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Article

Digital Capabilities, Integration into Global Innovation Networks, and Enterprise Innovation Performance

by
Shanwu Tian
1,
Xiaozhen Lai
1,*,
Lijun Dong
1 and
Xiurui Xu
2
1
School of Business, Qingdao University, Qingdao 266071, China
2
School of Politics and Public Administration, Qingdao University, Qingdao 266071, China
*
Author to whom correspondence should be addressed.
Systems 2025, 13(3), 212; https://doi.org/10.3390/systems13030212
Submission received: 23 February 2025 / Revised: 11 March 2025 / Accepted: 13 March 2025 / Published: 19 March 2025
(This article belongs to the Section Systems Practice in Social Science)
Browse Figure
Versions Notes

Abstract

:
Against the backdrop of accelerated global digital transformation and the shift toward open innovation models, this study examines how enterprises leverage digital capabilities to integrate into global innovation networks (GINs) and enhance innovation performance, while exploring the moderating role of organizational flexibility. Drawing on dynamic capability and social network theories, a multidimensional framework of digital capabilities (perception, operation, and coordination) and organizational flexibility (cultural, resource, and capability) is proposed. The empirical analysis of 343 Chinese multinational corporations using SPSS 27 and AMOS 24 reveals three key findings: (1) all dimensions of digital capabilities significantly improve innovation performance; (2) GIN integration partially mediates this relationship by facilitating resource acquisition and collaboration; and (3) capability flexibility positively moderates the GIN–performance link, while cultural and resource flexibility show no significant effects. This study advances digital capability research by emphasizing dynamic processes over static technology adoption and provides practical insights for balancing technological investments with organizational adaptability.

1. Introduction

With the acceleration of global economic digital transformation, the innovation model of enterprises is undergoing a fundamental shift from closed innovation to open collaborative innovation. The widespread application of digital technology not only reshapes the way resources are exchanged between organizations but also gives rise to a new innovation ecosystem centered around Global Innovation Networks (GINs) [1,2]. In this context, whether enterprises can integrate internal and external resources through digital capabilities, deeply embed into the global innovation network, and dynamically adapt to environmental changes in the process has become a key issue determining their innovation performance. However, the existing research still lacks a systematic exploration of how digital capabilities impact innovation performance through Integration into Global Innovation Networks (IIGIN), particularly concerning the boundary role of organizational flexibility in this process.
Currently, academic attention to digital capabilities is mostly focused on the application level of technology (such as big data analysis, cloud computing, etc.), viewing it as a tool capability for enterprise digital transformation [3,4]. However, the dynamic capability theory suggests that the essence of digital capability should be the dynamic process by which enterprises perceive, integrate, and reconstruct data resources to form sustainable competitive advantages [5]. From this perspective, digital capabilities not only cover the deployment of technological infrastructure but also emphasize the strategic flexibility of enterprises to coordinate resources and build an open innovation ecosystem in complex environments [6]. For example, General Electric (GE) integrates global R&D resources through digital twin technology, while Philips connects external innovation entities through an open innovation platform, both reflecting the core role of digital capabilities in driving global innovation network collaboration. However, a survey by Accenture (2021) shows that most companies face a “capability trap” in digital transformation: an excessive focus on technology investment while neglecting organizational structure and cultural fit, making it difficult to translate digital capabilities into improved innovation performance.
The existing research has shortcomings in the following aspects: Firstly, most of the literature explores the direct impact of digital capabilities on innovation performance in isolation [7], lacking in-depth analysis of the key mediating mechanism of global innovation network integration. Secondly, although social network theory emphasizes the promoting effect of heterogeneous resource acquisition and allocation on innovation performance [8], empirical support is still needed to demonstrate how digital capabilities can realize resource value by optimizing network embeddedness. Thirdly, organizational flexibility, as a strategic capability for enterprises to respond to dynamic environments [9], has not been fully incorporated into the existing framework in terms of its regulatory role, especially the multidimensional impact of cultural, resource, and capability flexibility, which urgently needs to be clarified.
In response to the above gaps, this article constructs a theoretical model of “digital capability global innovation network integration innovation performance” based on dynamic capability theory and social network theory, and introduces a three-dimensional adjustment mechanism of organizational flexibility (cultural flexibility, resource flexibility, and capability flexibility) to systematically reveal the path and boundary conditions of the impact of digital capability on innovation performance. Through empirical analysis of 343 Chinese multinational corporations, this article aims to answer the following questions: (1) How does the multidimensionality (perception, operation, and coordination) of digital capabilities differentially affect the integration and innovation performance of global innovation networks? (2) What intermediary role does the integration of global innovation networks play between digital capabilities and innovation performance? (3) How do different dimensions of organizational flexibility regulate the above relationship?
The theoretical contribution of this study lies in the following: firstly, breaking through the traditional “technology- oriented” paradigm of digital capability research, constructing a three-dimensional capability framework of “perception operation coordination” from the perspective of dynamic capabilities, and revealing the chain mechanism of its impact on innovation performance through the integration of global innovation networks; secondly, integrating social network theory and dynamic capability theory to clarify how digital capabilities can obtain heterogeneous resources by optimizing network embeddedness, providing a cross theoretical perspective for open innovation research; and thirdly, for the first time, organizational flexibility has been refined into three dimensions: culture, resources, and capabilities, analyzing its differential moderating effect in the process of transforming digital capabilities into innovative performance, and expanding the explanatory boundary of dynamic capability theory. On a practical level, this article provides a decision-making basis for enterprises to formulate digital transformation strategies and balance technology investment and organizational adaptability, especially for emerging market enterprises to achieve “curve overtaking” through global innovation networks.

