Digital–Intelligent Synergy Empowers Chinese Firms’ Internationalization: A Dual Perspective Based on Green Innovation and Stable Investment

Abstract

Amid the rapid growth of the digital economy and increasing global competition, the role of digital–intelligent technologies in enabling corporate internationalization has gained significant attention. From the perspective of “digital–intelligent synergy,” this study constructs a mediated moderation model to explore the impact mechanism of digital–intelligent synergy on corporate internationalization. The findings indicate that data assets, artificial intelligence, and digital–intelligent coupling coordination significantly enhance overseas revenue. Green technology innovation mediates this relationship, while investor stability exerts an asymmetrical moderating effect. This strengthens both the direct effect of digital–intelligent synergy on internationalization and its impact on green innovation, though not the path from green innovation to international performance. Further analysis indicates that self-use data assets significantly promote firm internationalization, while transactional data assets do not. Both AI technology and applications markedly enhance overseas expansion. For digital–intelligent coupling coordination, the level of coordination—not merely coupling intensity—positively affects internationalization level. By integrating green innovation and investor behavior perspectives, this study reveals the complex mechanisms through which digital–intelligent synergy empowers internationalization, offering theoretical and policy insights for corporate global expansion in the digital–green transition era.

1. Introduction

The current global economic landscape remains challenging, characterized by sluggish recovery, heightened geopolitical conflicts, and increasing protectionism. Corporate internationalization faces unprecedented uncertainties, with challenges extending beyond traditional market entry barriers [1] to include cultural adaptation [2], compliance risks [3], supply chain resilience [4], and sustainable development [5]. Despite these headwinds, Chinese listed companies have demonstrated notable resilience in overseas expansion. According to statistics from the China Association of Public Companies (CAPCO), in 2024, their overseas revenue reached CNY 9.44 trillion, a year-on-year increase of 7.97%. Overseas operations accounted for 14.3% of total revenue among entity firms, with sectors such as communications, automobiles, electronics, computers, and pharmaceuticals maintaining growth rates between 10% and 30% [6]. Meanwhile, forecasts from the China National Data Administration indicate that the digital economy will account for 35% of China’s GDP by the end of 2025 [7]. This figure underscores the continued dominance of the real economy and the absence of structural transformation in traditional tangible resource foundations. At the same time, however, the share of industrial digitalization has surpassed 80%, propelled by deepening digital transformation and rapidly evolving digital–intelligent technologies [8]. Against this backdrop, Chinese enterprises are demonstrating consistently strong overseas performance. This raises a core question: given that traditional tangible resource foundations have not fundamentally changed, can Chinese firms’ robust international expansion be attributed to a successful strategic shift from “hard resources” to “soft power” centered on digital and intelligent capabilities? Therefore, rigorously examining the relationship between digital–intelligent capabilities and internationalization levels, along with their underlying mechanisms and influencing factors, has become an urgent academic and practical imperative.

Data and intelligence are pivotal drivers of corporate transformation and competitive advantage. While existing research has examined their individual impacts on green performance and value creation [9,10,11], less attention has been given to their synergistic effects—particularly how they jointly influence corporate strategy and market expansion. In practice, data provides the foundational support for intelligence [12], and intelligence extends the application scope of data [13]. To deepen the analysis of this synergistic mechanism and extend the theoretical exploration of its implications, this study systematically incorporates the concept of “digital–intelligent synergy” within an established theoretical framework. First, digital–intelligent synergy represents a strategic organizational capability, encompassing the higher-order capacity for integration and reconfiguration required for firms to adapt to a digitalized environment [14]. Second, its core operating mechanism involves the strategic orchestration and coupled coordination of key complementary digital assets—data and artificial intelligence. This perspective is grounded in resource orchestration theory, which highlights how the synergistic configuration of complementary assets generates value [15]. Finally, from a value creation standpoint, this process serves as a critical micro-mechanism that drives digital transformation and reshapes pathways for value generation [16].

In summary, digital–intelligent synergy is a strategic organizational capability that integrates and orchestrates data assets, artificial intelligence, and their coupling coordination to enhance decision-making, operational efficiency, and value creation. It represents a systemic and dynamic process that transcends the sum of its parts, enabling intelligent, adaptive, and sustainable competitive advantages. It enables firms to integrate resources more efficiently, amplifying value creation [17] and creating opportunities to expand operational boundaries [18]. Textual analysis and proxy variables are currently the primary methods for measuring data assets and artificial intelligence indicators. Textual analysis involves extracting information content from structured texts [19,20], while proxy variables refer to using input or output variables highly correlated with data assets and AI as substitutes [21,22]. Direct research using “digital–intelligent synergy” as a keyword remains limited. Existing studies predominantly employ keywords such as digital intelligence, digital–intelligent integration, or digital–intelligence transformation [23,24,25], often treating digital–intelligent transformation as the core research subject without sufficiently considering the synergistic effects between data assets and artificial intelligence.

To effectively promote internationalization, digital–intelligent Synergy must operate through concrete and measurable mechanisms. Green technology innovation serves as a strategic facilitator for corporations to build market competitiveness [26,27], enabling them to circumvent green barriers and access international markets, green patents serve as the primary metric for assessing green technology innovation [28,29,30]. They are categorized based on patent type into green strategic innovation and green substantive innovation, gauged by the number of utility model patents and invention patents, respectively. Furthermore, based on the application stage, green technology innovation is divided into green technology application and green technology acquisition, typically measured by the quantity of green patents applied for and granted. Meanwhile, the internationalization process entails substantial uncertainties and financial risks that require stable investment as essential support for such expansion [31], the daily investor turnover rate is typically used as a direct proxy for investor stability [30,32]. Drawing on technological, environmental, and governance perspectives, this study investigates how Chinese listed companies can leverage digital–intelligent capabilities to foster green innovation, build a sustainable brand image and drive overseas revenue growth.

This study examines how digital–intelligent synergy promotes corporate internationalization, using data from Chinese A-share listed firms (2015–2024). The empirical findings reveal the following:

• Direct Impact: Data assets, artificial intelligence and their coupling coordination significantly enhance the level of corporate international business.
• Transmission Mechanism: Green technology innovation—whether substantive or symbolic, behavioral or outcome-based—acts as a key mediator in this relationship.
• Moderating Factor: Investor stability positively moderates the effect, strengthening the link between digital–intelligent synergy and international operations, as well as the effect between digital–intelligent synergy and green technology innovation.
• Component-specific Analysis: The analysis confirms that self-use data assets, AI technology, AI application and digital–intelligent coordination significantly enhance firm internationalization, while transactional data assets and digital–intelligent coupling intensity does not show a significant impact.

This study positions digital–intelligent synergy as the driver, green innovation as the guide, and investor stability as the stabilizer, offering a theoretical and empirical basis for building competitive advantage in a complex global environment. Compared to existing studies, this paper offers several potential contributions:

• Theoretical Framework Innovation: This study conceptualizes digital–intelligent synergy as a dynamic strategic capability and develops an integrated framework of “digital–intelligent driving—green orientation—investor stability—market expansion.” It reveals the mechanism through which digital–intelligent synergy drives green innovation, thereby facilitating international market entry. This provides a new theoretical explanation for unpacking the black box between digital–intelligent synergy and corporate internationalization.
• Boundary Condition Innovation: A key contribution lies in identifying “investor stability” as a critical contextual factor and boundary condition for the success of digital–intelligent strategies. This finding shifts the focus from “whether digital–intelligent strategies are effective” to “under what market conditions they work best,” highlighting the specific capital market environment on which strategic success depends and expanding existing understanding of digital–intelligent outcomes.
• Research Method Innovation: This study introduces the coupling coordination model from ecology into management research, creatively quantifying digital–intelligent synergy. This approach accurately captures the degree of coupling and coordination between corporate data assets and artificial intelligence, thereby establishes a necessary foundation for conducting refined empirical research in this field.
• Policy Tool Innovation: By integrating the coupling coordination model from environmental science, investment theory from finance, and strategic management theory, this study establishes a new research paradigm. Based on this, it proposes a “digital infrastructure—green finance—capital market” policy framework, facilitating the transformation of theoretical insights into actionable governance tools.

2. Theoretical Analysis and Research Hypotheses

2.1. Mechanism of Digital–Intelligent Synergy

Data serves as the foundation for the development of intelligence. Artificial intelligence operates via a data-driven paradigm rooted in deep learning [33], where intelligence arises from identifying statistical patterns in large-scale data instead of predefined rules. Statistical learning theory indicates that models generalize to unseen data [34], with performance heavily dependent on the scale and quality of training data. The law of large numbers indicates that richer data samples yield estimates closer to the true distribution. Diverse, large-scale data enables complex mappings and reduces overfitting [35], while independent datasets provide evaluation benchmarks for generalization and parameter tuning [36]. Ultimately, the scale, quality, and diversity of data define the ceiling of intelligent capability.

Intelligence empowers data primarily through “management” and “creation.” In management, machine learning-based data mining and knowledge discovery techniques extract valuable information from data [37], enabling efficient processing of unstructured raw data and deepening value extraction. In creation, generative AI learns the probability distribution of real data and samples from it [9], producing novel, valuable data. This mitigates data scarcity [38] and facilitates exploration of unknown domains via synthetic data. Thus, intelligence optimizes data into structured knowledge and expands its boundaries, acting as both an optimizer and an amplifier.

The interaction between data and intelligence forms a co-evolutionary closed-loop system, rather than a one-time linear process. Grounded in control theory, the “data intelligence closed loop” uses real-world data to optimize models while continuously generating authentic feedback data [39]. These data streams capture contextual performance, user preferences, and special cases [40], which are fed back into the data platform, enabling incremental learning and creating a flywheel effect [41]. Therefore, this loop transforms static model training into a dynamic, evolving intelligent agent, enhancing environmental adaptability, with flowing data serving as the sustained driver for iterative system improvement. Such a reciprocal, interdependent, and co-adaptive relationship aligns structurally with the concept of coupling coordination in ecosystem theory, wherein two subsystems evolve synergistically through mutual inputs and feedbacks [42]. Therefore, examining the coordination between data assets and AI through the lens of ecological coupling offers a theoretically coherent framework for capturing their synergistic evolution.

