Service-oriented manufacturing is an important trend in the development of the manufacturing industry. Accelerating the development of service-oriented manufacturing is of great significance for building a modern industrial system, making China a strong manufacturing country, and improving its global industrial competitiveness. With the development of digital technology, service-oriented manufacturing has become a data-intensive production and service process. Data are widely applied in typical scenarios such as research and development, production, delivery to users, and operation and maintenance. Through data insights, software definition, command and control, real-time data streaming and aggregation, it helps to optimize business decisions, achieve economies of scale, enhance flexibility, improve service responsiveness, and unleash value-creation capabilities. This can comprehensively improve the operational performance of service-oriented manufacturing. To further accelerate the development of service-oriented manufacturing, it is necessary to focus on shaping the advantages of manufacturing data factors, increasing the supply of manufacturing data, enhancing the innovation of data technologies and data security, accelerating the research and application of data technology standards, and promoting cross-border data flows.
Corporate venture capital (CVC) has emerged as a critical vehicle for integrating industrial operations with financial capital, enabling enterprises to pursue strategic synergy and value creation. The selection of investment models and the green supply chain integration are increasingly pivotal strategies for leveraging capital to drive sustainability and low-carbon transformation. Drawing on the theoretical framework of resource orchestration and co-evolution, this paper examines Contemporary Amperex Technology Co., Limited (CATL) as a typical case to explore the dynamic interplay between its CVC model selection and green supply chain integration strategy. The findings reveal that (1) policy regulation, technological innovation, and market competition act as environmental selection pressures that catalyze the co-evolution of CVC initiatives and architecture through renewed resource orchestration, while organizational adaptation logic guides the alignment of CVC models, together driving an evolutionary path of "technological breakthrough-economies of scale-ecosystem formation"; and (3) CVC enhances immediate supply chain integration by strengthening resource coordination, establishing a mutually reinforcing evolutionary loop. Accordingly, enterprises should dynamically align their CVC models with green business strategies, build responsive mechanisms across policy, technology, and market domains, and improve strategic agility through anticipatory resource deployment and ecological breakthroughs, advancing the sustainable development of industrial and supply chains.
Intelligent manufacturing inherently embodies the techno-economic characteristics commonly associated with general information technologies, such as permeability, substitutability, and synergy. Yet when deployed in manufacturing, its adoption typically entails prolonged experimentation and validation, giving rise to delays and heterogeneous outcomes in both implementation and performance. Much of the existing literature asks whether intelligent manufacturing enhances enterprises' innovation efficiency, fueling broader debates over productivity. The more fundamental question, however, is when and for which types of enterprises intelligent manufacturing enhances innovation efficiency-that is, the question of its effectiveness. Based on data from A-share listed manufacturing companies over the period 2010-2023, this paper examines the impact of intelligent manufacturing on enterprises' innovation efficiency from the perspective of the enterprise life cycle. The empirical results show that intelligent manufacturing, on the whole, improves enterprises' innovation efficiency. From the life cycle perspective, intelligent manufacturing exerts a positive effect on enterprises in the mature stage, but has no significant impact on enterprises in the growth or decline stages. Differences in innovation incentives, R&D capabilities, and organizational characteristics across life cycle stages are the main reasons for this pattern. Heterogeneity analysis further indicates that intelligent manufacturing improves innovation efficiency among mature-stage non-state-owned enterprises, high-technology enterprises, and technology-intensive enterprises, while exerting no significant effect on other types or stages of enterprises. In terms of the underlying mechanism, intelligent manufacturing enhances enterprises' innovation efficiency by strengthening their knowledge absorptive capacity. The findings of this paper offer policy implications for promoting intelligent manufacturing in support of boosting China's strength in manufacturing.
In the context of digital transformation, corporate environmental, social, and governance (ESG) performance plays a critical role in promoting open green innovation. Drawing on data from A-share listed companies from 2011 to 2022 in China, this paper examines the impact of ESG performance on open green innovation during the digital transformation. The results reveal that good ESG performance significantly promotes enterprises’ open green innovation, and this conclusion remains valid after several robustness tests. Moreover, the positive effect is more pronounced in regions with higher levels of digital economy development and among enterprises with greater digitization. Heterogeneity analysis reveals that ESG performance more strongly promotes open green innovation in three types of enterprises: high-tech enterprises, those in high-trust regions, and those with strict financing constraints. Based on these findings, enterprises should establish modern ESG management systems, fully leverage the dividends of regional digital economy development, and actively leverage digital transformation to enhance open green innovation ecosystems and ESG capability building.
