategic emerging industries themselves have certain
characteristics, and the challenges and opportunities encountered in digital transformation
and ESG practices may differ significantly from traditional industries. Therefore, the
research results may not apply to companies in other industries, limiting the extrapolation of
the research results. Future research could consider expanding the sample
selection range
.
4
Sample period limitations: Digital transformation is a dynamic process, and cor-
porate ESG performance may change over time. This study’s sample period spans from
Sustainability 2024,16, 5018 16 of 18
2011 to 2021. It is possible that the study’s time frame and data-gathering methods can-
not accurately capture the long-term effects of digital transformation on company ESG
performance. Future studies might think about extending the sample period.
To encapsulate, this study’s findings reveal the beneficial effects of digital transforma-
tion on corporate ESG performance and its mechanisms, providing important theoretical
and practical insights for companies undergoing digital transformation. However, due to
the regional, industrial, and temporal limitations of the sample companies, the general
applicability of the empirical results is constrained. Subsequent studies may delve deeper
into the varying degrees of influence that digital transformation exerts on ESG performance
for different types of companies and the variations across different regions and industries,
thereby providing more targeted recommendations for companies to formulate digital
transformation strategies.
Author Contributions: Conceptualization, M.Z.; methodology, M.Z.; software, W.L.; validation, W.L.;
data curation, W.L.; writing—original draft preparation, W.L.; writing—review and editing, W.L.;
supervision, M.Z.; project administration, M.Z.; funding acquisition, M.Z. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was funded by the Guizhou University of Finance and Economics Under-
graduate Student Research Projects in 2024, grant number 2024ZXSY261.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Conflicts of Interest: The authors declare no conflicts of interest.
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