The Impact of Digital Transformation on Financial Performance and

Green Development: Evidence from Chinese Manufacturing

Companies

Mohammad Alzyod

, Ling Yi

and Mahmoud Al-Sayed

2,3

Department of Accounting, Business School, The Hashemite University, Zarqa, Jordan

Centre for Research in Accounting, Accountability and Governance (CRAAG), Department of Accounting,

Southampton Business School, University of Southampton, Southampton, U.K.

College of Business, University of Doha for Science and Technology, Doha, Qatar

Keywords: Corporate Digital Transformation, Financial Performance, Green Performance, Sustainability.

Abstract: Digital transformation, driven by advancements in Artificial Intelligence (AI), Big Data, and the Internet of

Things (IoT), has become essential for modern manufacturing companies in reshaping their manufacturing

processes and business strategies. While prior research has largely focused on the financial benefits of digital

transformation, its environmental implications remain underexplored. This study examines the dual impact of

digital transformation on financial performance and green development, using panel data from Chinese A-

share listed manufacturing firms between 2010 and 2021. Applying a multiple regression model, the analysis

integrates Schumpeterian innovation theory and the Resource-Based View (RBV) to provide a comprehensive

understanding of how digitalisation influences both economic and environmental outcomes. The findings

reveal that digital transformation significantly enhances financial performance while also promoting

sustainable business practices. By bridging the gap in existing literature, this study offers new insights into

the broader value of digital transformation, providing practical implications for corporate decision-makers

and policymakers seeking to align financial growth with sustainability objectives.

1 INTRODUCTION

The rapid growth of the Internet and digital

technologies has significantly transformed the global

economy, shifting it from traditional structures to a

digital economy. As Liu, Liu, and Ren (2023) note,

the world is transitioning into an era of digital

business driven by technological advancements. In

this context, digital transformation has become a

strategic priority for manufacturing companies

aiming to enhance competitiveness and achieve

sustainable development. By integrating advanced

technologies such as Artificial Intelligence (AI), Big

Data, and the Internet of Things (IoT), businesses can

optimise resource allocation, streamline processes,

and adapt to the demands of a changing industrial

landscape (Ismail, Khater, & Zaki, 2017; Su et al.,

2023).

However, beyond economic gains, an urgent

question arises: Can digitalization drive

environmental sustainability? Given growing

regulatory and stakeholder pressures, understanding

how digital transformation supports green

development is crucial, especially in the

manufacturing sector—a major contributor to

environmental impact.

While digital transformation has shown

significant potential to improve economic efficiency,

its role in supporting green development is

increasingly gaining attention. Studies highlight that

digitalisation can enhance eco-friendly practices by

reducing resource consumption and environmental

pollution while improving operational efficiency

(Che & Wang, 2022; Wei & Sun, 2021). Moreover,

stakeholders now expect corporations to demonstrate

greater environmental responsibility, further

motivating businesses to align digital initiatives with

green objectives (Sui & Yao, 2023). Despite its

promise, much of the existing literature focuses

primarily on the economic benefits of digital

transformation, neglecting its non-economic impacts,

particularly in the manufacturing sector.

Alzyod, M., Yi, L. and Al-Sayed, M.

The Impact of Digital Transformation on Financial Performance and Green Development: Evidence from Chinese Manufacturing Companies.

DOI: 10.5220/0013470200003956

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 7th International Conference on Finance, Economics, Management and IT Business (FEMIB 2025), pages 259-266

ISBN: 978-989-758-748-1; ISSN: 2184-5891

259

To address this gap, this study investigates the

relationship between digital transformation, financial

performance, and green development in

manufacturing enterprises. This research is grounded

in Schumpeterian innovation theory and the

Resource-Based View (RBV), which together

provide a theoretical framework for understanding

how firms leverage digital capabilities to enhance

both financial and environmental performance.

Schumpeterian innovation theory explains how

technological advancements drive business

transformation and competitive advantage, while

RBV highlights the role of firm-specific digital

resources in achieving sustainable performance

outcomes. The analysis is particularly relevant in the

context of China's manufacturing sector, which

despite being the world's largest carbon emitter, is

undergoing a rapid digital transformation to achieve

sustainable growth (Du, Xie, & Ouyang, 2017; Zhang

et al., 2023). As China intensifies its commitment to

green and intelligent manufacturing, it provides a

valuable context for understanding how digitalisation

can be leveraged to support both economic and

environmental sustainability.

