2.1.1. Digital platform capabilities.

Zhu (2015) first conceptualized platform capability as the ensemble of technological resources that platform firms deploy to support online digital activities [28]. Drawing on dynamic-capability theory, Jin and Hurd (2018) extended Zhu’s framework to the digital context and introduced the notion of “digital platform capability,” defined as a firm’s ability to enlarge market share through platform-based search [29]. This conceptualization aligns with the dynamic-capability emphasis on adaptive resource reconfiguration and caters to the real-time data integration and dynamic asset restructuring demanded by big-data and cloud-computing infrastructures. In contemporary industry settings, non-digital platforms—such as physical trade fairs or offline supplier alliances—rely on manual information exchange, exhibit limited scalability, and fail to synchronize resources in real time. Conversely, digital platforms (e.g., industrial-internet platforms and cloud-based collaboration systems) leverage digital technologies to integrate internal and external data, dynamically reconfigure resources, and facilitate inter-organizational coordination. These distinctive attributes render “digital platform capability” a context-specific construct that is qualitatively different from Zhu’s original “platform capability.” Although non-digital platforms still exist—particularly in regions with low digital penetration or in certain offline industries—they offer servitized manufacturing firms far less utility than digital platforms when the objective is data-driven service innovation. Blaschke et al(2018), from the perspective of platform ecosystem theory [30], defined it as the ability to establish zero-boundary customer connections via digital platforms, reflecting the theory’s focus on networked value creation. Recent research has extended digital platform capabilities to the circular economy. Nemilentseva et al.’s (2025) review highlights that digital platforms can achieve resource cycling and sustainable value creation in developing countries through integration and reconfiguration capabilities [31]. This addresses waste management and resource scarcity challenges and confirms that digital platform capabilities promote adaptive resource optimization beyond organizational boundaries, within broader ecological and socio-economic contexts.

Building on these theoretical foundations, this study anchors its definition in the dynamic capabilities framework, which emphasizes firms’ capacity to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Considering the research context, digital platform capability is defined as an enterprise’s ability to obtain effective internal and external information via digital platform channels, driving optimization and innovation centered on a “platform network.” These channels provide standards, connections, rules, and IT capabilities to coordinate digital production, search, and interaction in response to changing customer needs. Digital platform capabilities include digital platform integration and analysis capabilities and digital platform reconfiguration capabilities. The construct comprises two dimensions rooted in theoretical literature: Digital platform integration and analysis capabilities is consistent with the resource-based view, referring to an enterprise’s ability to collect, store, process, and analyze both internal and external data, which highlights the value of rare and inimitable data resources. Digital platform reconfiguration capabilities is consistent with the dynamic capabilities theory, referring to the ability to flexibly configure internal and external resources through platform architecture design, enabling continuous adaptation of digital technology systems [32].

Building on the institutional theory proposed by DiMaggio and Powell (1983), which emphasizes two core mechanisms—the coercive mechanism of formal institutions and the mimetic mechanism of informal institutions [33]. We argue that the digital platform capabilities of service-oriented manufacturing firms are not solely driven by internal resources. Instead, they are significantly shaped and incentivized by the external institutional environment. At the formal institutional level, local governments in China (e.g., in Jiangsu and Zhejiang provinces, the key research contexts of this study) have implemented targeted policies to promote digital transformation, such as direct subsidies for digital infrastructure construction and tax incentives for firms investing in industrial internet platforms [18,19]. These policies effectively reduce the financial barriers for firms to build digital platforms: subsidies cover a portion of the costs of developing data collection systems (a core component of integration and analysis capabilities), while tax breaks alleviate the pressure of investing in modular function design (a key aspect of reconfiguration capabilities). As a result, firms in these regions are more motivated and capable of enhancing both dimensions of digital platform capabilities. At the informal institutional level, industry associations have played a pivotal role in establishing “digital platform standards,” such as unified data interface specifications and cross-enterprise data sharing protocols. These standards gradually evolve into industry norms, and firms face pressure to conform to avoid “institutional illegitimacy”—a status that could undermine their partnerships with upstream/downstream stakeholders and customer trust [33,34]. To gain legitimacy, many service-oriented manufacturing firms mimic the platform architectures and functional configurations of leading enterprises in the industry. This mimetic behavior accelerates the maturity of their own digital platform reconfiguration capabilities, as they can leverage proven designs to avoid trial-and-error costs.

