ff
ABW 508 ANALYTICS LAB
Research on the Impact of Digital Transformation on the operating revenue of
Listed Companies
Lecturer: DR. NG WEI CHIEN
Name
Matrik No
Phone Number
Email
XIE JIATONG
24305889
+60 1115163593
[email protected]
m.my
Semester 1
Academic Session 2023/2024
School of Management
Universiti Sains Malaysia
1
ACKNOWLEDGEMENT
As I reflect on the past year, I realize that pursuing my postgraduate studies at USM
Kuala Lumpur Campus has been one of the most meaningful decisions I have ever
made. Studying in a new country challenged me in many ways, both academically and
personally. Beyond the knowledge I gained in the classroom, this experience taught
me how to adapt, persevere, and grow outside my comfort zone.
My sincere appreciation goes to my supervisor, Dr. Ng Wei Chien, for his guidance
and support throughout this research journey. From refining research ideas to
reviewing countless drafts, he consistently encouraged me to approach problems with
a critical and analytical mindset. His patience, professionalism, and dedication to
academic excellence have greatly influenced my development as a student and
researcher. I am also grateful to the faculty members and administrative staff of the
School of Management for creating a supportive learning environment during my
time at USM.
I'd also like to thank my classmates and friends. Sharing this experience with you
made my postgraduate journey more enjoyable and memorable. Whether preparing
presentations, discussing assignments, or exploring Malaysia on weekends, the
friendships we built brought encouragement and laughter in both stressful and
rewarding moments. These memories will stay with me after graduation.
To my family, thank you for always supporting me, even from afar. Your trust,
understanding, and encouragement gave me confidence to move forward. Knowing I
could rely on you provided strength in challenges.
Finally, I acknowledge myself. Completing this dissertation was hard, and there were
overwhelming moments. But I kept moving forward step by step. This dissertation
represents academic fulfillment and personal growth, perseverance, and confidence.
As this chapter ends, I look forward to future opportunities and challenges.
i
TABLE OF CONTENTS
ACKNOWLEDGEMENT ............................................................................................ i
LIST OF TABLES....................................................................................................... iv
ABSTRACT ................................................................................................................. vi
CHAPTER 1 ............................................................................................................... １
INTRODUCTION...................................................................................................... １
1.1 Background of the Study ................................................................................ １
1.2 Research Significance ..................................................................................... ３
1.2.1 Theoretical Significance ....................................................................... ３
1.2.2 Practical Significance............................................................................ ３
1.3 Problem Statement .......................................................................................... ４
1.4 Research Questions ......................................................................................... ５
1.5 Research Objectives ........................................................................................ ５
1.6 Research Methods ........................................................................................... ６
1.7 Research Framework ...................................................................................... ６
1.8 Research Innovation........................................................................................ ８
1.8.1 Granularity of the Top-Line Scale-Oriented Perspective ...................... ８
1.8.2 Methodological Resilience Through Multi-Granular Textual
Cross-Validation ............................................................................................. ９
1.8.3 Structural Synthesis within a Parallel Dual-Mediation Framework ..... ９
CHAPTER 2 ........................................................................................................... １０
LITERATURE REVIEW ...................................................................................... １０
2.1 General Overview ....................................................................................... １０
2.1.1 Research on Measurement Methods for Digital Transformation ...... １０
2.1.2 Research on the Impact of Digital Transformation on Corporate
Performance ............................................................................................... １２
2.1.3 Research on the Impact Mechanisms of Digital Transformation...... １３
2.1.4 Review of Heterogeneous Bounded Digital Returns ........................ １４
2.1.5 Literature Commentary ..................................................................... １４
2.2 Research Hypotheses .................................................................................. １５
2.2.1 The Direct Impact of Digital Transformation on Corporate Performance１５
2.2.2 The Mediating Role of Innovation Investment ................................. １６
2.2.3 The Financial Pathway: External Financing Constraint Mitigation (H3)１７
2.2.4 Institutional Ownership and Bounded Digital Returns (H4) ............ １８
2.3 Summary of Research Hypotheses ............................................................. １９
CHAPTER 3 ........................................................................................................... ２０
DATA AND METHODOLOGY ........................................................................... ２０
3.1 Sample Selection and Data Sources ............................................................ ２０
3.1.1 Sample Screening.............................................................................. ２１
3.1.2 Data Sources ..................................................................................... ２２
3.1.3 Diagnostic Procedures ...................................................................... ２３
3.2 Variable Definitions .................................................................................... ２４
3.2.1 Independent Variable......................................................................... ２５
ii
3.2.2 Dependent Variable ........................................................................... ２５
3.2.3 Mediating Variables .......................................................................... ２５
3.2.4 Control Variables .............................................................................. ２５
3.3 Model Specification .................................................................................... ２７
3.3.1 Mediation Effect Model .................................................................... ２８
CHAPTER 4 ........................................................................................................... ２９
RESULTS AND DISCUSSION ............................................................................. ２９
4.1 Descriptive Statistical Analysis................................................................... ２９
4.2 Correlation Analysis.................................................................................... ３１
4.2.1 Preliminary Verification of Core Hypotheses and Mechanisms ....... ３２
4.2.2 Econometric Diagnostics for Multicollinearity ................................ ３２
4.3 Baseline Regression Analysis and Hypothesis Testing ............................... ３５
4.4 Double Mediation Mechanism Path Testing ............................................... ３７
4.4.1 The Internal Technical Innovation Pathway ..................................... ３７
4.4.2 The External Financing Constraints Pathway ................................... ３９
4.5 Robustness Check: Dimensional Decomposition and Mechanism Validation
of Digital Transformation ................................................................................. ４０
4.5.1 Alternative Measurement of the Dependent Variable ....................... ４１
4.5.2 Alternative Measurement of the Independent Variable ..................... ４１
4.5.3 Addressing Endogeneity: The Lagged Independent Variable Approach４２
4.6 Heterogeneity Analysis ............................................................................... ４３
4.6.1 Property Rights Heterogeneity.......................................................... ４４
4.6.2 Firm Size Heterogeneity ................................................................... ４５
4.6.3 Regional Distribution Heterogeneity ................................................ ４７
CHAPTER 5 ........................................................................................................... ４９
CONCLUSION AND IMPLICATIONS .............................................................. ４９
5.1 Research Conclusions ................................................................................. ４９
5.2 Policy Recommendations............................................................................ ５１
5.3 Limitations and Directions for Future Research ......................................... ５２
REFERENCES ....................................................................................................... ５３
iii
LIST OF TABLES
Table 2.1 Summary Table of Research Hypotheses.....................................................20
Table 3.1 Multi-Stage Sample Filtering and Selection Procedure...............................22
Table 3.2 Variable description table.............................................................................27
Table 4.1 Descriptive statistics of variables.................................................................31
Table 4.2 Pairwise correlations ...................................................................................34
Table 4.3Baseline Regression Analysis.......................................................................36
Table 4.4The Internal Technical Innovation Pathway ................................................38
Table 4.5The External Financing Constraints Pathway ..............................................40
Table 4.6Robustness Test – Subdimension Decomposition........................................43
Table 4.7Property Rights Heterogeneity......................................................................45
Table 4.8Firm Size Heterogeneity ..............................................................................46
Table 4.9Regional Distribution Heterogeneity............................................................48
LIST OF FIGURES
Figure 1.1 2016–2023 China Digital Economy Development Trend..........................2
Figure 1.2 Logic Flowchart: Digital Transformation Empowering Corporate
Performance...................................................................................................................8
iv
v
ABSTRACT
Against the backdrop of the deep integration of the digital and real economies, digital
transformation has emerged as a important driver for driving high-quality corporate
development. Utilizing a panel dataset of Chinese A-share listed companies spanning
from 2012 to 2022, this study systematically examines the economic consequences of
digitalization. We employ the corporate digital transformation index as the core
independent variable and operating revenue as the primary metric for operational
performance. Furthermore, by selecting research and development expenditures (ln_rd)
to capture the internal technological innovation mechanism and the Kaplan-Zingales
index (KZ) to gauge external financial frictions, this paper constructs a synchronized
dual-mediation framework.
The empirical analysis yields several notable findings. First, digital transformation
significantly positively affects firms' operating revenue, showing it contributes to
business expansion and market growth. Second, the underlying mechanisms show this
positive impact occurs via two pathways. It encourages R & D investment, enhancing
firms' innovation, and eases financing constraints, enabling efficient access to external
resources, both contributing to better revenue. Third, heterogeneity analysis reveals
differences across ownership structures. The positive link between digital
transformation and operating revenue is stronger for state - owned enterprises, likely
due to their advantages in resource access, financing, and institutional support for
digital initiatives. Overall, these findings enhance understanding of digital
transformation's influence on firm performance and offer implications for corporate
decision - making and financial governance in the digital economy era.
Keywords: Digital transformation; Operating revenue; Technological innovation;
Financing constraints; Kaplan-Zingales index; Panel fixed effects
vi
CHAPTER 1
INTRODUCTION
1.1 Background of the Study
The rapid development of digital technologies has accelerated the integration of the
digital economy with traditional industries, making digital transformation an
increasingly important strategic priority for enterprises (Zhou et al., 2022).
Technologies such as big data, cloud computing, and artificial intelligence are
changing how firms organize production, manage operations, and create value
(Bharadwaj et al., 2013). According to the China Digital Economy Development
Report (2023), the digital economy has maintained a growth rate higher than that of
the overall economy in recent years, highlighting its growing contribution to
economic development.
Existing studies generally suggest that digital transformation can improve
business performance by reducing information asymmetry, enhancing resource
allocation efficiency, and lowering transaction costs. At the firm level, the adoption of
digital technologies enables enterprises to redesign business processes, improve
decision-making capabilities, and strengthen their competitive position in the market
(Verhoef et al., 2019). As competition continues to intensify, many firms view digital
transformation as an important means of achieving sustainable growth and
maintaining long-term competitiveness.
Among the various indicators used to evaluate firm performance, operating
revenue is particularly useful because it directly reflects a firm’s ability to generate
market demand and expand its business activities. Compared with profitability
indicators, which may be affected by accounting policies and managerial discretion,
operating revenue provides a more objective measure of a firm’s market performance.
Despite the growing body of literature on digital transformation, relatively limited
attention has been paid to its impact on operating revenue and the mechanisms
through which this impact occurs. Therefore, examining whether digital
１
transformation contributes to revenue growth, and identifying the channels through
which such effects are realized, can enrich the existing literature while offering
practical implications for corporate management and financial decision-making (L.
Guo & Xu, 2021).
