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Robots are widely used in industrial manufacturing and service industries around the world. However, most of the previous studies on industrial robots use data at the national or industry level in the context of developed countries. This study examines the impact of imported industrial robots on firm innovation at the firm level in China.

Drawing on a large dataset of more than three million records in China, including non-publicly traded small and medium firms, the authors adopt a difference-in-differences method to investigate the impact and channels of industrial robots on firm innovation.

The authors find that the application of industrial robots increases firm innovation. Two possible channels are identified through which robots promote innovation: alleviation of financial constraints and the improvement of human capital. Further analysis shows that the effect of robots on innovation is more pronounced for firms that are highly dependent on external financing, belong to high-tech industries, import high-end robots, have insufficient supply of skilled labor and private firms (non-SOEs). The authors also find that industrial robots increase the firms' innovation quality and the marginal contribution of innovation to firms' total factor productivity.

This study provides big data evidence of the unintended positive consequences of industrial robots on firm innovation. The results are helpful to clarify the controversy of industrial robots. It also has important implications for government industrial policy making, firm innovation and human resource management.

Driven by the development of the global digital economy, knowledge management in industrial enterprises offers more possibilities for green innovation. Based on the perspective of external knowledge sources, this study aims to construct a panel regression model to explore the relationship between digital economy and industrial green innovation efficiency.

Panel data from 30 regions in China from 2011 to 2020 were selected as research samples. All data are obtained from national and provincial statistical yearbooks. Coupling coordination degree analysis, entropy method, panel regression analysis, robustness test and threshold effect test by Stata 16.0 were used to test the hypotheses.

The empirical results demonstrate the hypotheses and reveal the following findings: the digital economy is positively related to industrial green innovation efficiency and external knowledge sources, and external knowledge sources mediate the relationship between them. Moreover, based on the threshold test results, the digital economy has a double-threshold effect on industrial green innovation efficiency.

Based on the perspective of external knowledge sources, the proposed mediating mechanism between the digital economy and industrial green innovation efficiency has not been established previously, further enriching the research on the antecedents and outcomes of external knowledge sources. Moreover, this study estimated the direct influence mechanism and double-threshold effect of the digital economy on industrial green innovation efficiency from theoretical and empirical analysis, thus responding to the call of scholars and adding to existing research on how the digital economy affects the green transformation of industrial enterprises.

A new round of technological revolution is impacting various aspects of society. However, the importance of technology adoption in fostering firm innovation is underexplored. Therefore, this study aims to investigate whether robot adoption affects technological innovation and how human capital plays a role in this relationship in the era of circular economy.

Based on the robot adoption data from the International Federation of Robotics (IFR) and panel data of China's listed manufacturing firms from 2011 to 2020, this study uses regression models to test the impact of industrial robots on firm innovation and the mediating role of human capital.

The results demonstrate that the adoption of industrial robots can significantly promote high-quality innovation. Specifically, a one-unit increase in the number of robots per 100 employees is associated with a 13.52% increase in the number of invention patent applications in the following year. The mechanism tests show that industrial robots drive firm innovation by accumulating more highly educated workers and allocating more workers to R&D jobs. The findings are more significant for firms in industries with low market concentration, in labor-intensive industries and in regions with a shortage of high-end talent.

Due to data limitations, the sample of this study is limited to listed manufacturing firms, so the impact of industrial robots on promoting innovation may be underestimated. In addition, this study cannot observe the dynamic process of human capital management by firms after adopting robots.

The Chinese government should continue to promote the intelligent upgrading of the manufacturing industry and facilitate the promotion of robots in innovation. This implication can also be applied to developing countries that hope to learn from China's experience. In addition, this study emphasizes the role of human capital in the innovation-promoting process of robots. This highlights the importance of firms to strengthen employee education and training.

The adoption of industrial robots has profoundly influenced the production and lifestyle of human society. This study finds that the adoption of robots contributes to firm innovation, which helps people gain a deeper understanding of the positive impacts brought about by industrial intelligence.