2.2. The Impact of Digital–Intelligent Synergy on Internationalization Level

According to information processing theory, timely information processing improves a firm’s risk perception and decision accuracy [43]. As a fifth factor of production, data enhances the efficiency and depth of information processing, supporting more precise decision-making. In international expansion, firms encounter significant uncertainties and must process complex market, policy, and industry information to mitigate risks. Data technologies enable real-time analysis of global demand, offering accurate market insights [44]. Data-driven decisions also improve internationalization effectiveness [45]. Operational data from social and cross-border e-commerce platforms have become valuable assets, helping to identify market potential and bottlenecks and refine positioning and strategies. Therefore, converting data into assets enhances environmental sensing and strategic adaptation, providing essential impetus for global business growth.

Drawing on organizational learning theory [46], intelligent systems enable firms to learn continuously from overseas operational data, refining decision-making models iteratively. In supply chain management, intelligent algorithms forecast overseas demand fluctuations and optimize cross-border logistics [47]. In customer service, automated tools enhance response efficiency and satisfaction among international clients. For cultural integration, intelligent tools reduce barriers in cultural and institutional adaptation, cutting the costs of international compliance [48]. Overall, AI technologies strengthen a firm’s adaptability, operational optimization, and cross-border coordination, ultimately improving international performance.

Based on market power theory, digital–intelligent coupling coordination helps build competitive advantages [49] and strengthens market control [50], thereby enhancing international operations. In resource allocation, it provides analytical foundations and capabilities, using global supply chain data and intelligent algorithms to enable dynamic allocation and accurate demand forecasting, improving multinational operational efficiency [51]. For market competitiveness, this synergy facilitates scalable overseas expansion through precise customer insights and agile product customization. Regarding synergistic effects, composite technologies foster corporate optimization and ecological synergy [52]. Ultimately, digital–intelligent synergy creates an integrated process of risk perception and business response, driving a qualitative leap in internationalization.

Based on the above analysis, the following hypotheses are proposed:

H1a. 

Data assets can significantly enhance the level of corporate international operations.

H1b. 

Artificial intelligence can significantly enhance the level of corporate international operations.

H1c. 

Digital–intelligent coupling coordination can significantly enhance the level of corporate international operations.

2.3. The Mediating Role of Green Technology Innovation

Based on technological innovation theory, technological change can enhance innovation efficiency [53]. Digital–intelligent synergy promotes the steady advancement of green technology innovation through dual mechanisms of “internal co-construction” and “external coordination.” Within the firm, digital–intelligent technologies facilitate cross-departmental data sharing and process integration [54], enabling deep convergence of R&D, production, and management processes on digital platforms. By leveraging data-driven R&D decision-making and optimizing resource allocation [55], technologies such as digital twins accelerate the testing and iteration of green technology innovations [56], thereby reducing R&D uncertainty and enhancing the efficiency of green innovation. From an external perspective, platforms such as the industrial internet, built on digital–intelligent technologies, help break down information barriers among supply chains, research institutions, and customers [57]. This facilitates the cross-boundary flow of green technology information and mobilizes broader innovation resources to address complex environmental challenges [58]. In summary, digital–intelligent synergy comprehensively advances corporate green technology innovation by reducing innovation uncertainty, optimizing resource allocation, enabling innovation processes, and accelerating information flow.

As a core organizational capability, green technology innovation enhances competitive advantage and compliance in international operations. Rooted in the resource-based view, it creates heterogeneous resources that shape differentiated global competitiveness. Green innovation also signals commitment to sustainability, improving a firm’s position in global green value chains [59]. Institutional theory suggests it aids compliance with international environmental standards, enhancing legitimacy in high-end markets. Improved ESG ratings build trust among global investors and consumers [60], facilitating business expansion. Substantive green innovation reflects advanced technological capability, enabling high-value products that attract international partners and boost overseas revenue [27,61]. Symbolic green innovation reduces product carbon footprints to meet international standards [62]. Therefore, green technology innovation amplifies the effect of digital–intelligent resources and strengthens institutional compliance, serving as a mediating bridge.

Based on the above analysis, the following research hypothesis is proposed:

H2. 

Green technology innovation mediates the relationship between digital–intelligent synergy and the level of corporate international operations.

2.4. The Moderating Role of Investor Stability

In the process of enhancing corporate internationalization through digital–intelligent synergy, stable investors not only reflect the stability of the corporate equity structure but also strongly demonstrate the long-term perspective of investors. Based on resource dependence theory, a stable supply of resources provides a solid foundation for advancing internationalization via digital–intelligent synergy [63]. The development of overseas operations faces institutional and market risks, requiring stable and continuous capital support [64]. Stable investors provide “patient capital”, creating a resource buffer against uncertainties in the international market [65] and helping the firm withstand short-term market fluctuations during international expansion [66]. This endows management with the strategic determination to drive internationalization through digital–intelligent synergy. From a signaling perspective, a stable equity structure sends a credible signal to domestic and international stakeholders about the firm’s technological strength and long-term commitment. This reduces information asymmetry during internationalization and enhances international market recognition of the firm [67]. Furthermore, stable investors indicate that the firm is committed to long-term operations, with the willingness and behavior to continuously invest resources in adapting to local markets and fulfilling social responsibilities [68]. In summary, the stronger the investor stability, the more significant the promoting effect of digital–intelligent synergy on the level of internationalization.

Based on the above analysis, the following research hypothesis is proposed:

H3a. 

Investor stability positively moderates the relationship between digital–intelligent synergy and the level of internationalization.

From the resource-based view, green technology innovation is characterized by long R&D cycles, high asset specificity, and uncertain returns [69]. Stable investors provide solid financial support for advancing green technology innovation, helping firms withstand short-term profit pressures and ensuring the long-term dedicated investments required for digital–intelligent technologies to promote green R&D [70]. Thereby, they enhance the conversion efficiency from digital–intelligent collaboration to green technology innovation. From the perspective of agency theory, green technology innovation is oriented toward resource conservation and sustainable development [71], which may conflict with management’s performance-oriented goals. In the process of advancing green technology innovation through digital–intelligent synergy, investor stability can curb myopic behaviors driven by performance pressure among investors [72]. It also incentivizes managers to pursue green technology innovation in alignment with green development and dual-carbon goals [67], thereby allocating digital–intelligent resources such as data assets and artificial intelligence to green innovation areas with long-term strategic value. In summary, in the process of promoting green technology innovation through digital–intelligent synergy, investor stability not only provides a resource buffer and capital assurance at the resource level but also mitigates the conflict at the governance level between the long-term objective of green sustainability and management’s short-term performance goals. Consequently, it significantly strengthens the driving effect of digital–intelligent synergy on green technology innovation.

Therefore, the following research hypothesis is proposed:

H3b. 

Investor stability positively moderates the relationship between digital–intelligent synergy and green technology innovation.

First, based on signaling theory, green technology innovation faces information asymmetry in the international market [73]. Overseas stakeholders find it difficult to accurately assess the true value and long-term feasibility of Chinese enterprises’ green technology innovation. Stable investors, as a signal of commitment [74], convey credible information to the international market regarding the firm’s governance quality, financial stability, and green technology roadmap. This effectively ensures the quality and continuity of green technology innovation activities [75], thereby reducing verification costs for overseas stakeholders [76] and enhancing the acceptance of green technologies in the international market. Second, from a strategic management perspective, using green technology innovation to drive international revenue is a process fraught with uncertainties, representing a high-risk and high-commitment strategic decision [77]. In this process, investor stability grants management the capacity for risk-taking and strategic patience [78]. This enables firms to implement phased international strategic layouts in line with their technological innovation strategies and international market dynamics [79], while also allowing for dynamic adjustments based on market feedback. The strategic and gradual resource investment model, supported by investor stability, strongly facilitates the translation of green technology innovation into international competitive advantages.

Therefore, the following research hypothesis is proposed:

H3c. 

Investor stability positively moderates the relationship between green technology innovation and the level of internationalization.

Based on the above analysis, the conceptual model of this study is constructed, as shown in Figure 1.

Figure 1 presents the conceptual model illustrating how digital–intelligent synergy influences the level of enterprise internationalization. The model conceptualizes digital–intelligent synergy through three dimensions: data assets, artificial intelligence, and their coupling coordination. Green technology innovation plays a partial mediating role in this process, encompassing four aspects: strategic green innovation behavior, substantive green innovation behavior, strategic green innovation outcomes, and substantive green innovation outcomes. Investor stability serves as a moderator that not only influences the direct relationship between digital–intelligent synergy and internationalization but also regulates the mediating pathway involving green technology innovation. To clearly depict the core analytical framework of this study, Figure 1 includes only the main variables examined. The same set of control variables was employed in all empirical tests.

3. Research Design

3.1. Sample Selection and Data Sources

China launched its National Big Data Strategy in 2015, actively fostering the growth of the digital economy and related sectors. This study selected Chinese A-share listed companies from 2015 to 2024 as the initial sample, applying the following filters: (1) exclusion of financial firms; (2) removal of ST, *ST, or PT firms during the sample period; and (3) omission of observations with major missing data in key variables. All continuous variables were winsorized at the 1st and 99th percentiles to minimize outlier effects. Data were primarily sourced from the CNRDS and CSMAR databases.