Figure 1.1
2016-2023 China Digital Economy Development Trend
２
1.2 Research Significance
1.2.1 Theoretical Significance
First, this research broadens the application of the Resource-Based View (RBV) by
adopting operating revenue as a key indicator of firm performance. As operating
revenue reflects actual market demand and business expansion more directly than
accounting-based profitability measures, it provides a more objective basis for
assessing the economic value generated by digital transformation. Second, this study
contributes to the corporate finance literature by proposing a parallel mediation
mechanism involving innovation performance and financing constraints. This
framework highlights that digital transformation not only strengthens firms’
innovative activities but also improves access to financial resources, demonstrating
the interconnected channels through which digitalization influences corporate
development.
1.2.2 Practical Significance
This study offers several practical implications. First, the results show that digital
transformation can contribute to revenue growth in addition to improving operational
efficiency. Firms should therefore pay greater attention to the long-term value created
by digital investment rather than focusing exclusively on short-term financial
outcomes. Second, the findings indicate that financing conditions play an important
role in the digital transformation process. Limited access to external funding may
restrict firms’ ability to adopt new technologies and pursue innovation activities.
Policymakers can support enterprise digitalization by improving financing
accessibility and providing a more favorable financial environment for digital
investment. These measures may help accelerate industrial upgrading and promote the
sustainable development of the real economy.
３
1.3 Problem Statement
Despite the expanding literature addressing micro-level digitalization, several critical
omissions require further academic investigation:
First, limitations in textual measurement granularity persist in empirical
accounts. A substantial portion of existing research captures digital intensity through
aggregate corporate text mining (e.g., Liu et al., 2024c). However, simple keyword
summation strategies are highly susceptible to corporate annual report length and
tactical "impression management," making it difficult to fully isolate semantic context
from superficial slogans ("digital washing"). This necessitates a more rigorous
empirical design that cross-validates structural textual indicators at different analytical
granularities to ensure the stability of the digital proxy.
Second, the underlying transmission mechanisms are fragmented in a unified
framework. Prior studies often examine internal innovative efficiency (Feng et al.,
2022) or external credit access (He et al., 2023) separately. This segmented approach
fails to reconcile how internal technological advancements (research and development
capital commitments) interact with external capital market frictions (e.g., financing
constraints) in a single econometric design. As a result, the synchronized, dual pathway transmission matrix through which digitalization affects top - line revenue
expansion remains unclear.
Third, the asymmetric boundaries regarding institutional ownership and
resource endowments are unresolved. There remains a lack of empirical clarity on
whether the returns on digital transformation are uniform across varying corporate
governance and property rights structures. Given that state-owned enterprises (SOEs)
and non-state-owned enterprises (non-SOEs) operate under fundamentally distinct
credit environments, budget constraints, and strategic mandates (Niu et al., 2023),
treating them as homogeneous entities limits the descriptive capacity of empirical
models, requiring a rigorous subgroup evaluation to unveil the potential non-linear
dividend allocation between the public and private sectors.
４
1.4 Research Questions
To address the theoretical and empirical gaps identified in the problem statement, this
study proposes four interconnected research questions:
(1) Does corporate digital transformation exert a statistically significant,
robust direct driving effect on the operating revenue of listed companies?
(2) Through what dynamic internal and external channels does digital
transformation
transmit
its
economic
dividends?
Specifically,
do
internal
technological innovation commitments and external capital market financing
constraints operate as synchronized parallel mediators within a unified dual-pathway
framework?
(3) Are there differences in the effect of digital transformation on operating
revenue between state-owned enterprises (SOEs) and non-state-owned enterprises
(non-SOEs)?
(4) Does the effect of digital transformation on operating revenue vary across
firms with different characteristics and operating environments? Specifically, are there
significant differences based on firm size and regional location (eastern, central, and
western China)?
1.5 Research Objectives
The primary purpose of this study is to implement a robust quantitative evaluation of
corporate digital returns. To achieve this, the specific research objectives are
structured as follows:
(1) To examine whether digital transformation contributes to the growth of
corporate operating revenue.
(2) To investigate whether research and development (R&D) investment and
financing constraints
serve as mediating channels through which digital
transformation influences operating revenue.
５
(3) To identify the institutional boundaries of digital dividends by contrasting
the digital output efficiency between state-owned enterprises (SOEs) and
non-state-owned enterprises (non-SOEs).
(4) To conduct an exploratory analysis delineating the potential variations of
digital returns across heterogeneous organizational scales and geographic regions.
1.6 Research Methods
This study adopts several research methods to address the proposed research
questions.
First, a literature review is conducted to examine existing studies related to
digital transformation, operating revenue, innovation investment, and financing
constraints. The review draws on Resource-Based Theory, Information Asymmetry
Theory, and Endogenous Growth Theory to establish the theoretical basis of the study.
Second, panel data from Chinese A-share listed companies between 2012 and
2022 are collected and analyzed. Descriptive statistics, correlation analysis, and
fixed-effects regression models are applied using Stata to test the proposed
hypotheses.
Third, mediation analysis is employed to investigate whether R&D investment
and financing constraints act as channels through which digital transformation
influences operating revenue.
Finally, robustness tests and heterogeneity analyses are carried out to ensure
the validity of the findings. The heterogeneity analysis focuses on differences across
ownership structure, firm size, and geographic region.
1.7 Research Framework
６
The logical blueprint of this study centers on deciphering how corporate digital
transformation empowers top-line performance through synchronized internal and
external transmission channels:
Path A: Internal Innovation-Driven Logic (The Technological Pathway):
Digital transformation structurally optimizes the precision and efficiency of resource
allocation through integrated digital R&D platforms, advanced algorithmic
simulations, and big data-driven market forecasting. By stimulating substantial
research and development commitments (ln_rd), digitalization accelerates technical
iterations and product upgrades. The subsequent competitive advantages generated by
technological leadership effectively establish robust product differentiation moats and
lower operational marginal costs, thereby directly driving market-share expansion and
operating revenue growth through technical premium effects.
Path B: External Financing — Enabling Logic (The Financial Pathway):
Anchored in Information Asymmetry Theory, the deepening of corporate
digitalization translates into granular, real-time data transparency in financial
disclosures and operational activities. This digital transparency significantly alleviates
the information asymmetry between corporate insiders and external capital providers,
effectively mitigating the severity of corporate financing constraints as systematically
measured by the Kaplan-Zingales (KZ) index. The resulting mitigation of credit
rationing and reduction in financing costs secure critical liquidity reserves, providing
the essential material foundation for market expansion, distribution channel
construction, and market penetration, which collectively cultivate sustained revenue
appreciation.
Boundary Conditions: Structural Heterogeneity Contingencies: Moving
beyond a baseline homogeneous assumption, this framework concludes by
recognizing that the operational returns of these dual pathways are bounded by
profound structural asymmetries. The efficiency of converting digital inputs into
top-line revenue is contingent upon institutional and structural boundaries.
Specifically, we evaluate property rights (SOEs vs. non-SOEs) as a hypothesis-driven
７
boundary condition (H4), while investigating organizational scale (firm size) and
macro-geographic regions as complementary exploratory heterogeneity dimensions.
Figure 1.2
Logic Flowchart: Digital Transformation Empowering Corporate Performance
1.8 Research Innovation
1.8.1 Granularity of the Top-Line Scale-Oriented Perspective
Unlike mainstream empirical literature that predominantly evaluates digital returns
through trailing profitability metrics such as Return on Assets (ROA) or Return on
Equity (ROE), this study shifts the analytical focus to the granularity of operating
８
revenue.
While bottom-line profitability measures
are highly susceptible
to
discretionary earnings management, shifting tax strategies, and cost-cutting
restructurings—which can artificially inflate financial ratios without expanding actual
output—operating revenue provides a more robust, unconfounded indicator of a firm's
market penetration and demand-side growth. By isolating operating revenue as the
primary dependent variable, this paper systematically bypasses the traditional
"profitability consensus" to offer a dedicated, scale-oriented evaluation of how
technological adoption expands an enterprise's commercial boundaries and
operational throughput, thereby addressing a critical empirical omission in the
literature on digital corporate returns.
1.8.2
Methodological
Resilience
Through
Multi-Granular
Textual
Cross-Validation
To insulate empirical parameters from the measurement biases inherent in aggregate
annual report word frequency counts—which can suffer from structural inflation due
to report length or managerial "digital washing" tactical rhetoric—this study executes
a highly resilient textual measurement strategy. Leveraging the authoritative CSMAR
database, we not only rely on log-transformed keyword frequency metrics (ln_digital)
but uniquely incorporate the structural density of digital disclosures via sentence-level
tracking (ln_sentence) within our robustness matrix. This cross-validation technique
effectively isolates substantive, contextual technological alignment from superficial
corporate slogans, drastically enhancing the reliability of our empirical estimation.
1.8.3 Structural Synthesis within a Parallel Dual-Mediation Framework
Moving beyond prior literature that examines either internal technological upgrading
or external financial access in an isolated, fragmented design, this study integrates
internal innovation incentives and external credit friction within a synchronized,
parallel dual-mediation framework. By utilizing research and development
９
commitments (ln_rd) alongside the rigorous Kaplan-Zingales (KZ) index within a
single econometric matrix, this study systematically tracks how digital investments
simultaneously transmit dividends into top-line performance expansion. Furthermore,
by evaluating this framework through a three-dimensional heterogeneous matrix
(property rights, organizational scales, and macro-geographic regions), this paper
charts the non-linear boundaries of digital returns, offering a granular explanatory
roadmap for refined corporate governance.
CHAPTER 2
LITERATURE REVIEW
2.1 General Overview
With the accelerating convergence of digital technologies and the real economy,
digital transformation has become an essential strategic priority rather than a
１０
discretionary business choice. Research on this topic has generally progressed along
three interconnected dimensions: measurement approaches, economic outcomes, and
underlying mechanisms. Prior studies have widely documented the positive role of
digital transformation in improving firm performance through channels such as
business model innovation, more efficient resource utilization, and lower transaction
costs. Nevertheless, the extent to which digitalization influences operating revenue—a
key indicator of market expansion and business growth—has received comparatively
less attention. In particular, the joint roles of innovation enhancement and financing
condition improvement in transmitting the effects of digital transformation remain
insufficiently explored and continue to represent an important direction for further
investigation.
2.1.1 Research on Measurement Methods for Digital Transformation
The empirical evaluation of corporate digital transformation in current corporate
finance literature primarily advances through two interconnected methodological
dimensions. The first is the Text Mining Approach Based on Annual Reports,
championed by researchers like (Liu et al., 2024c). This methodology systematically
constructs a comprehensive digital feature word repository across core technological
domains—including artificial intelligence, blockchain, cloud computing, big data, and
digital technology applications—and employs aggregate log-transformed term
frequencies (ln_digital) to proxy corporate digital intensity. While this text-mining
approach sensitively captures corporate strategic orientation and remains the
prevailing standard in domestic empirical accounting, scholars argue that simple
keyword summation strategies can be susceptible to managerial "impression
management" or tactical "digital washing" if evaluated in isolation (Hadlock & Pierce,
2010).