In summary, the development of digital platform capabilities in service-oriented manufacturing firms is a hybrid outcome of internal resource allocation and external institutional push. This institutional perspective not only enriches our understanding of the antecedents of digital platform capabilities but also reinforces the necessity of exploring how such capabilities drive service innovation—especially in the context of China’s distinctive institutional environment for digital transformation.

2.1.2. Service innovation.

In 1988, Vandermerwe and Rada. proposed the concept of servitization, suggesting it is a shift for manufacturing firms from merely product provision to offering a “product - service package” centered on client firms, and a process of establishing this package as a source of value-addition [35]. Bian and Fan (2024) noted that service-oriented manufacturing enterprises are a new enterprise form emerging from the digital transformation of manufacturing [36]. These firms seamlessly integrate manufacturing and service activities, leveraging digital technologies to enhance their position in the global value chain. Building on prior studies, this paper views servitization as a new industrial form integrating manufacturing and services. Service-oriented manufacturing enterprises are those that innovate and optimize production organization and operational management, increase the proportion of service elements in inputs and outputs, and thus transform from primarily processing and assembly to a “manufacturing + service” model.

Service innovation, a key concept in servitization, has been extensively studied. In 1987, Betz introduced the concept of service innovation, emphasizing the application of new technologies to original processes and technologies to improve service efficiency [37]. With changing consumer demands, Gallouj and Weinstein(1997) defined service innovation as product or service improvements made by enterprises to meet customer needs at different stages [38]. In 2015, Skalen et al. described service innovation as a way for enterprises to seek or utilize external resources through technological means, integrating or creatively providing new service values [39]. Drawing upon these seminal studies, this research defines service innovation as an activity undertaken by servitized manufacturing firms. These firms utilize digital platforms, leveraging the exchange value of products as a foundation. Based on this foundation, they create value by offering users customized solutions that are tailored to their specific needs. In specific terms, firms make use of digital technologies, cloud computing, big data, and other such tools. They employ these tools to build or integrate intelligent digital platforms and deeply explore external resources, transforming them into actual productive capacity. By redesigning service interaction models, firms can achieve more efficient and personalized communication with users; optimizing service processes can eliminate redundant steps and enhance service response times; and innovating product functions can provide users with unexpected experiences. Whether introducing novel service designs, creating pioneering user interaction scenarios, building unique service delivery systems, or iterating and upgrading existing services, the ultimate goal is always to comprehensively enhance service quality and efficiency. In doing so, firms can increase user satisfaction and establish a unique competitive advantage in the face of intense market competition.

2.1.3. Digital platform capabilities and service innovation.

Institutional theory provides a critical foundation for the resource-based view. The resource-based view posits that the firm is a bundle of unique resources and capabilities that constitute the primary source of competitive advantage [40,41]. The institutional environment, however, largely determines which resources firms can acquire, accumulate, and deploy. Industrial policies may channel firms toward specific technologies or talent pools, while industry norms shape how resources are integrated and managed. For servitized manufacturers, the prevailing institutional climate surrounding digitalization compels them to build digital platforms; consequently, digital platform capabilities have evolved into a distinctive and valuable firm resource.Traditional RBV attributes competitive advantage to heterogeneous resources and capabilities [42], yet it offers limited insight into how those resources emerge and evolve [43]. Applying the VRIO lens to servitized manufacturers reveals why digital-platform capabilities constitute such a resource.Value. Superior integration-and-analytics modules mine massive internal and external data to detect latent demand and fine-tune service offerings, while reconfiguration routines allow rapid, low-cost adjustments to processes and resource mixes, neutralizing threats and exploiting market opportunities. Rarity. Building a fully fledged digital platform demands heavy, sustained investment in capital, technology, and specialist labour; only a minority of firms can assemble the requisite bundles. Imitability. Each platform embeds not only complex code and architecture but also firm-specific business processes, governance practices, and longitudinal customer data that are causally ambiguous and socially complex, making full replication prohibitively costly and time-consuming. Organization. Effective administrative structures and coordination mechanisms ensure that platform capabilities are systematically leveraged and continuously renewed rather than lying idle or depreciating. Dynamic-resource theory further argues that in volatile environments sustainable advantage requires continual integration, analysis, and reconfiguration of resources [44]. Because services are intangible and heterogeneous, servitized manufacturers must embed innovation across the entire “manufacturing + service” flow; platform-centric firms use real-time data to orchestrate production, search, and interaction, enabling rapid response to shifting customer needs [17,21]. We therefore examine service innovation through the two-dimensional lens of digital-platform integration-and-analysis capability and digital-platform reconfiguration capability.

