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To present the application of a business model for holistic uncertainty management for twenty‐first century manufacturing enterprises.

A questionnaire survey is carried out to UK manufacturing enterprises to collect relevant data, and analysis of variance (ANOVA), correlation analysis and cluster analysis are performed to infer the results.

It can be concluded that different manufacturing environments suffer different effects of underlying causes of uncertainty on product tardy delivery. The product tardy delivery performance in make‐to‐order (MTO) manufacturing environment is found significantly affected by a wide range of underlying causes of uncertainty. It is interesting to find that mixed‐mode (MM) manufacturing environment has an opposite outcome. Correlation results provide significant evidence that underlying causes of uncertainty do not have linear association with product tardy delivery. This finding reinforces the proposition that the effects of uncertainty are difficult to quantify due to the compound effect. The cluster analyses of the business environmental factors of the manufacturing enterprises in MM, make‐to‐stock (MTS) and MTO environments found that size of enterprise, product variety, product complexity, number of parts, ratio of buy vs make parts, the use of rough‐cut capacity planning, and the use of buffering or dampening techniques in production, influence the effects of underlying causes of uncertainty on product tardy delivery.

Only UK manufacturing enterprises are investigated. The results will be relevant to MTO, MM and MTS manufacturing environments.

The application of the business model has provided useful knowledge to MM, MTS and MTO manufacturing enterprises on which underlying causes of uncertainty are significantly affecting their product tardy delivery performance.

A holistic approach such as the business model has given a solid foundation for the enterprises to evaluate their performance. Using the knowledge of significant underlying causes of uncertainty, the enterprises could then prioritise the effort and devise suitable buffering or dampening techniques.

An integrated, exchangeable, sharable and distributed information environment is one of the crucial factors to ensure the competitive advantage of a virtual enterprise. A manufacturing model that describes an enterprise’s manufacturing capability information is an important part of such a distributed information infrastructure. This paper focuses on the definition of an object oriented manufacturing data model that can provide a consistent data structure for the construction of a manufacturing model for a virtual enterprise. The methodology that is employed to carry out the analysis and design of an object oriented manufacturing data model complies with the open distributed processing reference model. Unified modelling language (UML) class diagrams have been employed to represent the object oriented manufacturing data model with links to relevant ISO standards, which can be instantiated to generate a virtual, global manufacturing model. An experimental software system has been developed using ObjectStore OODBMS and Visual C++. An example manufacturing model for a simple virtual enterprise has been populated and can potentially be used to support product design and manufacturing decisions across a virtual enterprise.

The purpose of this paper is to investigate the relations between the digital transformation of manufacturing enterprises and enterprise innovation to provide inspiration for promoting the digitization and upgrading of China's manufacturing industry and facilitate enterprises' innovative development.

Based on a panel data of 584 listed manufacturing companies in China from 2016 to 2020, and using a fixed effects approach, this paper empirically examines the impact of digital transformation on corporate innovation in manufacturing enterprises, as well as examining the influential mechanisms and heterogeneity.

The research shows that the digital transformation of manufacturing enterprises has effectively improved the innovation capacity of enterprises and verifies different situations through heterogeneity. At the same time, the research clarifies the mediating effect of the innovation investment mechanism, cost control mechanism, and profit guarantee mechanism on the relations between the digital transformation of manufacturing enterprises and the innovation capacity of enterprises.

Based on Chinese manufacturing enterprise samples, this paper empirically tests the influential mechanism of digital transformation on the innovation capability of manufacturing enterprises, therefore clarifying the main mediating effects. This paper could provide inspiration for manufacturing companies to make good use of digital transformation rules according to their own situation and improve their innovation capabilities, thereby enhancing competitiveness in the digital economy era.

This study examines the mediating roles of the three dimensions of business intelligence (sensing capability, transforming capability and driving capability) in the relationship between the three dimensions of big data analytics capability (big data analytics management, technology and talent capabilities), and radical innovation among Chinese manufacturing enterprises.

A theoretical framework was developed using the resource-based view. The hypothesis was tested using empirical survey data from 326 Chinese manufacturing enterprises.

Empirical results show that, in the Chinese manufacturing context, business intelligence sensing capability, business intelligence transforming capability and business intelligence driving capability positively mediate the impact of big data analytics capability on radical innovation.

The results offer managerial guidance for leaders to properly use big data analytics capability, business intelligence and radical innovation as well as offering theoretical insight for future research in the manufacturing industry’s radical innovation.