The final dataset formed an unbalanced panel of 36,493 firm-year observations, comprising 5102 unique listed companies over the period from 2015 to 2024. On average, each firm was observed for approximately 7.15 years within the sample period. In terms of industry distribution, manufacturing enterprises accounted for over 60%. The Information Transmission, Software, and Information Technology Services sector, the Wholesale and Retail Trade sector, and the Production and Supply of Electricity, Heat, Gas, and Water sector ranked second to fourth in terms of firm representation. Regarding the temporal distribution, the sample size showed a consistent year-on-year increase, rising from 2015 observations in 2015 to 4932 observations in 2024.

3.2. Variable Definitions

3.2.1. Explanatory Variables

Digital–intelligent synergy (DIS). This construct was measured across three dimensions, including Data Assets (DA), Artificial Intelligence (AI), and the digital–Intelligent Coupling Coordination Degree (DICD).

Data Assets (DA): The measurement of data assets followed the approach of He Ying [19], involving textual analysis and keyword frequency counts in corporate annual reports. Using Python 3.9 and the jieba 0.42.1 library for word segmentation, the total frequency of keywords related to Self-use data assets (SDAs) and transactional data assets (TDAs) was extracted (Appendix A). The natural logarithm of the total frequency plus one served as a proxy for DA.

Artificial Intelligence (AI): The proxy for Artificial Intelligence was constructed based on the method of He Qin [20] (Appendix B). This measure calculates the natural logarithm of the sum of word frequencies for Artificial Intelligence Technology (AIT) and Artificial Intelligence Application (AIA), plus one.

Digital–Intelligent Coupling Coordination (DICD): The comprehensive development levels of DA and AI were first evaluated using the entropy method (Appendix C). Subsequently, a coupling coordination degree index between the two dimensions was constructed, based on the coupling coordination model proposed by Liu Yaobin [42]. This model is originally developed to assess synergistic interactions among ecological, social, and economic subsystems, quantifies the degree of mutual reinforcement and developmental alignment between two interdependent systems. Within the context of digital–intelligent integration, it effectively captures the extent of coupling and coordination between data assets and artificial intelligence. Such a dynamic aligns closely with the co-evolutionary and interactive features described in the mechanistic analysis.

The coupling degree quantifies the intensity of such interaction, whereas “coordination” reflects the positive and synergistic nature of their relationship. The coupling coordination degree integrates both the interaction strength and the level of harmonious development between systems, offering a comprehensive measure of their interdependent dynamics.

The coupling degree model for data assets and artificial intelligence (DICC):

$$C=2\sqrt{{\displaystyle \frac{{DA}_i·{AI}_i}{{\left({DA}_i+{AI}_i\right)}^2}}}$$

(1)

Here, the C-value has a range of [0, 1] and represents the coupling degree function between data assets and artificial intelligence.

The coordination degree model for data assets and artificial intelligence (DICT) is as follows:

$$T={\alpha DA}_i+{\beta AI}_i$$

(2)

Considering the equal importance of corporate data assetization and artificial intelligence development, the parameters were set as α = β = 0.5.

The coupling coordination degree model for data assets and artificial intelligence (DICD) is as follows:

$$D= \sqrt{C·T}$$

(3)

3.2.2. Explained Variable

Internationalization Level (IL): Following Di Lingyu [80], this study employed a firm’s overseas revenue to measure its internationalization level. The natural logarithm of overseas revenue plus one was used as a proxy variable for IL. For robustness, a dummy variable indicating the presence or absence of overseas revenue was constructed as an alternative measure.

3.2.3. Mediating Variable

Green Technology Innovation (GTI): Drawing on Johnstone N and Aghion P [28,29], the number of green patents served as a proxy variable for GTI. Based on Li X [81], green technology innovation was further classified into substantive and strategic innovation. To distinguish between innovation behavior and outcomes, GTI was subdivided into Substantive Green Innovation Behavior (GSubB), Substantive Green Innovation Outcome (GSubO), Strategic Green Innovation Behavior (GStrB), and Strategic Green Innovation Outcome (GStrO).

GSubB denotes the natural logarithm of the number of green invention patent applications. It measures early-stage investment in fundamental green R&D, reflecting exploratory innovation in core technologies. GSubO denotes the natural logarithm of the number of green invention patents granted. It represents certified high-quality innovations that have passed substantive examination, indicating a substantive technological edge with legal protection. GStrB denotes the natural logarithm of the number of green utility model patent applications. It captures R&D efforts focused on adaptive improvements and structural optimization of existing green technologies, reflecting incremental and practical innovation activities. GStrO denotes the natural logarithm of the number of green utility model patents granted. It gauges practically oriented innovations that have obtained fast legal protection, demonstrating capabilities in technology implementation and rapid market deployment.

3.2.4. Moderating Variable

Investor Stability (IS): Stable investors are defined as shareholder groups with a propensity for long-term holdings. Following Loh [32], investor stability was measured as the negative of the average turnover rate during the 30 trading days preceding each quarterly and annual earnings announcement. The annual indicator was constructed as the negative mean of these four periodic averages.

3.2.5. Control Variables

Consistent with the focus on intelligence, data, and internationalization, the following control variables were included: Cash Flow Ratio (Cashflow), Inventory to Total Assets Ratio (INV), Number of Directors (Board), Listing Age (ListAge), and Occupation of Funds by Largest Shareholder (Occupy).

Detailed definitions and explanations for all variables are provided in

Table 1. Definitions and Descriptions of Main Variables.

Type	Name	Symbol	Description
Explanatory Variable	Digital–Intelligent Synergy	DIS
	Data Assets	DA	Ln (Data Asset Word Frequency +1)
	Self-use Data Assets	SDAs	Ln (Self-use Data Asset Word Frequency +1)
	Transactional Data Assets	TDAs	Ln (Transactional Data Asset Word Frequency +1)
	Data Assets in Robustness Tests	DA-replace	The ratio of digital transformation-related intangible assets to total intangible assets.
	Artificial Intelligence	AI	Ln (AI Word Frequency +1)
	Artificial Intelligence Technology	AIT	Ln (AI Technology Word Frequency +1)
	Artificial Intelligence Application	AIA	Ln (AI Application Word Frequency +1)
	Artificial Intelligence in Robustness Tests	AI-replace	Following the measurement approach of Yao et al.
	Digital–Intelligent Coupling Coordination	DICD	Coupling Coordination Degree (AI & Data)
	Digital–Intelligent Coupling	DICC	Coupling degree (AI & Data)
	Digital–Intelligent Coordination	DICT	Coordination degree (AI & Data)
	DICD in Robustness Tests	DICD-replace	Coupling Coordination Degree (PCA)
Explained Variable	Internationalization Level	IL	Ln (Overseas Revenue +1)
		IL-dummy	Equals 1 if the firm has overseas revenue, and 0 otherwise.
Mediating Variable	Green Technology Innovation	GTI	Natural Logarithm of (Green Patents + 1)
	Substantive Green Innovation Behavior	GSubB	Ln (Green Invention Patent Applications +1)
	Substantive Green Innovation Outcome	GSubO	Ln (Green Invention Patent Grants +1)
	Strategic Green Innovation Behavior	GStrB	Ln (Green Utility Model Patent Applications +1)
	Strategic Green Innovation Outcome	GStrO	Ln (Green Utility Model Patent Grants +1)
Moderating Variable	Investor Stability	IS	(-) Avg Turnover Ratio (30 days pre-earnings announcement)
Control Variable	Cash Flow Ratio	Cashflow	Operating Cash Flow/Total Assets
	Inventory-to-Asset Ratio	INV	Net Inventory/Total Assets
	Board Size	Board	Ln (Number of Board Directors +1)
	Years Since Listing	ListAge	Ln (Years Since Listing +1)
	Tunneling by Largest Shareholder	Occupy	Other Receivables/Total Assets

.

3.3. Model Construction

3.3.1. Main Effect Model

To examine whether data assets, artificial intelligence, and their synergy affect corporate internationalization, a two-way fixed-effects regression model was constructed.

$${IL}_{i,t}={\alpha }_0+{\alpha }_1{DIS}_{i,t}+\sum {Controls}_{i,t}+\sum Year+\sum Id+{\epsilon }_{i,t}$$

(4)

Here, ${DIS}_{i,t}$ represents the digital–intelligent synergy level of firm $i$ in period $t$, including Data Assets (${DA}_{i,t}$), Artificial Intelligence (${AI}_{i,t}$), and the digital–Intelligent Coupling Coordination Degree (${DICD}_{i,t}$). ${IL}_{i,t}$ is the internationalization level of firm $i$ in the period t. ${Controls}_{i,t}$ represents the control variables. Year denotes time fixed effects, Id denotes individual fixed effects, and ${\epsilon }_{i,t}$ is the random error term. To address potential concerns of heteroskedasticity and serial correlation within firms over time, all regressions employ robust standard errors clustered at the firm level. This approach is consistently applied to subsequent models as well.

3.3.2. Mediation Effect Model

A mediation model was used to test whether green technology innovation plays a mediating role. The first step involved the main effect test, consistent with Model (4). The second step examined the effect of digital–intelligent synergy on green technology innovation.

$${GTI}_{i,t}={\beta }_0+{\beta }_1{DIS}_{i,t}+\sum {Controls}_{i,t}+\sum Year+\sum Id+{\epsilon }_{i,t}$$

(5)

In the mediation analysis, the broad GTI measure was substituted with its four specific components: Substantive Green Innovation Behavior (GSubB), Outcome (GSubO), Strategic Green Innovation Behavior (GStrB), and Outcome (GStrO). A significant ${\beta }_1$ suggests that digital–intelligent synergy exerts a significant effect on green technology innovation.

The third step evaluated the effect of digital–intelligent synergy on internationalization with the inclusion of the green technology innovation mediator.

$${IL}_{i,t}={\gamma }_0+{\gamma }_1{DIS}_{i,t}+{\gamma }_2{GTI}_{i,t}+\sum {Controls}_{i,t}+\sum Year+\sum Id+{\epsilon }_{i,t}$$

(6)

Here, the significance of coefficients ${\gamma }_1$ and ${\gamma }_2$ determined the mediating role: full mediation is present if ${\gamma }_1$ is insignificant but ${\gamma }_2$ is significant; partial mediation is indicated if both are significant; and no mediation exists if ${\gamma }_2$ is insignificant.