In the digital economy era, the operating logic of manufacturing enterprises has changed from product-driven to service-driven, and digital servitization has become an important realization path of the service-driven logic. To gain insight into the emerging phenomenon of digital servitization, this paper selects SANY Group as the case company to analyze the connotation and composition of digital servitization and reveal the impact mechanism of differentiated data endowments on digital servitization of manufacturing enterprises. This paper finds the following: (1) The digital servitization of a manufacturing enterprise usually goes through three stages: product + service, product x service, and product-service integration, which are also known as digital extended services, digital customized services, and digital ecosystem services; (2) differentiated data endowments that are mainly based on product data, user data, and platform data constitute the key driving factor for the digital servitization of manufacturing enterprises; data-insight affordance behaviors, and data-governance affordance behaviors, offer the key path for differentiated data endowments to influence the digital servitization of manufacturing enterprises. This paper deconstructs the digital servitization modes and constructs a theoretical model that describes the transmission among differentiated data endowments, data affordance behaviors, and digital servitization. It offers new perspectives and pathways for research on digital servitization, as well as valuable innovation.
Based on the perspective of digital resource orchestration, this study uses Gree Electric Appliances Inc. of Zhuhai as a case study and adopts a longitudinal single-case study method to explore the digital empowerment mechanism within the green innovation process of manufacturing enterprises. The study finds the following three points. (1) The digital empowerment for green innovation has evolved through three developmental stages: the traditional green development stage, the digital-driven green advancement stage, and the digital-green integration stage. Across these stages, green innovation manifests in three dimensions: process innovation, product innovation, and organizational innovation. (2) The specific process of digital resource orchestration is digital resource structuring-digital resource bundling-digital resource greening, which can be implemented through three distinct variations: incremental, integrative, and embedded. (3) In the traditional green development stage, dominated by policy-driven factors, improvement-oriented digital resource orchestration leads to localized green process innovation. In the digital-driven green advancement stage, where market-driven factors dominate, integrative orchestration supports green innovation across the entire value chain. In the digital-green integration stage, dominated by strategy-driven factors, embedded orchestration empowers green innovation at both the organizational and industrial-ecosystem levels.
The relationship between environmental regulation and green innovation has long been debated. One possible reason is that most studies overlook the dual heterogeneity of environmental regulations (command-and-control and market-based) and green innovation (strategic and substantive). Drawing on institutional theory and knowledge management theory, this study develops a theoretical framework of "environmental regulation-knowledge search-enterprise green innovation." Using multiple linear regression and fuzzy-set qualitative comparative analysis (fsQCA), this paper tests the hypotheses based on 285 enterprise survey responses. The findings show that command-and-control environmental regulation promotes only strategic green innovation, whereas market-based environmental regulation promotes only substantive green innovation. Knowledge search depth mediates the relationship between command-and-control environmental regulation and strategic green innovation, while knowledge search breadth mediates the relationship between market-based environmental regulation and substantive green innovation. Furthermore, the fsQCA results identify five causal configurations, including two configurations leading to high strategic green innovation and three leading to high substantive green innovation. These findings expand the research dimensions on how environmental regulation influences enterprise green innovation and offer insights for governments to adjust regulatory policies and for enterprises to respond to external institutional pressures.
Taking all A-share listed companies in China from 2009 to 2022 as research samples, this paper examines the relationship between customer enterprises' environmental, social, and governance (ESG) rating and supplier enterprises' green innovation. The empirical results show that customer enterprises' ESG rating has a positive impact on supplier enterprises' green innovation. Reducing the amount of funds of supplier enterprises, encouraging supplier enterprises to increase innovation investment, and improving the managers' green cognition of supplier enterprises are the three mechanisms through which customer enterprises' ESG rating promotes supplier enterprises' green innovation. Supplier enterprises' market power will negatively moderate the positive relationship between customer enterprises' ESG rating and supplier enterprises' green innovation. Heterogeneity analysis reveals that the positive influence of customer ESG rating on suppliers' green innovation is stronger under two conditions. Firstly, it is more pronounced when customers face greater legitimacy pressure or engage in more substantive ESG practices. Secondly, the effect is also amplified when suppliers lack credibility or operate under stricter environmental regulations. Further research shows that customer enterprises' ESG rating is more effective in promoting supplier enterprises' green innovation when the uncertainty of ESG rating results is low, and that supplier enterprises' green innovation contributes not only to supplier enterprises' own ESG rating but also to supplier enterprises' total factor productivity. This paper highlights the spillover effects of ESG rating pressure from the perspective of supplier enterprises and provides empirical evidence and managerial implications for promoting corporate green innovation.