2. Hypotheses

2.1. Digital Capability, Integration of Enterprises into Global Innovation Networks, and Enterprise Innovation Performance

In today’s fiercely competitive business environment, the rapid evolution of information technology has led to increasingly complex market and technological patterns for enterprises, and unpredictable external environments pose significant challenges to sustained innovation activities. Drucker pointed out in “Innovation and Entrepreneurship” that innovation does not rely on individual inspiration, but rather on purposeful, organized, and systematic practice, which requires multidimensional opportunity identification to achieve innovation transformation [10]. This viewpoint reveals the essential characteristics of innovation activities—as a collective collaborative process, it requires the participation of multiple subjects to continuously expand the boundaries of knowledge and capabilities [11]. The traditional closed innovation model is no longer able to meet the requirements of enterprises for innovation speed and quality, and the open innovation model has emerged as a result. Henry defines open innovation as a new model of collaborative innovation in which enterprises not only utilize internal resources but also integrate external entities, such as customers, suppliers, and research institutions, to achieve collaborative innovation. This model breaks the linear process of independent R&D to marketization in traditional closed innovation, accelerates the innovation process through the integration of internal and external resources, and improves the quality and diversity of innovation achievements [12]. Chesbrough further emphasizes that open innovation is a two-way knowledge flow paradigm centered on enterprises, which includes both the external commercialization of internal innovation results (outward flow) and the internal absorption of external knowledge and technology (inward flow). This two-way empowerment mechanism can effectively enhance the innovation capability and economic performance of enterprises [13], providing important theoretical support for this study.
With the evolution of theory, Martin Curley and Bror Salmelin proposed the new paradigm of “Open Innovation 2.0” (OI2) in “Open Innovation 2.0: A New Digital Innovation Model for Prosperity and Sustainability” [14]. OI2 integrates the three dimensions of technology, society, and policy, driving fundamental changes in society and industry through digital innovation to achieve sustainable development and smart living. Its core lies in creating shared value and promoting stakeholders to shift from “zero sum games” to “multi-party win-win”. Compared to traditional open innovation 1.0, OI2 emphasizes the development of new services and markets through systematic collaboration and the co-creation of open ecosystems. It elevates open innovation from a single technology cooperation to a systematic transformation tool that covers technology, society, and policy, highlighting the dual goals of economic prosperity and sustainable development achieved through the synergy of open ecosystems and digital technology. This theoretical framework provides a new strategic reference path for enterprises and policy makers.
Based on social network theory, this article focuses on how the International Innovation Network acquires and allocates heterogeneous resources through subsidiaries embedded in the host country’s social network, thereby affecting corporate innovation performance [8]. Multinational corporations gain insights into different innovation environments and competitor dynamics through cross-border operations, which not only enhances knowledge transfer efficiency but also continuously improves their innovation capabilities in cross-cultural contexts [15]. During this process, digital capability building has become a key element in adapting to the digital economy and promoting open innovation. Warner’s research reveals that dynamic digital capabilities, such as digital perception, acquisition, and reconstruction, have a significant promoting effect on corporate innovation performance [6]; Zhai argued that the application of digital technology enhances the overall performance of enterprises by improving market adaptability and collaborative innovation efficiency [16]. These studies collectively indicate that building a digital capability system has become an important strategic choice for enterprises to break through innovation bottlenecks. It is worth noting that in the wave of digitization and globalization, enterprise innovation strategies need to go beyond passive response modes. Christensen pointed out in “The Innovator’s Dilemma” that an excessive focus on high-end customer needs may lead companies to overlook strategic opportunities for disruptive innovation [17]; “The Innovator’s Solution” further emphasizes that companies should actively shape rather than passively respond to disruptive innovation [18]. This suggests that enterprises must establish forward-looking innovative thinking and systematically enhance the resilience of their innovation system through the collaborative evolution of open innovation networks and digital capability building. This theoretical understanding not only deepens the understanding of innovation-driven mechanisms, but also provides important insights for enterprise practice, constituting the significant practical significance of this study.
This study integrates the above researchers’ understanding of digital capabilities and proposes to divide digital capabilities into digital perception capabilities, digital operation capabilities, and digital resource coordination capabilities.
Digital perception capability refers to the ability of enterprises to identify opportunities and threats in the digital economy environment and perceive the value of digital innovation based on the trend of digital transformation. Specifically, it manifests as the perceived effectiveness of the external digital environment (digital technology dynamics, policy orientation, market competition, and changes in consumer demand), as well as the ability to proactively evaluate internal digital transformation management [19]. When enterprises have strong digital perception capabilities, they can not only keenly capture subtle differences and strategic opportunities in the digital field (Khin found through empirical research that the perception efficiency of data value has a positive impact on the output of digital innovation results [20]) but also effectively overcome international R&D barriers, such as language communication barriers and market information asymmetry, through in-depth analysis of the host country’s business environment [21]. This dual mechanism of environmental perception and data value recognition [22] enables enterprises to accurately locate heterogeneous resources in the global innovation network, avoid resource dissipation caused by redundant knowledge search, and significantly improve the construction efficiency, innovation research, and development breadth of the International Innovation Network. In summary, this article proposes the following research hypotheses:
H1a. 
There is a positive relationship between digital perception capability and IIGIN.
H2a. 
There is a positive relationship between digital perception capability and enterprise innovation performance.
Digital operation capability emphasizes the ability of enterprises to build digital solutions covering the entire value chain, including research and development, production, and marketing, based on industry insights and consumer data analysis. Unlike the perceptual ability that focuses on environmental scanning, this ability places more emphasis on data-driven resource optimization and allocation: Lynn’s research shows that under the constraints of data reliability and validity, enterprises can achieve the Pareto optimality of organizational resources through asset diversification and the digital restructuring of business processes [23], which provides dynamic resource pool support for the IIGIN. It is worth noting that this capability enhances the dual synergy of “technical architecture organizational management” in overseas R&D networks [24], which not only improves the efficiency of knowledge integration and reverse transfer in innovation networks but also unleashes the coupling value of heterogeneous innovation elements in cross-cultural contexts. In summary, this article proposes the following research hypotheses:
H1b. 
The relationship between digital operation capability and IIGIN is positively correlated.
H2b. 
There is a positive relationship between digital operation capability and enterprise innovation performance.
The collaborative capability of digital resources focuses on enterprises breaking down data silos and achieving the integration, sharing, and collaborative co-creation of complementary/competitive resources both internally and externally. The core feature that distinguishes this ability from the previous two is the linkage mechanism for building a digital ecosystem: according to Lavie’s resource synergy theory, the alignment of digital resources between enterprises and strategic partners is a key hub for transforming organizational elements into economic output [25]. Specifically, this capability not only reduces the difficulty of the cross-border allocation of talent, information [26], and other elements by constructing a bidirectional mapping relationship between “competitive elements and digital resources” [27] but also upgrades the knowledge flow in the IIGIN from linear transmission to networked interaction through the agile response mechanism of the value chain. This ecological collaborative model provides structural support for improving innovation performance. In summary, this article proposes the following research hypotheses:
H1c. 
There is a positive correlation between digital resource coordination capability and IIGIN.
H2c. 
There is a positive relationship between digital resource coordination capability and enterprise innovation performance.