By exploring the impact of industrial robots on firm innovation, this study offers crucial evidence at the firm level to comprehend the economic implications of robot adoption based on circular economy and human perspectives. Moreover, this study reveals that human capital is an important factor in how industrial robots affect firm innovation, providing an important complement to previous studies.

The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.

Based on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.

From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.

Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.

The purpose of this paper is to analyse the development and evolution of industrial innovation ecosystems of Around-Tongji Knowledge Economy Circle from the three levels mentioned above, focusing on knowledge-producing populations, core populations and service-supporting populations, and to further develop this research framework by combining with the latest developments.

Based on the five-helix theory and economic census statistical data, this paper adopts geographic information system technology and examines the characteristics of the industrial innovation ecosystem and the synergistic evolution process in Around-Tongji knowledge economy circle.

The knowledge product populations lead the development of industries in Around-Tongji Knowledge Economy Circle. It contributes political capital output for the government. It innovates community cooperation and governance mode, and it improves the natural ecological environment. In the face of the changes and challenges in the development environment, the future development must be recognised from the height of the iterative development of the interaction mode between university knowledge production and economic and social development.

Based on the five-helix theory and economic census statistical data, this paper examines the characteristics of the industrial innovation ecosystem and the synergistic evolution process in Around-Tongji Knowledge Economy Circle. It further expands the research framework used to develop a synergistic evolution model, which reveals the interactive and synergistic relationship among the populations and the evolution characteristics of the entire industrial innovation ecosystem. This paper also provides useful perspectives for the study of the industrial innovation ecosystem.

The relationship between industrial policy and exploratory innovation is imperfect.

The authors use Chinese high-tech enterprise identification policy (HTEP) as a natural experimental group to test policy impacts, spillover effects and mechanisms of action.

First, HTEP promotes exploratory innovation. In addition, HTEP has a greater impact on non-exploratory innovation. Second, HTEP has spillover effects in two phases: HTEP (2008) and the 2016 policy reform. HTEP affects exploratory innovation in nearby non-high-tech firms, and the policy effect decreases monotonically with increasing distance from the treatment group. Third, HTEP affects innovation capacity through financing constraints, technical personnel flow and knowledge flow, which explains not only policy effects but also spillover effects. Fourth, the analysis of policy heterogeneity shows that the 2016 policy reforms reinforce the positive effect of HTEP (2008). By deducting the effects of other policies, the HTEP effect is found to be less volatile. In terms of the continuity of policy identification, continuous uninterrupted identification has a crucial impact on the improvement of firms’ innovation capacity compared to repeated certification and certification expiration. Finally, HTEP has a crowding-out effect in state-owned enterprises and large firms’ innovation.

This paper contributes to the existing literature in several ways. First, the authors enrich the literature on industrial policy through exploratory innovation research. While previous studies have focused on R&D investment and patents (Dai and Wang, 2019), exploratory innovation helps firms break away from the inherent knowledge mindset and achieve sustainable innovation. Second, few studies have explored the characteristics of industrial policies. In this paper, the authors subdivide the sample into repeated certification, continuous certification and certification expiration according to high-tech enterprise identification. In addition, the authors compare the differences in policy implementation effects between the 2016 policy reform and the 2008 policy to provide new directions for business managers and policy makers. Third, innovation factors guided by industrial policies may cluster in specific regions, which in turn manifest externalities. This is when the policy spillover effect is worth considering. This paper fills a gap in the industrial policy literature by examining the spillover effects. Finally, this paper also explores the mechanisms of policy effects from three perspectives: financing constraints, technician mobility and knowledge mobility, which can affect not only the innovation of beneficiary firms directly but also indirectly the innovation of neighboring non-beneficiary firms.

Using the industrial data between 2000 and 2016, this study analysed the process of coupling and coordinated development of technological innovation and standardisation. Accordingly, the study considered the high-tech industry (five sub-sectors) in China as the research object.

Based on the summary of innovation and standardisation literature review, this study constructed a theoretical model of the influence of technological innovation and standardisation on industrial development from the perspective of the coupling system. Furthermore, the study employed multivariate linear regression analysis to explore coupling coordination relationships.