The rapid emergence and widespread permeation of digital technologies are driving the digital transformation of supply chains, offering an opportunity to enhance their sustainability. This paper analyzes the data of Chinese A-share listed companies and their suppliers from 2013 to 2021 and explores the impact that customer enterprises' digitalization exerts on supplier enterprises' environmental, social, and governance (ESG) performance from the perspective of digital empowerment. This paper finds that customer enterprises' digitalization has an enhancement effect on supplier enterprises' ESG performance, i.e., digitalization's empowering effect on the sustainable development of supply chains, which is an asymmetric effect. This effect is more significant when customer enterprises outshine supplier enterprises in terms of ESG performance primarily through two empowering effects: structural (i.e., improved supply-chain collaboration) and resource (i.e., alleviated factor constraints on suppliers). Heterogeneity analysis further reveals that this positive effect is more pronounced when local governments' environmental attention is higher or when the customersupplier relationship is closer. The effectiveness test shows that customer enterprises' digitalization boosts supplier enterprises' ESG performance and helps to enhance supply chain resilience. This paper enriches the research on the influencing factors and implementation mechanisms of sustainable supply chains, broadens the research boundaries of enterprise digitalization and supply chain resilience, and provides important enlightenment for promoting the sustainable development of supply chains in China and enhancing their resilience and security.
Environmental, social, and governance (ESG) serves as an important breakthrough for enterprises to advance sustainable development and promote high-quality development. It is also a key pathway for the capital market to move toward sustainability. A sound evaluation of the innovation value of ESG plays an important role in accelerating the establishment of China as a strong science and technology country in the new development stage. Using A-share listed companies in Shanghai and Shenzhen from 2012 to 2021 as the empirical sample, the present study conducts empirical tests based on an enterprise fixed effects model. The results show that: Firstly, stronger ESG performance improves corporate innovation efficiency and produces an efficiency-enhancing effect on innovation. This conclusion holds after a series of robustness checks and after addressing potential endogeneity. Secondly, ESG performance increases corporate innovation efficiency through three channels: higher R&D investment, relief of financing constraints, and lower agency costs. In other words, ESG improves innovation efficiency through stronger R&D incentives and cost-saving effects. Thirdly, the positive effect of ESG on innovation efficiency varies across enterprises. The effect is more pronounced in regions with higher marketization and among private enterprises and those with returnee executives. This paper provides empirical evidence on optimizing innovation resource allocation through ESG-driven strategic transformation in the new development stage.
Driven by the initiatives of data-driven intelligence empowerment,robots and artificial intelligence(AI)technology profoundly influence economic and social development.China is the world's largest market for robot adoption.This makes the impact of its industrial robot use on supply chains an important and urgent issue to explore.This paper uses Chinese A-share manufacturing listed firms from 2011 to 2019 to investigate the impact of industrial robot adoption at the enterprise level on customer stability.The empirical results indicate that an increase in the scale of industrial robot adoption will significantly improve the stability of enterprise customers.Mechanism testing shows that industrial robot adoption mainly enhances customer stability through the"technological progress effect"and"risk governance effect."Additionally,the heterogeneity analysis finds that the positive effect of industrial robot adoption on customer stability becomes more potent for firms that are capital-intensive,less digitally transformed,non-state-owned,and trading with younger customers.Finally,applying industrial robots is conducive to adding firm value by strengthening customer stability.This paper enriches the relevant literature on the empowerment of the digital economy and customer governance to some extent.The research also provides a reference for popularizing the adoption of AI technology in enterprises and promoting the high-quality development of supply chains.
Excellent corporate culture embodies an enterprise's values and represents a crucial informal institutional factor driving its high-quality development. Artificial intelligence (AI) is deeply integrated into various sectors of the Chinese economy, and corporate culture inevitably evolves alongside changes in organizational production modes. From the perspective of team spirit, this study empirically examines whether and how the adoption of AI influences corporate culture. The findings indicate that the adoption of AI substantially enhances team spirit within enterprises, and this effect remains robust after a battery of endogeneity and robustness checks. Mechanism analyses reveal that AI promotes team spirit through income creation, human capital upgrading, and increased market attention. Moreover, this positive effect is more pronounced in regions where Confucian cultural influence is weaker, and elevated team spirit boosts enterprises' market valuation. In the era of the digital economy, therefore, the deployment of AI actively shapes team spirit, thereby providing a scientific basis for cultivating an exemplary corporate culture aligned with socialist core values.
Artificial intelligence(AI)has reshaped the subject of product innovation and triggered transformations in product innovation strategies and processes.This study proposes a subject-strategy-process(SSP)framework for business intelligence(BI)for big data-driven product innovation through logical deduction,drawing on the theory of big data cooperative assets and an adaptive innovation perspective on enterprise-user interaction.The aim is to explore new mechanisms through which AI influences product innovation in manufacturing.This study indicates three aspects.Firstly,the two-way involvement of humans and AI forms a dual feedback-enhancement mechanism of factor combination and knowledge accumulation.This mechanism drives structural changes in innovation subjects and forms a new foundation for strategic and process transformations in product innovation.Secondly,the alignment between an enterprise's cognitive strategy about AI,competitive strategy,organizational culture,business model,and ecosystem jointly shapes the integrated application of AI in innovation processes.Thirdly,the new features of the big data-driven product innovation process include full-process diffusion from the fuzzy front end,nonlinear iteration of demand-solution pairs,and generative self-testing in intelligent manufacturing.Taken together,the study demonstrates that the SSP framework is well-suited to analyzing the new mechanisms of BI for big data-driven product innovation,which offers a fresh lens for examining the relationship between AI and product innovation.