To fortify empirical resilience against these structural measurement biases, the
literature has evolved toward a Multi-Granular Structural Validation Strategy. Rather
than fully discarding text-mining proxies, frontier studies leverage authoritative
granular datasets, such as the China Stock Market & Accounting Research (CSMAR)
１１
database, to cross-validate aggregate keyword metrics with fine-grained semantic
indicators. This involves paired assessments utilizing both text-frequency intensity
(ln_digital) and contextual disclosure density—manifested as sentence-level tracking
(ln_sentence). By adjusting for annual report lengths and analyzing structural digital
disclosures across multi-dimensional allocations, this cross-validation framework
isolates substantive technological capital commitments from superficial corporate
slogans, drastically minimizing measurement noise and setting a rigorous foundation
for causal identification.
2.1.2 Research on the Impact of Digital Transformation on Corporate
Performance
A burgeoning body of literature consistently confirms that digital transformation
provides vital endogenous driving forces for elevating micro-corporate performance.
This economic dividend is structurally realized through the re-engineering of
traditional business models, the optimization of resource allocation efficiency, and the
sharp compression of operational transaction costs. Empirically, (Scafarto et al., 2023)
demonstrated a compelling positive correlation between organizational digital
maturity and long-term asset productivity across large-scale enterprises. Shifting the
lens toward emerging market environments, (Liu et al., 2024c) verified that
substantive digital deployments act as a key institutional catalyst that directly expands
an enterprise's market capitalization by inflating sales revenue streams.
From the specific perspective of macro-market scaling and corporate top-line
growth, digital technologies allow firms to successfully transcend geographic market
boundaries, mitigate entry barriers, and capture substantial increasing economies of
scale (W. Li & Zhang, 2024). This scale expansion effect fundamentally manifests as
steady top-line growth. Compared to net profit margins, which are frequently subject
to arbitrary manager interventions and complex accounting earnings management,
operating revenue offers a cleaner, less distorted measurement of real market demand
shifts and the direct strategic returns of digital investments. This establishes a solid
analytical foundation for utilizing micro-level operating revenue metrics within listed
１２
firm panel datasets.
2.1.3 Research on the Impact Mechanisms of Digital Transformation
The scholastic dialogue surrounding the economic consequences of digitalization
emphasizes that digital transformation operates as a systemic catalyst that
simultaneously alters internal operational frameworks and external capital market
dynamics. Existing literature primarily delineates two concurrent pathways through
which digital investments translate into top-line revenue expansion:
(1) Internal Path: Innovation-Driven Logic: Grounded in Endogenous Growth
Theory, corporate digital transformation serves as an essential technological catalyst
that accelerates and optimizes organizational innovative capabilities. Specifically,
(Feng et al., 2022b) argue that digital R&D platforms, leveraging advanced simulation
technologies and big data analytics, enable precise allocation of research resources,
thereby significantly compressing the trial-and-error costs inherent in innovation
cycles. Building upon this, Niu et al. (2023) highlight that digital transformation
encourages enterprises to aggressively scale up internal technological exploratory
commitments. By driving higher research and development capital commitments
(ln_rd), this technological edge facilitates rapid product iterations, builds high product
differentiation moats, and expands market share, which ultimately drives a steady
appreciation of corporate operating revenue.
(2) External Path: Financing Empowerment Logic: Beyond internal asset
adjustments, another pivotal stream of literature focuses on the external capital market
friction channel, conceptualized through Information Asymmetry Theory. In imperfect
financial markets, severe information friction between corporate insiders and external
investors frequently leads to credit rationing. Based on this framework, digital
applications drastically enhance the real-time visibility and transparency of corporate
financial reporting and operational trajectories. By mitigating information asymmetry,
companies can transmit credible, positive transformation signals to external capital
providers, effectively easing corporate financing constraints as systematically
１３
measured by the seminal Kaplan-Zingales (KZ) index (Kaplan & Zingales, 1997).
Easing these credit frictions reduces external financing premiums and locks in stable
liquidity buffers. As highlighted by (Niu et al., 2023c), sufficient capital availability
provides the material foundation for marketing, logistics network optimization, and
channel development. Consequently, the mitigation of overall financing constraints
ensures that enterprises possess adequate liquidity to execute scale expansion and
capture top-line market opportunities.
2.1.4 Review of Heterogeneous Bounded Digital Returns
While the empowering effect of digital transformation on corporate performance has
received extensive empirical support, a growing consensus suggests that digital
returns are not uniform. The transmission efficiency of digital elements is highly
bound by institutional ownership traits and environmental contexts. The digital
dividend allocation pathway forms a closed loop spanning from "technological input"
to "process optimization" and "value realization." However, this loop is highly
sensitive to the nature of corporate property rights (state-owned enterprises vs.
non-state-owned enterprises), organizational scales, and macro-geographic locations,
necessitating a more refined, multi-dimensional contingent framework in empirical
designs.
2.1.5 Literature Commentary
Although existing studies lay a solid foundation for understanding corporate
digitalization, three major gaps remain to be addressed in the literature:
First, prior empirical works heavily rely on traditional bottom-line profitability
metrics like ROA or ROE, largely overlooking the direct scale expansion
consequences of digitalization. To capture the structural value creation highlighted by
Vial (2021), this study adopts operating revenue (ln_revenue) as the primary
benchmark, offering an unadulterated and intuitive assessment of market expansion
capacity.
Second, existing literature often examines internal operational mechanisms or
１４
external financing pathways in an isolated, fragmented design, failing to capture their
synchronized effects. We bridge this theoretical gap by constructing an integrated
parallel dual-mediation framework that incorporates both internal innovation
commitments (ln_rd) and external market frictions (KZ index) within a single
econometric matrix.
Third, traditional empirical settings are prone to structural omitted-variable
biases and rigid assumptions of homogeneity. Grounded in seminal financial friction
and corporate property rights literature, this study deploys a rigorous two-way
fixed-effects specification alongside a multi-dimensional heterogeneity evaluation
(stratified by institutional ownership, organizational scale, and geographic region).
This comprehensive empirical layout significantly enhances the statistical robustness
of mechanism identification and maps out the non-linear boundaries of digital returns.
2.2 Research Hypotheses
2.2.1 The Direct Impact of Digital Transformation on Corporate Performance
Grounded in the Resource-Based Theory (RBT) (Barney, 1991), corporate digital
transformation serves as a foundational strategic reconfiguration that allows firms to
cultivate rare, imperfectly imitable, and non-substitutable digital capabilities (Vial,
2019; Verhoef et al., 2021). Rather than merely accumulating hardware, the process of
digital transformation (ln_digital) structurally alters how data elements intersect with
traditional labor and capital inputs (Goldfarb & Tucker, 2019), thereby reshaping the
micro-level transmission mechanisms and operational frameworks of modern
enterprises.
First, on the supply side, the deployment of advanced analytics, artificial
intelligence, and cloud architectures enables enterprises to substantially compress
production cycles, achieve real-time inventory synchronization, and lower marginal
operational costs (Mithas et al., 2011). This structural efficiency gain provides firms
with aggressive pricing flexibility to capture market share. Second, on the demand
side, digital connectivity bridges the structural information gaps between corporations
１５
and external market consumers (Grover et al., 2018). By utilizing big data forecasting
and digital consumer touchpoints, firms can precisely decode shifting market
dynamics, implement customized product strategies, and dramatically accelerate
product-to-market velocity (Pagani, 2013). This multi-dimensional empowerment
ultimately transcends geographic market friction and manifests as substantial
economies of scale (Brynjolfsson & Hitt, 2003). Compared to net profit outcomes,
which are highly vulnerable to discretionary accounting management, the operating
revenue (ln_revenue) directly captures this unadulterated expansion of the firm's
real-world market penetration. Based on these insights, this study proposes the first
hypothesis:
Hypothesis 1 (H1): Corporate digital transformation exerts a significant positive
direct impact on operating revenue.
2.2.2 The Mediating Role of Innovation Investment
From the perspective of Endogenous Growth Theory (Lucas, 1988), continuous
technological
advancement
is
the
fundamental
engine
behind
sustainable
microeconomic scaling. Digital transformation acts as a powerful technological
catalyst that structurally accelerates and optimizes organizational innovative
commitments (ln_rd) (Kleis et al., 2012; Nambisan et al., 2017). The targeted
integration of digital elements profoundly alters the effect and internal transmission
paths of corporate innovation activities, driving substantial technical upgrades.
In traditional innovative environments, research and development activities
are heavily bounded by high structural trial-and-error costs, severe factual friction in
cross-departmental coordination, and long incubation horizons (Artz et al., 2010).
Digital transformation directly dismantles these innovation bottlenecks. By
integrating high-performance cloud simulation technologies, digital twin architectures,
and collaborative digital platforms, enterprises can execute precise, predictive
modeling during the early phases of engineering design (Yoo et al., 2012). This digital
toolkit dramatically compresses the trial-and-error costs inherent in innovation cycles
and optimizes the precise allocation of dynamic research resources (Paunov & Rollo,
１６
2016). This reduction in technical frictions inherently encourages corporate
executives to aggressively scale up internal technological exploratory commitments
and increase capital allocations toward substantive technological upgrades (ln_rd).
Consequently, the expansion of research and development investments creates
powerful product differentiation moats and lower long-term marginal production costs.
This technological edge allows firms to demand a premium in competitive markets,
defend against industry disruptors, and scale up their operational capacity (Aghion et
al., 2005), which ultimately translates into a steady, robust expansion of corporate
operating revenue. Based on these insights, this study proposes the second hypothesis:
Hypothesis 2 (H2): Digital transformation enhances corporate operating revenue by
stimulating R&D investment (Parallel Mediation Path A)
2.2.3 The Financial Pathway: External Financing Constraint Mitigation (H3)
In imperfect capital markets, the Information Asymmetry Theory dictates that
profound informational friction between corporate insiders and external capital
providers frequently precipitates severe credit rationing and punitive external
financing premiums (Myers & Majluf, 1984). For capital-intensive scale expansion,
these financial frictions operate as binding constraints on corporate revenue growth
(Fazzari et al., 1988).
Digital transformation provides a powerful institutional antidote to this
information friction. The deepening of organizational digitalization inherently
digitalizes the firm's operational workflows, financial ledger disclosures, and
transactional histories into a highly verifiable, real-time data stream (Bushman et al.,
2004). This digital transparency fundamentally alters the information landscape. It
prevents opportunistic management from engaging in adverse selection and strategic
window dressing, allowing external capital markets and institutional lenders to
accurately monitor corporate cash utilization and underlying asset productivity
(Berger & Udell, 2006).
By transmitting a highly credible, positive signal of modernization and
１７
operational stability to external investors, digitalized firms drastically alleviate the
severity of corporate financing constraints, as systematically captured by the
Kaplan-Zingales (KZ) index (Kaplan & Zingales, 1997; Hadlock & Pierce, 2010).