This is among the first studies to examine three dimensions of big data analytics capability on the manufacturing industry’s radical innovation by considering the mediating role of three dimensions of business intelligence.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

This study aims to examine the impact of China's “Manufacturing and Internet Integration Development Pilot Demonstration Project” (MIP) policy on the digital transformation (DT) and labor structure optimization (LSU) of manufacturing enterprises, reveal the relationship between DT and LSU at the micro level and investigate the mechanism between them.

This study employs MIP as a quasi-natural experiment and develops a time-varying difference-in-difference (DID) model based on a sample of 2,445 Chinese A-share listed manufacturing enterprises in the Shanghai and Shenzhen markets from 2013 to 2021.

The implementation of MIP significantly increases DT by 0.4366 and optimizes LSU by 0.0507. By enhancing the two mediated variables of organizational learning inputs (SI) and employees' personal digital cognition (PDC), DT can optimize the LSU of pilot enterprises by 0.035 and 0.034, according to the results of the mechanism analysis. The study also reveals that the impact of MIP on LSU is highly heterogeneous. With effects of 0.0691 and 0.0632, the optimization effect is more pronounced in state-owned firms and firms with low ownership concentration, respectively.

This study demonstrates the dual effects of the MIP pilot on DT and LSU. In addition, this study pioneers research on the significance of optimizing the labor structure through SI and PDC on the basis of DT, which provides an empirical foundation for the Chinese Government to expand the scope of MIP pilots and revise policy content, as well as for manufacturing enterprises to upgrade the labor structure.

Under the influence of the digital economy, the digital transformation of manufacturing enterprises has attracted widespread attention from scholars at home and abroad due to its uniqueness and importance. However, the existing literature is still deficient in the inner mechanism of digital transformation and lacks research on the digital transformation process. Through in-depth interviews and rooted theory, this paper focuses on the key factors of digital development of manufacturing enterprises and constructs a model of the digital transformation process.

This paper takes China's manufacturing enterprises as research projects. First, through a literature search, the authors proposed three key factors for constructing sustainable competitiveness from digital transformation, that is, core technology innovation, business model re-engineering and organizational structure optimization. Second, the authors adopted the grounded theory approach to identify three sustainable competitive factors of manufacturing enterprises resulting from digital transformation and constructed a theoretical model. Third, based on Chinese manufacturing enterprises data, the authors used an empirical study to validate the three influencing factors. Finally, the authors identified the inner mechanism of the digital transformation of enterprises and put forward suggestions for enterprises to maintain sustainable competitiveness.

First, based on the existing literature, this paper identified the importance of digital transformation of manufacturing enterprises, and the essence of digital transformation of manufacturing companies was found to lie in the change in the combined effect of technological, market and organizational innovation. Second, based on the grounded theory, through in-depth interviews with manufacturing enterprises on digital transformation and open coding of interview transcripts, it was concluded that the key factors for the digital transformation manufacturing companies are core technology change, business model re-engineering and organizational structure optimization. Third, based on a dynamic perspective, the authors evaluated and validated digital transformation. The study found that manufacturing enterprises can improve their digital transformation capabilities through the trajectory of “core technology change – business model re-engineering – organizational structure optimization” to improve their sustainable competitive advantage.

Firstly, this study proposed three key factors for constructing sustainable competitiveness from digital transformation, that is, core technology innovation, business model re-engineering and organizational structure optimization, which enriched the theory of transformation and upgrading. Secondly, this study constructs the structural equation model of digital transformation and discovers the key factors pf digital transformation.

Based on the research of free trade agreements on alleviating service trade policy heterogeneity and its impact on manufacturing exports, this article aims to not only provide a basis for China's strategy of promoting regional economic integration, but also provide a policy reference for the manufacturing industry to expand the export market space.

This study uses the two principles of “answering” and “scoring” to quantify the indicators of service trade policy heterogeneity to test the relationship between heterogeneity of service trade policy, free trade agreement and manufacturing export.

According to empirical study, the export of Chinese manufacturing firms is severely hampered by the variety of service trade regulations, and the bigger the enterprise, the more hampered it is. In comparison to communications, transport and commerce, the financial industry's policy heterogeneity has a greater negative impact on certain industries. The major methods used to reduce the impact of service trade policy heterogeneity on manufacturing exports are product price increases and product quantity reductions. Also, by reducing the heterogeneity of service trade regulations and fostering industrial exports, the free trade agreement that China has signed can be quite successful. The open commitment in the area of national treatment, however, can reduce policy heterogeneity and advance manufacturing.