Digital platform integration and analysis capabilities encompass the overall process of collecting, analyzing, using, and interpreting data to transform it into actionable insights. This process creates value and builds competitive advantages for firms in today’s economy [45,46]. Drucker highlighted that in the new economy, a firm’s innovation capacity hinges on its ability to efficiently access external knowledge. In the digital economy, digital knowledge has become an indispensable resource for service-oriented manufacturing enterprises. However, merely collecting and analyzing internal data is no longer sufficient for enterprise development. Companies must mine, integrate, analyze, and utilize complex external data to identify information that truly suits their development strategies and promotes innovation.

Moreover, transforming data into actionable decisions requires not only efficient data processing techniques but also the ability to foster cross-departmental collaboration. Breaking down silos within organizations allows data to flow freely, unlocking its full potential and driving innovation. Lehrer et al. (2018) found that firms leveraging digital platforms to collect and analyze customer data exhibit stronger innovation capabilities, enabling them to offer customized products and services [47].

Service innovation theory emphasizes that innovation originates both internally and externally within a firm. Internally, enhancing digital platform integration and analysis capabilities allows service-oriented manufacturing enterprises to improve employees’ familiarity with business processes and supply chain dynamics. This familiarity fosters valuable creativity and effective innovation [48]. Externally, these capabilities enable firms to establish connections with external organizations, expand customer information databases, and accurately predict market trends. By providing personalized customer experiences, firms can reduce market risks and enhance the effectiveness of their service innovations.

In the external realm, digital platform integration and analysis capabilities play an even more pivotal role. By connecting with external organizations, enterprises can significantly expand their customer information databases, enabling precise market predictions. offering highly personalized products and services. This interaction also helps customers gain a deeper understanding of the enterprise’s product positioning and service culture, fostering long-term relationships. The synergy between internal and external processes reduces market risks and amplifies the effectiveness of service innovations, providing enterprises with a competitive edge in the market. Based on this analysis, we propose the following hypothesis:

H1a: The ability to integrate and analyze digital platforms in service-oriented manufacturing firms has a positive impact on service innovation.

Digital platform reconfiguration capabilities refer to a firm’s ability to utilize the flexible architecture of digital platforms to reconfigure internal and external resources [49]. This ability enables enterprises to proactively repair and optimize existing value-creating processes. It allows firms to overcome cognitive and operational barriers, bridge capability gaps with competitors, and achieve sustained optimization in dynamic environments.

Firstly, Value co-creation theory emphasizes that value creation is based on service exchanges between parties. During these exchanges, in-depth interactions and complementary strengths drive innovation. Service-oriented manufacturing enterprises with digital platform reconfiguration capabilities can leverage the modular architecture of digital platforms [50]. They can establish digital business models with other platform enterprises, exchange goods and services with stakeholders, share information, and collaborate within the platform ecosystem. This enables the rapid adaptation to new partners and applications, optimizing or replacing existing processes.

Additionally, digital platforms’ openness and transparency provide enterprises with valuable insights for developing new products or processes. drawing on established models. Firms can easily access customer feedback and preferences, enhancing the competitiveness of their new services [21]. This accelerates innovation in service-oriented manufacturing enterprises. Kopalle et al.(2020) explored how modular digital platforms offer effective pathways for resource reconfiguration, supporting enterprises in delivering high-quality, diverse innovative products and services [51].

In the digital economy, firms are not isolated but are part of a dynamic digital ecosystem. The openness of digital platforms attracts numerous developers, suppliers, and customers, forming an interdependent and co-evolving ecosystem. Within this ecosystem, enterprises can harness external innovation resources, collaborate on R&D projects, and share technological achievements with partners. This accelerates innovation iteration and forms a powerful innovative synergy, driving technological progress and industrial upgrading across the sector.

The stronger a service-oriented manufacturing enterprise’s digital platform reconfiguration capabilities, the richer its customer-related innovative elements, and the more likely it is to achieve disruptive innovation. Based on this, we propose:

H1b: The ability to reconfigure digital platforms in service manufacturing firms has a positive impact on service innovation.

4.4.1. Main effect and mediation effect tests.

This study employs hierarchical regression analysis to test the hypotheses, with the results presented in Table 4. Model 2 shows that digital platform integration and analysis capability has a significant positive impact on service innovation (β = 0.514, p < 0.01), supporting Hypothesis H1a. Similarly, digital platform reconfiguration capability significantly positively impacts service innovation (β = 0.273, p < 0.01), verifying Hypothesis H1b.