This research seeks to answer the following key

questions: What is the impact of digital

transformation on the financial performance of

manufacturing companies? What is the impact of

digital transformation on the green development of

manufacturing firms?

Using data from Chinese A-share listed

manufacturing companies between 2010 and 2021,

this study employs textual and content analysis

alongside a panel two-way fixed-effects model to test

the hypotheses.

The contributions of this research are twofold.

First, it enriches the understanding of digital

transformation by exploring its impact on both

financial and green development, particularly in the

context of emerging markets. Second, by integrating

Schumpeterian innovation theory and the Resource-

Based View, this study develops a robust theoretical

and empirical framework that links digital

transformation to financial and environmental

outcomes.

The paper is structured as follows: Section 2

reviews the theoretical background and develops the

hypotheses. Section 3 outlines the research

methodology. Section 4 presents the findings and

discussion. Finally, Section 5 concludes with

reflections on the study’s implications and

limitations.

2 LITERATURE REVIEW

2.1 Theoretical Framework

Digital transformation is the process by which

businesses integrate advanced technologies, such as

Artificial Intelligence (AI), Big Data, and the Internet

of Things (IoT), into their operations. This

transformation allows companies to improve

processes, reduce inefficiencies, and adapt to

changing market demands. According to

Schumpeterian innovation theory, innovation is the

main driver of economic growth. Companies that

successfully adopt and implement digital

technologies gain a competitive edge by enhancing

their productivity and operational capabilities

(Anthony, 2021). The Resource-Based View (RBV)

theory complements this understanding by focusing

on the unique resources that a company can leverage

to achieve success. According to RBV, resources that

are valuable, rare, and hard to imitate—such as

advanced technologies, skilled employees, or

specialised knowledge—can help businesses sustain

long-term advantages (Abbasi Kamardi et al., 2022).

In terms of green development, RBV highlights how

firms can use digital tools and innovations to

implement sustainable practices, reduce

environmental harm, and meet regulatory

requirements (Okorie et al., 2023). By combining

these two theories, this study aims to explore how

digital transformation affects both financial

performance and green development, emphasising

the unique role of digital technologies in achieving

these dual goals.

2.2 Hypotheses Development

2.2.1 Digitisation and Financial

Performance

Digital transformation has a significant impact on

financial performance by improving efficiency,

reducing costs, and enhancing decision-making.

Technologies such as AI and Big Data enable

businesses to collect and process large amounts of

information, leading to better insights and faster

responses to market changes (Sun et al., 2022). For

example, digital tools help streamline production

processes by integrating data across different

departments, breaking down information silos, and

improving overall productivity (Hanelt et al., 2021).

Moreover, digital transformation allows

companies to strengthen relationships with their

customers by providing personalised services and

FEMIB 2025 - 7th International Conference on Finance, Economics, Management and IT Business

260

improved communication channels. These

advancements enable firms to respond to customer

demands more effectively, thereby increasing

customer satisfaction and loyalty (Gupta et al., 2020).

Additionally, businesses can use digital tools to

optimise their supply chains, enhance resource

allocation, and achieve cost savings (Bughin,

LaBerge, & Mellbye, 2017).

The competitive advantages gained through

digital transformation are especially important in

uncertain economic environments. By using digital

tools, businesses can better adapt to external changes,

improve operational flexibility, and maintain their

market position (Siachou, Vrontis, & Trichina, 2021).

H1a: Digital transformation in manufacturing

companies has a positive link with financial

performance.

2.2.2 Digitisation and Green Development

Digital transformation also plays a crucial role in

promoting green development. Advanced

technologies allow businesses to use resources more

efficiently, reduce waste, and minimise their

environmental footprint. For instance, AI-driven

systems can optimise energy use and identify ways to

reduce carbon emissions (Chen, 2022). Similarly, Big

Data analytics enables firms to monitor

environmental performance in real time, helping them

meet sustainability goals and comply with

environmental regulations (Wang, Wang, & Chen,

2022).