3.3.3. Mediated Moderation Model

First, a moderation model was used to test whether investor stability moderates the direct effect of digital–intelligent synergy on internationalization. A significant δ₂ indicates the presence of this moderation.

$${IL}_{i,t}={\delta }_0+{\delta }_1{DIS}_{i,t}+{\delta }_2{{DIS}_{i,t}\times IS}_{i,t}+{\delta }_3{IS}_{i,t}+\sum {Controls}_{i,t}+\sum Year+\sum Id+{\epsilon }_{i,t}$$

(7)

Second, model (8) examined its moderating effect on the relationship between digital–intelligent synergy and green technology innovation.

$${GTI}_{i,t}={\rho }_0+{\rho }_1{DIS}_{i,t}+{\rho }_2{{DIS}_{i,t}\times IS}_{i,t}+{\rho }_3{IS}_{i,t}+\sum {Controls}_{i,t}+\sum Year+\sum Id+{\epsilon }_{i,t}$$

(8)

Contingent on the established mediation (implying ${\rho }_1$ is significant), a significant ${\rho }_2$ indicates that investor stability moderates the DIS → GTI link.

Finally, the moderating role of investor stability on the second stage (GTI → IL) was tested.

Scenario A: If the direct effect is not moderated in Model (7):

$${IL}_{i,t}={\theta }_0+{\theta }_1{DIS}_{i,t}+{\theta }_2{IS}_{i,t}+{\theta }_3{GTI}_{i,t}+{\theta }_4{{IS}_{i,t}\times GTI}_{i,t}+\sum {Controls}_{i,t}+\sum Year+\sum Id+{\epsilon }_{i,t}$$

(9)

A significant ${\theta }_4$ would indicate this moderation, alongside ${\theta }_1$ and ${\theta }_3$, confirming the main and mediation effects, respectively.

Scenario B: If the direct effect is moderated in Model (7):

$$\begin{gathered} {IL}_{i,t}={\omega }_0+{\omega }_1{DIS}_{i,t}+{\omega }_2{IS}_{i,t}+{\omega }_3{{IS}_{i,t}\times DIS}_{i,t}+{\omega }_4{GTI}_{i,t}+{\omega }_5{{IS}_{i,t}\times GTI}_{i,t} \\ +\sum {Controls}_{i,t}+\sum Year+\sum Id+{\epsilon }_{i,t} \end{gathered}$$

(10)

A significant ${\omega }_1$ confirms the main effect; ${\omega }_3$ confirms moderation of the direct DIS → IL effect; ${\omega }_4$ confirms the mediation exists. A significant ${\omega }_5$ indicates that investor stability also moderates the second-stage GTI → IL path.

In the above models, green technology innovation (GTI) variable encompasses both green innovation behavior and outcome. To account for the objective time lag between innovation activities and their measurable results, the empirical analysis adopted a refined treatment: data on green innovation outcomes were lagged to capture the impact of prior innovation efforts on contemporaneous internationalization performance, whereas data on green innovation behavior were contemporaneously measured.

4. Analysis of Empirical Results

4.1. Descriptive Statistics

Descriptive statistics in

Table 2. Descriptive Statistics.

Variables	(1)	(2)	(3)	(4)	(5)
	Number	Mean	SD	Min	Max
DA	36,493	3.363	0.672	1.792	5.961
SDAs	36,493	3.319	0.645	1.792	5.855
TDAs	36,493	0.618	0.856	0	3.970
DA-replace	36,493	12.98	5.652	0	20.07
AI	36,493	1.522	1.295	0	4.500
AIT	36,493	1.055	1.204	0	4.407
AIA	36,493	0.899	1.037	0	3.738
AI-replace	36,493	0.983	1.212	0	4.913
DICD	36,493	0.122	0.0935	0.0176	0.503
DICC	36,493	0.562	0.245	0.175	1.000
DICT	36,493	0.0477	0.0754	0.000535	0.385
IL	36,493	9.588	9.942	0	24.50
IL-dummy	36,493	0.489	0.500	0	1
GSubB	36,493	0.623	0.982	0	4.511
GStrB	36,493	0.575	0.910	0	4.043
GSubO	36,493	0.330	0.687	0	3.871
GStrO	36,493	0.603	0.922	0	4.094
IS	36,493	−0.0339	0.0365	−0.266	0
Cashflow	36,493	0.0486	0.0664	−0.167	0.267
INV	36,493	0.128	0.111	0	0.719
Board	36,493	2.094	0.195	1.609	2.708
ListAge	36,493	2.045	0.963	0	3.466
Occupy	36,493	0.0125	0.0207	6.10 × 10−5	0.184

show that the mean value of data assets (DA) is 3.363. Self-use data assets (SDAs) account for the majority, while transactional data assets (TDAs) remain limited, reflecting inactive data asset transactions. The mean artificial intelligence (AI) level is 1.522, with AI technology (AIT) slightly higher than AI application (AIA), suggesting challenges in translating technical capability into practical use. The digital–intelligent coupling coordination (DICD) is low (mean = 0.122), indicating preliminary synergy between DA and AI. The internationalization Level (IL) varies considerably across firms. Substantive innovation (GSubB) and strategic innovation behavior (GStrB) show similar means, but the outcome of substantive innovation (GSubO) is significantly lower, pointing to its slower and more difficult nature. Investor stability (IS) is generally high with little variation. Among the control variables, CashFlow is stable on average but negative for some firms. Inventory levels (INV) vary widely, with some firms holding zero inventory and others facing accumulation risks. Board size is concentrated, ListAge is broadly distributed, and fund occupation (Occupy) differs notably across firms.

4.2. Diagnosing Multicollinearity

To mitigate potential multicollinearity arising from the multiple interaction terms in the model, all interaction terms and their constituent variables were mean-centered to reduce multicollinearity. Following this procedure, the relevant interaction terms were computed using the centered variables, and subsequent empirical tests were conducted based on these centered values.

Thereafter, four sets of variance inflation factor (VIF) tests were performed; the first set involves Variance Inflation Factor tests among the three components of the explanatory variables and the control variables, while the remaining three sets pertain to a distinct group of variables that appear together in the model, with the results reported in

Table 3. Variance Inflation Factor Test.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	VIF	1/VIF	VIF	1/VIF	VIF	1/VIF	VIF	1/VIF
DA	1.48	0.673819	1.14	0.878975
AI	6.07	0.164864			1.12	0.892584
DICD	7.13	0.140261					1.14	0.877161
Cashflow	1.01	0.988062	1.07	0.938570	1.06	0.946213	1.06	0.942343
INV	1.02	0.984527	1.06	0.939880	1.04	0.957144	1.05	0.952426
Board	1.00	0.998427	1.04	0.962550	1.04	0.961081	1.04	0.962390
ListAge	1.02	0.981003	1.49	0.670024	1.49	0.669433	1.49	0.670666
Occupy	1.01	0.991096	1.06	0.946950	1.05	0.949578	1.05	0.949399
IS			1.51	0.662804	1.51	0.662574	1.51	0.663118
DA × IS			1.09	0.916001
AI × IS					1.08	0.927453
DICD × IS							1.10	0.906075
GSubB			2.97	0.336868	2.97	0.337055	3.00	0.333187
GSubO			2.25	0.445275	2.25	0.445346	2.25	0.445176
GStrB			3.68	0.271377	3.62	0.275995	3.64	0.274494
GStrO			3.63	0.275302	3.63	0.275379	3.63	0.275267
GSubB × IS			2.83	0.352968	2.83	0.352841	2.86	0.349199
GSubO × IS			2.14	0.466794	2.14	0.467770	2.14	0.467310
GStrB × IS			3.48	0.287708	3.42	0.292102	3.44	0.290635
GStrO × IS			3.21	0.311965	3.21	0.311895	3.21	0.311620
Mean VIF	2.47		2.10		2.09		2.10

. Specifically, column (1) presents the VIF values for all explanatory and control variables; column (3) focuses on data assets (DA) along with their associated mediating, moderating, and interaction terms; columns (5) and (7) respectively report the VIFs for variable groups related to artificial intelligence (AI) and digital–intelligence coupling coordination (DICD). Across all tests, the VIF values remain below the common threshold of 10, indicating that serious multicollinearity is not a concern in the specified models.

4.3. Baseline Regression

4.3.1. Data–Intelligent Synergy and Internationalization Level

Table 4. Digital–Intelligent Synergy and Internationalization Level.

Variables	(1)	(2)	(3)
	IL	IL	IL
DA	0.431 ***
	(3.02)
AI		0.182 ***
		(2.71)
DICD			3.060 ***
			(2.74)
Constant	6.811 ***	6.849 ***	6.871 ***
	(7.79)	(7.84)	(7.87)
Obs.	36,493	36,493	36,493
Adj. R2	0.815	0.815	0.815
Id	YES	YES	YES
Year	YES	YES	YES
Controls	YES	YES	YES

Note: *** represent statistical significance at the 1% levels; t-statistics are reported in parentheses.

reports the baseline regression results examining the impact of Data Assets (DA), Artificial Intelligence (AI) and digital–intelligent coupling coordination (DICD) on internationalization level (IL). The results demonstrate that data assets, artificial intelligence, and digital–intelligent coupling coordination all exert a significantly positive effect on overseas revenue at the 1% level, thereby enhancing the level of corporate internationalization. Therefore, Hypothesis H1 is supported.

