Manufacturing Industry In China Research Articles

Financial Sector Liberalisation and the Climbing of Manufacturing GVCs

The National 14th Five-Year Plan and 2035 Vision Outline emphasise the need to promote the integration of China's manufacturing industry and service industry, so as to achieve the upgrading of China's manufacturing industry to the high end of the global value chain. Based on the OCED input-output data and the World Bank STRD data, the study empirically explores the impact of financial sector openness on the upgrading of the manufacturing industry's global value chain by using the global multiregional input-output model and the panel model. The study finds that both the quantity and quality of financial sector openness have a significant effect on the upgrading of manufacturing GVCs, with the quality of financial sector openness having a more pronounced effect, and this conclusion still holds after considering robustness; accordingly, it puts forward policy recommendations for financial sector openness to promote the upgrading of manufacturing GVCs.

High-performance work systems and employee emotional well-being in the manufacturing industry in China

High-performance work systems and employee emotional well-being in the manufacturing industry in China

Impact of digital technology innovation on carbon intensity: evidence from China's manufacturing A-share listed enterprises.

Current studies do not provide a consensus on whether digital technology innovation can reduce enterprise carbon intensity despite the rise of the digital economy. This paper examines the role and influence pathway of digital technology innovation on enterprise carbon intensity using data from A-share listed enterprises in China's manufacturing industry from 2012 to 2021. The findings indicate that (1) digital technology innovation has been found to significantly reduce enterprise carbon intensity, as confirmed by numerous robustness and endogeneity tests. However, its inhibitory effect on carbon intensity shows a marginal decreasing trend. (2) In the heterogeneity analysis, it was found that digital technology innovation significantly reduces the carbon intensity of consuming coal, coke, kerosene, and diesel. From various perspectives, including enterprise, industry, and external environment, there are significant differences in the carbon reduction effects of digital technology innovation. (3) The analysis of impact paths reveals that digital technology innovation can affect enterprise carbon intensity through three paths: improving productivity, enhancing green innovation efficiency, and adjusting energy consumption. (4) Upon further analysis, it was discovered that the spillover effect of digital technology innovation is more pronounced in the industry cohort of enterprises. Additionally, digital technology innovation plays a positive role in enhancing enterprise ESG performance. The paper's findings offer empirical evidence and decision-making references for the government to develop reasonable policies for reducing carbon emissions, promoting green and low-carbon enterprise transformation, and actively and steadily achieving the goal of carbon peaking and carbon neutrality.

Research on the Transformation and Upgrading of China's Manufacturing Export from the Perspective of Global Value Chain

With the in-depth development of the global economy, China's manufacturing export is facing an urgent need for transformation and upgrading. From the perspective of global value chain, this paper analyzes the position and challenges of China's manufacturing industry in the global value chain, discusses the motivation and path of export transformation and upgrading, and puts forward corresponding strategic suggestions. The study shows that China's manufacturing industry is gradually climbing from low end to high end in the global value chain, but it still faces problems such as technical bottleneck and brand lack. Therefore, it is necessary to increase investment in technological innovation and brand building, enhance export competitiveness, and realize the sustainable development of manufacturing industry.

Balancing clean and quality production goals: A multi-analytical approach to scrutinizing the influencing factors in China's manufacturing industry

Under the strategies of "high-quality development" and "dual carbon goals", the manufacturing industry in China is confronted with the simultaneous challenges of ensuring the production of high-quality goods and adopting environmentally friendly production practices. The integration of cleaner production and quality management has received widespread attention; however, existing research cannot comprehensively identify the interactive factors between quality management and cleaner production and analyze their mechanism of action. This research employed a multi-faceted approach involving meetings, interviews, and online data to access primary materials, and complemented by grounded theory and quality tool (5M1E), a total of 18 quality factors that exert a substantial impact on the attainment of clean production objectives were ultimately identified. To meticulously assess the interrelationships among factors, this study employed the interval value hesitant preference relationship DEMATEL-AISM technique. The findings of the study emphasize the significance of Employee Quality Skill Level (F1) and Production Environment Quality (F15) as critical quality factors that should be given priority and careful consideration by managers aiming for a cleaner production process. The results of this study have the potential to enhance the manufacturing industry's ability to predict and prevent quality and environmental issues, thereby ensuring the long-term sustainability of clean production and effective quality management.