Furthermore, digital technologies encourage

collaboration and resource sharing among

organisations. This leads to innovative solutions for

green growth, such as shared energy systems or

collaborative waste management practices (Shang et

al., 2023). The integration of digital tools into

business operations creates a "double enhancement"

effect, where companies can simultaneously improve

production efficiency and achieve energy savings

(Chen, 2022).

While digital transformation may involve high

initial costs, these investments often yield long-term

benefits. Reduced information asymmetry, improved

transparency, and lower borrowing costs further

enhance the financial and environmental outcomes of

digital initiatives (Liu, Liu, & Ren, 2023).

H1b: Digital transformation in manufacturing

companies has a positive link with green

development.

3 METHODOLOGY

3.1 Sample Selection and Data Sources

This study examines the impact of digital

transformation on the financial performance and

green development of manufacturing companies. The

research focuses on A-share manufacturing

companies listed on the Chinese Stock Exchanges

between 2010 and 2021. This timeframe was chosen

to capture the evolution of digital transformation in

China's manufacturing sector, particularly in response

to government initiatives promoting digitalisation

and sustainability. The sample consists of 2,151

companies, selected due to their diverse

representation of the manufacturing sector, operating

under competitive market conditions and regulatory

oversight, making them well-suited for this analysis.

The data for corporate financial performance and the

Digital Transformation Index were obtained from the

CSMAR database, a widely recognised source for

research on China's capital market. Green

development was measured using Green Total Factor

Productivity (GTFP), calculated with the Slack-

Based Measurement (SBM) model and the Green

Manufacturing and Logistics (GML) index,

incorporating undesired outputs such as emissions.

Data for GTFP were sourced from the China

Statistical Yearbook, provincial and city-level

yearbooks, company annual reports, and the WIND

database.

3.2 Measurement of Variables

Green development in the manufacturing sector

reflects a balance between economic and

environmental performance, representing a "win-

win" situation for firms (Alexopoulos, Kounetas, and

Tzelepis, 2018). This study measures green

development using Green Total Factor Productivity

(GTFP), calculated with the Slack-Based

Measurement (SBM) model and the Green

Manufacturing and Logistics (GML) index. GTFP

evaluates inputs such as capital, labour, and expected

outputs, alongside undesired outputs like CO₂ and

SO₂ emissions. Capital input is calculated based on

capital stock changes, labour input is measured by the

total number of employees, and expected outputs are

proxied by total revenue. Undesired outputs are

estimated using pollutant emissions data, derived

from provincial and municipal statistical yearbooks.

This comprehensive approach ensures a robust

assessment of green development.

The Impact of Digital Transformation on Financial Performance and Green Development: Evidence from Chinese Manufacturing

Companies

261

Financial performance is assessed using Return

on Total Assets (ROA), a widely used indicator in

empirical research. ROA captures how efficiently a

firm utilises its assets to generate profits, making it a

reliable measure of economic performance and

facilitating comparisons with prior studies.

Digital transformation, a multifaceted concept, is

measured through text analysis of annual reports.

Python is used to identify keywords related to digital

technologies, such as "big data," "AI," "blockchain,"

and "cloud computing." A Digital Transformation

Quotient is then calculated by determining the

frequency of these terms relative to the total word

count in each report. To ensure credibility and

consistency, pre-compiled metrics from the CSMAR

database are also employed.

Following prior studies, several control variables

are included: (1) enterprise size, measured as the

natural logarithm of total assets; (2) enterprise age,

defined as the number of years since the firm’s

establishment; (3) growth rate, calculated as the year-

on-year revenue increase; (4) gearing ratio,

representing the ratio of total debt to equity; and (5)

equity concentration, expressed as the percentage of

shares held by the largest shareholder. These control

variables ensure that the analysis accounts for firm-

specific characteristics that may influence the

relationships among financial performance, green

development, and digital transformation.

