The impact of artificial intelligence on total factor productivity: empirical evidence from China’s manufacturing enterprises

Wang, Keliang; Sun, Ting-Ting; Xu, Ru-Yu

doi:10.1007/s10644-022-09467-4

Cited by 30 publications

(10 citation statements)

References 106 publications

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“…There are several contributions of this article: firstly, it enriches research in the field of intelligent manufacturing. Intelligent manufacturing research has traditionally focused primarily on corporate performance [ 4 , 39 ], while green innovation research has been primarily based on macro data [ 40 ], lacking microscopic mechanisms. Research on green innovation primarily focuses on environmental regulation [ 41 , 42 ], green credit [ 43 ], and digital transformation [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

“…Information technologies such as AI, blockchains, cloud computing, and big data are booming, and smart manufacturing is becoming an important strategic direction. In terms of China, since 2015, the smart manufacturing pilot demonstration projects have been issued to promote manufacturing industry transformation [ 4 ]. Meanwhile, to successfully run those projects, the government has taken a series of security measures such as increasing financial support and improving services.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The impact of smart manufacturing demonstration projects on green innovation of Chinese firms: Based on random forest methods

Yang,

Wang,

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The impact of smart manufacturing demonstration projects on green innovation of Chinese firms: Based on random forest methods

Yang,

Wang,

et al. 2024

Heliyon

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“…The advantage of artificial intelligence is that it can integrate and analyses huge amounts of data, build market prediction models, and give forward-looking advice on product design to better meet consumer demand. When the products produced by manufacturing enterprises can more accurately match the needs of consumers, after a series of production, sales efficiency will be greatly improved, the production plant can obtain timely equipment configuration, greatly reducing the economic losses caused by the mismatch between supply and demand [2].…”

Section: Artificial Intelligence Improves Market Matching Degreementioning

confidence: 99%

“…It is understood that AI, as an important technology leading the fourth technological revolution, has slowly begun to penetrate the manufacturing sector [1]. Capgemini's research report stated that more than 50% of the top manufacturing companies in Europe apply AI, followed by 30% in Japan and 28% in the United States [2]. The full integration of AI with manufacturing has brought about a complete paradigm shift in technology.…”

Section: Introductionmentioning

confidence: 99%

Development trend and Challenge Analysis of Manufacturing Industry Under the Background of Artificial Intelligence

Weng

2024

HBEM

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Artificial Intelligence (AI), as a new technology emerging in recent years, has received great attention in various fields. For the manufacturing industry, the ability to integrate artificial intelligence technology into the production process of manufacturing can improve the productivity of manufacturing enterprises. This paper discusses the development trend of the manufacturing industry in the context of artificial intelligence technology. It analyses several advantages of AI technology applied to the manufacturing industry, including innovation in technology, helping enterprises to achieve the optimization of human resources as well as improving the matching degree of the market. Based on the above advantages, the integration of artificial intelligence in the manufacturing industry can be further developed. At the same time, this paper also analyses the current integration of artificial intelligence technology problems and difficulties and puts forward suggestions. The results of this paper can be proved for the integration of artificial intelligence into the manufacturing industry, confirming that the integration of artificial intelligence plays a great role in the development of the manufacturing industry.

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“…Von Joerg et al (2022) [19] found that small and medium-sized manufacturing enterprises can create new business models by effectively utilizing AI resources. Wang et al (2022) [20] found that artificial intelligence can significantly improve the total factor productivity of manufacturing enterprises. Specifically implemented from three aspects: technological innovation, human capital optimization, and market matching.…”

Section: The Concept Of Urban Advanced Manufacturing Developmentmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM.

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The impact of artificial intelligence on total factor productivity: empirical evidence from China’s manufacturing enterprises

Cited by 30 publications

References 106 publications

The impact of smart manufacturing demonstration projects on green innovation of Chinese firms: Based on random forest methods

The impact of smart manufacturing demonstration projects on green innovation of Chinese firms: Based on random forest methods

Development trend and Challenge Analysis of Manufacturing Industry Under the Background of Artificial Intelligence

The Evaluation Prediction System for Urban Advanced Manufacturing Development

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