Reflections on the Construction of Strategic Performance Management System in Chinese Manufacturing Enterprises

In the surge of global economic integration, China's manufacturing industry is poised at the forefront of a transformational upgrade. Beyond the hum of production lines, the vital pulse lies within the robust rhythm of Strategic Performance Management Systems (SPMS). Yet, studies indicate that despite China's manufacturing sector advancing towards "intelligent manufacturing" and innovation, there are numerous areas within strategic performance management systems that urgently demand enhancement. In the coming years, for China's manufacturing industry to secure higher value positioning within the global supply chain and assert dominance in niche markets, it must seize the nerve center of strategic performance management. Confronted with a series of challenges including slowing growth, rising costs, and tightening environmental regulations, manufacturing enterprises urgently need to construct an efficient and precise performance management system. However, in reality, many enterprises still exhibit a weak strategic execution, lacking a systematic performance orientation and employee incentive mechanisms, often resulting in a disconnect between strategic objectives and performance management. The factors affecting the effective implementation of strategic performance management within enterprises are intricate. Internal factors primarily encompass the rigidity of corporate culture, bottlenecks in leadership style and employee engagement, as well as hindrances in information sharing and communication mechanisms. External factors involve the volatility of market environments, unclear policy directions, and intensifying international competition. The formulation of a comprehensive performance management system undoubtedly holds profound significance for optimizing resource allocation, reinforcing core competitiveness, and enhancing strategic responsiveness.

Will advancement in technologies bring fear and damage human employment? Evidence from China's manufacturing industry.

Advancement in technologies such as robotic industries and artificial intelligence bring fear among human being that jobs will be substituted by robots. Base on the panel data of 28 China's manufacturing industries, this research analyzed the impact of technical progress bias on employment. First, we calculate the technical progress bias index of 28 industries base on the stochastic frontier model with transcendental logarithm function found 16 industries were toward the skilled labor while the remaining 12 industries were toward the unskilled labor. Second, the empirical results show that technical progress bias has a positive impact on the total manufacturing employment and significant positive effect on the unskilled labor, while no significant impact on skilled labor employment. Third, the threshold effect test proves that if taking industry value-added per capita or R&D capital stock as threshold variable, the threshold about the impact exist, making the impact on skilled labor was insignificant.

Manufacturing intelligentization and technological innovation: Perspectives on intra-industry impacts and inter-industry technology spillovers

This study empirically analyzes the impacts of manufacturing intelligentization on technological innovation using panel data from 29 manufacturing industries in China for the period 2012–2019. The research findings indicate that manufacturing intelligentization has significantly positive impacts on intra-industry technological innovation. Moreover, technological innovation exhibits path dependence, in which prior innovation accumulation is advantageous for current technological innovation. Furthermore, the spatial Durbin model indicates that intelligentization has both horizontal and vertical technology spillover effects on technological innovation. Specifically, the intelligentization of horizontal- and forward-linked industries has positive impacts on technological innovation in the focal industry. Additionally, technological innovation exhibits significant spatiotemporal dependence, as manifested by the negative spillover effects of prior technological achievements in other industries on current technological innovation in the focal industry.

Analysis of the spatiotemporal evolution and influencing factors of green development level in the manufacturing industry

The manufacturing sector is the main battlefield of energy saving and carbon reduction in China, and vigorously promoting energy saving and carbon reduction in manufacturing and enhancing the green development level are the key links to support China's realization of the dual-carbon goal. The article adopts the SBM-GML model to measure the level of green development of the manufacturing industry in China. Based on this, it analyzes the spatio-temporal characteristics and the evolution law of the level of green development of the manufacturing industry by using the Dagum Gini Coefficient and Kernel Density Estimation. Using a spatial econometric model to explore the influencing factors of the level of green development of the manufacturing industry. The study finds that the green development level of the manufacturing industry has achieved remarkable results in recent years, but there are differences in the development level of each region. The regional differences in the level of green development of the manufacturing industry are significant. The optimization of manufacturing structure is a key factor influencing the level of green development of the manufacturing industry, and there is a positive spatial spillover effect of manufacturing structure optimization. However, The green development of the manufacturing industry shows a negative spatial spillover effect. The article proposes optimization paths based on the requirements of dual-carbon targets and regional characteristics, which is an important inspiration and reference for the green development level of the manufacturing industry in the world.

Intelligence level evaluation and influencing factors analysis of equipment manufacturing industry in the Yangtze River Delta.