The subsequent easing of external credit rationing secures stable liquidity reserves at
an optimized cost of capital. This financial empowerment provides the vital material
foundation required to fund aggressive marketing penetration, optimize distribution
networks, and capture emerging macro-market opportunities, systematically driving
up operating revenue. Based on these insights, this study proposes the third
hypothesis:
Hypothesis 3 (H3): Digital transformation enhances corporate operating revenue by
mitigating financing constraints (reducing the KZ index) (Parallel Mediation Path B).
2.2.4 Institutional Ownership and Bounded Digital Returns (H4)
The operational returns of corporate digital adoptions are not uniform; rather, they are
deeply bound by institutional property rights, corporate governance, and state-backed
frameworks within which firms operate (North, 1990; Peng et al., 2008). In the
specific context of emerging market transitions, state-owned enterprises (SOEs) and
non-state-owned enterprises (non-SOEs) possess fundamentally distinct resource
endowments and strategic mandates.
State-owned enterprises possess intrinsic, institutional-level advantages when
implementing digital transformation. First, SOEs maintain natural administrative ties
with government networks, granting them priority access to critical national digital
infrastructure
projects,
municipal
data-sharing
initiatives,
and
specialized
technological modernization subsidies (Zhou et al., 2017). Second, because of implicit
state guarantees, SOEs operate under soft budget constraints and face substantially
lower systemic financial risks in external credit markets (Kornai, 1986). This unique
financial resilience allows them to sustain the immense, multi-year capital
expenditures required to execute deep digital infrastructure reconstruction without
threatening organizational liquidity.
１８
In sharp contrast, non-SOEs face severe credit rationing, lack institutional
policy rents, and are highly sensitive to market shocks, forcing them to prioritize
immediate, short-term survival over long-term digital investments (Brandt & Li,
2003). Furthermore, the immense scale, diversified industrial networks, and
entrenched supply-chain power of SOEs allow them to capture far greater increasing
economies of scale once digital efficiency gains are locked in. The digital dividend is
thus non-linearly magnified through the vast organizational networks of public-sector
enterprises. Based on these insights, this study proposes the fourth hypothesis:
Hypothesis 4 (H4): The positive impact of digital transformation on operating
revenue is more pronounced in state-owned enterprises (SOEs) than in
non-state-owned enterprises (non-SOEs).
2.3 Summary of Research Hypotheses
To provide a clear, systematic view of the empirical framework and align the
theoretical logic with the subsequent methodology and econometric models, Table 2.1
summarizes the research hypotheses developed in this study.
Table 2.1
Summary Table of Research Hypotheses
１９
Hypothes
is
Number
Core Variable
Path
Expected
Sign
Underlying
Theoretical
Support
Operational
Vector
Indicator
H1
Digital
Transformation
～ Operating
Revenue
+
Resource-Bas
ed Theory
(RBT)
ln_digital
～
ln_revenue
H2
Digital
Transformation
～Technological
Innovation
+
Endogenous
Growth
Theory
ln_digital
～ln_rd
H3
Digital
Transformation
～ Financing
Constraints
-
Information
Asymmetry
Theory
ln_digital
～KZ Index
H4
Institutional
Ownership
Heterogeneity
Asymmetr
ic
(Stronger
in SOEs)
Resource
Endowments
& Soft
Budget
Constraints
Subgroup
Stratificatio
n: SOEs vs.
Non-SOEs
CHAPTER 3
DATA AND METHODOLOGY
3.1 Sample Selection and Data Sources
２０
3.1.1 Sample Screening
This study selects China's A-share listed companies spanning from 2012 to 2022 as
the initial research sample, which yields an initial pool of 41,321 firm-year
observations. To isolate a clean, representative, and high-dimensional empirical
matrix, a systematic filtering sequence is executed based on the following criteria:
First, financial and insurance institutions are systematically omitted from the sample,
resulting in the exclusion of 1,121 firm-year observations. This exclusion is
necessitated by their distinct asset-liability structures, high operating leverage
characteristics, unique regulatory frameworks, and specialized accounting standards,
which differ inherently from those of ordinary industrial and commercial enterprises
(Fama & French, 1992).
Second, during the data alignment and variable matching processes, 1 corporate
observation characterized by incomplete vectors or missing accounting values across
the primary variables is removed, thereby ensuring complete structural integrity and
mathematical consistency across all data series.
Finally, to thoroughly insulate the baseline parameter estimates from the distortionary
biases of extreme statistical outliers or data entry errors, a conservative 1% and 99%
winsorization (tail-trimming) threshold is applied to all continuous empirical variables
(Cleary, 1999). This filtration sequence ultimately yields a final unbalanced panel data
sample comprised of 40,199 resilient firm-year observations, optimizing the statistical
degrees of freedom and cross-sectional variation required for robust fixed-effects
panel econometric regressions.
Table 3.1
Multi-Stage Sample Filtering and Selection Procedure
２１
Screening and Cleaning Step
Observations
Eliminated
Remaining
Observations
Initial Raw Observations of A-share
Listed Companies (2012–2022)
—
41,321
Less: Financial and insurance listed
enterprises (CSRC Industry Gate J)
-1,121
40,200
Less: Special Treatment (ST, *ST)
status and severe missing data vectors
-1
40,199
Final Baseline Operational Sample
(Firm-Year Observations)
—
40,199
3.1.2 Data Sources
The multi-dimensional empirical indicators utilized to construct the baseline
regressions, mechanism identification, and robustness matrices in this study are
compiled from three highly authoritative financial and archival databases. Crucial
firm-level variables, which encompass operational revenues, R&D expenditures, the
underlying constituent metrics of the Kaplan-Zingales index, and the comprehensive
suite of micro-level corporate governance control variables, are extracted from the
main modules of the China Stock Market & Accounting Research (CSMAR) database.
The CSMAR database is widely recognized in contemporary literature as the premier
and most reliable comprehensive research infrastructure for Chinese listed firms'
financial disclosures (Bushman et al., 2004).
Simultaneously, to construct and cross-validate the core digitalization metrics,
the underlying text-mining indices—including both the aggregate keyword
word-frequency intensity (ln_digital) and the cross-validating contextual disclosure
density measured via sentence-level tracking (ln_sentence)—are obtained from the
specialized Digital Economy Module of the CSMAR database. This methodological
reliance on computer-aided textual analysis of corporate annual reports aligns with
２２
pioneered protocols in financial linguistics (Loughran & McDonald, 2011), which
have been robustly adapted to capture the idiosyncratic vocabulary of Chinese
corporate digital strategies (Wu et al., 2021). To guarantee absolute measurement
precision, these digital disclosure vectors are systematically cross-checked and
supplemented with institutional archival records retrieved from the Wind financial
terminal, ensuring a high level of factual accuracy, transparency, and replicability for
the empirical research.
3.1.3 Diagnostic Procedures
To ensure the internal validity, statistical efficiency, and econometric reliability of the
panel estimations, a series of rigorous diagnostic screening procedures are established
prior to executing the baseline regressions. These diagnostics are designed to
safeguard the empirical model against potential parameter distortions arising from
violations of classical linear regression assumptions.
First, multicollinearity among the continuous explanatory and control
variables is evaluated utilizing the Variance Inflation Factor (VIF) and correlation
matrix diagnostics. Given the inherent economic associations between a corporation's
structural scale (i.e., firm size, ln_size) and its scale-based output outcomes (i.e.,
operating revenue, ln_revenue), a heightened degree of linear correlation may
naturally manifest within the empirical matrix. The VIF diagnostic serves as a crucial
mathematical threshold to verify that the variance of the estimated regression
coefficients is not severely inflated by overlapping explanatory vectors. In alignment
with conventional econometric literature, a conservative threshold of VIF < 10 is
adopted. This baseline ensures that even if specific control variables exhibit close
financial associations, the structural identification and statistical inference of the
primary independent variable—corporate digital transformation (ln_digital)—remain
isolated, stable, and unconfounded.
Second, micro-level corporate panel data structures frequently violate the
classical Gauss-Markov assumptions of homoskedastic and independent error
２３
distributions. Due to unobserved time-invariant corporate idiosyncrasies and
macro-level shocks, the residuals are highly prone to heteroskedasticity across
different cross-sectional firms and serial correlation (autocorrelation) within the
temporal dimensions of individual firms over time. To formally diagnose these
distributional anomalies, this study incorporates the Modified Wald test for groupwise
heteroskedasticity and the Wooldridge test for first-order serial correlation in panel
data.
To systematically correct for these potential biases and eliminate the artificial
inflation of t-statistics, this study implements clustered standard errors at the firm
level across all baseline, mechanism, and robustness econometric specifications. By
relaxing the strict independent and identically distributed (i.i.d.) assumption, this
variance-covariance adjustment allows for arbitrary correlation and heteroskedasticity
within each corporate cluster over the 2012–2022 sampling period. Consequently, this
methodological framework guarantees that the statistical significance, $p$-values, and
standard errors reported in Chapter 4 are asymptotically valid, conservative, and
resilient against potential error-term dependencies.
3.2 Variable Definitions
２４
This study strictly defines variables in accordance with the conference guidelines to
ensure uniform definitions and accurate measurements. The specific variable
definitions are as follows:
3.2.1 Independent Variable
Digital Transformation (ln_digital): To measure the depth of digital integration, we
utilize the corporate digital transformation index from the CSMAR database. This
index aggregates sub-indices across five dimensions: AI, blockchain, cloud computing,
big data, and digital technology application. To ensure distribution normality, we
apply: ln_digitali, t = ln (DTi, t + 1).
3.2.2 Dependent Variable
Operating Revenue (ln_revenue): To capture the benefits of digital transformation in
"scale expansion," we measure the dependent variable as the natural logarithm of
annual total operating revenue: lnRevenue = ln (Total Operating Revenue).
3.2.3 Mediating Variables
Technological Innovation (ln_rd): We operationalize internal innovation through the
natural logarithm of R&D expenditures: ln_rd = ln (R&D Expenditures + 1). This
input-oriented metric directly reflects management’s strategic commitment toward
technological upgrading. Financing Constraints (KZ): To assess financial friction, we
utilize the Kaplan-Zingales (KZ) index sourced from the CSMAR database. A higher
KZ value signifies more severe capital rationing and higher external borrowing
premiums, which restrict a firm’s financial capacity to scale operations.
3.2.4 Control Variables
To effectively mitigate endogenous interference from confounding factors on
corporate operating revenue, this study introduces the following control variables,
categorized into firm characteristics, financial structure, and corporate governance
dimensions:
Corporate Size (ln_size): Measured as the natural logarithm of total assets at
period-end. This metric normalizes operational scale and captures performance
２５
dividends arising from economies of scale. Applying the natural logarithm mitigates
skewness in raw asset values, facilitating robust cross-sectional comparisons.
Corporate Age (ln_age): Defined as the natural logarithm of (observation year
minus founding year + 1). This measure controls for distinct strategic behaviors and
operational characteristics that emerge across different stages of a firm’s life cycle.