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Table 4. Results of regression analysis of main and mediating effects.

https://doi.org/10.1371/journal.pone.0333382.t004

In Model 3, after controlling for other variables, knowledge creation significantly positively influences service innovation (β = 0.383, p < 0.01). In Model 4, adding knowledge creation to the established relationship between digital platform integration and analysis capability and service innovation shows that knowledge creation continues to have a significant positive effect on service innovation (β = 0.317, p < 0.01), and the regression coefficient of digital platform integration and analysis capability decreases from 0.514 to 0.478. This indicates that knowledge creation partially mediates the relationship between digital platform integration and analysis capability and service innovation, verifying Hypothesis 2a. Model 5 shows that when knowledge creation is added to the relationship between digital platform reconfiguration capability and service innovation, knowledge creation still significantly positively affects service innovation (β = 0.312, p < 0.01). The regression coefficient of digital platform reconfiguration capability decreases from 0.273 to 0.269, indicating that knowledge creation partially mediates this relationship as well, supporting Hypothesis 2b.

To further test the mediating effect of knowledge creation, a Bootstrap test was conducted on Model 4 using the Process plug-in. The results presented in Table 5. The results indicate that the indirect effect of digital platform integration and analysis capability on service innovation through knowledge creation is 0.135, with a 95% confidence interval of [0.056, 0.226], supporting Hypothesis 2a. The indirect impact of digital platform reconfiguration capabilities on service innovation through knowledge creation is 0.081, with a 95% confidence interval of [0.029, 0.033], further supporting Hypothesis 2b.

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Table 5. Mediating effect test results.

https://doi.org/10.1371/journal.pone.0333382.t005

4.4.2. Moderating effect test.

The moderating role of environmental uncertainty was tested using the Process program developed by Hayes(2012), with the results shown in Table 6 [67]. The interaction term between knowledge creation and service innovation has coefficients (β = −0.216, p < 0.01) indicating that environmental uncertainty negatively moderates the relationship between knowledge creation and service innovation, verifying Hypothesis 3.

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Table 6. Tests of the moderating effect of environmental uncertainty.

https://doi.org/10.1371/journal.pone.0333382.t006

To visually demonstrate the moderating effect, environmental uncertainty was plotted to show its moderating influence on the relationship between knowledge creation and service innovation. As depicted in Fig 2, high environmental uncertainty weakens the impact of knowledge creation on service innovation, whereas low environmental uncertainty enhances this impact.

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Fig 2. The moderating effect of environmental uncertainty.

https://doi.org/10.1371/journal.pone.0333382.g002

According to Fig 2, environmental uncertainty has different impacts on service innovation by moderating the relationship between knowledge creation and service innovation. In the case of high and low environmental uncertainty, the moderating effect of environmental uncertainty on the relationship between knowledge creation and service innovation presents different influencing mechanisms.

1. (1) The influencing mechanism under high environmental uncertainty

Under the high environmental uncertainty, the external environment is full of unstable factors, such as rapid technological progress, market demand fluctuations, policy changes, etc., which make service-oriented manufacturing enterprises face greater risks and uncertainties. In this environment, while digital platform capabilities can facilitate knowledge creation, the effectiveness and relevance of knowledge creation may be greatly affected. Due to the lack of stable market demand and forecast information, enterprises may encounter more difficulties in the process of knowledge creation, which in turn affects their ability to innovate. Specifically, knowledge creation may be too scattered or unclear, and it is difficult to form actual service innovation. ompanies’ R&D and innovation activities may become more conservative or delayed due to market uncertainty, leading to a slowdown or poor performance of service innovation. Therefore, under the high environmental uncertainty, the role of knowledge creation in promoting service innovation is weakened, and even if the capabilities of digital platforms are enhanced, it may be difficult for enterprises to effectively transform them into innovation results.

1. (2) The influencing mechanism under low environmental uncertainty

Under the low environmental uncertainty, the external environment is relatively stable, the market demand is relatively predictable, and the technology and policy changes are relatively slow. Such an environment is conducive to the strategic planning and resource allocation of enterprises, so that enterprises can carry out knowledge creation in a more targeted manner. Enterprises can rely on digital platform capabilities to effectively capture and analyze data, accumulate and innovate knowledge, and drive service innovation more smoothly. Specifically, knowledge creation can focus more on actual needs and market directions, and form innovative achievements with high application value. A stable market environment helps enterprises to gain more opportunities for trial and error in the innovation process, reduce risks, and accelerate the process of service innovation.

