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The purpose of this paper is to investigate the impact of intellectual capital (IC) and its components (human, structural, relational and innovation capitals) on financial performance (FP) at different life cycle stages.

The study uses the data from Chinese manufacturing listed companies during 2014–2018. The modified value added intellectual coefficient (MVAIC) model is employed as the measurement of IC efficiency. Finally, multiple regression analysis is used to test the research hypotheses.

This study shows that the impact of IC on FP is different across life cycle stages. Specifically, at the birth stage, human capital (HC), structural capital (SC) and innovation capital (INC) have a positive impact on FP. At the growth and mature stages, all IC components contribute to FP improvement. HC and SC play an important role at the revival stage, while only HC positively affects FP at the decline stage.

The findings may help corporate managers to make optimal strategies to improve FP by effective utilization of IC resources in the complex and competitive business environment. Meanwhile, companies can invest in the core elements of IC at different stages of development, so as to maximize the contribution of IC to company value.

This is among the few studies to explore the impact of IC on FP of manufacturing listed companies in the Chinese context from the perspective of life cycle. It also makes novel contributions in measuring IC by the MVAIC model with the inclusion of relational capital and INC that are largely neglected in previous research.

The purpose of this paper is to examine the impact of intellectual capital (IC) and its components (human, structural and relational capitals) on the performance of manufacturing listed companies in China. This paper also investigates the impacts of company ownership, industry attributes and region on the IC-performance relationship.

The study uses the data of 953 manufacturing companies listed on the Shanghai and Shenzhen Stock Exchanges over the period 2012–2016. The modified value-added intellectual coefficient (MVAIC) model is applied to measure IC efficiency. Finally, multiple regression analysis is employed to test the research hypotheses.

This study reveals that IC can enhance firm performance in China's manufacturing sector. Overall, earnings are affected by physical capital, human capital (HC) and structural capital (SC), and profitability and productivity are influenced by physical capital, HC, SC and relational capital. Physical capital is the most influential contributor to firm performance. In addition, state-owned enterprises have a greater impact of IC on firm performance than private-owned enterprises; high-tech manufacturing companies have higher IC performance than non-high-tech manufacturing companies; manufacturing companies in China's eastern region have higher IC performance than the counterparts in central and western regions.

The findings may help managers, stakeholders and policymakers in developing countries to effectively and efficiently manage their IC resources.

This is the first study to evaluate IC and its relationship with firm performance among Chinese manufacturing listed companies using the MVAIC model.

The objective of this study is to investigate how the coronavirus disease 2019 (COVID-19) pandemic affects firms' financial management in China's manufacturing sector. In addition, the authors analyze the changes in various financial indicators before and during the COVID-19 pandemic. Further, the authors make a cross-country comparison of the COVID-19's impact on financial management between China and Romania.

The study uses the balanced panel data of 2,272 manufacturing listed companies from 2019 to 2020, and applies the t-test method and multiple regression method.

The results show that firms' financial performance in most manufacturing sub-sectors decreased during the observed period. In addition, the authors find that equity financing, proper liquidity management and an expanded firm scale can improve firms' financial performance. The authors further compare the results with the Romanian results, and find that the negative impact of debt-to-equity ratio on firms' financial performance in Romania is greater than that in China and the positive impact of financial autonomy ratio and working capital ratios is greater in China than that in Romania.

The findings can help corporate managers make the best financial management decision in response to crisis.

This study is one of the pioneers that analyze how manufacturing companies carried out their financial management during the COVID-19 crisis in the Chinese context, and provides a cross-country analysis of corporate financial management practices in China and Romania.

The purpose of this paper is to explore and compare the extent of intellectual capital (IC) and its four components in high-tech and non-high-tech small and medium-sized enterprises (SMEs) operating in China’s manufacturing sector, and to examine the relationship between IC and the performance of high-tech and non-high-tech SMEs.

The study uses the data of 116 high-tech SMEs and 380 non-high-tech SMEs listed on the Shenzhen stock exchanges during 2012–2016. The modified value added intellectual coefficient (MVAIC) model is used incorporating four components, namely, capital employed, human capital, structural capital and relational capital. Finally, multiple regression analysis is utilized to test the proposed research hypotheses.