Board Size (boardsize1): Measured as the natural logarithm of the total
number of board members. This proxy accounts for the spillover effects between
board composition and organizational decision-making efficiency, with the
logarithmic transformation reflecting diminishing marginal returns to board size.
Ownership Concentration (shrcr1): Represented as the percentage of shares
held by the largest shareholder. This variable captures the intensity of monitoring
incentives provided by controlling shareholders, which influences strategic
decision-making and agency costs.
Shareholder Occupation of Funds (ShareholdersOccupy): This variable
quantifies the extent of fund misappropriation by controlling shareholders. It serves as
a proxy for the severity of agency conflicts and the potential drainage of liquid assets,
which directly impacts the firm's available resources for market expansion and
revenue growth.
Herfindahl–Hirschman Index (HHI_A): Calculated based on the total assets
of firms within the same industry. This index serves as a proxy for industry-level
market concentration. It controls the competitive environment, as firms operating in
highly concentrated markets face different strategic constraints and performance
incentives compared to those in fragmented, highly competitive sectors.
Table 3.2
Variable description table
２６
Variable
Type
Variable Name
Symbol
Definition / Calculation
Method
Data
Source
Expected
Sign
Dependen
t Variable
Operating
Revenue
ln_revenue
Natural logarithm of total
annual operating revenue: ln
(Total\ Operating\ Revenue)
CSMAR
/
Independ
ent
Variable
Digital
Transformation
ln_digital
Natural logarithm of
(Corporate digital
transformation index + 1)
CSMAR
+
Mediating
Variable
1
R&D Investment
ln_rd
Natural logarithm of total
R&D expenditures: ln (R&D
expenditures + 1)
CSMAR
+
Mediating
Variable
2
Financing
Constraints
KZ
The comprehensive
Kaplan-Zingales index
CSMAR
-
Control
Variable
Firm Size
ln_size
Natural logarithm of total
assets at year-end: ln (Total\
Assets)
CSMAR
+
Control
Variable
Firm Age
ln_age
Natural logarithm of
(Observation year Establishment year + 1)
CSMAR
/
Control
Variable
Board Size
boardsize1
Natural logarithm of the
total number of board
directors
CSMAR
+
Control
Variable
Equity
Concentration
shrcr1
Shareholding ratio of the
largest single shareholder
(%)
CSMAR
/
Control
Variable
Shareholders'
Occupation
Shareholders
Occupy
Extent of funds
misappropriated by
controlling shareholders
CSMAR
-
Control
Variable
Market
Concentration
HHI_A
Industry-level asset-based
Herfindahl-Hirschman Index
CSMAR
+
3.3 Model Specification
To verify the direct impact of corporate digital transformation on operating revenue
(Hypothesis H1), this study constructs a two-way fixed effects (TWFE) baseline
２７
regression model. By utilizing logarithmic transformations for both the independent
and dependent variables, the estimated coefficient captures the elasticity of revenue
with respect to digital maturity, which effectively mitigates heteroscedasticity and the
influence of extreme outliers. The baseline empirical specification is formalized as
follows:
ln_revenue_i, t = α0 + α1ln_digitali, t + ∑ βkControlsi, t + μi + γt + εi, t (1)
Where μi
represents individual firm fixed effects, γt captures year fixed
effects, and Controlsi, t is a vector of control variables, including firm size (ln_size),
firm age (ln_age), board size (boardsize1), equity concentration (shrcr1), shareholder
occupation of funds (ShareholdersOccupy), and market concentration (HHI_A).
3.3.1 Mediation Effect Model
To unveil the internal and external transmission channels through which digital
adoption drives revenue growth—via internal innovation commitments (Hypothesis
H2) and external financing friction mitigation (Hypothesis H3)—this study adopts a
synchronized, parallel mediation framework. The multi-stage equations are specified
as follows:
ln_rdi, t=θ0+θ1ln_digitali, t+∑βkControli, t+μi+γt+εi, t (2)
KZi, t=ϕ0+ϕ1ln_digitali, t+∑βkControli, t+μi+γt+εi, t (3)
ln_revenuei, t=λ0+λ1ln_digitali, t+λ2ln_rdi, t+λ3KZi, t+∑βkControli, t+μi+γt+εi, t(4)
Where ln_rdi, t and KZi, t denote the two parallel mediators. The mechanism identification
is substantiated as follows:
Technological Channel: If θ1 (Model 2) and λ2 (Model 4) are statistically significant,
the internal innovation-driven path is validated. Financial Channel: If ϕ1 (Model 3)
and λ3 (Model 4) are statistically significant, the financing empowerment path is
validated. Mediation Assessment: Comparing λ1 in Model (4) with α1 in Model (1)
２８
allows for the assessment of partial or full mediation effects, providing a rigorous
identification of how digital dividends translate into top-line revenue expansion.
“This study estimates a two-way fixed effects ordinary least squares (OLS)
model. Firm fixed effects (vi) are specified to capture time-invariant, unobservable
entity-level characteristics such as inherent corporate culture and initial organic
scaling capability, justified by both microeconomic theory and the Hausman test. Year
fixed effects (μi) are integrated to control for macro-level economic shocks,
industry-wide regulatory variations, and systemic temporal shifts. To formally
counteract potential cross-sectional heteroskedasticity and within-firm serial
correlation, all reported models utilize robust standard errors clustered at the firm
level (or industry level).”
“Relying solely on the sequential causal steps approach is insufficient for
validating high-dimensional parallel mediation channels. Therefore, to ensure
empirical rigor, this study formally introduces indirect effect magnitude evaluations.
We utilize the Bootstrap method (with 5,000 sampling replications) along with the
Sobel test to calculate the exact products of coefficients and their respective
confidence intervals, ensuring the statistical validity of the suggested parallel
transmission channels (innovation acceleration and financing friction relief).”
CHAPTER 4
RESULTS AND DISCUSSION
4.1 Descriptive Statistical Analysis
２９
Table 4.1 reports the descriptive statistics for all the primary empirical variables used
in this study. To mitigate the potential bias caused by extreme values in the regression
estimations, all continuous variables have been winsorized at the 1% and 99% levels
during the initial data processing stage.
Operating Revenue (ln_revenue): The log of operating revenue for the sample
firms has a mean value of 21.459 and a standard deviation of 1.469. A significant span
exists between the maximum and minimum values, indicating that the sample covers
listed companies of various operating scales. This variation provides a solid
foundation for evaluating the heterogeneous impacts of digital transformation on
corporate operating revenue.
Degree of Digital Transformation (ln_digital): As the core independent
variable of this study, its mean value is 3.034 with a standard deviation of 1.282.
Notably, the minimum value is 0.000, while the maximum value reaches 5.932. This
wide range clearly reflects a striking "great divergence" in the digital transformation
process among Chinese listed companies at this stage: some traditional or
conservative firms have not yet substantially initiated digital deployment, whereas
leading firms have achieved deep integration of digital technologies. This substantial
individual variation provides an ideal data setting for capturing the marginal
economic consequences of digital transformation.
Research and Development Investment (ln_rd): The sample mean is 9.791
with a standard deviation of 9.044. The immense gap between the extreme values
(ranging from a minimum of 0.000 to a maximum of 21.364) indicates that the
innovation-driven strategies among listed companies are highly diversified, with some
firms depending heavily on R&D inputs, while others show insufficient innovative
momentum.
Financing Constraints (KZ Index): The mean value is 1.312 with a standard
deviation of 2.420. The KZ index ranges from -6.340 to 7.226, illustrating that the
financial friction and financing pressures faced by the sample firms in the capital
３０
market vary drastically.
Basic Corporate Characteristics: Corporate size (ln_size) has a mean of 22.197,
and corporate age (ln_age) has a mean of 2.074, suggesting that the majority of the
sample firms are in mature and stable stages of development.
Corporate Governance and Market Structure: The average board size
(boardsize1) is approximately 8.4 individuals. The shareholding ratio of the largest
shareholder (shrcr1) averages 33.597%, indicating that ownership concentration
generally falls within a reasonable and relatively controlling range. The mean value of
major shareholders' fund misappropriation (ShareholdersOccupy) is -0.019. The
industry concentration index (HHI_A) has a mean of 0.123, showing that the sample
industries overall exhibit a monopolistically competitive or fully competitive
landscape.
Table 4.1
Descriptive statistics of variables
Variable
ln revenue
ln digital
In sentence
ln size
ln age
boardsize1
shrcr1
ShareholdersOccupy
HHI A
KZ
ln rd
Obs
40507
40507
40507
40507
40507
40507
40507
40507
40465
40507
40507
Mean
21.459
3.034
2.891
22.197
2.074
8.412
33.597
-.019
.123
1.312
9.791
Std. Dev.
1.469
1.282
1.221
1.306
.931
1.616
14.801
.048
.132
2.42
9.044
Min
18.327
0
0
19.77
0
5
8.217
-.275
.022
-6.34
0
Max
25.691
5.932
5.631
26.31
3.401
14
73.984
.096
.786
7.226
21.364
4.2 Correlation Analysis
Before implementing multi-variable panel regressions, a preliminary statistical
screening was executed to map the linear interrelationships among the key variables.
３１
Table 4.2 reports the comprehensive Pearson correlation matrix covering the primary
dependent variable (ln_revenue), the core independent variable (ln_digital), the
alternative independent proxy (ln_sentence), the key mediating mechanism variable
(ln_rd), and the firm-level control covariates (including KZ).
4.2.1 Preliminary Verification of Core Hypotheses and Mechanisms
As outlined in the full correlation matrix, the pairwise correlation coefficient between
the core independent variable (ln_digital) and the dependent variable (ln_revenue) is
0.114, which is statistically significant at the 1% level (p < 0.01). This positive
association provides an initial baseline indication that aligns with the theoretical
direction of Hypothesis H1, suggesting that deep corporate digital transformation is
empirically associated with top-line revenue expansion. Furthermore, the alternative
proxy for digital transformation (ln_sentence) shows a consistent positive correlation
of 0.111 with ln_revenue (p < 0.01), supporting the reliability of the core indicator
configuration.
The Strategic Innovation Leg: Digital transformation (ln_digital) exhibits a
robust and significant positive correlation with the mediating variable, research and
development investment (ln_rd, coefficient = 0.424, p < 0.01). Concurrently, the
alternative digital index (ln_sentence) tracks closely with ln_rd at 0.428 (p < 0.01).
The Innovation-Performance Link: In turn, the mediating variable (ln_rd)
displays a significant positive relationship with the ultimate outcome metric
(ln_revenue; coefficient = 0.136, p < 0.01).
These statistical trends logically conform to the anticipated mediation
framework proposed in this study, providing preliminary micro-level evidence that
corporate digitalization is closely tied to increased R&D commitments, which
subsequently connect to enhanced operational scale.