4.3.2. Discussion on Baseline Regression

Data assets and artificial intelligence serve not only as production tools but also as critical assets and core drivers of internationalization strategies, providing strong empirical evidence for firms to integrate them into their core resource base to expand into international markets. Existing research has often focused on the independent functions of information technology, such as enhancing risk perception, market insight, and supply chain optimization [43,44,46], or emphasized its role in reducing costs and shaping competitive advantages [48,49]. This study confirms that the effective implementation of an internationalization strategy relies not only on the separate application of data assets or AI technologies, but also on their deep integration and systemic coordination. This finding not only aligns with the view that composite technological applications can promote holistic optimization and ecological synergy [52], but further clarifies that synergy itself functions as a mechanism. Its value does not stem from the simple accumulation of individual technologies or resources, but from the leap in overall effectiveness achieved through structural coordination. Therefore, this study theoretically advances the current discussion on “how digital technologies promote internationalization,” shifting the focus from “tool-enabled empowerment” to “systemic synergy,” and offers a new explanatory pathway for understanding how firms can build sustainable international competitiveness through digital–intelligent integration.

4.4. Robustness and Endogeneity Tests

4.4.1. Replacing the Explained Variable

A dummy variable for the internationalization level (IL_dummy) was constructed based on the presence of overseas business revenue. As shown in

Table 5. Regression Results after Replacing the Explained Variable.

Variables	(1)	(2)	(3)
	IL	IL	IL
DA	0.019 **
	(2.53)
AI		0.007 **
		(2.00)
DICD			0.118 **
			(2.03)
Constant	0.383 ***	0.384 ***	0.385 ***
	(8.52)	(8.55)	(8.58)
Obs.	36,493	36,493	36,493
Adj. R2	0.811	0.811	0.811
Id	YES	YES	YES
Year	YES	YES	YES
Controls	YES	YES	YES

Note: *** and ** represent statistical significance at the 1%, 5% levels, respectively; t-statistics are reported in parentheses.

, the significantly positive coefficients in columns (1)–(3) reaffirm support for Hypotheses H1a, H1b, and H1c.

4.4.2. Replacing the Explanatory Variables

To ensure robustness, alternative measures were adopted. Following Qi H. [21], data assets were proxied using “digital transformation intangible assets,” measured as the share of Digital-related intangible assets in total intangible assets. The significantly positive result in column (1) of

Table 6. Regression Results after Replacing the Explanatory Variables.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	IL	IL	IL	IL	IL	IL
DA-replace	0.012 *
	(1.68)
AI-replace		0.083 *
		(1.66)
DICD-replace			0.298 ***
			(3.44)
DA-narrow				0.279 **
				(2.24)
AI-narrow					0.270 ***
					(3.81)
DICD-narrow						8.121 ***
						(4.12)
Constant	6.611 ***	6.728 ***	6.881 ***	6.769 ***	6.857 ***	6.879 ***
	(7.50)	(7.66)	(7.89)	(7.72)	(7.84)	(12.22)
Obs.	36,493	36,493	36,493	36,493	36,493	36,493
Adj. R2	0.815	0.815	0.815	0.815	0.815	0.815
Id	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES
Controls	YES	YES	YES	YES	YES	YES

Note: ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively; t-statistics are reported in parentheses.

confirms the robustness of H1a. Similarly, following Yao J. [22], an alternative AI word frequency measure was used, and its positive effect in column (2) again supports H1b.

An alternative index of digital–intelligent coupling coordination was derived via principal component analysis (PCA) based on standardized measures of data assets (DA) and artificial intelligence (AI). The result presented in column (3) confirms that its relationship with internationalization level is still significant at the 1% level, lending additional support to Hypothesis H1c.

To capture the core concepts of data assets and AI more precisely, the study constructs narrow dictionaries by filtering the original keyword lists to retain the most representative terms (Appendix A and Appendix B). After recalculating the coupling coordination degree using these refined measures, the results in columns (4)–(6) remain consistent with the main findings, supporting H1a, H1b, and H1c.

4.4.3. Lagged Variables

To mitigate endogeneity due to reverse causality or dynamic factors, one-period lagged values of the explanatory variables were used in the regression. The results in

Table 7. Regression Results Lagged by One Period.

Variables	(1)	(2)	(3)
	IL	IL	IL
L.DA	0.310 **
	(2.10)
L.AI		0.151 **
		(2.28)
L.DICD			2.372 **
			(2.19)
Constant	7.141 ***	7.175 ***	7.184 ***
	(7.10)	(7.15)	(7.15)
Obs.	30,668	30,668	30,668
Adj. R2	0.824	0.824	0.824
Id	YES	YES	YES
Year	YES	YES	YES
Controls	YES	YES	YES

Note: ***, ** represent statistical significance at the 1%, 5% levels, respectively; t-statistics are reported in parentheses.

, which account for time trends and reduce reverse causality concerns, confirm the robustness of H1a, H1b, and H1c.

4.4.4. Modifying the Fixed Effects Specification

To further examine the impact of digital–intelligent synergy at the industry level, the models were re-estimated. As detailed in Appendix D, industries are classified according to China’s National Economic Industry Classification Standard [82]. As shown in

Table 8. Regression Results at the Industry Level.

Variables	(1)	(2)	(3)
	IL	IL	IL
DA	0.613 ***
	(2.67)
AI		0.904 ***
		(8.97)
DICD			11.920 ***
			(7.74)
Constant	6.097 ***	5.988 ***	6.112 ***
	(4.77)	(4.70)	(4.80)
Obs.	30,668	30,668	30,668
Adj. R2	0.824	0.824	0.824
Id	YES	YES	YES
Year	YES	YES	YES
Controls	YES	YES	YES

Note: *** represent statistical significance at the 1% levels; t-statistics are reported in parentheses.

, data assets, AI, and digital–intelligent coupling coordination all exhibit significantly positive effects on internationalization at the industry level.

4.4.5. Interactive Fixed Effects

To account for variation in digital–intelligent development and international policies across industries and regions, industry–year and region–year interactive fixed effects are incorporated into the baseline regression specifications, respectively. Industries are classified according to China’s National Economic Industry Classification Standard (Appendix D), and regions are defined at the provincial administrative level. Baseline regression results including individual, year, and industry–year fixed effects are reported in columns (1)–(3) of

Table 9. Regression Results with Interactive Fixed Effects.

Variables	Ind × Year Fixed			Region × Year Fixed
	(1)	(2)	(3)	(4)	(5)	(6)
	IL	IL	IL	IL	IL	IL
DA	0.445 ***			0.402 ***
	(3.05)			(2.81)
AI		0.185 ***			0.181 ***
		(2.74)			(2.71)
DICD			3.228 ***			2.947 ***
			(2.86)			(2.67)
Constant	6.718 ***	6.743 ***	6.767 ***	6.918 ***	6.955 ***	6.972 ***
	(7.80)	(7.84)	(7.87)	(7.75)	(7.81)	(7.83)
Obs.	36,483	36,483	36,483	36,493	36,493	36,493
Adj. R2	0.816	0.816	0.816	0.816	0.816	0.816
Id	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES
Ind × Year	YES	YES	YES	-	-	-
Region × Year	-	-	-	YES	YES	YES
Controls	YES	YES	YES	YES	YES	YES

Note: *** represent statistical significance at the 1% levels; t-statistics are reported in parentheses.

, whereas those with individual, year, and region–year fixed effects appear in columns (4)–(6). The findings demonstrate that hypotheses H1a, H1b, and H1c remain robust even after the inclusion of these interactive fixed effects.

4.4.6. Instrumental Variable Approach

To address potential endogeneity, this study employs an instrumental variable (IV) approach. The instrumental variable is a predicted provincial digitalization index constructed using a leave-one-out procedure based on data from the CSMAR Database, which satisfies the relevance and exclusion restriction conditions. The regional digital environment provides a foundation for firms’ digital investment and synergy, ensuring the relevance of the IV. As a macro-level predicted value based solely on the digital performance of other firms within the same province, the IV isolates the direct influence of a firm’s own digitalization. Furthermore, to control for potential confounding effects from regional digital conditions via economic, financial, informational, technological, and infrastructural channels, province-level controls are included in the analysis: economic development (per capita GDP), financial development (total deposits and loans to GDP ratio), R&D intensity (R&D expenditure to GDP ratio), internet penetration (internet users to population ratio), technology market development (technology transaction value to GDP ratio), transportation infrastructure (road density), and openness (total import and export to GDP ratio). Data are sourced from the National Bureau of Statistics and provincial statistical yearbooks.

The IV tests in

Table 10. Predicted Province-Year Digital Index as an Instrumental Variable.

Variables	DA		AI		DICD
	(1)	(2)	(3)	(4)	(5)	(6)
	First Stage	Second Stage	First Stage	Second Stage	First Stage	Second Stage
IV	0.0588 ***		0.2082 ***		0.0136 ***
	(14.4915)		(24.3113)		(22.1605)
DA		1.8020 *
		(1.7684)
AI				0.5090 *
				(1.7862)
DICD						7.8066 *
						(1.7817)
Constant	−1.5953 ***		−1.0439		−0.1704 ***
	(−2.9825)		(−1.3514)		(−3.0238)
Obs.	31,110	31,110	31,110	31,110	31,110	31,110
R-squared	0.890	−0.005	0.855	0.001	0.894	0.001
Controls FE (Firm&Province)	Yes	Yes	Yes	Yes	Yes	Yes
Id FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Kleibergen–Paap rk Wald F	210.010 [16.38]		591.059 [16.38]		491.106 [16.38]
Kleibergen–Paap rk LM	173.641 ***		386.393 ***		333.882 ***

Note: *** and * represent statistical significance at the 1% and 10% levels, respectively; t-statistics are reported in parentheses.

show that in the first-stage regressions, the IV is positively and significantly correlated with each endogenous explanatory variable at the 1% level. The Kleibergen–Paap rk Wald F statistics all exceed the 10% critical value (16.38), and the Kleibergen–Paap rk LM statistics are all significant at the 1% level, ruling out concerns of weak instruments and underidentification. The second-stage estimates indicate that, after controlling for endogeneity, the positive effects of data assets, artificial intelligence, and digital–intelligent coupling coordination on firm internationalization remain statistically significant. In summary, these results from the instrumental variable estimations, which mitigate potential endogeneity issues, provide further support for the robustness of the main findings regarding the positive role of data assets, AI, and digital–intelligent coupling coordination in promoting firm internationalization.