The Yangtze River Delta (YRD) bears the vital task of driving the growth of China's equipment manufacturing industry (EMI) intelligence as an advanced region. Fostering the transformation and upgrading of the EMI in the YRD and constructing a modern production mode is vital to developing and reforming China's manufacturing industry. This paper uses industrial robot data to assess the level of intelligence (LoI) in the EMI from 2016 to 2019. The OLS (ordinary least squares) model is used for the measurements, and the MQ (the modified contribution index) is used to estimate the degree of contribution from a host of variables. It is identified that the LoI is on the rise. However, excluding railways, aerospace, shipbuilding, and other transportation equipment manufacturing, the LoI is significantly higher than in other subsectors. It is also identified that technological innovation ability, human capital density, and enterprise cost pressure govern the industry's LoI. Moreover, while there is a difference in the main influencing factors in LoI within different industries, R&D investment, technological innovation ability, and enterprise cost pressure have the most significant impact across most equipment manufacturing sub-industries.

Research on the mechanism and policies of polycentric agglomeration for promoting the upgrading of China's position in the global production value chain

Recently, with the increasing trend of anti-globalization, China's manufacturing industry has encountered instability in the supply of upstream products in international trade, a challenge that has given rise to an urgent demand for ways to improve China's position in the global production chain. In response to this challenge, the 14th Five-Year Plan puts forward the strategic goal of building multi-level, multi-node, networked city clusters and trade powerhouses, which provides new development opportunities for China to strengthen its position in the global production chain through the model of polycentric agglomeration. In order to gain a deeper understanding of the impact of polycentric agglomeration on the position of enterprises in the global production chain, this project will use the instrumental variable estimation method to conduct an empirical study. This method can help us accurately identify the causal relationship between polycentric agglomeration and global production chain position and assess the differential impact of different agglomeration patterns on firms' production chain position. Through this study, we expect to reveal how polycentric agglomeration can enhance firms' global production chain positions through mechanisms such as optimizing resource allocation, promoting technological innovation, and strengthening industrial chain synergies. In addition, we plan to explore the impact of polycentric agglomeration on the evolution of firms' roles in global value chains and on international trade competitiveness. The objective of this study is to provide targeted recommendations for policymakers and enterprises to help them achieve high-quality development of China's manufacturing industry by optimizing the structure of polycentric agglomeration in the context of global production chain restructuring. Through this comprehensive study, we expect to provide scientific decision support and action guidelines for China's sustainable upgrading and competitiveness enhancement in global production chains.

The Influence of Digital Transformation on the Reconfigurability and Performance of Supply Chains: A Study of the Electronic, Machinery, and Home Appliance Manufacturing Industries in China

In this era of intense global competition, supply chains are facing challenges in coping with emerging market issues. Within diverse industries worldwide, supply chains are experiencing accelerated reconfiguration, with one of the most notable transformations being the digitalization of supply chain operations. But the literature lacks empirical evidence about how digital transformation effectively contributes to it. Thus, this paper delves into the implications of the supply chain digital transformation (SCDT) and supply chain reconfigurability (SCR) on its overall performance. Cross-sectional data from 379 respondents in the machinery, electronics, and home appliance manufacturing industries were collected through a closed questionnaire. Utilizing a hybrid approach involving the Partial Least Squares Structural Equation Model (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA), this study employs a cross-validation of the conceptual model. Initially, the PLS-SEM is employed to unveil the direct impact of SCDT on supply chain performance (SCP), as well as the intermediary effect of SCR. Building on this foundation, the fsQCA method is further utilized to investigate the configuration paths that enable enterprises to achieve high SCP under the combined influence of SCR and SCDT. The research results affirm the significant influence of SCDT on SCP. Likewise, the findings highlight the crucial intermediary role of SCR between SCDT and SCP. Ultimately, three distinct configurations driving high supply chain performance are identified: technical, management, and flexible configurations, each playing a unique role.

The Impact of Digital Transformation on the High-quality Development of the Manufacturing Industry

The digital transformation of manufacturing industry facilitates enterprises to access and utilize data information to improve the efficiency of resource allocation, expand product and service business models, and accumulate human capital stock to empower the high- quality development of manufacturing industry by creating a large number of databases. This paper adopts panel data of 30 provinces and cities in China from 2011-2020, and uses system GMM to verify the correlation between two variables. It also introduces environmental regulation as a threshold variable to analyze the threshold effect. The results of the study indicated that: firstly, the association between digital transformation of manufacturing industry and high-quality development of manufacturing industry shows a U-shaped relationship, secondly, the digitalization of manufacturing industry exhibits different impact effects on the four dimensions of high-quality development of manufacturing industry, and lastly, only when the environmental regulation is within a certain level, it can have a driving effect on high-quality development of manufacturing industry. Therefore, it is important to grasp the strategic opportunities of digital economy, strengthen the integration of digital economy and traditional industries, accelerate the digital transformation of manufacturing enterprises, and improve the high-quality development of China's manufacturing industry.