2.2. The Impact of Enterprise IIGIN on Enterprise Innovation Performance

The existing research indicates that to avoid technological path dependence, enterprises often seek partnerships with high-capability firms for cooperation [28]. The global innovation network offers numerous scientific and innovative enterprises, facilitating the acquisition of cutting-edge technologies [29]. Moreover, IIGIN expands avenues for enterprises to secure funding. According to Schumpeter’s innovation theory, capital plays a crucial role in R&D and innovation. Insufficient funding can lead to reduced investment in technological R&D, potentially compromising market competitiveness. IIGIN enables access to global financial services and opportunities for direct capital investment overseas [2]. IIGIN not only enables enterprises to access advanced cross-border technological resources and improve innovation investment returns, but also serves as a key way to expand into new markets and meet the needs of international customers [30,31]. This integration enhances the international competitiveness of the product, helps establish cooperative relationships, integrates resources from all parties, and thus improves overall innovation performance [32]. In summary, this paper sets forth the following research hypothesis:
H3. 
There is an affirmative correlation between IIGIN and enterprise innovation performance.

2.3. The Mediating Role of Enterprises in IIGIN

The digital sensing capability within an enterprise is crucial for identifying and accessing various knowledge assets and resources in the global innovation network. This ability reduces search costs and minimizes efforts related to redundant knowledge to the greatest extent possible [33]. Additionally, it can also help companies mitigate external threats [34], thus promoting its IIGIN. In addition, a company’s digital operational capabilities enhance the synergy between its technological infrastructure and organizational structure. This enhancement has improved companies’ ability to absorb and integrate knowledge [24], thereby enhancing their efficiency in participating in global innovation networks. In addition, the ability to collaborate with digital resources coordinates internal and external systems, integrating complementary and competitive resources within a company [27]. This ability reduces the complexity of resource allocation across geographical and temporal dimensions [26] and lowers the coordination and supervision costs of managing overseas R&D operations. By minimizing redundant investments and resource waste, these digital capabilities improve enterprise efficiency and promote IIGIN. As IIGIN enterprises, they can access diverse knowledge and resources [35]. This exposure helps them reduce innovation uncertainty by learning and imitating the successful innovation paths of other companies. It also helps to develop new products that meet the constantly changing needs of consumers, thereby improving their innovation performance [32]. Overall, IIGIN accelerates enterprises’ technological progress, fosters product innovation, builds brand recognition, expands market reach, and enhances global influence. These outcomes collectively strengthen enterprises’ innovative capabilities and competitiveness in the global marketplace. In summary, this paper sets forth the following research hypothesis:
H4. 
The IIGIN acts as a mediator in the interaction between digital capabilities and innovation performance.

