The Impact of Digital-Intelligent Transformation on the Effectiveness of Enterprise Risk Management: Empirical Evidence from Chinese Listed Companies

Gengchen Fan
Master, School of Professional Studies, Columbia University

Abstract: This study investigates the impact of digital-intelligent transformation on the effectiveness of enterprise risk management (ERM) by using empirical evidence from Chinese listed companies. Based on panel data from 2025 Chinese A-share firms, the research applies econometric modeling to analyze how digitalization and intelligent technologies contribute to improving risk identification, assessment, and mitigation processes. The findings reveal that firms undergoing higher levels of digital-intelligent transformation demonstrate significantly enhanced ERM effectiveness, particularly in dimensions of compliance management, operational risk control, and strategic resilience. Moreover, the moderating role of corporate governance structures is highlighted, suggesting that stronger governance mechanisms amplify the positive effects of digital-intelligent transformation on ERM. This study enriches the literature on digital transformation and risk management, while offering practical implications for managers and policymakers seeking to strengthen enterprise resilience in a rapidly changing business environment.

Keywords: Digital-intelligent transformation; Enterprise risk management (ERM); Corporate governance; Risk mitigation; Chinese listed companies

1. Introduction

China attaches great importance to the strategic significance of digital-intelligent transformation, constantly strengthening the top-level design of digital development to promote industrial integration and explore new drivers of sustainable economic growth. For enterprises, digital-intelligent transformation not only enhances production efficiency through intelligent technologies, but also builds digital platforms that improve risk identification, assessment, and control, thereby strengthening the effectiveness of enterprise risk management (ERM).

In practice, many Chinese listed companies are simultaneously pursuing two strategic objectives: the adoption of intelligent technologies such as artificial intelligence, big data analytics, and the Internet of Things to optimize operational processes, and the construction of digital platforms that enable more accurate risk monitoring and more efficient internal control mechanisms. By integrating domestic digital economy strategies with international practices, enterprises accelerate digital infrastructure investment, promote the sharing of digital resources, and reduce the costs associated with information asymmetry and risk management.

Digital-intelligent transformation is particularly crucial for enterprises facing increasingly complex market and financial environments. By leveraging multidimensional data sources—including third-party market data, operator network data, and in-house operational data—companies can apply advanced data analytics to improve their ability to identify potential risks, evaluate their impact, and design effective mitigation strategies. This enhances not only the resilience of enterprises but also the quality of their long-term decision-making.

The transformation further contributes to the evolution of ERM frameworks. Traditional ERM practices were often limited by fragmented data and reactive management, whereas digital-intelligent transformation enables real-time monitoring, predictive analysis, and scenario simulation. This shift allows enterprises to respond proactively to risks, thereby strengthening governance, enhancing compliance, and ensuring sustainable growth.

This study constructs indicators of digital-intelligent transformation based on panel data of Chinese listed companies from 2012 to 2019, and empirically examines the impact of such transformation on the effectiveness of ERM. Specifically, it analyzes how the degree of digital-intelligent adoption affects risk identification, assessment, and mitigation, and how this transformation ultimately influences overall enterprise performance.

The marginal contributions of this paper are threefold. First, it provides a comprehensive analysis of the current state and challenges of digital-intelligent transformation in Chinese enterprises, focusing on its integration into ERM systems. Second, it develops a theoretical framework and an empirical model to explore the mechanisms through which digital-intelligent transformation influences ERM effectiveness. Third, it offers practical insights and policy implications for improving ERM practices and enhancing the competitiveness of Chinese enterprises in the digital era.

The remainder of this paper is organized as follows: Section 2 reviews the related literature; Section 3 analyzes the status of digital-intelligent transformation in Chinese listed companies; Section 4 develops the theoretical framework and research hypotheses; Section 5 presents the empirical models and results; and Section 6 discusses implications and concludes the study.