Research on Key Technologies of Artificial Intelligence for Intelligent Manufacturing

Intelligent manufacturing, as the main form of future manufacturing, is the highland of a new round of competition in the global manufacturing industry, which will fundamentally change the production and manufacturing methods and technological and economic paradigms formed by human beings since the industrial revolution. At present, the intelligent manufacturing industry is still in the early stage of development, which is characterized by three typical characteristics: continuous growth of market size, strong promotion by national governments and diversification of competition subjects. At present, China's manufacturing industry is facing transformation, and the manufacturing industry is bound to need to transform to intelligent manufacturing. Industrial robots are the main content of current intelligent manufacturing, how to fully apply artificial intelligence technology to the manufacturing of industrial robots, so that its manufacturing efficiency, manufacturing accuracy and other product attributes are improved, to maximize its economic benefits, has a particularly important application value. The application of intelligent manufacturing and robots can not only promote the faster development of manufacturing, but also reconstruct new forms of manufacturing. Based on this, the paper first summarizes the application technology of intelligent manufacturing, and then analyzes the key technology of robot application for reference to relevant workers.

The Mechanism of Digital Economy in Promoting the Transformation and Upgrading of China’s Manufacturing Industry

The era of China's digital economy has arrived, Chinese government is actively exploring how to use the technological changes brought about by the digital economy to promote China's economic development in the next 30 years. As the pillar industry of China's economy, manufacturing has maintained rapid growth in the past 30 years and has made great contributions to China's economic development. However, there are also many challenges at present, such as,China's manufacturing industry is at the low end of the global industrial chain, and the added value is very low; Backward technology and poor competitiveness in the international market. In this context, this paper analyzes the impact of the digital economy on the transformation and upgrading of the manufacturing industry from a qualitative perspective ,attempts to explore the way how China's manufacturing industry can use digital technology to achieve intelligent transformation, value chain upgrading, and industrial structure upgrading, so as to achieve comprehensive transformation and upgrading , in the end, gain long-term development momentum.

"Machine replacement" or "job creation": How does artificial intelligence impact employment patterns in China's manufacturing industry?

Artificial intelligence (AI), as an important engine for promoting high-quality economic development, should not be overlooked in terms of its impact on the employment of the labor force while promoting the digital and intelligent transformation of industries. In the face of the complex international environment and non-systemic shocks, it is of great significance to explore whether it is "machine replacement" or "job creation" in the process of the integration of AI and industry, as well as the impact of technological progress on the employment pattern of the labor force, in order to promote the economic development, respond to and solve the employment problem. It is of great significance to promote economic development and cope with and solve the employment problem. Based on the task model, this paper analyses the mechanism of the impact of AI on the employment pattern of manufacturing industry. Meanwhile, based on the provincial panel data of China's manufacturing industry from 2011 to 2020, it empirically examines the impact of AI on the total employment, employment structure and employment quality of the labor force, and analyses the multiple responses of AI on the employment pattern of the manufacturing industry. The study shows that: Firstly, the level of development of AI and the total amount of employment is a positive U-shaped relationship, the short term is dominated by the substitution effect, and the long term is dominated by the creation effect; Secondly, with regard to the employment structure, low-skilled labor is more likely to be replaced. The financial, accommodation and catering industries are relatively less affected by the spillover effects of the manufacturing industry; Third, with regard to the employment quality, the gap between urban and rural incomes has eased, with per capita net income of rural residents rising to a higher degree than per capita disposable income of urban residents. Thus, in order to further address the impact of AI on the employment patterns of the labor force, the level of AI development should be increased while expanding employment channels, paying attention to labor force skills training, reinforcing the leading role of developed regions, and accelerating regional integration and urban-rural integration, so as to share the dividends of technological progress.