The study results revealed that high coupling coordination between technological innovation and standardisation is highly conducive to industrial development. Moreover, requirements for standardisation levels differ owing to different stages and characteristics in each segmented industry.

This study primarily contributes to the literature by using a bibliometrics tool to summarise related literature on innovation and standardisation and provides a new perspective of reviewing, and it also offers new evidence on the coupling coordination relationship between innovation and standardisation in the high-tech industry.

This paper aims to analyze the role and advantages of knowledge resources in the carbon emission reduction of the industrial chain, and how it can be used to promote the carbon emission reduction of the industrial chain, so that the industry can better achieve the saving of energy and the reduction of emission.

This paper argues that the traditional resource-plundering industrial chain production method can no longer meet the needs of sustainable development of the green and low-carbon industrial chain, and builds the coupling and coordination of knowledge technology innovation drive and industrial chain carbon emission reduction mechanism, in the four dimensions of industrial chain organization, government support, internet support and staff brainstorming, put forward suggestions for knowledge resources to drive carbon emission reduction in the industrial chain.

This paper holds that the use of knowledge resource advantages can better help industrial chain enterprises to carry out technological innovation, knowledge resource digital platform construction, knowledge resource overflow and transfer, application and management of network information technology, so as to reduce carbon emission in industrial chain.

This paper contributes to the discussion about the high-quality implementation of the revitalization strategy of the industrial chain and also deepens research on the knowledge resource-driven carbon emission reduction of the industrial chain. Further, this paper enriches the role of knowledge resources in the industrial industry, and the theoretical results support the advantages of knowledge resource in the field of chain carbon emission reduction.

The research question is how can a company implement a circular innovation in a supply network context? Leveraging the main conceptual and interpretative models of the industrial marketing and purchasing thinking, this study aims to investigate the interplay between the process of circular innovation development and the changes in the structure and dynamics of the supply network in which innovation takes place.

This research applies a case study design focusing on participant interaction dynamics. The case relates to an industrial company producing an innovative coating solution for compostable packaging. The data used to develop the case study came from multiple sources but primarily from semistructured interviews that cover the implementation of the circular innovation and the configuration of the circular network.

The dynamics of interconnected relationships can configure a circular network that interconnects business and non business actors through vertical, horizontal and heterogeneous relationships. The network configuration is supported by the new mobilizer actor that facilitates the sharing of circular knowledge within the circular network, together with the sharing of a market orientation and entrepreneurial orientation within the supply network, through the educational learning path.

This paper aims to contribute to a new understanding of how circular innovation can be developed, adopted and diffused. In a network, when circular innovation takes place, the focal issue is not the new product or technology in itself but how such innovation is developed and implemented by and through the reconfiguration of the business and non-business relationships into circular network.

In light of the current challenges in the business environment, firms, particularly those involved in supply chains, must foster innovation. In this context, the current study employs the theory of dynamic organizational capabilities (DOCs) to track supply chain learning capabilities (SCLCs) and independently uncover their relationship to innovation at both the product and process levels. Similarly, the study intends to investigate the influence of technological turbulence (TT) on these relationships as a moderating variable.

The data were gathered using in-person interviews with 189 CEOs with some supply chain management proficiency from small and medium-sized enterprises (SMEs) in the industrial zones of eastern Egypt. The study used a survey approach to collect data, and the SEM-PLS technique was utilized to analyze the data.

Study findings revealed that SCLCs positively affect product and process innovation. In addition, TT positively moderates the relationship between SCLCs and product and process innovation, except for risk-taking capability. Further theoretical and practical implications are derived from the study findings.

This research adds to the knowledge of the dynamic capabilities theory (DCT), which affects how firms interact with their external environment. Studying learning capabilities are employed as essential competencies to counterbalance high levels of TT in the external environment in terms of innovative performance and vice versa if firms do not attempt to strengthen their dynamic learning capabilities in supply chains. In addition, this study contributes to the literature by studying learning capabilities from the external perspective, where SCLCs are being developed as a new variable to improve innovation.