5. Further Analysis

5.1. The Mediating Effect of Green Technology Innovation

5.1.1. Substantive Green Innovation Behavior

Substantive green innovation behavior in this study refers to the number of green invention patent applications.

Table 11. Mediating Effect of Substantive Green Innovation Behavior.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	IL	GSubB	IL	IL	GSubB	IL	IL	GSubB	IL
DA	0.431 ***	0.137 ***	0.400 ***
	(3.02)	(7.33)	(2.81)
AI				0.182 ***	0.061 ***	0.168 **
				(2.71)	(8.21)	(2.51)
DICD							3.060 ***	1.106 ***	2.806 **
							(2.74)	(8.59)	(2.53)
GSubB			0.230 ***			0.231 ***			0.229 ***
			(3.45)			(3.46)			(3.44)
Constant	6.811 ***	0.052	6.799 ***	6.849 ***	0.688 ***	6.834 ***	6.871 ***	0.076	6.853 ***
	(7.79)	(0.54)	(7.79)	(7.84)	(7.09)	(7.84)	(7.87)	(0.79)	(7.86)
Obs.	36,493	36,493	36,493	36,493	36,493	36,493	36,493	36,493	36,493
Adj. R2	0.815	0.703	0.815	0.815	0.703	0.815	0.815	0.704	0.815
Id	YES	YES	YES	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES	YES	YES	YES
Controls	YES	YES	YES	YES	YES	YES	YES	YES	YES

Note: ***, ** represent statistical significance at the 1%, 5% levels, respectively; t-statistics are reported in parentheses.

presents the mediation test results. Columns (1)–(3) show that substantive green innovation behavior mediates the relationship between data assets and internationalization revenue, with significantly positive outcomes confirming this effect. Similar mediating roles are observed in the relationships involving artificial intelligence and digital–intelligent coupling coordination. These results provide partial support for Hypothesis H2.

Specifically, firms utilize big data to analyze global environmental trends and market demand, and apply machine learning to simulate experiments and optimize R&D processes. Through digital–intelligent synergy, they integrate resources and accelerate innovation decision-making, thereby increasing investment in core technology R&D. This enhances firms’ innovation vitality and establishes a technical foundation for global expansion.

5.1.2. Substantive Green Innovation Outcome

Table 12. Mediating Effect of Substantive Green Innovation Outcome.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	IL	GSubO	IL	IL	GSubO	IL	IL	GSubO	IL
DA	0.300 **	0.063 ***	0.289 *
	(2.04)	(4.93)	(1.96)
AI				0.146 **	0.022 ***	0.142 **
				(2.20)	(4.31)	(2.14)
DICD							2.268 **	0.494 ***	2.177 **
							(2.09)	(5.45)	(2.01)
GSubO			0.184 **			0.185 **			0.183 **
			(2.09)			(2.10)			(2.08)
Constant	7.816 ***	0.082	7.801 ***	7.840 ***	0.082	7.824 ***	7.846 ***	0.089	7.830 ***
	(8.55)	(1.12)	(8.53)	(8.58)	(1.12)	(8.57)	(8.58)	(1.22)	(8.57)
Obs.	30,668	30,668	30,668	30,668	30,668	30,668	30,668	30,668	30,668
Adj. R2	0.824	0.641	0.824	0.824	0.641	0.824	0.824	0.641	0.824
Id	YES	YES	YES	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES	YES	YES	YES
Controls	YES	YES	YES	YES	YES	YES	YES	YES	YES

Note: ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively; t-statistics are reported in parentheses.

presents the results for the mediating effect of substantive green innovation outcomes in the relationship between digital–intelligent synergy and internationalization, providing partial support for Hypothesis H2.

Specifically, the integration of data assets and AI enhances patent quality, helps firms avoid potential conflicts, and increases patent grant rates. Meanwhile, digital–intelligent synergy improves innovation chain efficiency, enabling more effective transformation of R&D outcomes and strengthening intellectual property barriers in international markets.

5.1.3. Strategic Green Innovation Behavior

Table 13. Mediating Effect of Strategic Green Innovation Behavior.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	IL	GStrB	IL	IL	GStrB	IL	IL	GStrB	IL
DA	0.431 ***	0.065 ***	0.423 ***
	(3.02)	(4.08)	(2.97)
AI				0.182 ***	0.041 ***	0.177 ***
				(2.71)	(6.38)	(2.64)
DICD							3.060 ***	0.507 ***	2.996 ***
							(2.74)	(4.77)	(2.69)
GStrB			0.127 **			0.125 **			0.126 **
			(2.03)			(2.00)			(2.02)
Constant	6.811 ***	−0.282 ***	6.847 ***	6.849 ***	−0.270 ***	6.883 ***	6.871 ***	−0.272 ***	6.905 ***
	(7.79)	(−3.15)	(7.83)	(7.84)	(−3.03)	(7.89)	(7.87)	(−3.04)	(7.91)
Obs.	36,493	36,493	36,493	36,493	36,493	36,493	36,493	36,493	36,493
Adj. R2	0.815	0.663	0.815	0.815	0.663	0.815	0.815	0.663	0.815
Id	YES	YES	YES	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES	YES	YES	YES
Controls	YES	YES	YES	YES	YES	YES	YES	YES	YES

Note: ***, ** represent statistical significance at the 1%, 5% levels, respectively; t-statistics are reported in parentheses.

presents the mediating effect of strategic green innovation behavior in the relationship between digital–intelligent synergy and internationalization. The results confirm its mediating role, providing further partial support for Hypothesis H2.

Digital–intelligent synergy delivers timely market feedback and user needs, facilitating swift patent filings for environmental equipment or process improvements tailored to specific international requirements or regulatory shifts. It also enhances cross-departmental collaboration, allowing firms to adapt more flexibly to diverse environmental standards and strengthen market responsiveness. These rapidly accumulated competitive advantages ultimately help mitigate internationalization risks.

5.1.4. Strategic Green Innovation Outcome

Table 14. Mediating Effect of Strategic Green Innovation Outcome.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	IL	GStrO	IL	IL	GStrO	IL	IL	GStrO	IL
DA	0.300 **	0.066 ***	0.293 **
	(2.04)	(3.70)	(1.99)
AI				0.146 **	0.051 ***	0.140 **
				(2.20)	(7.22)	(2.12)
DICD							2.268 **	0.675 ***	2.193 **
							(2.09)	(5.64)	(2.02)
GStrO			0.113 *			0.109 *			0.110 *
			(1.68)			(1.65)			(1.65)
Constant	7.816 ***	0.029	7.813 ***	7.840 ***	0.046	7.835 ***	7.846 ***	0.043	7.842 ***
	(8.55)	(0.28)	(8.55)	(8.58)	(0.45)	(8.59)	(8.58)	(0.42)	(8.59)
Obs.	30,668	30,668	30,668	30,668	30,668	30,668	30,668	30,668	30,668
Adj. R2	0.824	0.683	0.824	0.824	0.683	0.824	0.824	0.683	0.824
Id	YES	YES	YES	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES	YES	YES	YES
Controls	YES	YES	YES	YES	YES	YES	YES	YES	YES

Note: ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively; t-statistics are reported in parentheses.

presents the mediating effects of strategic green innovation outcomes in the relationship between digital–intelligent synergy and internationalization. The results confirm that digital–intelligent synergy enhances internationalization by fostering such outcomes, thus providing full support for Hypothesis H2.

The findings suggest that digital–intelligent synergy helps identify high-value technological enhancements and facilitates the rapid implementation of outcomes. Therefore, strategic green innovation serves as an effective approach for firms to achieve international expansion at relatively lower cost, build brand loyalty, expand market share, and ultimately elevate their level of internationalization.

5.1.5. Discussion on Mediating Effect

This study finds that digital–intelligent synergy simultaneously drives both substantive and strategic green innovation, enabling them to collectively serve the firm’s international competitiveness. This supports the view that substantive green innovation, as an advanced technological capability, can be translated into higher value-added products and enhance overseas revenue [27,61], while also confirming the role of strategic innovation in reducing product carbon footprints [62]. These findings deepen existing research that regards green technology innovation as a signal of sustainable development for gaining international competitive advantage [59,60], and shift the perception of green innovation from a “cost center” to a “functional asset”.

The study not only echoes the perspective that composite technology applications can promote holistic optimization and ecological synergy, but also reveals a systematic mechanism of “digital–intelligent synergy—dual-path green innovation—internationalization,” thereby providing an integrated explanation for understanding how firms can build green international competitiveness through digital–intelligent integration.

5.2. Mediated Moderation Effects

5.2.1. The Moderating Effect of Investor Stability on the Main Relationship

Based on Model (7),

Table 15. Moderating Effect of Investor Stability (DIS → IL).