In the area of market access, the effect of export is superior to the open promise. Thus, in order to effectively support the stabilization of international trade, China should actively encourage the negotiation and signing of higher-quality and mutually beneficial free trade agreements.

Drawing on the “Resource- Capability – Competitive advantage” framework within Natural Resource-Based View, this study is purposed to address problems of manufacturing companies “turning into the green” issues, providing references for manufacturing companies to achieve green competitive advantage via internal motivation and identity perspectives.

This study collected survey data from 112 Chinese manufacturing listed enterprises from the Shanghai Stock Exchange, and obtained 418 questionnaires. Following an empirical design, hierarchical regression analysis and Bootstrapping analysis were applied to examine these hypotheses.

Results show that green innovation behaviors positively mediate the positive relationship between organizational environmental identity and green competitive advantage. Furthermore, slack resource not only positively moderates the interaction effect between organizational environmental identity and green product innovation behaviors, but also moderates the mediation role of green product innovation behavior in the main effect.

The study is limited to the data samples and dynamic evolution between variables. More longitudinal designs and diverse enterprise fields will be considered in future.

The findings provide guidance for manufacturing enterprises to gain green competitive advantage in green manufacturing era from identity value origins. Moreover, the results provide practical suggestions for manufacturing enterprises to enhance green innovation behaviors and integrate useful but slack resources.

The findings provide implications for manufacturing industry to increase both the awareness of organizational environmental identity and green innovation behaviors, all of which helps promote the corporate environmental responsibility.

The study breaks the limitation of the existing literature which focuses on the influence of external push factors (e.g. environmental regulation), and bring new visions in constructing green competitive advantage in green manufacturing era, not only extending the identity theory and upper echelon theory, but also enriching the current studies on green management. Also, these findings provide deeper and new insights into risks and uncertain features of green manufacturing era, analyzing how polluted manufacturing enterprises obtain green competitive advantages through the mediating role of green innovation behaviors and the moderating role of slack resource.

With the deep development of the new technological revolution and industrial transformation, the development, application, expansion and integration of digital technology provide opportunities for transforming the manufacturing industry from traditional manufacturing to intelligent manufacturing. However, little research currently focuses on analyzing the influencing factors of intelligent development in this field. There is a lack of research from the perspective of the digital innovation ecosystem to explore the intrinsic mechanism that drives intelligent development. Therefore, this article starts with high-end equipment manufacturing enterprises as the research subject to explore how their digital innovation ecosystem promotes the effectiveness of enterprise intelligent development, providing theoretical support and policy guidance for enterprises to achieve intelligent development at the current stage.

This article constructs a logical framework for the digital innovation ecosystem using a “three-layer core-periphery” structure, collects data using crawling for subsequent indicator measurement and assessment and uses the fuzzy set Qualitative Comparative Analysis method (fsQCA) to explore how the various components of the digital innovation ecosystem in high-end equipment manufacturing enterprises work together to promote the development of enterprise intelligently.

This article finds that the various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises, through mutual coordination, can help improve the level of enterprise intelligence. Empirical analysis shows four specific configuration implementation paths for the digital innovation ecosystem of high-end equipment manufacturing enterprises to promote intelligent development. The core conditions and their combinations that affect the intelligent development of enterprises differ in each configuration path.

Firstly, this article discusses the practical problems of intelligent transformation and development in the manufacturing industry and focuses on the intelligent development effectiveness of various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises in the context of digitalization. Secondly, this article uses crawling, text sentiment analysis and other methods to creatively collect relevant data to overcome the research dilemma of being limited to theoretical analysis due to the difficulty in obtaining data in this field. At the same time, based on the characteristics of high-end equipment manufacturing enterprises, the “three-layer core-periphery” digital innovation ecosystem framework constructed in this article helps to gain a deep understanding of the development characteristics of the industry's enterprises, provides specific indicator analysis for their intelligent development, opening the “black box” of intelligent development in the industry's enterprises and bridging the gap between theory and practice. Finally, this study uses the fsQCA research method of configuration analysis to explore the complexity of the antecedents and investigate the combined effects of multiple factors on intelligent development, providing new perspectives and rich research results for relevant literature on the intelligent development of high-end equipment manufacturing enterprises.