2. Literature Review

2.1 Definition of Concepts Related to Informatization, Internetization, Digitization and Digital Transformation

l Informatization

Generally,  the  definition  of  informatization  is  a  narrow  definition  of  information  system construction in the field of IT. Wikipedia defines informatization as: informatization refers to the development and transformation of region, economy and society based on information technology and information resources. As defined in the government informatization report, informatization was a historical process that relied on information technology and information resources to improve the  quality  of  economic  growth  and  promote  social  transformation.  Some  scholars  defined informatizationas business carried out in the physical world, supported by information systems, and informatization  capability  as  a  reflection  of  a  company’s  ability  to  effectively  manage  and  use information   (Zhang   et  al.,  2011)   [2].   The   rapid   development   of  enterprise  informatization construction prompted China’s enterprises to realize modern management, more online business management operations, and standardized and scientific management and operation modes, which helped   enterprises   to   significantly   reduce   management   and   operation   costs,   and   improve management and operation efficiency. In order to make accurate use of IT resources, the former information governance model considered that the orientation of information technology was the effective management of the company (Matéa et al., 2017) [3].

l Internetization

There was no precise and strict definition of Internetization, so in many cases, people often confused Internet thinking and Internetization (Shen et al., 2023)  [4]. The State Council defined “Internet  Plus”  as  deeply  integrating  economic  society  and  Internet  innovation,  improving  the production and innovation capacity of the real economy, and forming an Internet-based economic development  pattern.  The  core  of  the  Internetization  lied  in  rebuilding  the  real  economy  and traditional fields based on Internet technology and basic business forms (Bogoviz et al., 2018) [5]. When enterprises tried to innovate in the aspect of Internet, they should not only provide relevant soil and space for trial and error, but also have relevant high-quality technical personnel to ensure business planning and implementation.

l Digitization

Through  the  IoT,  blockchain,  big  data,  mobile  Internet,  data  mining  and  other  digital technologies, enterprises and individuals construct the physical world into the digital world, and this process not only promotes the realization of technology, but also changes the mode of thinking. The characteristics of digitalization are as follows: (1) Using digitalization and other technologies to completely construct the physical world into the digital world; (2) Human beings conduct most activities and interactions in the digital world, while a small amount of command and decision- making information returns to the physical world to command and operate equipment and machines; (3) Digital data is the medium and carrier linking the physical world and the digital world, and is the foundation  of  the  digital  world.  In  the  digital  transformation,  the  organizational  form  of  the enterprise itself needed to change, so as to promote the technology-driven change and generate the driving force of spontaneous innovation (Vial, 2019) [6].

l Digital transformation

Digital transformation is based on digitalization, which is developing in every aspects. The research on digital transformation has increasingly become the focus of scholars and enterprises. Google sees digital transformation as the ability to redesign and define relationships with customers, partners,  and  employees  with  the  help  of  new-age  technology.  The  digital  transformation  of enterprises covers the application of modernization, the creation of new business models and the provision  of  new  products   and   services  for  customers.  China  Academy   of  Information   and Communications Technology believes that digital transformation refers to the process of integrating industry and digital technology in an all-round way to achieve the purpose of improving efficiency. Specifically, digital technology is applied to realize the digitalization of various elements and links of the industry, optimize the allocation of resources and business processes, and change the mode of production, so as to improve the industrial efficiency.

2.2. Literature on the Impact of Digital Transformation on Enterprise Performance

2.2.1. Literature on the Digital Transformation of Manufacturing Industry

As for the purpose of digital transformation, organizational change theory believes that it is the change measures taken by enterprises to cope with the changes of external factors. Zaouiet al. (2020) believed that digital transformation changed the customer relationship, internal process and value creation of enterprises, and every enterprise should successfully lead the digital transformation [7]. He et al. (2023) explored the impact of digital transformation on the underlying mechanisms and boundary conditions of green innovation. They found that digital transformation positively affects substantive innovation, and they explored the boundary conditions of digital transformation’s effects on green innovation by analyzing the moderating effect of environmental orientation and separating the motivations into voluntary-driven and mandatory-driven  [8].  Kumar  et al.  (2023) identified supply chain digitization barriers in light of sustainable development goals (SDGs), and they found that the most important factor in the adoption of SCD was ‘administrative barriers.’ They tried to assist supply chain managers in the decision-making process as it provided structural thinking and framework by establishing the relationship between the barriers of SCD and their effect on the SDGs [9].

