Research on capital allocation efficiencies with four-dimensional factor capitals from China’s intelligent manufacturing enterprises

Wang, Qiong; Geng, Changran; Hai-tao, E.; Song, Jiarui

doi:10.1371/journal.pone.0270588

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…Tey are both inputs made by enterprises in R&D activities and represent their ability to achieve breakthrough innovation. Tis is supported by the study of Wang et al [14], which found that both human capital and knowledge capital of the frms contribute to intelligent performance. Terefore, when considering the R&D investment of a company, the two must be included together, so as to objectively refect the actual degree of R&D investment of the company.…”

Section: Infuencing Factors Of Intelligent Manufacturingmentioning

confidence: 79%

The Impact of R&D Investment and Policy Support on Intelligent Manufacturing Level: Based on the fsQCA Method

Qin

Yao

2023

Mathematical Problems in Engineering

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Intelligent manufacturing is the main direction of manufacturing transformation and upgrading in developing countries. However, the issue of how to systematically improve the level of intelligent manufacturing in enterprises has not been effectively solved. The analyses in this study are based on data collected from a sample of 45 manufacturing companies in China from 2016 to 2018, and fuzzy set qualitative comparative analysis (fsQCA) was used to explore the impact of R&D, policy support, and enterprise characteristics on the level of intelligent manufacturing. The results find that three modes and four configurations for enterprises achieve high-level intelligent manufacturing. Specifically, firstly, enterprises with larger scale and better benefits can achieve high-level intelligent manufacturing with less R&D investment. Secondly, large enterprises with a high leverage ratio and high R&D capital investment can sacrifice short-term profitability and complete intelligent transformation through lower human capital investment. With the help of policy support, we maintain a high human capital investment and accelerate the completion of the intelligent upgrade. Thirdly, small companies can use higher R&D human capital investment to achieve the goal of intelligence. These findings provide valuable insights for enterprises to effectively achieve intelligent transformation.

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Section: Infuencing Factors Of Intelligent Manufacturingmentioning

confidence: 79%

The Impact of R&D Investment and Policy Support on Intelligent Manufacturing Level: Based on the fsQCA Method

Qin

Yao

2023

Mathematical Problems in Engineering

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“…By integrating and reconstructing knowledge and technology, technological talents transfer their professional and R&D knowledge to grassroots employees, thus improving overall workforce quality. In addition, their strong desire for knowledge and keen observation of technological talents enables them to quickly absorb advanced knowledge and technology and apply them to production operations [ 59 ], improve the technological innovation ability of enterprises, and then promote high-quality development of the manufacturing industry by enhancing production capacity [ 60 ], improving production processes, promoting product upgrading, and optimizing management modes.…”

Section: Analysis Of the Empirical Resultsmentioning

confidence: 99%

A pathway towards high-quality development of the manufacturing industry: Does scientific and technological talent matter?

LI,

YAO

2024

PLoS ONE

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Against the background of the accelerated evolution of the new round of scientific and technological revolution and industrial change, scientific and technical talents, as essential innovation resources, play an important role in promoting the high-quality development of the manufacturing industry. Based on the panel data of 30 provinces in China from 2012 to 2021, the article constructs a fixed-effects model and systematically researches the impact of scientific and technological talents on the high-quality development of the manufacturing industry. The results show that scientific and technical talents play a significant role in promoting the high-quality development of the manufacturing industry, and the upgrading of the consumption structure and the accumulation of productive service industries play a mediating role. Heterogeneity analysis found that the promotion effect of scientific and technical talents is more favorable in the eastern region, medium-technology level manufacturing, and labor-intensive manufacturing. Among the three sub-dimensions of scientific and technological talents, the scale of scientific and technical talents has the most significant impact on the development of the manufacturing industry. The analysis of the spatial spillover effect finds that scientific and technological talents will have a positive spillover effect on the development of the manufacturing industry in neighboring areas. The study provides a basis for relevant departments to formulate effective strategies and policies.

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“…The quantitative research on regional IM is few, and the index system of IM is lacking. Compared with the analysis of the connotation of IM [10,29,59,60] and the theoretical model of a coupled coordination system between DE and IM constructed in this paper, the development level of IM in each provincial administrative region is comprehensively calculated from four dimensions of intelligent R&D investment, intelligent technology, intelligent products and intelligent application, considering the availability and quality of data. Finally, the index system of the DE and IM coupling systems with 8 factor layers and 24 index layers is selected.…”

Section: Index Compositionmentioning

confidence: 99%

Digital Economy and Intelligent Manufacturing Coupling Coordination: Evidence from China

Zhang,

Meng

2023

Systems

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The digital economy uses its own digital information advantages to reduce the intensity of energy consumption brought by economic growth. Intelligent manufacturing achieves cost reduction and efficiency through the integration of manufacturing and intelligence as well as digitalization and information technology. The two have become a new engine for sustainable economic development at present, and they can promote and influence each other. However, there is a lack of research on the relationship between them. In this regard, this study aims to build a coupling coordination model of digital economy and intelligent manufacturing and to make an empirical analysis using the data of Chinese provincial administrative regions in order to provide a theoretical reference for promoting sustainable economic development. The research finds that (1) the digital economy and intelligent manufacturing are mainly cross-coupled from four aspects: infrastructure, technological innovation, product optimization and organizational change. The development level and speed of the former are significantly higher than those of the latter, and the gap does not decrease with time. The two have a strong correlation, but there is no high-quality coupling coordination. (2) The main obstacle factors to the digital economy lie in the imperfect supporting facilities, the short board of technological innovation and the lack of technological application capacity. Intelligent manufacturing lacks intelligent application and technological innovation. (3) Influencing factors such as opening to the outside world, economic development, high-level talent input, industrial structure and innovation emphasis have different effects on their coupling and coordinated development in different regions. (4) The spatial correlation test shows that the coupling coordination degree of each region is spatially positively correlated. This research helps to promote the coupling and coordinated development of the digital economy and intelligent manufacturing.

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Research on capital allocation efficiencies with four-dimensional factor capitals from China’s intelligent manufacturing enterprises

Cited by 4 publications

References 28 publications

The Impact of R&D Investment and Policy Support on Intelligent Manufacturing Level: Based on the fsQCA Method

The Impact of R&D Investment and Policy Support on Intelligent Manufacturing Level: Based on the fsQCA Method

A pathway towards high-quality development of the manufacturing industry: Does scientific and technological talent matter?

Digital Economy and Intelligent Manufacturing Coupling Coordination: Evidence from China

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