3.3 Model Specification

Accordingly, the study employed the following

regression models to examine the relationships

among digital transformation, financial performance,

and green development:

𝐺𝐷

,

= 𝛽



+ 𝛽𝐷𝑖𝑔

,

+ ∑ 𝜂𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠

,

+ 𝜀

,

𝐹𝑃

,

= 𝛽



+ 𝛽𝐷𝑖𝑔

,

+ ∑ 𝜂𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠

,

+ 𝜀

,

In these models, the dependent

variable 𝐺𝐷

,

represents the degree of green

development exhibited by manufacturing firms, while

𝐹𝑃

,

captures the level of financial performance

achieved by the firms. The key independent variable

𝛽𝐷𝑖𝑔

,

reflects the extent of digital transformation

undertaken by the firm. Control variables, denoted as

𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠

,

, account for factors such as enterprise

size, age, growth rate, gearing ratio, and equity

concentration. Finally, 𝜀

,

represents the random

error term, capturing unexplained variation in the

models.

4 RESULTS AND DISCUSSION

4.1 Descriptive Statistics and

Univariate Analysis

Table 1. Summary Statistics.

Var. Obs. Mean SD Min Media

Max

FP 14550 5.8 30.06 -141.8 6.93 871.5

GD 14550 1.8 0.70 0.00 1.87 4.8

14550 0.8 0.85 0.00 0.55 14.9

Size 14550 9.5 0.51 8.66 9.52 11.1

Growth 14550 20.3 39.9 -29.36 9.79 245.7

14550 40.1 19.7 5.08 39.26 88.5

1 14550 56.5 14.91 22.19 56.91 87.9

14550 7.8 1.03 4.1 8.1 9.1

Table 1 summarises the descriptive statistics for

14,550 observations of 2,151 listed A-share

manufacturing companies. Among the sample firms,

financial performance (FP) has a mean of 5.809, a

standard deviation of 30.065, and ranges from -

1481.865 to 871.503, reflecting substantial variation

among firms. Green development (GD) averages

1.846, with a standard deviation of 0.702 and a range

of 0 to 4.828, indicating diverse environmental and

sustainability efforts. Digital Transformation (Dig)

has a mean of 0.844, ranging from 0 to 14.925. This

highlights a generally low level of digitalisation,

consistent with the early adoption phase of digital

transformation in China’s manufacturing sector. For

control variables, firm size (Size) shows a mean of

9.593 and minimal variation, with values between

8.660 and 11.123. Growth rates (Growth) vary

widely, averaging 20.308 with a range of -29.360 to

245.708. The gearing ratio (Lev) averages 40.189,

spanning from 5.089 to 88.583, reflecting diverse

financial strategies. Equity concentration (Top1) has

a mean of 56.585, ranging from 22.190 to 87.970,

indicating variations in ownership structure. Firm

ages range from 9 to 40 years, showing minimal

disparity. These variations across key variables

justify their inclusion in the study to understand how

digital transformation impacts financial and green

performance.

The univariate analysis, in Table 2, shows that the

correlation coefficient between digital transformation

and financial performance is 0.006, which is not

statistically significant with indicating that H1a

cannot be confirmed through correlation analysis.

FEMIB 2025 - 7th International Conference on Finance, Economics, Management and IT Business

262

The correlation coefficient between digital

transformation and green development is 0.042,

significant at the 1% level, indicating a weak positive

correlation. Although statistically significant, the low

degree of correlation suggests a minimal effect of

digital transformation on green development.

Advanced analytical methods, such as multiple

regression, are required to further explore these

relationships while controlling for other variables.

Firm size has a correlation coefficient of 0.350 with

green development, significant at the 1% level,

indicating a moderate positive relationship. This

suggests that larger firms are more likely to achieve

higher green development scores, presenting a

potential confounding variable. Similarly, leverage

has a correlation coefficient of 0.141 with green

development, also significant at the 1% level,

implying that firms with higher leverage tend to

perform better in green development. These

relationships highlight the need to account for these

factors when analysing the impact of digital

transformation on financial performance and green

development.