Therefore, under the low environmental uncertainty, the relationship between knowledge creation and service innovation is stronger, and enterprises can more smoothly transform knowledge creation into actual service innovation results. High environmental uncertainty reduces the role of knowledge creation in promoting service innovation, and enterprises may face more challenges in this environment, resulting in poor service innovation results. Low environmental uncertainty provides a more stable environment for innovation, and knowledge creation can more effectively drive service innovation. In the face of different degrees of environmental uncertainty, enterprises need to flexibly adjust their innovation strategies to maximize the advantages of digital platform capabilities and improve the effectiveness of service innovation.

6.2.1. Theoretical implications.

Centered on the core context of digital transformation and service innovation in service-oriented manufacturing enterprises, this study advances the existing theoretical framework and expands the scope of relevant literature in a focused and in-depth manner across the following four dimensions. This is achieved through the integration of multi-theoretical perspectives and rigorous empirical validation, thereby yielding targeted and profound theoretical contributions:

Firstly, this study enriches research on the antecedents of service innovation and extends the literature boundaries in the service-oriented manufacturing field. Existing research on the antecedents of service innovation has predominantly focused on traditional manufacturing enterprises or pure service firms, with insufficient attention devoted to service-oriented manufacturing enterprises—entities defined by the in-depth integration of “manufacturing + service”. Furthermore, these studies rarely incorporate “digital platform capability”—a distinctive characteristic of the digital era—into their antecedent frameworks [21,61]. By leveraging the unique attributes of service-oriented manufacturing enterprises, namely “the embedding of services throughout the entire manufacturing process” and “customer participation in value co-creation” [3,4], this study empirically demonstrates the positive driving role of digital platform capabilities (encompassing digital platform integration and analysis capability, and digital platform reconstruction capability) in service innovation. Specifically, integration and analysis capability enables accurate alignment with market demands through data mining, while reconstruction capability facilitates responses to service iterations via flexible resource allocation. Collectively, these two capabilities form a dual-path mechanism characterized by “data-driven + dynamic adaptation”. This finding not only addresses the gap in the “digital capability dimension” within the antecedents of service innovation but also extends service innovation research from traditional contexts to the emerging domain of service-oriented manufacturing, thereby providing a benchmark framework for investigating the relationship between digital elements and innovation in this field.

Secondly, this study expands the research perspective on digital platform capability and opens the “capability-innovation” transformation black box. Prior literature on digital platform capability has primarily explored its overall impact on business model innovation and innovation performance from a macro perspective [75,76], or treated it merely as a moderating variable to examine contextual effects [77]. However, it has failed to decompose the micro-mechanisms underlying “how digital platform capability influences service innovation” [14,26]. This study fills this gap through two key breakthroughs: First, it decomposes digital platform capability into a two-dimensional construct consisting of “integration and analysis capability” and “reconstruction capability”, and verifies the heterogeneous contributions of these two dimensions to service innovation—overcoming the cognitive limitation of treating digital platform capability as a unidimensional concept. Second, it introduces knowledge creation as a mediating variable and validates the transformation path of “digital platform capability → knowledge spiral (socialization-externalization-combination-internalization) → service innovation” [52,54]. This mechanistic insight shifts research on digital platform capability from a “result-oriented” paradigm to a “process-oriented” one, thereby advancing the understanding of management science regarding the micro-level functioning of digital capabilities.

Thirdly, this study integrates institutional theory and the resource-based view to construct the chain logic of “institution-capability-innovation”. Previous studies have generally treated the institutional environment and firms’ internal capabilities as independent drivers of innovation: some have focused on the direct impact of institutions on innovation, while others have emphasized the core role of internal resources [34,43]. However, there remains a lack of research integrating the interactive relationship between these two factors [26]. This study is the first to empirically validate the chain path of “institutional environment →digital platform capability→knowledge creation→service innovation”. Specifically, the institutional environment does not exert a direct effect on innovation; instead, it influences innovation indirectly by “empowering firms’ internal digital capabilities”. For example, government subsidies for digitalization reduce the barriers to platform construction, enabling firms to acquire scarce digital resources, which are then converted into innovative outputs through knowledge creation. This finding not only provides contextualized empirical evidence for the cross-application of Institutional Theory and the Resource-Based View but also constructs a complete logical loop of “external institution - internal capability - intermediate process - innovation outcome”, thereby offering an integrated paradigm for innovation research from a cross-theoretical perspective.