2.4. The Regulatory Role of Organizational Flexibility

Organizational flexibility, as a dynamic capability, can help organizations respond in a timely manner when changes occur in the realms of both internal and external contexts, thus stabilizing the organization’s market position, maintaining the organization’s competitive advantage, and improving innovation success and organizational performance [36,37]. Organizational flexibility is a multidimensional concept; Sanchez [9] classified it into resource flexibility and coordination flexibility, another group of scholars classified it into resource flexibility and competence flexibility, and subsequent studies added the dimension of cultural flexibility to enable a more comprehensive study of organizational flexibility. This article refers to the research of other scholars and proposes to divide organizational flexibility into three dimensions: cultural flexibility, resource flexibility, and capability flexibility.

2.4.1. The Moderating Role of Cultural Flexibility on IIGIN and Enterprise Innovation Performance

Cultural flexibility refers to an organization’s ability to embrace a value system that promotes openness, inclusiveness, innovation, and a people-centered approach, encouraging its members to continuously acquire new information and skills [38]. The cultural flexibility of a company enhances its organizational culture, thereby further enhancing its ability to acquire, transform, and apply external knowledge, and thus improving its overall resource integration capability. Thus, enhancing the positive role of IIGIN in improving corporate innovation capabilities. At the same time, by consciously identifying and adjusting factors that are not conducive to the development of the company, the company can have a more sensitive observation ability and the ability to resist risks, reduce the mismatch between its development and the external environment [39], better cope with the risks associated with deep IIGIN, reduce the excessive dependence of enterprises on the global innovation network, and thus weaken the negative impact of excessive IIGIN on enterprise innovation performance. In summary, this article proposes the following research hypothesis:
H5a. 
Cultural flexibility has a constructive influence on the positive relationship between IIGIN and enterprise innovation performance.

2.4.2. The Moderation of Resource Flexibility on IIGIN and Enterprise Innovation Performance

Resource flexibility refers to the ability of enterprises to quickly, economically, and easily transform existing resources into another type of resource to cope with external environmental changes, thereby improving the scope of resource utilization and conversion efficiency [40]. Resource flexibility emphasizes the strategic integration of resources through different channels, while dynamically expanding resource utilization to help businesses seize emerging opportunities [41]. It can promote the rapid conversion of resources among network members, provide sufficient resource support for the internal innovation of enterprises, and strengthen the positive impact of IIGIN on enterprise innovation performance. Resource flexibility mainly promotes innovation in two ways: one is to alleviate the resource demands generated by enterprises in response to changes and subside the risk for resource shortages in innovation activities; the second is to encourage the utilization of available resources and the sharing of knowledge, in order to enhance innovation capabilities without incurring excessive costs [42]. In today’s competitive landscape, accelerating the pace of new product releases is increasingly recognized as strategically valuable for enhancing enterprise innovation performance [43], and resource flexibility plays an important role in this process. In summary, this article proposes the following research hypothesis:
H5b. 
Resource flexibility has a positive effect on the relationship between IIGIN and innovation performance.

2.4.3. The Moderation of Capability Flexibility on IIGIN and Enterprise Innovation Performance

Capability flexibility refers to the ability of an enterprise to identify new resources and their scope of application and integrate and allocate resources reasonably when faced with changes in the external environment in order to maximize the effectiveness of resources. Capability flexibility enables enterprises to dynamically adapt to their surrounding environment, actively change by utilizing the environment, seize the first mover advantage, and quickly carry out innovative activities. Compared to resource flexibility, which mainly focuses on resource acquisition and utilization, capability flexibility emphasizes adaptation and responses to external environmental changes, as well as resource discovery and strategic allocation [9]. In today’s increasingly uncertain environment, it has become increasingly difficult to maintain a competitive advantage solely through resource acquisition. Capability flexibility plays a crucial role in addressing the limitations of resource flexibility by integrating various resources and enhancing their overall value, effectively responding to the unpredictability of the external environment. Innovation essentially involves risks and uncertainties. Enterprises with high flexibility capability can more effectively respond to these risks, quickly adapt to external changes, and thus increase the likelihood of successful innovation. In conclusion, the following research hypothesis is proposed:
H5c. 
Capability flexibility actively regulates the connection between IIGIN and enterprise innovation performance.
The preceding analysis and theoretical deduction are synthesized in Figure 1, which depicts the theoretical model of this study.

3. Methods

3.1. Data Sources and Sample Selection

The data for this paper originate from a nationwide survey conducted in 2024, focusing on the impact of digital competence on enterprises’ IIGIN. The survey targeted enterprises across the country with operations overseas and was completed by executives and senior management personnel. The questionnaire comprised 37 questions covering aspects such as enterprise digital capability, the status of overseas operations, operational strategies, and more. This comprehensive approach aimed to provide insights into enterprise digital capability, overseas operations, and innovation performance. Both online and offline methods were employed for data collection, resulting in a total of 400 valid questionnaires. The offline methods were mainly distributed to relevant personnel of enterprises during lectures, as well as during visits to enterprises and when enterprise personnel came to school for classes. Among them, a total of 73 offline questionnaires were distributed and 69 were collected. Out of 342 valid samples, there were a total of 273 valid samples online and 69 valid samples offline, excluding invalid, duplicate questionnaires and questionnaires that do not meet the requirements of overseas business enterprises. As a result, 342 valid samples were retained for analysis, and their descriptive statistical characteristics are detailed in Table 1.