Table 2. Correlation analysis results

FP GD Dig Size Growth Lev Top1 Age

FP 1.000

GD 0.036*** 1.000

Dig 0.006 0.042*** 1.000

Size 0.044*** 0.350*** -0.050*** 1.000

Growth 0.136*** -0.064*** 0.003 -0.085*** 1.000

Lev -0.171*** 0.141*** -0.049*** 0.480*** -0.157*** 1.000

Top1 0.098*** 0.014* 0.052*** 0.007 0.210*** -0.192*** 1.000

Age -0.074*** 0.078*** -0.088*** 0.436*** -0.431*** 0.401*** -0.457***1.000

Note: *** p<0.01, ** p<0.05, * p<0.1

4.2 Multivariate Regression Analysis

Table 3 presents the findings from the regression

analysis investigating the influence of digital

transformation on financial performance (columns 1

and 2) and green development (columns 3 and 4). As

shown in column 2 of the table, after controlling for

variables such as enterprise size, enterprise growth

rate, enterprise gearing ratio, equity concentration,

and age of the enterprise, the coefficient for digital

transformation was 0.5863, significant at the 1%

level. This provides evidence for a significant

positive correlation between digital transformation

and financial performance, supporting Hypothesis

H1a, consistent with Schumpeter’s theory of

innovation, which underscores technological

advancement as a driver of economic growth. These

findings align with prior empirical research in

different contexts. For instance, Ji et al. (2022) and

Nasiri et al. (2020) found similar positive effects in

Western economies, where digitalisation contributed

to financial performance through increased

operational efficiency and competitive advantage.

However, in emerging markets, the impact of digital

transformation varies due to differences in digital

infrastructure, regulatory environments, and firm

capabilities (Xie et al., 2021; Huang & Wang, 2023).

Compared to studies on developed economies, where

digital adoption is more advanced, the results of this

study suggest that firms in China are still in a

transitional phase, with digital transformation

providing financial benefits primarily through

improved scalability, integration, and efficiency of

information flow. Digitalisation enhances internal

efficiency and reduces information asymmetry,

ultimately boosting performance.

From the results of the control variables, firm size

significantly affects financial performance, with a

coefficient of 12.8692 (p<0.01), suggesting that

larger firms benefit from economies of scale.

Leverage has a negative coefficient of -0.5470

(p<0.01), aligning with the "pecking order" theory

that higher leverage increases financial risk. The

positive coefficient of 5.2561 (p<0.01) for company

age indicates that older firms typically exhibit better

financial results due to established market positions

and customer loyalty.

Table 3. Multivariate regression analysis

VARIABLES

(1)

(2)

(3)

(4)

Dig

0.594***

(

3.58

)

0.586***

(

3.55

)

0.024***

(

14.31

)

0.029***

(

13.98

)

Size -

12.869***

(

6.44

)

0.514***

(

67.76

)

Growth -

0.084

(

4.03

)

-0.001***

(

-9.92

)

Lev -

-0.547***

(

-7.79

)

0.005

(

0.73

)

Top1 -

0.109***

(

3.49

)

-0.008***

(

-5.33

)

Age -

5.256***

(

7.46

)

-0.132***

(

-8.65

)

Constant

4.836***

(19.01)

148.318***

(

-8.65

)

2.067***

(10.84)

-1.761***

(-12.51)

Observations 14,550 14,550 14,550 14,550

R-squared 0.00651 0.055 0.082 0.19

Firm FE YES YES YES YES

Year FE YES YES YES YES

Note: *** p<0.01, ** p<0.05, * p<0.1

The Impact of Digital Transformation on Financial Performance and Green Development: Evidence from Chinese Manufacturing

Companies

263

Columns 3 and 4 present the regression results on

digital transformation’s impact on green

development. The coefficient of 0.0296 (p<0.01)

confirms a significant positive relationship,

supporting H1b. Firms adopting digital

transformation are more likely to finance eco-friendly

technologies and sustainable practices, enhancing

resource efficiency and supply chain management,

aligning with the Resource-Based View (RBV). This

theory suggests that firms with advanced digital

resources are better positioned for sustainability, as

supported by Gu et al. (2023) and Chen et al. (2023).