4.2.2 Econometric Diagnostics for Multicollinearity
Furthermore, a comprehensive diagnostic tracking of the correlation matrix
３２
coordinates reveals that the vast majority of absolute correlation coefficients between
any two explanatory variables or controlling covariates fall well below the critical
econometric threshold of 0.80. While firm size (ln_size) and operating revenue
(ln_revenue) demonstrate a strong positive correlation (0.881, p < 0.01), this closely
aligns with standard corporate finance intuition, where larger corporate entities
systematically command higher baseline book values of operating revenue. "As
presented in the correlation matrix, the Pearson correlation coefficient between firm
size (ln_size) and operating revenue (ln_revenue) exhibits a heightened value of 0.881.
Given that ln_size represents a firm’s structural scale and ln_revenue reflects its
scale-dependent output, a strong linear association is theoretically anticipated in
corporate finance literature. To verify whether this high correlation induces severe
multicollinearity that could potentially distort the empirical parameters, a formal
Variance Inflation Factor (VIF) diagnostic was executed following the regression. The
empirical results indicate that the maximum VIF among all explanatory and control
variables remains strictly below the conservative threshold of 10 (with a mean VIF
significantly lower than 5). Consequently, the linear interdependence between ln_size
and ln_revenue does not compromise the structural stability, identification efficiency,
or statistical inference of the primary independent estimator (ln_digital)."
According to established econometric conventions, severe multicollinearity
typically distorts conditional estimations only when the absolute correlation
coefficients among independent variables widely exceed 0.80. Since the pairwise
correlations among the independent covariates remain within standard safety
boundaries, this systematic dispersion indicates that the selected indicators
successfully capture distinct financial, strategic, and governance dimensions of
corporate operations, lowering the prospective probability of severe multicollinearity
within
the
baseline
model
specifications
high-dimensional fixed effects panel regression.
３３
and
validating
the
choice
of
Table 4.2
Pairwise correlations
Variables
(1) ln_revenue
(2) ln_digital
(1)
1.000
0.114*
(0.000)
(3) ln_sentence
0.111*
(0.000)
(4) ln_size
0.881*
(0.000)
(5) ln_age
0.333*
(0.000)
(6) boardsize1
0.245*
(0.000)
(7) shrcr1
0.198*
(0.000)
(8) ShareholdersOc~y
0.036*
(0.000)
(9) HHI_A
0.053*
(0.000)
(10) KZ
0.107*
(0.000)
(11) ln_rd
0.136*
(0.000)
*** p<0.01, ** p<0.05, * p<0.1
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
1.000
0.994*
(0.000)
0.092*
(0.000)
-0.097*
(0.000)
-0.080*
(0.000)
-0.101*
(0.000)
0.103*
(0.000)
-0.051*
(0.000)
-0.076*
(0.000)
0.424*
(0.000)
1.000
0.091*
(0.000)
-0.100*
(0.000)
-0.081*
(0.000)
-0.102*
(0.000)
0.103*
(0.000)
-0.051*
(0.000)
-0.079*
(0.000)
0.428*
(0.000)
1.000
0.391* 1.000
(0.000)
0.268* 0.144*
(0.000) (0.000)
0.192* -0.091*
(0.000) (0.000)
0.015* -0.188*
(0.003) (0.000)
0.092* 0.069*
(0.000) (0.000)
0.132* 0.402*
(0.000) (0.000)
0.126* -0.034*
(0.000) (0.000)
1.000
0.020*
(0.000)
-0.011*
(0.032)
0.049*
(0.000)
0.051*
(0.000)
-0.109*
(0.000)
1.000
0.002
(0.639)
0.070*
(0.000)
-0.118*
(0.000)
-0.091*
(0.000)
1.000
-0.022*
(0.000)
-0.191*
(0.000)
0.067*
(0.000)
1.000
0.028*
(0.000)
-0.073*
(0.000)
1.000
-0.026*
(0.000)
1.000
Note: Parentheses contain p-values rather than t-statistics. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively.
３４
4.3 Baseline Regression Analysis and Hypothesis Testing
To systematically evaluate the direct impact of corporate digital transformation on
operating revenue and test Hypothesis H1, this study estimates a multi-variable panel
data regression model with dual fixed effects. Column (1) displays the bivariate
baseline regression without control covariates, while Column (2) reports the fully
specified model incorporating firm-level corporate governance indicators, financial
characteristics, and industry structural controls alongside high-dimensional fixed
effects.
Impact of the Core Independent Variable: In the baseline bivariate
specification reported in Column (1), the estimated coefficient of digital
transformation frequency (ln_digital) on operating revenue (ln_revenue) is 0.160,
passing the significance test at the 1% level (t = 13.613). This demonstrates a
substantial, unconditional positive relationship between digitalization and corporate
revenue scale when omitting other firm-level characteristics.
In the fully specified model presented in Column (2), which accounts for the
full suite of micro-level control variables, the coefficient of ln_digital converges to
0.032 but maintains its statistical significance at the 1% level (t = 4.864). From an
economic standpoint, this coefficient indicates that, ceteris paribus, a 1% increase in a
firm's digital transformation intensity is associated with a marginal expansion of 0.032%
in operating revenue. This empirical finding provides rigorous statistical backing for
Hypothesis H1. It suggests that by deeply integrating digital technologies and data
assets into corporate operations, enterprises can refine business processes, alleviate
transactional information asymmetries, and reconstruct market boundaries, thereby
consistently enhancing top-line revenue generation capacity.
Performance of Control Covariates: * Firm Size (ln_size): Displays a heavily
significant positive effect on revenue (coefficient = 0.867, t = 45.623), confirming that
larger enterprises command extensive operating networks and substantial asset
accumulation, which naturally yields a higher baseline operating scale.
３５
Firm Age (ln_age): Shows a positive coefficient of 0.038 that is significant at
the 1% level (t = 3.028), suggesting that more established firms benefit from
accumulated operational experience and stable market shares. Funds Occupied by
Large Shareholders (ShareholdersOccupy): Exhibits a positive relationship significant
at the 1% level (coefficient = 0.464, t = 3.656), which may reflect a concurrent book
value phenomenon related to specific related-party fund allocations in the short term.
Industry Concentration (HHI_A): Captures a significant negative coefficient of -0.297
(t = -3.377). Since a higher Herfindahl-Hirschman Index denotes greater market
concentration (approaching monopoly), this negative coefficient suggests that heavily
concentrated industry structures tend to compress the expansionary drive of individual
micro-entities, whereas a competitive market ecology is more conducive to
optimizing corporate revenue scale.
Table 4.3
Baseline Regression Analysis
(1)
(ln_revenue)
0.160***
(13.613)
ln_digital
ln_age
ln_size
boardsize1
shrcr1
ShareholdersOccupy
HHI_A
20.978***
(587.367)
40241
0.893
0.879
Constant
Observations
R-squared
Adj. R-squared
t statistics in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
３６
(2)
ln_revenue
0.032***
(4.864)
0.038***
(3.028)
0.867***
(45.623)
0.004
(0.999)
0.001
(0.692)
0.464***
(3.656)
-0.297***
(-3.377)
2.023***
(4.800)
40199
0.949
0.943
4.4 Double Mediation Mechanism Path Testing
To systematically investigate the underlying mechanisms through which corporate
digital transformation empowers firm operating revenue, this study constructs a
double mediation framework. Following the theoretical hypotheses established in
Chapter 3, the transmission pathways are examined from two distinct dimensions: the
internal strategic innovation channel (proxied by the logarithm of research and
development expenditures, ln_rd) and the external financial friction channel (proxied
by the Kaplan-Zingales financing constraints index, KZ). Based on the standard
stepwise
mediation
evaluation
convention,
the
empirical
results
utilizing
high-dimensional fixed effects panel regressions are consolidated and reported in
Table 4.4 and 4.5.
4.4.1 The Internal Technical Innovation Pathway
Columns (2) and (3) of Table 4.4 present the empirical verification of the technical
innovation transmission channel utilizing the logarithm of R&D expenditures (ln_rd)
as the mediating variable.
First-Stage Regression (Column 2): When modeling R&D investment (ln_rd)
as the dependent variable, the estimated coefficient of digital transformation
(ln_digital) is 0.4288, which is statistically significant at the 1% level (t = 8.227). This
indicates that digital transformation provides a robust driving force that motivates
firms to scale up their research and development commitments. Practically, the
integration of digital technologies lowers information search frictions and minimizes
the misallocation of innovative resources.
Second-Stage Regression (Column 3): Upon incorporating the mediating
variable ln_rd into the baseline specification, the coefficient of R&D investment on
operating revenue (ln_revenue) is significantly positive (0.0131, t = 7.615), proving
that technology accumulation and innovative updates directly expand a firm’s
operational performance scale. Concurrently, the coefficient of the core independent
３７
variable ln_digital slightly decreases from 0.0316 in the benchmark model to 0.0260,
while maintaining its significance at the 1% level (t = 3.988).
This joint statistical significance demonstrates that the technological
innovation pathway plays a valid partial mediation role in transmitting the effects of
digitalization to corporate operating revenue. Consequently, Hypothesis H2 is
strongly supported by the empirical data.
Table 4.4
The Internal Technical Innovation Pathway
(ln_digital)
(ln_age)
(ln_size)
(boardsize1)
(shrcr1)
(ShareholdersOccupy)
(HHI_A)
(1)
(ln_revenue)
0.0316***
(4.864)
0.0376***
(3.028)
0.8674***
(45.623)
0.0045
(0.999)
0.0006
(0.692)
0.4643***
(3.656)
-0.2966***
(-3.377)
(2)
(ln_rd)
0.4288***
(8.227)
0.8540***
(8.610)
0.9526***
(9.925)
-0.0291
(-0.893)
-0.0247***
(-4.268)
1.0388
(0.971)
-2.6017***
(-4.486)
2.0227***
(4.800)
40199
0.9495
0.9428
-13.0756***
(-6.307)
40199
0.9073
0.8950
(ln_rd)
Constant
Observations
R-squared
Adj. R-squared
t statistics in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
３８
(3)
(ln_revenue)
0.0260***
(3.988)
0.0264**
(2.212)
0.8550***
(44.303)
0.0048
(1.093)
0.0009
(1.110)
0.4507***
(3.556)
-0.2625***
(-3.089)
0.0131***
（7.615）
2.1940***
(5.189)
40199
0.9501
0.9435
4.4.2 The External Financing Constraints Pathway
Columns (2) and (3) of Table 4.5 present the empirical verification of the external
financial friction channel utilizing the Kaplan-Zingales index (KZ) as the mediating
metric.
First-Stage Regression (Column 2): Modeling the KZ index as the dependent
variable, the estimated coefficient of digital transformation (ln_digital) is significantly
negative (-0.0545, t = -2.680). As a lower KZ index score means alleviation of capital
rationing and a reduction in external financial friction, this negative coefficient shows
that corporate digitalization mitigates external financing constraints mainly by
improving information transparency and optimizing the firm's creditworthiness
environment.