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	IL	IL	IL	IL	IL	IL
DA	0.431 ***	0.408 ***
	(3.02)	(2.86)
AI			0.182 ***	0.174 ***
			(2.71)	(2.59)
DICD					3.060 ***	2.878 ***
					(2.74)	(2.58)
IS		1.807 *		1.970 *		1.984 *
		(1.71)		(1.86)		(1.87)
DA × IS		5.432 ***
		(4.11)
AI × IS				1.761 ***
				(2.65)
DICD × IS						29.406 ***
						(3.20)
Constant	6.811 ***	6.926 ***	6.849 ***	7.035 ***	6.871 ***	7.045 ***
	(7.79)	(7.80)	(7.84)	(7.94)	(7.87)	(7.95)
Obs.	36,493	36,493	36,493	36,493	36,493	36,493
Adj. R2	0.815	0.815	0.815	0.815	0.815	0.815
Id	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES
Controls	YES	YES	YES	YES	YES	YES

Note: *** and * represent statistical significance at the 1% and 10% levels, respectively; t-statistics are reported in parentheses.

reports the moderating effect of investor stability on the relationship between digital–intelligent synergy and internationalization. Columns (1)–(2), (3)–(4), and (5)–(6) present the moderation tests for data assets, artificial intelligence, and digital–intelligent coupling coordination, respectively. With the inclusion of the interaction terms (DA × IS, AI × IS, DICD × IS), both the explanatory variables and interaction terms show coefficients significant at the 1% level, indicating that investor stability amplifies the positive effect of these factors on internationalization. In international strategy implementation, stable investors not only provide financial support but also supply resources and risk mitigation, thereby validating Hypothesis H3a.

5.2.2. The Moderating Effect of Investor Stability Between Digital–Intelligent Synergy and Green Technology Innovation

Based on Model (8),

Table 16. Moderating Effect of Investor Stability (DIS → GTI).

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	GSubB	GSubO	GStrB	GStrO	GSubB	GSubO	GStrB	GStrO	GSubB	GSubO	GStrB	GStrO
DA	0.134 ***	0.025 *	0.065 ***	0.082 ***
	(7.17)	(1.94)	(4.05)	(4.68)
AI					0.060 ***	0.010 *	0.040 ***	0.037 ***
					(8.03)	(1.93)	(6.33)	(4.94)
DICD									1.079 ***	0.254 ***	0.501 ***	0.572 ***
									(8.43)	(2.80)	(4.74)	(4.75)
DA × IS	0.493 ***	0.445 **	−0.097	0.680 ***
	(2.86)	(2.35)	(−0.77)	(3.19)
AI × IS					0.312 ***	0.286 ***	0.076	0.186
					(3.72)	(3.04)	(1.08)	(1.52)
DICD × IS									3.688 ***	3.121 **	0.431	2.931 *
									(2.95)	(2.23)	(0.47)	(1.81)
IS	0.471 ***	0.258 **	0.220 *	0.214	0.508 ***	0.280 **	0.227 *	0.283	0.511 ***	0.281 **	0.236 **	0.283
	(3.76)	(2.15)	(1.84)	(1.21)	(4.03)	(2.30)	(1.90)	(1.63)	(4.06)	(2.33)	(1.98)	(1.62)
Constant	0.098	0.141 *	−0.256 ***	0.036	0.117	0.152 *	−0.246 ***	0.068	0.128	0.156 *	−0.246 ***	0.070
	(0.99)	(1.66)	(−2.81)	(0.31)	(1.19)	(1.78)	(−2.71)	(0.59)	(1.30)	(1.83)	(−2.71)	(0.61)
Obs.	36,493	30,668	36,493	30,668	36,493	30,668	36,493	30,668	36,493	30,668	36,493	30,668
Adj. R2	0.703	0.641	0.663	0.683	0.704	0.641	0.663	0.683	0.704	0.641	0.663	0.683
Id	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
Controls	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES

Note: ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively; t-statistics are reported in parentheses.

presents the test results for the moderating effect of investor stability in the relationship between digital–intelligent synergy and green technology innovation. The significance of the interaction terms (GTI × DA, GTI × AI, GTI × DCID) indicates whether a moderating effect exists. The results show that investor stability positively moderates the impact of digital–intelligent synergy on both substantive green innovation behavior (GSubB) and substantive green innovation outcomes (GSubO), as shown in columns (1), (5), (9) and (2), (6), (10), respectively.

However, investor stability exerts a differentiated moderating role in the relationship between digital–intelligent synergy and strategic green innovation. Specifically, it does not significantly moderate the effect of digital–intelligent synergy on strategic green innovation behavior, as shown in columns (3), (7), and (11). In terms of strategic green innovation outcomes, investor stability moderates the relationships involving data assets and digital–intelligent coupling coordination (columns 4 and 12), but not the relationship involving artificial intelligence (column 8).

Overall, investor stability primarily moderates the pathways through which digital–intelligent synergy influences green innovation “outcomes” and substantive green innovation. This can be attributed to the financial support and risk tolerance provided by stable investors, which facilitate sustained and in-depth green innovation activities. Strategic green innovation “behavior” involves relatively lower costs and does not directly affect core business operations or product transformation, making it less sensitive to investor stability. Conversely, substantive green innovation and green innovation outcomes often accompanies strategic initiatives such as product renewal and business expansion [27], thereby requiring the assurance of stable investors. In conclusion, Hypothesis H3b is partly supported.

5.2.3. The Moderating Effect of Investor Stability Between Green Technology Innovation and Internationalization

Based on Model (9),

Table 17. Moderating Effect of Investor Stability (GTI→IL).

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	IL	IL	IL	IL	IL	IL	IL	IL	IL	IL	IL	IL
DA	0.378 ***	0.245 *	0.400 ***	0.241 *
	(2.66)	(1.86)	(2.81)	(1.75)
AI					0.160 **	0.141 *	0.169 **	0.139 *
					(2.41)	(1.92)	(2.53)	(1.89)
DICD									2.636 **	2.177 *	2.815 **	2.164 *
									(2.38)	(1.80)	(2.53)	(1.79)
IS	1.713 *	2.994 *	1.706 *	3.147 **	1.909 *	3.526 **	1.799 *	3.571 **	1.888 *	3.402 **	1.834 *	3.497 **
	(1.65)	(1.93)	(1.66)	(2.02)	(1.73)	(2.30)	(1.66)	(2.32)	(1.71)	(2.21)	(1.69)	(2.26)
DA × IS	5.308 ***	6.501 ***	5.436 ***	6.295 ***
	(3.98)	(3.34)	(4.11)	(3.26)
AI × IS					1.653 **	2.129 *	1.787 ***	2.101 *
					(2.46)	(1.94)	(2.68)	(1.91)
DICD × IS									28.395 ***	37.295 ***	29.587 ***	36.031 **
									(3.07)	(2.62)	(3.22)	(2.53)
GSubB	0.225 ***				0.224 ***				0.224 ***
	(3.37)				(3.37)				(3.37)
GSubB × IS	0.057				0.269				0.091
	(0.05)				(0.23)				(0.08)
GSubO		0.189 **				0.187 **				0.187 **
		(2.21)				(2.19)				(2.19)
GSubO × IS		−1.396				−1.020				−1.339
		(−0.62)				(−0.45)				(−0.59)
GStrB			0.130 **				0.128 **				0.129 **
			(2.08)				(2.07)				(2.08)
GStrB × IS			−0.396				−0.769				−0.649
			(−0.38)				(−0.73)				(−0.62)
GStrO				0.107 *				0.109 *				0.109 *
				(1.92)				(1.66)				(1.66)
GStrO × IS				−0.069				−0.424				−0.326
				(−0.04)				(−0.26)				(−0.20)
Constant	6.903 ***	7.241 ***	6.961 ***	7.244 ***	7.006 ***	7.409 ***	7.071 ***	7.420 ***	7.015 ***	7.406 ***	7.080 ***	7.411 ***
	(7.79)	(7.10)	(7.85)	(7.11)	(7.92)	(7.29)	(7.98)	(7.31)	(7.93)	(7.29)	(7.99)	(7.30)
Obs.	36,493	30,668	36,493	30,668	36,493	30,668	36,493	30,668	36,493	30,668	36,493	30,668
Adj. R2	0.815	0.824	0.815	0.824	0.815	0.824	0.815	0.824	0.815	0.824	0.815	0.824
Id	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
Controls	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES

Note: ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively; t-statistics are reported in parentheses.

presents the moderating effect of investor stability on the relationship between green technology innovation and internationalization. The coefficients of all interaction terms are insignificant, indicating that investor stability does not moderate the latter stage of the mediation pathway, i.e., between green innovation and internationalization. This implies that overseas revenue generation relies more on corporate strategic planning and core capability building, where green technology innovation serves as a tactical tool to overcome trade barriers and comply with environmental regulations. While stable external funding offers financial security, it does not directly affect overseas customers’ purchasing decisions, explaining the lack of a moderating effect in this pathway. Therefore, Hypothesis H3c is not supported.

In the above empirical analysis, the green innovation outcome variable is lagged to align with a time lag between digital–intelligence synergy, innovation outcomes, and subsequent internationalization performance. This approach helps to clearly identify the sustained impact of green innovation outcome on international revenue. Meanwhile, DIS may enhance innovation efficiency and accelerate the application of green technologies, exerting a more immediate effect on internationalization within the same period. To ensure that the core findings are not driven by specific modeling choices—such as variable centering or lag structures—and to test the robustness of the mechanism, Appendix E reports estimation results using uncentered and non-lagged variables. The results confirm that the mediating role of green technology innovation between DIS and internationalization remains robust. Moreover, the moderating effects of investor stability are consistent with those reported in the main text: it positively moderates the DIS → IL path and the DIS → GTI path, but does not significantly moderate the GTI → IL path. Therefore, the hypotheses H3a (supported), H3b (partially supported), and H3c (not supported) hold across both model specifications, further reinforcing the reliability of the conclusions.

5.2.4. Discussion on Mediated Moderation Effects

The study reveals that investor stability exerts an asymmetric moderating effect along the strategic pathway of “digital–intelligent synergy—green innovation—internationalization.” Stable investors provide “patient capital” and continuous resource support [63,65], strengthening the role of digital–intelligent synergy in driving green technology innovation and international expansion. This confirms theoretical expectations from behavioral strategy and the resource-based view, indicating that a stable ownership structure helps curb managerial myopia [72], ensures sustained investment in long-term strategies such as digital–intelligent integration and green R&D [70,71], and supports firms in overcoming short-term fluctuations while building market credibility during internationalization [66,67].