Control variables also influence green

development. Firm size (0.5141, p<0.01) indicates

that larger firms allocate more resources to

sustainability, consistent with RBV. However, firm

growth (-0.0011, p<0.01) and firm age (-0.1324,

p<0.01) negatively impact green development,

suggesting resource constraints in fast-growing firms

and adaptation challenges in older firms. Comparing

digital transformation’s effects, its impact on

financial performance is numerically stronger, yet the

higher R-squared for green development suggests

greater explanatory power. While financial gains are

immediate, green development offers long-term

economic and environmental benefits, reinforcing

sustainability’s strategic importance.

These findings align with prior research on

digitalisation and sustainability. Hart and Ahuja

(1996) and Xue et al. (2022) found that digital

innovations drive long-term environmental gains,

particularly in high-carbon industries. In contrast,

studies on developed economies (Porter & van der

Linde, 1995; Beier et al., 2020) indicate that financial

priorities often overshadow environmental

objectives. However, in emerging economies like

China, increasing regulatory pressures make digital

transformation a more crucial driver of green

development. Firms adopting smart technologies

enhance efficiency while reducing waste and

emissions (Zheng et al., 2023), positioning digital

transformation as essential for sustainable growth in

manufacturing.

4.3 Robustness Test

To further test the robustness of the main findings, the

continuous digital transformation variable (Dig) was

replaced with a dummy variable (Dig_dum), as

presented in Table 4. This use of alternative

measurement enables an examination of the

relationship between digital transformation and its

effects on financial performance (FP) and green

development (GD). It seeks to determine if the impact

is not only dependent on the magnitude of digital

transformation, but also holds substantial

significance, even when digital transformation is

considered as a binary condition (i.e., the presence or

absence of digital transformation). Table 4

summarises the results of this alternative

measurement.

Table 4. Regression results with alternative measure.

VARIABLES

(1)

(2)

Dig_dum

3.1079***

(3.50)

0.0857**

(2.28)

Size

12.8566***

(

5.96

)

0.5130***

(

59.55

)

Growth

0.0844***

(

3.88

)

-0.0011***

(

-9.39

)

Lev

-0.5468***

(-7.38)

0.0005

(0.72)

Top1

0.1096***

(

3.21

)

-0.0018***

(

-4.89

)

Age

5.2921***

(7.37)

-0.1350***

(-8.31)

Constant

-151.0299***

(-8.28)

-1.7894***

(-11.55)

Observations 14,550 14,550

R-s

uare

0.0511 0.189

umber of

rou

s 2,151 2,151

Note: *** p<0.01, ** p<0.05, * p<0.1

The effect of digital transformation remains strong

even after replacing continuous variables with

dummy variables. Dig_dum is statistically significant

at the 1% level for financial performance (FP) and the

5% level for green development (GD), with control

variables showing consistent significance across

models. To test robustness, post-2020 data was

excluded to account for the COVID-19 shock, which

could introduce endogeneity. Regression results in

Table 5 confirm that Dig remains highly significant at

the 1% level in both FP and GD, with control

variables maintaining their influence. Excluding post-

2020 data confirms the robustness of the findings,

reinforcing H1a and H1b—digital transformation

significantly impacts financial performance and

green development in the manufacturing sector.

FEMIB 2025 - 7th International Conference on Finance, Economics, Management and IT Business

264

Table 5. Regression results with excluding Covid's effect.

VARIABLES

(

)

(

)

Dig

1.0235***

(4.88)

0.0274***

(13.65)

Size

16.2110***

(4.84)

0.5264***

(78.63)

Growth

0.0685***

(4.34)

-0.0011***

(-14.03)

Lev

-0.5220***

(-7.95)

0.0001

(0.14)

Top1

0.0864

(1.37)

-0.0019***

(-3.63)

Age

4.8707***

(6.43)

-0.1318***

(-5.77)

Constant

-180.7830***

(-6.69)

-2.1689***

(-15.95)

Observations 11,477 11,477

R-square

0.045 0.177

Number of

rou

s 1,745 1,745

Note: *** p<0.01, ** p<0.05, * p<0.1

5 CONCLUSION

This study investigates the impact of digital

transformation on financial performance and green

development in Chinese A-share manufacturing firms

from 2010 to 2021.