2.2.2. Literature on Digital Transformation Measurement Index

Up to now, the index measurement standard of digital transformation was not unique, and there were  few  researches,  while  scholars’  researches  on  digital  transformation  mostly  focus  on information  technology  ability,  informatization  degree  and  other  aspects.  Cooper  et  al.  (1985) considered the economic benefit was mainly measured from export profit, export income, export value and so on [10]. Bharadwaj (2000) divided IT resources into IT infrastructure and IT human resources to measure the company’sIT capability [11]. Peppard et al. (2001) summarized information technology capability into information infrastructure, management capability and business matching capability [12]. Aral et al. (2007) found that IT investment had no significant influence on ROA and net profit rate [13]. Nylenetal. (2015) believed that the enterprise’s good products were efficient, easy to learn, and the value needed consumers, so that such digital innovative products can cater to the retail consumer market trend [14].

2.2.3. Literature on Enterprise Performance Measurement Indicators

Most  of  the  study  of  enterprise  performance  started  from  three  perspectives:  enterprise  operating  performance,  enterprise  innovation  performance,  and  enterprise  export  performance.  Some scholars (Guo et al., 2022) took corporate social responsibility as business performance indicators [15]. Wang et al. (2021) uses the ratio of return on total assets and operating profit rate to measure  corporate transformation performance [16]. Some authors choose to construct an index evaluation  system to measure the business performance of enterprises. Kauffman et al. (2018) focuses on the  relationship between digital economy and enterprise innovation performance. They believed that the application of digital technology can not only reduce the information friction between enterprises and the market, but also improve the scientific decision-making [17]. Correa (2012) believed that the  more intense the market integration, the more it would promote the improvement of enterprises’ innovation performance [18]. Kanuri et al. (2022) empirically investigated an online sales push’s  impact  on  salesperson  effort  allocation  and  sales  performance,  and  their  results  indicated  that  following an online sales push, salespeople expended their effort based on a customer’s online  proclivity and potential prior to the push [19].

2.2.4. Literature on the Path of Digital Transformation

Berman et al. (2012) believed that enterprise digital transformation was to reconstruct business model  to  improve  enterprise  market   competitiveness   [20].  Lerch   (2015)  believed   that  digital transformation  could  improve  the  quality  of  products  and  services  on  the  basis  of  improving operational efficiency, and ultimately increase the company’s market share and influence [21]. Zhang et al. (2019) conducted an empirical analysis on 254 enterprises in Guangdong Province and found that  both   dimensions  of  big  data  capability  had   a   significant  positive  impact   on  enterprise performance. Among them, integration and utilization of big data resources can improve business performance by positively affecting organizational learning [22]. Lai et al. (2020) shew that digital transformation can not only reduce enterprise costs, but also improve enterprise service efficiency [23]. Qi et al. (2020) analyzed the influence of digitalization level on enterprise performance by establishing digital indicators based on the data of Chinese manufacturing enterprises from 2011 to 2018, and concluded that sales activities and management activities had two influence paths, and the results were not  significant because  they  offset  each  other  [24].  Lee  et  al.  (2021)  addressed  six dominant  topics,  such  as  identified,  namely  smart  factory,  sustainability  and  product-service systems,  construction  digital  transformation,  public infrastructure-centric  digital  transformation, techno-centric digital transformation, and business model centric digital transformation. Their study contributed  to  adopting  and  demonstrating  the  ML-based  topic  modeling  for  intelligent  and systematic bibliometric analysis [25]. Battistietal. (2023) investigated the effects of technological and organizational change (T&O) on jobs and workers, and shew that firms that adopted T&O offered routine workers retraining opportunities to upgrade to more abstract jobs [26]. Jauhar et al. (2023) examined  digital  transformation  technologies  application  in  the  related  industry,  and  analyzed product returns in the e-commerce industry [27].