However, the study finds that investor stability does not play a moderating role in the “GTI → IL” path. This differs from some of the theoretical expectations derived from signaling theory [74,76] and the strategic commitment perspective [77,78,79]. One possible explanation is that in international markets, the credibility of green technology innovation relies more heavily on direct signals such as international certification and localized partnerships, which may dilute the indirect signaling effect of equity stability. Meanwhile, during the highly uncertain process of commercializing green innovation outcomes, management’s risk-averse tendencies may be reinforced, leading to a preference for incremental rather than breakthrough internationalization strategies, which may interrupt the development chain of “stable investment—strategic innovation—international expansion.” These findings suggest that the strategic value of investor stability is highly context-dependent, being more relevant in supporting technology integration and innovation incubation than in directly facilitating the international marketization of innovation performance.

5.3. Segmented Explanatory Variables

5.3.1. Regression Results on Segmented Data Assets

Table 18. Segmented Data Assets and Internationalization Level.

Variables	(1)	(2)	(3)
	IL	IL	IL
DA	0.431 ***
	(3.02)
SDAs		0.425 ***
		(2.98)
TDAs			0.084
			(1.12)
Constant	6.811 ***	6.803 ***	6.779 ***
	(7.79)	(7.77)	(7.74)
Obs.	36,493	36,493	36,493
Adj. R2	0.815	0.815	0.815
Id	YES	YES	YES
Year	YES	YES	YES
Controls	YES	YES	YES

Note: *** represent statistical significance at the 1% levels; t-statistics are reported in parentheses.

reports the baseline regression results of segmented data assets on the corporate Internationalization Level (IL). Data assets are categorized into Self-use data assets (SDAs) and transactional data assets (TDAs) to further distinguish the effects by data type. The results reveal that SDAs significantly enhance internationalization, whereas TDAs show no significant relationship with IL. This suggests that data assets deeply integrated into a firm’s internal operations are the primary driver of overseas expansion. Although TDAs can generate cash flow, they have not yet become a significant factor in boosting international business at the current stage.

5.3.2. Regression Results on Segmented Artificial Intelligence

Table 19. Segmented Artificial Intelligence and Internationalization Level.

Variables	(1)	(2)	(3)
	IL	IL	IL
AI	0.182 ***
	(2.71)
AIT		0.088 *
		(1.66)
AIA			0.254 ***
			(3.50)
Constant	6.849 ***	6.796 ***	6.845 ***
	(7.84)	(7.77)	(7.82)
Obs.	36,493	36,493	36,493
Adj. R2	0.815	0.815	0.815
Id	YES	YES	YES
Year	YES	YES	YES
Controls	YES	YES	YES

Note: ***, * represent statistical significance at the 1%, 10% levels, respectively; t-statistics are reported in parentheses.

reports the baseline regression results of Artificial Intelligence (AI) on the corporate Internationalization Level (IL). When AI is categorized into AI Technology (AIT) and AI Application (AIA), both dimensions show significantly positive coefficients. This suggests that AI technologies and AI applications serve as key strategies in strengthening firms’ competitive advantage in international markets.

5.3.3. Regression Results on Segmented Digital–Intelligent Coupling Coordination

The influence of digital–intelligent coupling coordination on internationalization is examined through three metrics: the Digital–Intelligent Coupling Coordination Degree (DICD), Coupling Degree (DICC), and Coordination Degree (DICT). Columns (2)–(3) in

Table 20. Segmented Digital–Intelligent Coupling Coordination and Internationalization Level.

Variables	(1)	(2)	(3)
	IL	IL	IL
DICD	3.060 ***
	(2.74)
DICC		−0.089
		(−0.59)
DICT			3.510 **
			(2.22)
Constant	6.871 ***	6.749 ***	6.844 ***
	(7.87)	(7.69)	(7.81)
Obs.	36,493	36,493	36,493
Adj. R2	0.815	0.815	0.815
Id	YES	YES	YES
Year	YES	YES	YES
Controls	YES	YES	YES

Note: ***, ** represent statistical significance at the 1%, 5% levels, respectively; t-statistics are reported in parentheses.

reveal that the coupling degree alone has no significant effect, while the coordination degree plays a critical role. Integrating these findings with earlier results, it can be inferred that although both data assets and AI individually facilitate internationalization, the sheer intensity of their coupling does not directly determine international outcomes. In contrast, the coordination degree—reflecting interactive quality and harmonious development—proves essential.

To further examine the findings under alternative weighting schemes, the study recalculated the coupling coordination degree by progressively varying the weight of data assets from 0.9 to 0.1 (and correspondingly, that of artificial intelligence from 0.1 to 0.9), and then regressed it on the level of internationalization.

As shown in Figure 2, the curve plotted based on the estimated coefficients under different weights indicates that the positive main effect remains statistically significant across all weighting settings. Furthermore, the trend in the coefficient estimates suggests that, within the context of the sample and measurement approach adopted in this study, the variation in data asset (DA) disclosure contributes more substantially to changes in the synergy index. This may reflect the critical role of data infrastructure development at the current stage.

5.3.4. Discussion on Segmented Explanatory Variables

The regression results on segmented data assets and artificial intelligence reveal that proprietary data assets, AI technologies, and AI applications all significantly enhance the level of internationalization, consistent with findings that digital–intelligent capabilities enhance international competitiveness through improved risk perception and market insight [43,44]. Transactional data assets do not yet exhibit a significant positive impact on overseas revenue at this stage, which aligns with the current early exploratory phase of data asset trading in China. The results reveal that the coordination degree, rather than the mere coupling degree, serves as the key driver of international outcomes. The findings indicate that the key to development lies not in the mechanical coupling of data assets and AI, but in their organic integration and in-depth coordination to advance the level of internationalization.

Furthermore, analysis across alternative weight assignments consistently indicates a positive relationship between digital–intelligent coupling coordination and internationalization. Within this study’s context, the contribution of data assets to the synergy index is pronounced. This pattern reinforces that the current stage of development depends not on the mere aggregation of digital resources, but on their strategic, synergistic integration. It underscores why transactional data assets alone show limited impact, while the deliberate coordination of self-use data assets with AI technologies and applications emerges as a central driver of international competitiveness.

6. Conclusions, Implications and Future Directions

6.1. Conclusions

Firstly, digital–intelligent synergy serves as a significant driver for firms to enhance their level of internationalization. Data assets and artificial intelligence can not only function independently but their coupling and coordination generate a synergistic effect with multiplied benefits. This underscores the need to treat data and intelligence as integrated strategic resources.

Secondly, green technology innovation is a critical pathway through which digital–intelligent synergy drives internationalization. It incentivizes both substantive and strategic green innovation activities and outcomes, thereby helping firms build a differentiated competitive advantage and lay the foundation for entering international markets.

Simultaneously, investor stability is an important factor influencing the realization of the internationalization effects of digital–intelligent synergy. Stable investors strengthen the impact of digital–intelligent synergy on both green technology innovation and the level of internationalization, providing crucial resource support for the digital–intelligent transformation and the exploration of green technologies.

Finally, the internationalization effects of digital–intelligent synergy exhibit significant heterogeneity when examining the constituent dimensions separately. Within the current stage and specific context of this research, the results suggest that internal data integration, AI technologies and applications, and digital–intelligent coordination play a more critical role in advancing internationalization.

6.2. Implications

6.2.1. Theoretical Implications

This study introduces the coupling coordination measurement method from the environmental field into the evaluation of digital–intelligent synergy effects. It constructs and validates an analytical framework integrating digital–intelligent technologies, green innovation, and value effects. This reveals the internal mechanism through which digital–intelligent synergy influences the internationalization level of Chinese firms. By verifying investor stability as an asymmetric influencing factor, this study deepens the understanding of the boundaries of investor stability’s role.

6.2.2. Practical Implications

For firms: The focus of technology application should shift from isolated uses to deep integration. Firms should leverage digital–intelligent synergy to rationally promote green technology innovation, reduce technical trade barriers, and shape a green and sustainable brand image. They need to develop green technologies and products with international competitiveness, transforming environmental pressures into market advantages.

For investors and financial institutions: They should fully realize their important value in enabling digital–intelligent effects by optimizing investment decision-making processes and avoiding short-term performance pressures. Providing long-term, stable, and strategic “patient capital” is essential for supporting firms’ international expansion.

For policymakers: Tailored support policies should be formulated to ensure targeted implementation. Encouraging and enhancing the participation of long-term investors can increase corporate commitment to sustainable innovation. Building an ecosystem that fosters cross-sector collaboration among data, artificial intelligence, and green technologies will help build competitive advantage in the global market.

6.3. Limitations and Future Directions

6.3.1. Measurement Method

The study employed a coupling coordination model but lacked granular measurement of qualitative dimensions, such as the depth of integration between data assets and AI within organizations or changes in management processes. Future research could adopt case studies, surveys, or text analysis to develop composite indicators that better capture the quality and progression of digital–intelligent integration.

6.3.2. Mechanism Exploration

While green technology innovation is identified as a key mediator, other pathways—such as enhanced organizational resilience or business model transformation—may also explain how digital–intelligent synergy influences internationalization. Factors like top management team characteristics and external policy uncertainty represent additional boundary conditions to examine. Further study could integrate dynamic capability theory or behavioral strategy perspectives to unravel this complex causal network.

6.3.3. Contextual Boundary

This study is primarily based on domestic green technology innovation and investor stability within the Chinese context. Whether digital–intelligent synergy can facilitate the acquisition of international green patents and attract global investors, thereby directly driving internationalization, remains an open question. Future research could extend the examination to international settings to explore how such mechanisms operate across different institutional and cultural environments.

6.3.4. Generalizability of Findings

Conclusions are primarily based on data from Chinese listed firms, limiting generalizability across different institutional contexts, cultures, or types of enterprises. Future work could conduct cross-national comparative studies to test the applicability and evolution of these findings under diverse systems and technological conditions.