Second-Stage Regression (Column 3): When ln_digital and the KZ index are
included in the regression, the coefficient of financing constraints on operating
revenue is significantly negative (-0.0076, t = -3.032), indicating that lower external
financial friction (lower KZ value) frees up corporate expansion capacity and drives
revenue. Concurrently, the coefficient of ln_digital remains robustly significant at
0.0312 (t = 4.800).
The mechanism analyses show that the enabling effect of digital
transformation on Chinese listed enterprises' operating revenue is not a simple direct
shock. Instead, it works through a dual-channel network of "awakening internal
technological innovation" and "alleviating external financial frictions."
３９
Table 4.5
The External Financing Constraints Pathway
(ln_digital)
(ln_age)
(ln_size)
(boardsize1)
(shrcr1)
(ShareholdersOccupy)
(HHI_A)
(1)
(ln_revenue)
0.0316***
(4.864)
0.0376***
(3.028)
0.8674***
(45.623)
0.0045
(0.999)
0.0006
(0.692)
0.4643***
(3.656)
-0.2966***
(-3.377)
(2)
(KZ)
-0.0545***
(-2.680)
1.9710***
(43.500)
-0.3105***
(-8.092)
-0.0033
(-0.247)
-0.0019
(-0.776)
-4.6843***
(-13.167)
-0.1750
(-1.025)
2.0227***
(4.800)
40199
0.9495
0.9428
4.3036***
(5.215)
40199
0.6857
0.6439
(KZ)
Constant
Observations
R-squared
Adj. R-squared
t statistics in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
(3)
(ln_revenue)
0.0312***
(4.800)
0.0526***
(3.989)
0.8651***
(45.597)
0.0044
(0.994)
0.0006
(0.675)
0.4288***
(3.389)
-0.2979***
(-3.393)
-0.0076***
(-3.032)
2.0554***
(4.881)
40199
0.9495
0.9428
4.5 Robustness Check: Dimensional Decomposition and Mechanism Validation
of Digital Transformation
To ensure the statistical validity of the baseline findings and rule out the possibility
that the positive nexus between digital transformation and corporate performance is
driven by measurement errors or potential endogeneity, this section conducts
comprehensive robustness assessments. The evaluation matrix consists of three
primary strategies: alternative measurement of the dependent variable, alternative
measurement of the independent variable, and a lagged independent variable approach.
The consolidated empirical results are summarized in Table 4.6.
４０
4.5.1 Alternative Measurement of the Dependent Variable
In the baseline analysis, corporate performance is operationalized using the logarithm
of operating revenue (ln_revenue). To verify whether the growth premium enabled by
digitalization systematically translates into bottom-line profitability, this study
reconstructs the dependent variable by using the logarithm of corporate net profit
(ln_profit) as an alternative proxy."To ensure the operational resilience of the primary
findings, this study introduces the natural logarithm of operating profit (ln_profit$ as
an alternative dependent variable. While the baseline dependent variable (ln_revenue)
effectively captures top-line market expansion and business scale, ln_profit reflects
net bottom-line efficiency, cost-control capabilities, and overall financial health.
Utilizing ln_profit as an alternative performance indicator guarantees that the
identified promotional effects of digitalization are not merely artifactual drivers of
revenue volume, but genuinely contribute to structural profitability and economic
value added."
The empirical estimation under this alternative formulation is reported in
Column (2) of Table 4.6. The regression coefficient for digital transformation
(ln_digital) yields a value of 0.054, which passes the statistical significance threshold
at the 1% level (t = 4.452). This statistical coordinate demonstrates that the corporate
digital transition does not merely yield a top-line volume expansion but
simultaneously exerts a significant positive impact on firm-level net profitability.
Hence, the baseline conclusion remains robust across different financial dimensions
of corporate growth.
4.5.2 Alternative Measurement of the Independent Variable
The baseline model utilizes text-mining word frequency counts (ln_digital) to capture
the intensity of corporate digital deployment. However, single-word frequencies may
contain measurement noise due to textual context differences or corporate narrative
variations. To circumvent potential measurement errors, this study employs the
４１
logarithm of the total number of sentences mapping digital features in the annual
reports (ln_sentence) as a more refined alternative index.
As illustrated in Column (3) of Table 4.6, when using this structurally
decomposed independent variable, the estimated coefficient of ln_sentence is 0.032
and remains highly significant at the 1% level (t = 4.663). The consistency in both the
magnitude and significance of this coefficient proves that even when changing the
analytical granularity of textual disclosure from specific word tokens to
contextualized sentences, the empowering role of digitalization on revenue growth
remains structurally sound.
4.5.3 Addressing Endogeneity: The Lagged Independent Variable Approach
To mitigate potential reverse causality—wherein firms with expanding operating
revenue are more financially capable of initiating digital strategies—this study
re-estimates the model by lagging the independent variable by one fiscal period
(L1_ln_digital). This temporal separation ensures that the digital transformation
precedes the observed financial outcomes, blocking instantaneous feedback loops.
The lagged regression outcome is detailed in Column (4) of Table 4.6. The
coefficient of L1_ln_digital is estimated at 0.030 and is statistically significant at the 1%
level (t = 4.472). This significant positive effect confirms that historical digital
investment serves as a continuous driver for subsequent revenue streams. The fact that
the positive impact persists under a lagged specification strongly suggests that the
baseline findings are not severely contaminated by simultaneous endogeneity bias.
The multi-dimensional testing matrix reported in Table 4.6 demonstrates high
data resilience. Across all specifications involving alternative variables or dynamic
temporal lags, the positive coefficient on digital transformation remains highly stable
and consistently significant at the 1% level, validating the baseline empirical findings
of this thesis.
４２
Table 4.6
Robustness Test – Subdimension Decomposition
(ln_digital)
(ln_age)
(ln_size)
(boardsize1)
(shrcr1)
(ShareholdersOccupy)
(HHI_A)
(1)
Baseline
Regression
(2)
ln_profit
0.032***
(4.864)
0.038***
(3.028)
0.867***
(45.623)
0.004
(0.999)
0.001
(0.692)
0.464***
(3.656)
-0.297***
(-3.377)
0.054***
(4.452)
-0.390***
(-19.410)
0.905***
(37.768)
-0.001
(-0.161)
0.003**
(2.232)
-0.139
(-0.567)
0.160
(1.348)
(ln_sentence)
(3)
ln_sentence L1_ln_digital
0.038***
(3.024)
0.868***
(45.689)
0.004
(1.000)
0.001
(0.681)
0.465***
(3.661)
-0.296***
(-3.372)
0.032***
(4.663)
L1_ln_digital
Constant
2.023***
(4.800)
40199
0.943
-0.704
(-1.366)
34576
0.761
Observations
r2_a
t statistics in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
(4)
2.016***
(4.786)
40199
0.943
0.069***
(3.263)
0.861***
(42.520)
0.005
(0.964)
0.001
(0.941)
0.448***
(3.447)
-0.301***
(-3.131)
0.030***
(4.472)
2.094***
(4.701)
34322
0.943
4.6 Heterogeneity Analysis
Although the preceding baseline regressions and mechanism tests confirm that digital
transformation exerts a widespread empowering effect on corporate operating revenue,
the release of this digital dividend may exhibit non-uniform characteristics due to
profound structural variations in resource endowments, such as ownership structures,
firm sizes, and geographic locations. To shed light on the boundaries of how
digitalization empowers operational performance, this section bifurcates the sample
into distinct subsamples along these three structural dimensions for multi-group panel
fixed effects estimations.
４３
4.6.1 Property Rights Heterogeneity
Table 4.7 presents the heterogeneity estimation results across different corporate
ownership types. The baseline regression indicates that in the sample of State-Owned
Enterprises (SOEs), the impact coefficient of digital transformation on operating
revenue is 0.049, statistically significant at the 1% level. In contrast, for
non-State-Owned Enterprises (non-SOEs), the coefficient is only 0.018, significant at
the 5% level. This suggests that the revenue-driving effect of digital transformation is
substantially stronger in SOEs.
The underlying economic logic for this finding is twofold: First, there are
significant disparities in the 'resource scale effects' of digital transformation. Digital
transformation is a capital-intensive, high-risk, and long-cycle systemic project that
entails substantial sunk costs, such as the establishment of industrial internet
platforms and data mid-offices. SOEs generally possess massive asset scales, stable
cash flows, and high risk tolerance, allowing them to achieve economies of scale in
digital investments and more rapidly translate digital technologies into revenue
increments.
Second, SOEs benefit from a natural alignment with policy dividends. Under
the macro-strategy of vigorously developing the digital economy, SOEs, as the
vanguard of the national economy, enjoy preferential access to government support,
including special fiscal subsidies, tax incentives, and low-interest policy loans for
digitalization. This powerful external resource endowment heavily amplifies the
revenue-generating efficacy of their digital pursuits. Non-SOEs, however, frequently
encounter financial constraints such as 'financing hurdles and high borrowing costs'
during transformation. Consequently, their digital investments tend to pivot toward
short-cycle, localized business applications (such as simple online marketing), which
struggle to exert a strategic, structural pull on overall operating revenue in the short
run.
４４
Table 4.7
Property Rights Heterogeneity
(1)
SOEs
0.049***
(4.577)
0.059**
(2.012)
0.787***
(19.286)
0.007
(0.940)
0.001
(0.682)
0.526**
(2.276)
-0.463***
(-3.195)
3.818***
(4.127)
13198
0.954
0.948
(ln_digital)
(ln_age)
(ln_size)
(boardsize1)
(shrcr1)
(ShareholdersOccupy)
(HHI_A)
Constant
Observations
R-squared
Adj. R-squared
t statistics in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
(2)
non-SOEs
0.018**
(2.224)
0.008
(0.555)
0.893***
(47.682)
0.002
(0.487)
0.001
(0.788)
0.418***
(2.749)
-0.174*
(-1.751)
1.507***
(3.650)
26943
0.942
0.933
4.6.2 Firm Size Heterogeneity
A firm's operational scale directly dictates its risk-hedging capacity, structural agility,
and capital orchestration space. Utilizing the median value p50 of the firm size
logarithm (ln_size) as the demarcation line, this study divides the sample into
"Large-scale Firms" and "Small-scale Firms." The corresponding estimation results
are summarized in Columns (1) and (2) of Table 4.8.
Interpretation of Empirical Data: In the large-scale firm group reported in
Column (1), the estimated coefficient of the core independent variable ln_digital is
0.041, passing the significance test at the 1% level (t = 4.901). For the small-scale
firm group reported in Column (2), the regression coefficient is 0.024, which also
maintains robust statistical significance at the 1% level (t = 2.861).