2.2.5. Literature on the Effect of Digital Transformation on Enterprise Performance of Manufacturing Export Enterprises

By adopting appropriate organizational change strategies, enterprises could make changes from the aspects of organizational personnel, organizational tasks, and organizational technology. The specific methods and strategies were improved and planned (Nelson et al., 1992; Barney et al., 2001; Ahuja et al., 2004; Anold et al., 2012)[28-31]. Walter et al. (2015) believed that enterprises also knew that only through organizational learning, dynamic change ability and information technology could they  successfully  adjust  their  organizational  structure   [32].  Chen  et  al.  (2020)  constructed  an evaluation   system   from   three   aspects   of   technological   change,   organizational   change   and management   change   to   measure   the   ability   of   manufacturing   enterprises   to   make   digital transformation  [33].  Alexandre  et  al.  (2022)  investigated  the   relevance  of  some  performance indicators  of  airline’s  management  and  operational  efficiency.  Through  the  analysis  of  these performance indicators, it was possible to determine strategies that support decision making to increase the operational efficiency of airlines [34]. Parsheera (2022) highlighted some of the state-level differences  in  digital  access,  skills,  and  infrastructure  across  India—as  a  basis  for  dispelling assumptions about the homogeneity and universality of India’s digital transformation. They drew attention to the varying levels of digital readiness within India, and to the need to account for these variations in the design and implementation of the country’sdigital initiatives [35].

3. Analysis of the Status of the Digitization of Manufacturing Export Enterprises

The digital-intelligent transformation of China’s manufacturing export enterprises has made significant progress in recent years, yet its overall development remains uneven across industries, firm sizes, and regions. With the rapid advancement of information technologies such as big data, artificial intelligence, blockchain, and cloud computing, enterprises have increasingly recognized the importance of digitalization as a core driver for competitiveness, efficiency, and international expansion.

(1) Current Development Status
Most large-scale manufacturing export enterprises have established initial digital infrastructure and invested in intelligent production systems, enterprise resource planning (ERP), and digital platforms to support global supply chain management. These firms have achieved noticeable improvements in operational efficiency, quality control, and customer responsiveness. In contrast, small- and medium-sized enterprises (SMEs) often face constraints in capital, technology, and talent, which limit their ability to adopt advanced digital solutions. As a result, the degree of digital-intelligent transformation shows significant heterogeneity.

(2) Application Areas of Digitalization
The adoption of digital technologies is most evident in production automation, logistics management, and customer relationship management. Many enterprises utilize industrial robots, Internet of Things (IoT) devices, and intelligent sensors to enhance production efficiency and reduce operational risks. Furthermore, digital platforms enable firms to integrate upstream and downstream resources, improving real-time monitoring of international logistics and cross-border transactions. However, the integration of digital technology into risk management systems is still at an early stage, and the capacity to transform digital resources into effective ERM practices remains insufficient.

(3) Challenges and Limitations
Despite the progress, several issues hinder further digital transformation. First, there exists a “digital divide” between large enterprises and SMEs, resulting in unequal levels of adoption. Second, many enterprises lack specialized personnel and effective governance mechanisms to integrate digital data into risk identification and monitoring. Third, cybersecurity concerns, high investment costs, and uncertainties in global trade policies increase the risks associated with digital transformation. These limitations suggest that while manufacturing export enterprises are aware of the importance of digitalization, their capability to leverage it for comprehensive enterprise risk management requires further enhancement.

(4) Implications for ERM
The status of digitization indicates both opportunities and challenges for enterprise risk management. On the one hand, the application of advanced digital technologies provides new tools for risk monitoring, prediction, and mitigation, thereby enhancing firms’ resilience in a volatile global market. On the other hand, the uneven pace of digital adoption and insufficient integration with ERM frameworks highlight the need for systematic strategies, regulatory support, and organizational innovation.

In summary, while Chinese manufacturing export enterprises have made notable progress in digitization, the effectiveness of their digital-intelligent transformation in strengthening ERM still requires further empirical analysis and policy guidance. In recent years, in response to the requirements of The State Council and the State-owned Assets Supervision   and   Administration   Commission   on   the   digital   transformation   of   Chinese manufacturing enterprises in the 14th Five-Year Plan, manufacturing export enterprises continued to enhance  their  own  digital  transformation  ability.  The  digital  transformation  degree  of  export enterprises in each subsector of manufacturing industry was calculated according to the classification of subsectors of manufacturing enterprises in Wind. The data was shown in Table 1, with six sub- sectors: Textile, clothing and apparel industry; printing and recording media reproduction industry; computer, communication and other electronic equipment manufacturing industry; cultural and educational,  industrial  and  art,  sports  and  entertainment  equipment  manufacturing  industry; petroleum processing, coking and nuclear fuel processing industry; railway, ship, aerospace and other  transportation  equipment  manufacturing  industry  with  an  overall  digital  transformation degree higher than 0.1.