This study contributes to academic literature and

practice by bridging financial and non-financial

outcomes of digital transformation. Its practical and

managerial implications are significant. For

enterprises, the findings emphasise the need to

integrate digital technologies to enhance financial

performance and sustainability, particularly in

energy-intensive industries where efficiency gains

reduce costs and support regulatory compliance. For

industry leaders, the study highlights the competitive

advantages of digital adoption, urging firms to

prioritise innovation to remain resilient in evolving

markets. Policymakers can use these insights to

design targeted incentives that promote digital

transformation, reduce regional disparities in

digitalisation, and align corporate sustainability

efforts with environmental goals. These findings are

especially relevant for economies undergoing

industrial restructuring, offering evidence-based

guidance on the role of digital technologies in

sustainable growth.

Despite its contributions, this study has

limitations. Geographic heterogeneity in financial

impacts warrants further exploration, and the focus on

manufacturing limits generalisability to other

industries. Assumed linear relationships may

oversimplify complex dynamics, and mediating

factors remain unexplored. Future research should

examine other sectors, assess specific digital

technologies, and consider regional policy and

cultural differences to provide a more nuanced

understanding of digital transformation’s broader

implications.

REFERENCES

Abbasi Kamardi, A., et al. (2022). Developing sustainable

competitive advantages from the lens of resource-based

view: Evidence from the IT sector of an emerging

economy. Journal of Strategic Marketing, 1(23), 1–23.

Alexopoulos, I., Kounetas, K., & Tzelepis, D. (2018).

Environmental and financial performance: Is there a

win-win or a win-loss situation? Evidence from Greek

manufacturing. Journal of Cleaner Production, 197,

1275–1283.

Anthony Jnr, B. (2021). Managing digital transformation of

smart cities through enterprise architecture: A review

and research agenda. Enterprise Information Systems,

15(3), 299–331.

Anthony Jnr, B., et al. (2021). Digital transformation with

enterprise architecture for smarter cities: A qualitative

research approach. Digital Policy, Regulation and

Governance, 23(4), 355–376.

Bughin, J., LaBerge, L., & Mellbye, A. (2017). The case for

digital reinvention. McKinsey Quarterly, 2017(1), 26–

41.

Che, S., & Wang, J. (2022). Digital economy development

and haze pollution: Evidence from China.

Environmental Science and Pollution Research, 29(48),

73210–73226.

Chen, P. (2022). Relationship between the digital economy,

resource allocation and corporate carbon emission

intensity: New evidence from listed Chinese

companies. Environmental Research Communications,

4(7).

Chen, S., Yang, Y., & Wu, T. (2023). Research on the

impact of digital transformation on green development

of manufacturing enterprises. Frontiers in Energy

Research, 10, 1045328.

Ching, N. T., et al. (2022). Industry 4.0 applications for

sustainable manufacturing: A systematic literature

review and a roadmap to sustainable development.

Journal of Cleaner Production, 334.

Ciasullo, M. V., & Lim, W. M. (2022). Digital

transformation and business model innovation:

Advances, challenges, and opportunities. International

Journal of Quality and Innovation, 6(1), 1–6.

Dawson, A., Hirt, M., & Scanlan, J. (2016). The economic

essentials of digital strategy.

Du, K., Xie, C., & Ouyang, X. (2017). A comparison of

carbon dioxide (CO2) emission trends among provinces

in China. Renewable and Sustainable Energy Reviews,

73, 19–25.

The Impact of Digital Transformation on Financial Performance and Green Development: Evidence from Chinese Manufacturing

Companies

265

Feliciano-Cestero, M. M., Ameen, N., Kotabe, M., Paul, J.,

& Signoret, M. (2023). Is digital transformation

threatened? A systematic literature review of the factors

influencing firms’ digital transformation and

internationalization. Journal of Business Research,

157, 113546.

Gu, R., et al. (2023). The impact of industrial digital

transformation on green development efficiency

considering the threshold effect of regional

collaborative innovation: Evidence from the Beijing-

Tianjin-Hebei urban agglomeration in China. Journal

of Cleaner Production, 420.

Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T., &

Potapov, D. (2020). Digital analytics: Modeling for

insights and new methods. Journal of Interactive

Marketing, 51(1), 26–43.