The digital reconstruction of value chain plays an important role in achieving the goal of moving up the value chain in participatory manufacturing enterprises (PMEs). This study aims to investigate the configuration path for the realization of the digital reconstruction performance of value chain in PMEs. Taking 122 PMEs embedded in the Haier COSMOPlat ecosystem as research objects, we change the traditional research perspective and use fuzzy-set qualitative comparative analysis to explore how technology resources, information resources, relationship resources, and digital services provided by industrial internet platform empowerment synergistically affect the digital reconstruction performance of enterprise value chain. This study obtains two groups of high (non-high) performance, namely, product-driven and service-driven; and relationship trapped and information-trapped. The results show that the impact of industrial internet platform empowerment on the digital reconstruction performance of value chain exhibits equifinality across PMEs at different levels and stages of development.
Brand competitiveness is a significant indicator of high-quality development of enterprises and a pivotal factor in an enterprise’s pursuit of long-term growth. Using panel data on Chinese A-share listed companies from 2007 to 2021, this study constructs a measure of brand competitiveness using textual analysis and empirically examines its effect on corporate investment efficiency. The results reveal that brand competitiveness mitigates inefficient investment through two distinct channels: the reputation constraint mechanism and the information acquisition mechanism. These effects are more pronounced in environments characterized by intense market competition, advanced managerial cognition, and sustained brand competitiveness. Additionally, brand competitiveness is shown to significantly enhance total factor productivity, thereby supporting high quality development of enterprises. The findings offer practical implications for enterprises aiming to strengthen brand competitiveness and advance toward high-quality development.
The mechanisms by which value chains are reconfigured during corporate digital transformation have attracted growing attention from both academia and industry. Drawing on technology empowerment theory, we conduct a longitudinal dual-case study of Huawei and Midea to explore the dynamic evolution mechanism of value chain reconfiguration during digital transformation. Specifically, this study aims to find the pathways through which value chain reconfiguration occurs, analyze the mechanisms of value transmission, and examine the similarities and differences in reconfiguration mechanisms between infrastructure-oriented and application-oriented enterprises. The findings reveal that digital technologies serve as the engine and driving force of value chain reconfiguration by empowering resource combinations that integrate data and traditional resources to enhance resource reconfiguration capabilities, innovating resource leverage approaches, and driving value chain reconfiguration. Based on these insights, we develop a research framework of “digital technologies → resource orchestration → value chain reconfiguration.” Furthermore, the mechanism of value chain reconfiguration evolves across three stages, namely, digitalization, networking, and intelligentization. Given their different starting points, motivations, and depths of digitalization, infrastructure oriented and applicationoriented enterprises exhibit distinct mechanisms of value chain reconfiguration at different stages of digital transformation. These findings enrich the understanding of value chain reconfiguration in digital transformation and provide managerial implications for practitioners.
With the advancement of technology, innovation ecosystems based on technological resources gradually emerge. However, little attention has been paid to the internal mechanisms driving the evolution of such ecosystems in extant literature. This study conducts a longitudinal case analysis of iFLYTEK’s ecosystem-building process and finds that the evolution of a technological resource-driven innovation ecosystem comprises three stages: technological accumulation, technological openness, and technological empowerment. Each stage exhibits distinct technological characteristics that underpin different resource orchestration approaches and value co-creation models. In the technological accumulation stage, the company forms an integrated value-creation model by structuring resources. In the technological openness stage, a shared value co creation model is shaped through resource capability development. In the technological empowerment stage, a model emerges via resource leveraging. Based on these findings, this study proposes a theoretical model of the evolution and value co-creation of technological resource-driven innovation ecosystems. This model not only enriches theoretical research on innovation ecosystems and resource orchestration but also provides practical insights for building other technological resource-driven innovation ecosystems.
To explore the process mechanism of digital innovation in manufacturing enterprises to promote enterprise performance and to reveal its synergistic, diffusion, and value-added effects, as well as the contextual effects of continuous digital investment, th
Artificial intelligence (AI) has a significant spillover effect and plays a crucial role in driving technological progress and high-quality development. The introduction of new quality productive forces points out the direction for the advancement of Chin
Against the backdrop of deep digital-real economy integration and intensifying global innovation competition, accelerating digital transformation and platform transformation to shape a multidimensional, interactive, and co-evolutionary innovation ecosyste