STUDY ON THE SUSTAINABLE DEVELOPMENT OF CHINA'S EXPORT MANUFACTURING ENTERPRISES IN THE YANGTZE RIVER ECONOMIC BELT UNDER THE BACKGROUND OF "DOUBLE CARBON"

As a core part of the national economy, manufacturing is the key to achieving sustainable economic development. However, the development of China's manufacturing industry faces many problems, such as poor industrial structure, lack of key core technologies, and serious overcapacity, which seriously affect the quality of China's manufacturing industry. The Yangtze River Economic Belt not only has great strategic significance for coordinating the development of the east, middle and west China, but also a demonstration belt for the sustainable development of manufacturing industries. Amid the Chinese government's "double carbon" implementation in 2020, how to transform and upgrade these export manufacturing enterprises and find a path to sustainable development is a major challenge they will have to face. Therefore, on the basis of combing domestic and foreign literature, taking "quality-sustainable development-sustainable development of manufacturing industry" as the logic line, and combine the theory of sustainable development, environmental Kuznets curve theory, equatorial principle theory, and corporate governance theory. Second, it measures and evaluates the sustainable development of manufacturing industry in the Yangtze River Economic Belt from 2007 to 2019 through quantitative research. Conclusions: When assessing the status, the quality level of sustainable development of manufacturing industry in the Yangtze River Economic Belt has been steadily improving, more than 80% of the provinces and cities in the Yangtze River Economic Belt have high and medium quality. Finally, targeted countermeasures and proposals are put forward.

A Feasibility Study on the Application of Artificial Intelligence on the Human Resource Practices among Manufacturing Companies in China

This paper presents a feasibility study on the integration of artificial intelligence (AI) into human resource (HR) practices within the manufacturing sector of China. With the rapid advancement of AI technologies, industries worldwide are exploring its potential applications to streamline operations and enhance efficiency. However, the adoption of AI in HR functions, particularly within manufacturing companies in China, remains relatively unexplored. This study aims to assess the feasibility of implementing AI-driven solutions in various HR processes such as recruitment, training, performance evaluation, and employee engagement. The research methodology involves a combination of qualitative and quantitative approaches. Primary data will be collected through surveys, interviews, and focus group discussions with HR professionals, managers, and employees from a diverse range of manufacturing companies across different regions in China. Additionally, secondary data from relevant literature, industry reports, and case studies will be analysed to gain insights into current trends, challenges, and best practices associated with AI adoption in HR. Key factors influencing the feasibility of AI integration will be examined, including technological readiness, organizational culture, regulatory environment, cost-benefit analysis, and potential socio-economic implications. The study will also explore the perceived benefits and concerns regarding the use of AI in HR practices, such as improved recruitment accuracy, enhanced employee productivity, data privacy concerns, and ethical considerations. Furthermore, the research will identify potential barriers and enablers to successful AI implementation and provide recommendations for policymakers, HR practitioners, and organizational leaders to navigate the challenges and leverage the opportunities presented by AI in the manufacturing sector. By shedding light on the feasibility and implications of AI adoption in HR practices, this study seeks to contribute to the ongoing discourse on the future of work and technological innovation in China's manufacturing industry.

The impact of population aging on FDI: A panel data analysis based on 27 segments in China's manufacturing industry.

Foreign Direct Investment (FDI) is an important part of China's new "double-cycle" development pattern. Among the many factors affecting FDI, will the aging population have an impact on manufacturing, the key industry for FDI? This paper examines the direct and indirect effects of an aging population on FDI using panel data from 27 manufacturing sub-industries in China between 2005 and 2020. It is found that (1) the deepening of the population's aging negatively affects FDI inflows and this result continues to hold after a series of robustness tests. (2) Using labor quantity and labor cost as mediating variables, it is found that the population's aging indirectly affects FDI by reducing labor quantity and increasing labor cost. (3) The heterogeneity analysis study finds that the deepening of the population's aging significantly inhibits FDI in labor-intensive and capital-intensive industries among manufacturing sub-industries, and the inhibitory effect on FDI in technology-intensive industries is not significant. This study provides meso-evidence to support the findings of existing studies and provides suggestions and insights for the government to formulate relevant policies to actively cope with aging.

THE MODERATING ROLE OF ORGANIZATIONAL SUPPORT ON THE RELATIONSHIP BETWEEN GREEN SUPPLY CHAIN PRACTICES, GOVERNANCE AND SUSTAINABLE ECONOMIC PERFORMANCE: EVIDENCE FROM CHINA

Recently, sustainable economic performance (SEP) has been the foremost requirement due to uncertainty of the economy and environment. This issue needs best practices and governance and also needs researchers’ focus. Thereby, the paper aims to assess green supply chain (SC) practices, green SC governance and their role in the achievement of SEP in the manufacturing sector of China. The paper also attempts to explore the moderating effect of organizational support in proposed framework. PLS-SEM model was employed to test model’s reliability and validity. The very same method has applied to test the linkage among outlined constructs. Obtained results confirms the moderating impact of organizational support among traditional governance, relational governance and the achievement of SEP in the manufacturing industry in China. This article provides guidelines to the regulators in developing policies related to the achievement of SEP using green SC practices and governance.