3.2. Variable Measurement

The data for this paper were collected through surveys, with the variables assessed on a 5-point Likert-type scale, where ‘1’ represents a complete lack of agreement or a very low level of agreement, and ‘5’ represents a complete agreement or a very high level of agreement. The measurement of the variables was based on well-established research. Among them, the independent variable is digital capability (including digital perception, operation, and resource coordination capabilities), which comprises 10 items, such as “enterprises can identify and recognize data sources with commercial potential”. The mediating variable is integration in the global innovation network and is measured by 7 items, such as “degree of joint ventures and cooperation with foreign companies”. The moderating variable is organizational flexibility (encompassing cultural, resource, and capability flexibility), which includes 13 items, such as “employees can contribute when and where the company needs them”. The dependent variable is innovation performance, measured by 7 items, such as “the enterprise has opened up new markets”. Control variables include seven factors: age, size, revenue, and four additional variables. Table 2 provides a comprehensive explanation of variable definitions.

3.3. Common Method Bias Test

This study utilized the Harman one-way test to test for common method bias [44]. The results indicate that the first principal component accounts for 11.495% of the variance, which is below the 50% threshold, suggesting no significant issue of common method bias.

4. Analysis and Results

4.1. Descriptive Statistics and Univariate Analysis

In this paper, descriptive statistics and correlation analyses were conducted on the variables, with the results presented in Table 3. Each variable exhibits a Pearson correlation coefficient below 0.8. Additionally, this study assessed multicollinearity using the Variance Inflation Factor (VIF), finding that all predictor variables have VIF values below the threshold of 10, indicating no significant multicollinearity issues.

4.2. Regression Analysis and Hypothesis Testing Results

During the regression examination, the independent variables and moderating variables were initially standardized to create the interaction term for the two-way effects. According to the model specifications, the regression analysis was conducted by incorporating control variables, independent variables, moderating variables, and interaction terms into the model in an orderly manner.

4.2.1. Main Effect Test

The hierarchical regression results of the main impacts are shown in Table 4. Hypothesis testing is based on the significance level of the path coefficients (p-value, *, **, and *** respectively indicate significance at the 10%, 5%, and 1% levels) and effect size (β value). We set the significance level to 0.1. If the path coefficient is significant and the direction is as expected, then the hypothesis is supported; otherwise, it is not [45].
Model 1 is the regression model of the control variables on innovation performance. It is evident from Model 1 that the regression coefficient of the control variable “enterprise age” is positive and significant (β = 0.199, p < 0.01), indicating that the growth of the enterprise’s operating years has a positive impact on the enterprise’s innovation performance. The coefficient of “enterprise size” is positive and significant (β = 0.285, p < 0.01), indicating that the expansion of the enterprise’s size has a positive impact on innovation performance. The coefficient of “average annual business revenue” is also positive and significant (β = 0.285, p < 0.01), indicating that the expansion of enterprise scale enhances enterprise innovation performance. Model 2 is a regression model of the effects of control variables and independent variables on innovation performance. The results showed that the regression coefficients of “digital perception ability” (β = 0.158, p < 0.01), “digital operation ability” (γ = 0.146, p < 0.01), and “digital resource coordination ability” (α = 0.166, p < 0.01) on innovation performance were positive and significant, verifying hypotheses H2a, H2b, and H2c. Model 3 is the regression model of the control variable and the mediator variable (IIGIN) on innovation performance. The results show that IIGIN (β = 0.368, p < 0.01) is significantly and positively correlated with innovation performance, validating hypothesis H3. To further examine the potential nonlinear effects of IIGIN, the squared term of IIGIN was tested, and the results showed that the coefficient was not significant (β = 0.046, p > 0.1).
Model 5 is the regression model of control variables on IIGIN. Model 6, on the other hand, is the regression model of control variables and the three dimensions of digital capability on IIGIN. The results show that digital perception capability (β = 0.274, p < 0.01), digital operation capability (β = 0.201, p < 0.01), and digital resource coordination capability (β = 0.218, p < 0.01) all have a significant positive effect on IIGIN, supporting hypotheses H1a, H1b, and H1c.
Model 4 is a full regression model of control variables, three-dimensional digital capabilities and enterprises’ integration into the global innovation network on enterprise innovation performance, which is used to test the mediating role of enterprises’ integration into the global innovation network. Compared with model 2, after adding intermediary variables, digital perception ability(β = 0.112, p < 0.01), digital operation ability(β = 0.126, p < 0.01) and digital resource coordination ability(β = 0.133, p < 0.01) still have a significant positive impact on enterprise innovation performance, but the coefficients have weakened, indicating that enterprises’ integration into the global innovation network plays a partial intermediary role in the relationship between digital ability and enterprise innovation performance. Additionally, the robustness test of the mediation effect was conducted using the SPSS 27 Process procedure. The total mediation effect value of IIGIN was 0.2475 at the 95% confidence level, with a confidence interval of [0.1477, 0.3519], excluding 0. This indicates that the mediation effect of IIGIN is significant, validating hypothesis H4.