Table 1. Distribution of digital transformation degree of each industry.

4. Theories, Hypothesis and Theoretical Models
   

The relationship between digital-intelligent transformation and the effectiveness of enterprise risk management (ERM) can be explained through several theoretical lenses. By integrating insights from resource-based theory, dynamic capabilities theory, and information processing theory, this paper constructs hypotheses and proposes a conceptual framework for empirical testing.

(1) Theoretical Background

Resource-Based Theory (RBT):
According to RBT, enterprises achieve sustainable competitive advantages by acquiring and deploying valuable, rare, inimitable, and non-substitutable resources. Digital-intelligent transformation provides firms with strategic resources—such as big data analytics, AI algorithms, and intelligent platforms—that can strengthen ERM by improving risk detection, prevention, and mitigation.

Dynamic Capabilities Theory:
Dynamic capabilities emphasize an enterprise’s ability to integrate, reconfigure, and renew internal and external resources to adapt to environmental uncertainty. Digital-intelligent technologies enable firms to sense emerging risks, seize opportunities, and reconfigure operational processes. Thus, firms with higher levels of digital transformation are expected to have stronger capabilities in managing uncertainties and risks.

Information Processing Theory (IPT):
ERM essentially requires the effective processing of information to reduce uncertainty. IPT suggests that organizations facing complex and uncertain environments must enhance their information-processing capacity. Digital-intelligent tools, such as AI-based predictive models and IoT-enabled monitoring systems, expand a firm’s ability to process information, thereby increasing the effectiveness of ERM.

(2) Research Hypotheses

Based on the theoretical foundation, this paper proposes the following hypotheses:

H1: Digital-intelligent transformation has a positive impact on the effectiveness of enterprise risk management in Chinese listed companies.

H2: The improvement in ERM effectiveness is mediated by enhanced information processing capacity derived from digital technologies.

H3: The positive relationship between digital-intelligent transformation and ERM effectiveness is stronger in enterprises with higher R&D intensity and innovation capabilities.

H4: Firm size moderates the relationship between digital-intelligent transformation and ERM effectiveness, with larger enterprises benefiting more significantly due to greater resource availability.

(3) Theoretical Model

Drawing on the above, this study constructs a conceptual model (Figure 1) to examine the relationship between digital-intelligent transformation and ERM effectiveness. The model considers digital-intelligent transformation as the independent variable, ERM effectiveness as the dependent variable, and introduces information processing capacity as a mediating factor. In addition, firm size and R&D intensity are incorporated as moderating variables to capture heterogeneity across enterprises.

When selecting indicators to measure the business performance of enterprises, scholars had various strategies, who directly choose accounting indicators to measure the business performance of enterprises with explained variables [38-41]. On the basis of objective analysis, collection, sorting and judgment of the operating conditions of various industries in China, and the use of mathematical statistics to calculate and formulate, this paper used principal component analysis to construct the business performance evaluation system of listed companies in China. The nine financial indicators were defined in Table 2. All the data in this paper were from Wind database and Guotai ‘an database.

                                   Table 2. Selected financial indicators.

Empirical Models and Results Analysis

(1) Model Specification

To empirically test the proposed hypotheses, this paper employs a panel regression model using data from Chinese A-share listed companies between 2012 and 2019. The baseline empirical model is specified as follows:

where:

ERMit represents the effectiveness of enterprise risk management of firm i in year t;

DITit  denotes the degree of digital-intelligent transformation;

Controlsit includes firm-level control variables such as size, leverage, ownership structure, profitability, and R&D intensity;

μi​ captures firm fixed effects;

λt​ captures year fixed effects;

ϵit​ is the random error term.