４５
Economic Mechanism Analysis: Although digital deployment serves as a valid
revenue driver across both groups, the digital empowerment efficiency of large-scale
enterprises is approximately 70.8% higher than that of their smaller counterparts. This
finding unveils a typical "increasing marginal scale effect" of the digital economy at
the micro-firm level. Large-scale corporations benefit from standardized workflows,
multi-tiered application scenarios, and comprehensive data asset accumulation,
enabling deep structural integration of digital modules across R&D, supply chains,
and marketing channels. On the contrary, small-scale enterprises are routinely
constrained by the financial barriers of digital implementation and a shortage of
professional tech talent. Their digital practices often linger at the surface level of basic
hardware procurement, resulting in a lower conversion rate of digital inputs into
top-line revenue.
Table 4.8
Firm Size Heterogeneity
(ln_digital)
(ln_age)
(ln_size)
(boardsize1)
(shrcr1)
(ShareholdersOccupy)
(HHI_A)
Constant
Observations
R-squared
Adj. R-squared
t statistics in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
(1)
Large-scale Firms
0.041***
(4.901)
0.001
(0.025)
0.890***
(37.106)
0.000
(0.036)
-0.001
(-0.528)
0.629***
(3.335)
-0.206***
(-2.785)
1.764***
(3.234)
19860
0.944
0.935
４６
(2)
Small-scale Firms
0.024***
(2.861)
0.049***
(2.940)
0.881***
(41.050)
0.005
(0.881)
0.000
(0.142)
0.262*
(1.823)
-0.097
(-0.997)
1.575***
(3.515)
19931
0.876
0.851
4.6.3 Regional Distribution Heterogeneity
Table 4.9 reports the exploratory heterogeneity estimation results across geographic
regions. The empirical estimates demonstrate that the digital transformation
coefficient for enterprises located in the Central and Western regions is 0.035, while
the corresponding coefficient for enterprises in the Eastern region is 0.029, with both
estimates being statistically significant at the 1% level. This descriptive variance
indicates that the positive association between digital transformation and corporate
operating
revenue
manifests
with
asymmetrical
intensity
across
different
macroeconomic territories.
While the Eastern region conventionally possesses more sophisticated digital
infrastructure and a higher concentration of technology-led market participants, the
marginally higher coefficient observed in the inland provinces could be tentatively
interpreted through two theoretical perspectives. First, from the framework of the law
of diminishing marginal returns, enterprises in the Eastern region, acting as early
adopters of the digital economy, may have achieved a state of relative maturity in
baseline information technology deployments. Consequently, further incremental
investments in digitalization within these highly saturated corporate environments
might yield standardized marginal returns regarding top-line revenue expansion.
Second, the descriptive divergence points toward a potential late-mover
pattern or suggestive catch-up tendency among enterprises within the Central and
Western regions. Characterized by a lower initial baseline of digital adoption, these
enterprises may experience more pronounced marginal gains when introducing
standardized digital applications, potentially supported by supportive policy
frameworks such as the national "East Data West Computing" strategy. However,
because the current econometric specification does not incorporate explicit contextual
channels or structural variables to formally identify a regional catching-up mechanism,
this geographic variation should be interpreted with caution. It is presented primarily
as an exploratory empirical pattern, demonstrating that the operational implications of
４７
corporate digitalization are subject to geographical and institutional boundary
conditions.
Table 4.9
Regional Distribution Heterogeneity
(ln_digital)
(ln_age)
(ln_size)
(boardsize1)
(shrcr1)
(ShareholdersOccupy)
(HHI_A)
Constant
(1)
eastern enterprises
0.029***
(3.716)
0.040***
(2.741)
0.882***
(38.930)
0.001
(0.244)
0.001
(0.717)
0.624***
(3.860)
-0.321***
(-2.972)
1.744***
(3.487)
28608
0.952
0.945
Observations
R-squared
Adj. R-squared
t statistics in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
４８
(2)
central and western enterprises
0.035***
(3.020)
0.052**
(2.249)
0.846***
(29.703)
0.017**
(2.011)
0.000
(0.237)
0.167
(0.820)
-0.143
(-1.217)
2.316***
(3.776)
11578
0.949
0.943
CHAPTER 5
CONCLUSION AND IMPLICATIONS
5.1 Research Conclusions
This study empirically examined the impact of corporate digital transformation on
operating revenue using a sample of Chinese A-share listed companies from 2012 to
2022. The findings are summarized as follows:
Primary Association Framework (H1): Consistent with Hypothesis H1,
corporate digital transformation (ln_digital) maintains a statistically significant
positive association with corporate operating revenue (ln_revenue). This empirical
evidence indicates that the integration of digital architectures correlates with top-line
market expansion and business scale proliferation. This statistical pattern aligns with
strategic management literature, suggesting that the structural implementation of
digital technologies enables enterprises to reconfigure organizational routines and
expand their market boundaries (Hanelt et al., 2021; Verhoef et al., 2021; Zhai et al.,
2022). However, given the observational nature of the panel dataset, this relationship
is framed as a robust statistical association derived via a panel fixed-effects
identification strategy rather than a definitive causal mechanism.
Suggestive Transmission Channels (H2 & H3): The empirical results present
suggestive evidence regarding parallel transmission channels, though they should be
interpreted with caution as formal indirect-effect testing warrants further specialized
statistical verification in future inquiries:
The Innovation-Driven Path (H2): The estimates suggest that digitalization is
positively associated with internal technological exploratory commitments (ln_rd),
which subsequently correlates with enhanced revenue outcomes, potentially adjusting
R&D trial-and-error costs (Wen et al., 2022).
４９
The Financing Channel Path (H3): Regarding the financing constraint
mechanism, a precise clarification of the sign logic for the Kaplan-Zingales (KZ)
index is required. Because a higher numerical value of the KZ index mathematically
operationalizes a more severe degree of corporate financing distress, corporate
digitalization is found to be negatively associated with the KZ index, while the KZ
index is negatively associated with operating revenue. This interlocking sign
directional logic indicates a plausible mediation channel whereby digitalization
mitigates informational asymmetries, thereby optimizing external credit accessibility
and positively correlating with high-growth operations (He et al., 2023).
Exploratory Heterogeneity Conditions (H4 & Additional Tests): The
operational implications of corporate digitalization exhibit asymmetrical descriptive
patterns across different institutinal and geographic domains:
Ownership Heterogeneity (H4): In terms of ownership characteristics, the
baseline estimated coefficient for State-Owned Enterprises (SOEs) appears
descriptively higher than that observed for non-SOEs. This descriptive variance
reflects the institutional landscape of emerging markets, where SOEs utilize their
resource endowments, policy provisions, and infrastructure access to absorb digital
infrastructure (Qiao et al., 2025). Nevertheless, because formal cross-group
coefficient equality tests or structural interactive specifications are not explicitly
modeled, this descriptive variance does not imply a statistically significant difference
in operational sensitivity between the two sectors and must be interpreted with strict
academic caution.
Additional Regional Heterogeneity: Supplementary regional exploratory
estimations show that the estimated coefficient for enterprises in the Central and
Western regions is descriptively larger than that for enterprises located in the Eastern
region. While this pattern potentially hints at a late-mover advantage or descriptive
catch-up tendency facilitated by lower digital technology replication costs in inland
areas, it is presented strictly as an auxiliary exploratory empirical pattern rather than a
primary hypothesis-driven contribution.
５０
5.2 Policy Recommendations
Based on the empirical evidence delineated in this study, the following concise and
evidence-based recommendations are formulated for corporate executives and
regional policy coordinators:
Deepen the Multi-Dimensional Integration of Digital Systems with Core R&D
Frameworks. Mechanism analysis indicates that R&D investment represents a highly
plausible parallel channel linking digital adoption to top-line performance. Therefore,
corporate managers should avoid superficial or fragmented digital adoption, such as
treating digitalization merely as an administrative or peripheral IT hardware
procurement task. Instead, enterprises must deeply embed digital applications into
core research and development workflows (Bharadwaj et al., 2013). By deploying
advanced technological architectures—such as digital-twin R&D platforms and
data-driven demand forecasting systems—firms can systematically optimize the
allocation efficiency of innovation inputs (Feng et al., 2022). This strategic alignment
ensures that digital investments generate substantive technological upgrades that
support long-term revenue growth, rather than serving as strategic window dressing.
Mitigate Capital Market Frictions Through Targeted Credit Allocation
Channels. Given that the mitigation of financing constraints (the KZ index pathway)
represents an important structural channel for operational expansion, financial
regulatory institutions should design specialized financial instruments to reduce
capital frictions during the initial stages of corporate transformation. Financial entities
are encouraged to innovate credit assessment frameworks, such as piloting modular
digital asset-pledged lending programs tailored specifically for private enterprises and
small-and-medium enterprises (SMEs) that face higher asset-liability friction.
Concurrently, highly digitized industrial leaders should be encouraged to enhance the
accessibility of their industrial internet platforms and technical mid-offices (Hein et
al., 2020). Implementing collaborative "platform-sharing" frameworks allows smaller
market participants to access low-cost, modular digital tools, thereby lowering the
initial capital friction and entry barriers for resource-constrained firms.
５１
Implement Balanced Regional Digital Frameworks Aligned with Local
Absorptive Capacities. Reflecting the additional empirical finding that enterprises in
the Central and Western regions demonstrate a descriptively high revenue sensitivity
to digital deployment, macroeconomic policy should continue to support regional
digital infrastructure integration. Policy coordinators should maintain targeted fiscal
support
for
novel
digital
infrastructure
development—including
5G
telecommunication networks and centralized computing nodes—in inland economic
zones to narrow the geographic digital divide (Pan et al., 2021). However, these
supportive policies must avoid top-down strategic mandates; instead, public digital
resource allocation should be rigorously aligned with localized corporate absorptive
capacities to ensure that newly deployed digital infrastructure genuinely helps inland
firms overcome traditional geographic informational barriers.
5.3 Limitations and Directions for Future Research
To ensure methodological transparency, several structural limitations of the current
design must be formally acknowledged, providing clear pathways for future academic
inquiries: Research Limitations:
Observational Design and Omitted Variables: The empirical strategy relies on
an observational panel fixed-effects framework. Although firm and year dual fixed
effects absorb time-invariant individual characteristics and macro-temporal shocks,
the potential for time-varying, localized industry-year interactive disruptions cannot
be completely ruled out, meaning the results denote statistical associations rather than
definitive causal impacts.
Measurement Boundaries of Text Proxies: The independent variable utilizes
annual report keyword text-mining to capture digitalization. While widely accepted,
this index predominantly reflects managerial strategic disclosures and promotional
rhetoric rather than capturing actual granular financial expenditures, software
capitalization scales, or physical hardware integration.
５２
Performance Metric Scope: The dependent variable focuses exclusively on top-line
operating revenue. This operational scope leaves other critical dimensions of
bottom-line corporate performance—such as net margin structures, cost efficiency
ratios, or total factor productivity—unexamined within the baseline specification.
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