4.2.2. Moderating Effect Test

Table 5 presents the results of the hierarchical regression analysis, which assesses the moderating effect.
In Table 5, in Model 2, the interaction term between IIGIN and cultural flexibility is positive (β = 0.010) but does not reach the threshold of significance, indicating that the moderating effect of cultural flexibility on the positive correlation between IIGIN and innovation performance is not significant, and Hypothesis H5a is not supported. In Model 3, the interaction term between IIGIN and resource flexibility is negative (β = −0.012) and also fails to meet the criteria for statistical significance, suggesting that the moderating effect of resource flexibility on the positive correlation between IIGIN and innovation performance is not significant, and Hypothesis H5b is not supported. In Model 4, the interaction term between IIGIN and capability flexibility is positive (β = 0.067) and significant at the 10% level, indicating that capability flexibility can strengthen the positive correlation between IIGIN and innovation performance, and Hypothesis H5c is supported.

4.3. Reliability and Validity Testing

This study indicates that the reliability of all factors measured by Cronbach’s alpha is above the acceptable level of 0.7, suggesting that the scale has strong reliability. In terms of validity, the convergent validity of each structure was tested through confirmatory factor analysis (CFA), where the standardized factor load (λ) of each item not only met but also exceeded the threshold of 0.5, and the average variance extraction (AVE) score also exceeded the threshold of 0.5, indicating that the survey has strong convergent validity. In addition, the model adaptability refers to the standards of Hu and Bentler: CFI > 0.90, TLI > 0.90, RMSEA < 0.08, and SRMR < 0.06 [46]. Based on this, this study used AMOS 24 to analyze the relevant data and obtained the following data: χ2/df = 1.714, GFI = 0.858, IFI = 0.934, NFI = 0.855, CFI = 0.933, and RMSEA = 0.046. All have reached the recommended threshold. In summary, all these important indicators indicate that the questionnaire design and structural model of this study have reached a high level.

5. Conclusions and Discussion

This paper examines the impact of digital competencies on enterprise innovation performance through the lens of global innovation networks. It employs a questionnaire survey targeting senior and middle management personnel in domestic enterprises. The survey explores differences in digital capabilities across innovation efforts and globalization trends, alongside various dimensions of organizational flexibility and governance mechanisms. Additionally, it investigates the moderating effects of different dimensions of organizational flexibility. The findings of this paper encompass four main aspects.
Firstly, there is a positive correlation between a company’s digital capability and its innovation performance. The digital capability of enterprises has become the core driving force for improving innovation performance through multidimensional collaboration. Digital perception capability enables enterprises to keenly capture market dynamics and technological trends, providing data support for innovation directions. The digital operation capability accelerates the conversion efficiency of innovative achievements by optimizing production processes and resource allocation. The ability to coordinate digital resources breaks down internal “information silos”, integrates internal and external data resources, and builds an open and collaborative innovation ecosystem. The combined effect of these three factors significantly enhances the innovation efficiency of enterprises in product research and development, service optimization, and market response. Meanwhile, this study validates the viewpoint of scholars such as Jun [47] that digital capability is the key for enterprises to maintain competitiveness in the digital economy era. High-level digital capabilities can not only reduce transaction costs through dynamic resource restructuring [7] but also expand the boundaries of corporate behavior through intelligent decision-making, promoting sustained product or service innovation. However, the research also warns that the improvement of digital capabilities needs to be balanced. If companies excessively invest in technology and ignore market demand [48], or fail to properly evaluate the cost–benefit of digital transformation [5], it may lead to resource misallocation and a marginal decline in innovation performance. Therefore, in practice, enterprises need to coordinate the construction of digital capabilities from a strategic perspective. On the one hand, we should promote the coordinated development of digital perception, operation, and coordination capabilities in stages based on our own development stages. On the other hand, it is necessary to strengthen data governance capabilities, prevent data security risks [49], ensure the efficient mining of data value, and maximize the positive effect of digital capabilities on innovation performance.