The findings of this paper reveal that there is significant difference in MVAIC between high-tech and non-high-tech SMEs. The results further indicate a positive relationship between IC and financial performance of high-tech and non-high-tech SMEs. Specifically, IC is positively associated with firms’ earnings, profitability and operating efficiency. Additionally, capital employed efficiency, human capital efficiency and structural capital efficiency are found to be the most influential value drivers for the performance of two types of SMEs while relational capital efficiency possesses less importance.

This paper will provide a valuable framework for executives, managers and policy makers in managing IC within the Chinese context.

To the best knowledge of the authors, this is the first empirical study that has been conducted on high-tech and non-high-tech SMEs in the manufacturing sector in China.

The digital economy has become a new engine for economic development, promoting the upgrading and transformation of traditional industries as well as fostering emerging industries and forms of business. Nonetheless, how does the digital economy affect innovation? The research objective is to explore the specific impact of the digital economy on innovation output.

This paper innovatively adopts the dynamic panel data model (DPDM) to carry out an empirical study on the impact of the digital economy on innovation output, through the observation of 30 provincial-level administrative regions in China. Furthermore, the paper innovatively analyzes the impact of different dimensions of the digital economy on innovation output and the impact of the digital economy on different dimensions of innovation output.

It is found that the digital economy is conducive to boosting innovation output considering innovation continuity. Specifically, the driving impact of core industries and enterprise application of digital economy on innovation output is more prominent, but the driving impact of infrastructure and personal application on innovation output is not fully played. Meanwhile, the driving impact of the digital economy on the innovation output quality is more significant than that digital economy on the innovation output quantity.

This study employs a DPDM for the first time to investigate the specific impact of the digital economy on innovation output, and contributes to the existing literature on the digital economy and digital economy-driven innovation. The findings offer a comprehensive explanation for the impact of the digital economy on innovation output, which has reference value for the formulation of innovation policies driven by digital economy, thereby providing impetus for the sustained and stable development of China's economy.

The purpose of this paper is to examine the impacts of research and development (R&D) investment and environmental, social and governance (ESG) performance on green innovation performance. This paper also investigates the moderating effect of ESG performance between R&D investment and green innovation performance.

The study uses the data of 223 Chinese listed companies over the period 2015–2018. The ESG indices issued by SynTao Green Finance are used to measure ESG performance. Green innovation performance is measured by the total number of green patents, the number of green invention patents and the number of green non-invention patents. Finally, multiple regression analysis is applied to test the research hypotheses.

The results show that R&D investment has a positive impact on green innovation performance and ESG performance can increase the number of green invention patents. In addition, ESG performance moderates the relationship between R&D investment and green innovation performance.

The findings may help managers and policymakers in developing countries to make ecological innovation strategies to achieve corporate sustainability.

This is the first study to examine the impacts of R&D investment and ESG performance on green innovation performance in the context of China, an emerging market.

Proper topic selection is an essential prerequisite for the success of research. To study this, this article proposes an important concerned factor of topic selection-topic popularity, to examine the relationship between topic selection and team performance.

The authors adopt extracted entities on the type of gene/protein, which are used as proxies as topics, to keep track of the development of topic popularity. The decision tree model is used to classify the ascending phase and descending phase of entity popularity based on the temporal trend of entity occurrence frequency. Through comparing various dimensions of team performance – academic performance, research funding, relationship between performance and funding and corresponding author's influence at different phases of topic popularity – the relationship between the selected phase of topic popularity and academic performance of research teams can be explored.

First, topic popularity can impact team performance in the academic productivity and their research work's academic influence. Second, topic popularity can affect the quantity and amount of research funding received by teams. Third, topic popularity can impact the promotion effect of funding on team performance. Fourth, topic popularity can impact the influence of the corresponding author on team performance.

This is a new attempt to conduct team-oriented analysis on the relationship between topic selection and academic performance. Through understanding relationships amongst topic popularity, team performance and research funding, the study would be valuable for researchers and policy makers to conduct reasonable decision making on topic selection.