Hanelt, A., et al. (2021). A systematic review of the

literature on digital transformation: Insights and

implications for strategy and organizational change.

Journal of Management Studies, 58(5), 1159–1197.

Hart, S. L., & Ahuja, G. (1996). Does it pay to be green?

An empirical examination of the relationship between

emission reduction and firm performance. Business

Strategy and the Environment, 5(1), 30–37.

Ismail, M. H., Khater, M., & Zaki, M. (2017). Digital

business transformation and strategy: What do we know

so far? Cambridge Service Alliance, 10(1), 1–35.

Ji, H., et al. (2022). Digital transformation and financial

performance: The moderating role of entrepreneurs’

social capital. Technology Analysis & Strategic

Management, 1(18), 1–18.

Liu, X., Liu, F., & Ren, X. (2023). Firms' digitalization in

manufacturing and the structure and direction of green

innovation. Journal of Environmental Management,

335, 1–15.

Nasiri, M., et al. (2020). Digital-related capabilities and

financial performance: The mediating effect of

performance measurement systems. Technology

Analysis and Strategic Management, 32(12), 1393–

1406.

Okorie, O., et al. (2023). Digital transformation and the

circular economy: Creating a competitive advantage

from the transition towards net zero manufacturing.

Resources, Conservation & Recycling, 189, 1–15.

Peter, B., & Alexander, B. (2020). Digital business model,

digital transformation, digital entrepreneurship: Is there

a sustainable ‘digital’? Sustainability, 12(5239), 5239–

5239.

Porter, M. E., & Heppelmann, J. E. (2014). How smart,

connected products are transforming competition.

Harvard Business Review, 92(11), 64–88.

Santoalha, A., Consoli, D., & Castellacci, F. (2021). Digital

skills, relatedness, and green diversification: A study of

European regions. Research Policy, 50(9), 1–15.

Schumpeter, J. A., & Nichol, A. J. (1934). Robinson's

economics of imperfect competition. Journal of

Political Economy, 42(2), 249–259.

Shang, Y., et al. (2023). Does enterprise digital

transformation contribute to carbon emission

reduction? Micro-level evidence from China.

International Review of Economics and Finance, 86, 1–

13.

Shi, C., Yang, Y., & Wu, T. (2023). Research on the impact

of digital transformation on green development of

manufacturing enterprises. Sustainability, 10, 1–15.

Siachou, E., Vrontis, D., & Trichina, E. (2021). Can

traditional organizations be digitally transformed by

themselves? The moderating role of absorptive capacity

and strategic interdependence. Journal of Business

Research, 124, 408–421.

Su, J., Wei, Y., Wang, S., & Liu, Q. (2023). The impact of

digital transformation on the total factor productivity of

heavily polluting enterprises. Scientific Reports, 13(1),

6386–6392.

Sui, B., & Yao, L. (2023). The impact of digital

transformation on corporate financialization: The

mediating effect of green technology innovation.

Innovation and Green Development, 2(1), 1–15.

Sun, C., et al. (2022). Enterprise digital transformation and

debt financing cost in China’s A-share listed

companies. Oeconomia Copernicana, 13(3), 783–829.

Wang, S., Wang, X., & Chen, S. (2022). Global value

chains and carbon emission reduction in developing

countries: Does industrial upgrading matter?

Environmental Impact Assessment Review, 97, 1–12.

Wei, Z., & Sun, L. (2021). How to leverage manufacturing

digitalization for green process innovation: An

information processing perspective. Industrial

Management & Data Systems, 121(5), 1026–1044.

Xu, J., et al. (2023). Impacts of digital transformation on

eco-innovation and sustainable performance: Evidence

from Chinese manufacturing companies. Journal of

Cleaner Production, 393, 1–15.

Zhang, X., et al. (2023). Can digital transformation drive

green transformation in manufacturing companies?

Based on socio-technical systems theory perspective.

Sustainability, 15(3), 1–15.

Zheng, R., et al. (2023). How does digitalization drive

carbon emissions? The inverted U-shaped effect in

China. Environmental Impact Assessment Review, 102,

107203.

FEMIB 2025 - 7th International Conference on Finance, Economics, Management and IT Business

266