Secondly, IIGIN is positively correlated with enterprise innovation performance. The empirical results indicate a positive correlation between IIGIN and enterprise innovation performance. Although some scholars have proposed an inverted U-shaped relationship between IIGIN and enterprise innovation performance, this study did not observe this conclusion. This may be due to the fact that this study focuses on Chinese companies with overseas operations, many of which are in the early stages of IIGIN, and therefore do not exhibit an inverted U-shaped relationship. IIGIN enables enterprises to strategically position themselves in the global market. Through international cooperation and exchange, firms can gain a deeper understanding of diverse market demands and develop effective market entry strategies [50]. In addition, IIGIN enables firms to better respond to the international policy environment and obtain necessary policy support. This is crucial for promoting innovation activities and improving innovation performance in the international market [34].
Thirdly, IIGIN plays a valuable mediating role between digital capabilities and enterprise innovation performance. Enhancing digital capabilities is crucial for firms to adapt to digital transformation and develop flexible innovation strategies [6]. As firms become more efficient by enhancing their digital capabilities, they are also increasingly interested in joining the global innovation network to share knowledge and exchange technology with global partners. Open innovation platforms facilitate collaboration and knowledge transfer between enterprises [2]. Through IIGIN, firms can access a wider range of information and resources, enabling them to maintain innovation and respond to market changes [31]. Additionally, IIGIN is also influenced by firms’ digital capabilities. Firms with stronger digital capabilities can better identify and leverage innovation opportunities in these networks through efficient information management and decision-making. This, in turn, has improved enterprises’ global innovation performance.
Fourthly, capability flexibility plays an important positive regulatory role in the process of IIGIN and influencing enterprise innovation performance. Capability flexibility is crucial for maximizing the efficiency of utilizing various resources within the global innovation network, thereby promoting enterprise innovation [51]. In addition, capability flexibility helps enterprises improve innovation performance by effectively integrating resources and quickly adapting to external changes, such as developing new products, increasing market share, and increasing revenue [52]. Meanwhile, empirical research results show that cultural flexibility and resource flexibility do not have a significant moderating effect on the relationship between IIGIN and enterprise innovation performance. There may be several reasons for this. Firstly, cultural flexibility may exhibit different forms of influence in different organizations and environments. The questionnaire design of this paper may not fully capture the expression and role of cultural flexibility in the current context, and therefore cannot prove its moderating effect. Secondly, resource flexibility involves the ability to quickly transform and reconfigure resources. If companies do not fully utilize this dynamism during their IIGIN, its impact on innovation performance may be minimal. Finally, global innovation networks are inherently complex, with multiple relationships and interactions that may mask or mitigate the moderating effects of cultural and resource flexibility.
There are certain research limitations in this paper that warrant further in-depth exploration in the future. Firstly, this study chose product innovation as the measure for assessing innovation performance rather than the traditional indicator of new product output. This decision is grounded in the reality that product innovation focuses more on the output performance of innovation results, while new product output value tends to reflect the commercialization and industrialization effects of innovation results. The product innovation data used in this study come from questionnaire surveys, which are primary data, and may differ in directness and objectivity compared with new product output value, which is objectively assessed through sales. Therefore, future research could consider using new product output value as a measurement tool to enhance the robustness of the findings. In addition, the influence of IIGIN on different innovation performance indicators can be explored to enhance the general applicability pertaining to the theory. Secondly, the focus of study for this research is Chinese firms with overseas operations, which has certain limitations. Future research may consider not limiting itself to Chinese firms and selecting target firms globally to enhance the general applicability of the findings. Meanwhile, the sample size of this study is mainly large enterprises, and the universality of the conclusions in small- and medium-sized enterprises needs to be verified in the future. Finally, the questionnaire method is used in the research design of this paper to quickly and sensitively capture the effect of enterprise digital capability and IIGIN on enterprise innovation performance, but it also exposes this study to the impact of the common methodological bias inherent in questionnaire research. Therefore, we look forward to the diversification of subsequent research methodologies and innovations in research technology.