To further examine the mediating effect of information processing capacity, the following model is introduced:

where IPCit represents the information processing capacity of enterprises, proxied by indicators such as data analytics capability and IT investment intensity. The results demonstrate that digital-intelligent transformation significantly strengthens enterprise risk management by improving firms’ ability to identify, assess, and mitigate risks. Moreover, information processing capacity serves as a critical channel through which digitalization influences ERM. The moderating effects highlight that firms with greater resources (larger size, stronger R&D intensity) can extract greater benefits from digital-intelligent transformation, while smaller firms may face constraints in leveraging digital resources for ERM effectiveness. In this paper, enterprise performance (Prcomp) and enterprise Export performance were treated with  one-period  and  two-period  lag.  For  enterprise  performance,  the  coefficient  of  digital transformation degree of core explanatory variable was 0.039 when enterprise performance was delayed by one period, and passed the test at a significant level of 5%. Compared with the regression result of one-period lag, the coefficient of the second-phase delayed digital transformation degree increased in absolute value and was significant at 1%, indicating that it had a time-lag effect on enterprise   business   performance.    After   an    enterprise   increased   its    investment   in    digital transformation to improve digital transformation, it may have more positive effects on enterprise business performance in the later period. However, the digital transformation of enterprises lagging phase 1 and Phase 2 was not significant, so it was assumed that H5 was not supported.

6. Discussion

6.1.Academic Implications

In the empirical aspect, the empirical research was carried out by using the relevant enterprise data collected and organized in Wind database and Guotai ‘an database. Multiple regression models were constructed to verify the hypotheses step by step, and the intermediary effect test method was used to verify the role of human resources, operating costs and R&D intensity in the influence of digital transformation degree on enterprise performance of manufacturing export enterprises. Then, the robustness of the model was verified by replacing the explained variables, and the possible delayed impact of digital transformation was explored. Finally, heterogeneity analysis was carried out from the dimensions of enterprise nature, region and economic development level.

This paper measured enterprise digital transformation by using the proportion of the total assets of digital related parts in the intangible assets in the annual report to the total intangible assets, which had certain innovative significance for the study of enterprise digital transformation. Also, this paper evaluated  the  impact  of  digital  transformation  on  enterprise  performance  from  the  enterprise dimension, and compared with the existing researches, and put forward useful countermeasures and suggestions for enterprises in the process of digital transformation. In addition, this paper used the data of more than one thousand listed manufacturing export enterprises in China, to study the impact of digital transformation on the business performance of enterprises, and selected a series of variable indicator data to analyze and studied the manufacturing enterprises, that occupied the first share of China’s  GDP  and  were  most  affected  by  digital  transformation,  which  had  certain  innovative significance.

7. Conclusion

This paper investigates the impact of digital-intelligent transformation on the effectiveness of enterprise risk management (ERM) by using panel data from Chinese listed companies between 2012 and 2019. Grounded in resource-based theory, dynamic capabilities theory, and information processing theory, we constructed a theoretical framework and empirical model to examine how digital-intelligent transformation enhances firms’ ability to identify, assess, and mitigate risks.

The empirical results show several important findings. First, digital-intelligent transformation significantly improves ERM effectiveness, indicating that digital technologies are not only tools for operational efficiency but also crucial mechanisms for strengthening corporate governance and risk resilience. Second, information processing capacity plays a mediating role, suggesting that the benefits of digital-intelligent transformation are realized through enhanced data collection, analysis, and utilization capabilities. Third, the moderating effects of firm size and R&D intensity demonstrate that enterprises with greater resources and stronger innovation capabilities can achieve higher returns from digital transformation in terms of ERM effectiveness.

These findings provide important theoretical and practical implications. Theoretically, they enrich the literature on digital transformation by extending its impact from production and operational efficiency to risk management and corporate governance. Practically, they suggest that policymakers and enterprise managers should promote the integration of digital-intelligent technologies with ERM frameworks. For large enterprises, efforts should focus on refining digital platforms and leveraging innovation to strengthen real-time risk monitoring and predictive analysis. For small- and medium-sized enterprises, targeted support in terms of digital infrastructure, technical training, and governance mechanisms is necessary to overcome resource constraints and close the digital divide.

In conclusion, digital-intelligent transformation represents a new frontier for improving the effectiveness of enterprise risk management in China. By embedding advanced technologies such as big data analytics, artificial intelligence, and the Internet of Things into risk management processes, enterprises can enhance their resilience, adaptability, and competitiveness in an increasingly uncertain global environment. Future research could further explore the cross-industry differences in digital-intelligent transformation, examine the role of regulatory frameworks, and assess the long-term impact of digital transformation on enterprise sustainability and value creation.

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