The purpose of this paper is to generate grinding trajectory of unknown model parts simply and efficiently. In this paper, a method of grinding trajectory generation of hybrid robot based on Cartesian space direct teaching technology is proposed.

This method first realizes the direct teaching of hybrid robot based on 3Dconnexion SpaceMouse (3DMouse) sensor, and the full path points of the robot are recorded in the teaching process. To reduce the jitter and make the speed control more freely when dragging the robot, the sensor data is processed by Kalman filter, and a variable admittance control model is established. And the joint constraint processing is given during teaching. After that, the path points are modified and fitted into double B-splines, and the speed planning is performed to generate the final grinding trajectory.

Experiment verifies the feasibility of using direct teaching technology in Cartesian space to generate grinding trajectory of unknown model parts. By fitting all the teaching points into cubic B-spline, the smoothness of the grinding trajectory is improved.

The whole method is verified by the self-developed TriMule-600 hybrid robot, and it can also be applied to other industrial robots.

The main contribution of this paper is to realize the direct teaching and trajectory generation of the hybrid robot in Cartesian space, which provides an effective new method for the robot to generate grinding trajectory of unknown model parts.

The purpose of this paper is to improve the corrosion resistance of anodized 6063 Al alloy inertial air–water separator by means of silane technology and to investigate the effect of corrosion-generated surface roughness changes on aerodynamic performance.

The BTSE-KH560 double-layer silane film treatment technique is used to close micropores on the anodic oxide film surface. The microstructure of the coating is observed by scanning electron microscopy, the coating structure of the specimens is determined by X-ray diffraction (XPS) and the corrosion resistance is determined by electrochemical and salt-spray tests. Computational fluid dynamics is also used to calculate the effect of roughness and analyse the change in separator performance.

The silane film deposited on the surface of the anodic oxide film acts as a good seal against microporous defects on the surface of the anodic oxide film and reduces the surface roughness. Electrochemical and salt-spray tests show that the silane film improved the corrosion resistance of the anodized film. The roughness produced by the corrosion deteriorates the performance of the separator.

The porous structure of the anodized coating makes it easier for corrosive ions to enter the substrate and cause pitting corrosion. Therefore, in this study, the corrosion behaviour of the coating in the marine environment and its effect on aerodynamic performance are investigated using a BTSE-KH560 double-layer silane coating with a sealing effect.

The purpose of this paper is to propose a novel strategy of optimal parameters configuration and placement for sensitive equipment.

In this study, clamped thin plate is considered as the foundation form, and a novel composite system is proposed based on the two-stage isolation system. By means of the theory of mechanical four-pole connection, the displacement amplitude transmissibility from the thin plate to precision equipment is derived. For the purpose of performing optimal design of the composite system, a novel multi-objective idea is presented. Multi-objective particle swarm optimization (MOPSO) algorithm is adopted as an optimization technique, which can achieve a global optimal solution (gbest), and selecting the desired solution from an equivalent Pareto set can be avoided. Maximum and variance of the four transmitted peak displacements are considered as the fitness functions simultaneously; the purpose is aimed at reducing the amplitude of the multi-peak isolation system, meanwhile pursuing a uniform vibration as far as possible. The optimization is mainly organized as a combination of parameter configuration and placement design, and the traversal search of discrete plate is performed in each iteration for the purpose of achieving the global optimum.

An important transmissibility based on the mechanical four-pole connection is derived, and a composite vibration isolation system is proposed, and a novel optimization problem is also defined here. This study reports a novel optimization strategy combined with artificial intelligence for parameters and placement design of precision equipment, which can promote the traditional view of two-stage vibration isolation.

Two-stage vibration isolation systems are widely applied to the vibration attenuation of precision equipment, but in these traditional designs, vibration participation of foundation is often ignored. In this paper, participation of foundation of equipment is considered, and a coherent new strategy for equipment isolation and foundation vibration is presented. This study shows a new vision of interdisciplinary including civil engineering, mechanical dynamics and computational science.