Author Contributions

Conceptualization, X.L.; methodology, S.T.; software, X.L.; validation, X.L. and L.D.; formal analysis, X.L.; investigation, X.L.; resources, S.T.; data curation, X.L.; writing—original draft preparation, X.L.; writing—review and editing, S.T.; visualization, X.X.; supervision, X.X.; project administration, L.D.; funding acquisition, S.T. and X.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Shandong Provincial Natural Science Foundation (ZR2023QG153) and National Social Science Key Found of China (23AGL001).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Systems 13 00212 g001
Figure 1. Theoretical framework.
Figure 1. Theoretical framework.
Systems 13 00212 g001
Table 1. Statistical descriptors of sample’s properties.
Table 1. Statistical descriptors of sample’s properties.
AttributeClassificationNumberPercentage (%)
Enterprise Age1–3 years113.2
4–10 years9327.2
11–20 years14442.1
Over 20 years9427.5
Enterprise Size50 or less people154.4
51–100 people329.4
101–500 people8224
501–1000 people12135.4
1001 or more people9226.9
Average Annual Operating IncomeUp to USD 142,857 72
USD 144,285–1,428,571 308.8
USD 1,430,000–7,142,857 14843.3
USD 7,142,857–14,285,714 11232.7
Over USD 14,285,714 4513.2
Ownership TypeState-owned enterprise277.9
Collective enterprise5716.7
Private enterprise11032.2
Foreign capital enterprise14843.3
Main Business TypesOnline focus277.9
Offline focus10931.9
Online + offline20660.2
Table 2. Variable definition.
Table 2. Variable definition.
VariableSymbolVariable Declaration
Independent variableDigPerDigital perception ability
DigOpeDigital operation capability
DigResDigital resource coordination capability
Mediating variableGloInnoIntegrating into global innovation networks
Moderating variableCulFleCultural flexibility
ResFleResource flexibility
CapFleCapability flexibility
Dependent variableprefInnovation performance
Control variableAgeEnterprise age
SizeEnterprise size: the number of existing employees in the enterprise
RevenueAverage annual operating income level
OwnershipOwnership type (dummy variable)
IndustrySegmented industries (dummy variable)
AreaLocation (dummy variable)
ModelBusiness model (dummy variable)
Table 3. Mean, standard deviation, and correlation coefficient of each variable.
Table 3. Mean, standard deviation, and correlation coefficient of each variable.
VariableMean ValueStandard Deviation123456789101112131415
1. Age2.940.8211
2. Size3.711.0940.490 **1
3. Revenue3.460.9010.407 **0.523 **1
4. Ownership3.110.952−0.0370.0670.0781
5. Industry2.230.9360.019−0.049−0.085−0.0151
6. Area2.030.4390.0210.0050.084−0.141 **0.0411
7. Model2.520.6390.0610.0750.012−0.007−0.0020.0291
8. DigPer3.7990.993920.272 **0.354 **0.297 **0.042−0.085−0.005−0.0361
9. DigOpe3.93760.86570.306 **0.333 **0.335 **0.0650.002−0.0180.020.376 **1
10. DigRes3.90740.983180.326 **0.365 **0.334 **−0.002−0.028−0.0620.010.433 **0.518 **1
11. GloInno1.640 0.481 0.240 **0.284 **0.232 **-0.068-0.067-0.0050.0180.457 **0.351 **0.427 **1
12. CulFle1.840 0.540 0.150 **0.126 *0.198 **0.0450.0030.020-0.0490.365 **0.301 **0.307 **0.298 **1
13. ResFle1.770 0.483 0.165 **0.198 **0.179 **0.0590.018-0.010-0.0830.398 **0.279 **0.326 **0.303 **0.515 **1
14. CapFle1.940 0.407 0.278 **0.366 **0.305 **0.0410.0390.0110.0400.276 **0.277 **0.268 **0.255 **0.154 **0.179 **1
15. pref3.9140.844270.450 **0.559 **0.454 **-0.015-0.0680.0190.0430.480 **0.473 **0.519 **0.479 **0.285 **0.286 **0.593 **1
Note: * and ** respectively indicate significance at the 5%, and 1% levels.
Table 4. Main effect hierarchical test.
Table 4. Main effect hierarchical test.
VariableDependent Variable: Innovation PerformanceMediating Variable: GloInno
Model 1Model 2Model 3Model 4Model 5Model 6
Age0.199 ***0.123 **0.146 ***0.118 **0.143 **0.032
Size0.285 ***0.204 ***0.231 ***0.196 ***0.148 ***0.026
Revenue0.172 ***0.092 **0.119 ***0.092 **0.146 **0.029
DigPer 0.158 *** 0.112 *** 0.274 ***
DigOpe 0.146 *** 0.126 *** 0.201 ***
DigRes 0.166 *** 0.133 *** 0.218 ***
GloInno 0.368 ***0.316 ***
Ownershipcontrolcontrolcontrolcontrolcontrolcontrol
Industrycontrolcontrolcontrolcontrolcontrolcontrol
Areacontrolcontrolcontrolcontrolcontrolcontrol
Modelcontrolcontrolcontrolcontrolcontrolcontrol
R-sq0.380 0.5090.5160.5320.1370.382
Adj. R-sq0.3670.4950.5040.5170.1190.363
F Value29.264 ***34.363 ***44.320 ***34.126 ***7.589 ***20.432 ***
Observations342342342342342342
Note: ** and *** respectively indicate significance at the 5%, and 1% levels.
Table 5. Hierarchical regression results of regulatory effects.
Table 5. Hierarchical regression results of regulatory effects.
VariableDependent Variable: Innovation Performance
Model 1Model 2Model 3Model 4
Age0.105 **0.105 **0.103 **0.108 **
Size0.181 ***0.181 ***0.182 ***0.181 ***
Revenue0.105 **0.104 **0.104 **0.103 **
CulFle0.0580.0620.0650.057
ResFle−0.004−0.009−0.01−0.006
CapFle0.339 ***0.339 ***0.340 ***0.366 ***
GloInno0.303 ***0.304 ***0.301 ***0.305 ***
GloInno × CulFle 0.010
GloInno × ResFle −0.012
GloInno × CapFle 0.067 *
OwnershipControlControlControlControl
IndustryControlControlControlControl
AreaControlControlControlControl
ModelControlControlControlControl
R-sq0.5930.5930.5930.597
Adj. R-sq0.5800.5790.5790.582
F Value43.759 ***40.009 ***40.015 ***40.564 ***
Observations342342342342
Note: *, **, and *** respectively indicate significance at the 10%, 5%, and 1% levels.
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Tian, S.; Lai, X.; Dong, L.; Xu, X. Digital Capabilities, Integration into Global Innovation Networks, and Enterprise Innovation Performance. Systems 2025, 13, 212. https://doi.org/10.3390/systems13030212

AMA Style

Tian S, Lai X, Dong L, Xu X. Digital Capabilities, Integration into Global Innovation Networks, and Enterprise Innovation Performance. Systems. 2025; 13(3):212. https://doi.org/10.3390/systems13030212

Chicago/Turabian Style

Tian, Shanwu, Xiaozhen Lai, Lijun Dong, and Xiurui Xu. 2025. "Digital Capabilities, Integration into Global Innovation Networks, and Enterprise Innovation Performance" Systems 13, no. 3: 212. https://doi.org/10.3390/systems13030212

APA Style

Tian, S., Lai, X., Dong, L., & Xu, X. (2025). Digital Capabilities, Integration into Global Innovation Networks, and Enterprise Innovation Performance. Systems, 13(3), 212. https://doi.org/10.3390/systems13030212

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