Fourthly, this study clarifies the boundary constraints of environmental uncertainty and advances research on innovation boundary mechanisms. While existing studies have acknowledged the impact of environmental uncertainty on innovation, they have mostly conceptualized it as a “single external disturbance”, failing to distinguish between its different dimensions and rarely exploring its moderating effect on the “knowledge creation - service innovation” chain [59,73]. This study classifies environmental uncertainty into three distinct dimensions: “institutional uncertainty”, “market uncertainty”, and “technological uncertainty”, and empirically confirms that environmental uncertainty exerts a significant negative moderating effect on the relationship between knowledge creation and service innovation. Under conditions of high uncertainty, the time window for knowledge creation is compressed, and the costs of resource coordination increase—ultimately reducing the efficiency of converting knowledge into innovation [60,74]. This finding not only clarifies the “boundary constraint effect” of environmental uncertainty but also provides an analytical framework of “uncertainty type - innovation path matching” through dimensional decomposition, thereby enhancing the theoretical application of environmental factors in innovation research.

6.2.2. Practical implications and recommendations.

Based on the study results, we put forward suggestions to promote the development of the Service-based manufacturing companies, which provide references for the government to formulate relevant policies and are of great practical significance.

Firstly, service-oriented manufacturing enterprises need to construct a “two-dimensional digital platform capability” dynamic empowerment system, systematically enhancing digital platform integration and analysis capabilities as well as digital platform reconfiguration capabilities through strategic planning. Specifically: First, enterprises should focus on the refined construction of digital platform integration and analysis capabilities, increasing investment in technologies such as artificial intelligence, big data analytics, and cloud computing to enhance the data processing and analysis capabilities of digital platforms. They should establish a cross-departmental data hub to achieve real-time integration of customer interaction data, supply chain data, and market dynamic data. Leveraging natural language processing and machine learning algorithms for demand forecasting, enterprises should also establish strategic partnerships with technology vendors to obtain advanced technical support and solutions. Second, enterprises need to increase the modular design of digital platform reconfiguration capabilities. They can adopt a microservice architecture to disassemble core functional modules, allowing quick invocation and recombination according to business needs, and design a flexible and scalable digital platform architecture to rapidly adapt to market changes and enterprise requirements. Meanwhile, developing standardized API interfaces, they should build a “service innovation alliance” with upstream and downstream partners, jointly test new service models, and shorten the iteration cycle of service solutions.

Secondly, service-oriented manufacturing enterprises need to transform information obtained through digital platforms into valuable new knowledge, creating an efficient “information-knowledge-innovation” conversion engine to drive service innovation. Specifically: First, enterprises should construct a knowledge management system, build a semantically linked “service knowledge map” based on digital platform data, perform labeling processing on unstructured information, form a reusable solution knowledge base, and promote knowledge collection, storage, sharing, and application. Second, cross-departmental collaboration should be encouraged to promote communication and cooperation among different departments, break down information silos, and accelerate knowledge flow. “Innovation sandbox” workshops should be established, and multi-department joint seminars organized, requiring each department to submit service innovation proposals based on digital platform data. Finally, employee training should be strengthened, and a knowledge conversion assessment mechanism established. The conversion rate of digital platform information should be incorporated into departmental KPIs, with assessment standards set. Meanwhile, certification training should be carried out to enhance employees’ knowledge conversion and innovation capabilities, creating new value for the enterprise.

Thirdly, service-oriented manufacturing enterprises should design a dynamic adaptation framework of “environmental resilience-innovation strategy”, reduce the impact of environmental uncertainty on service innovation through a three-level response mechanism, and adopt proactive adaptation strategies to mitigate its negative effects on service innovation. Specifically: First, establish an external environment monitoring system to track market dynamics, technological development trends, and policy changes in real time. Through data analysis and forecasting, potential risks and opportunities are identified in advance to provide a basis for enterprise decision-making.

Second, adopt agile management methods to enhance the enterprise’s adaptability and innovation capabilities. Build a corporate culture that encourages innovation and fosters a relaxed innovative atmosphere, enabling employees to dare to try new ideas and approaches. Finally, flexibly adjust resource allocation according to environmental changes to ensure the effective utilization of enterprise resources. In highly uncertain environments, enterprises can integrate third-party data services with internal platform data to construct a “market uncertainty index”, which automatically triggers the “innovation team” mechanism when the index exceeds a threshold. In stable environments, long-term technological service innovation can be invested in, and long-term cooperation platforms established with core customers and scientific research institutions to enhance the overall quality and speed of service innovation.