Research on the Influence Mechanism of the Across-Industrial-Chain Investment Speed on Innovation Performance of AI Enterprises: Improvement Path of Artificial Intelligence Technology Application
Author(s) -
Yan Chen,
Fan Si,
Xiying Lu,
Xin Li
Publication year - 2021
Publication title -
mobile information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.346
H-Index - 34
eISSN - 1875-905X
pISSN - 1574-017X
DOI - 10.1155/2021/6149746
Subject(s) - investment (military) , computer science , path (computing) , chain (unit) , industrial organization , panel data , empirical research , industrial engineering , business , operations research , econometrics , economics , philosophy , physics , epistemology , astronomy , politics , political science , law , programming language , engineering
This paper presents a regression analysis by using the system generalized method of moments (SYS-GMM) model as the main regression model and combining it with the fixed effect of panel data and acquires the basic empirical research data from Wind database. The research shows that the speed of cross-industrial-chain investment can improve the innovation ability of AI enterprises, and AI enterprises with deep technology accumulation can improve their innovation performance in the rapid across-industrial-chain investment. In this paper, an across-industrial-chain investment decision path model for AI enterprises is proposed for the first time, suggesting that AI enterprises should pay attention to the related factors of industry and AI enterprises when making across-industrial-chain investment decisions. This helps to express the determination of investment, integration, and reconstruction to the target AI enterprises, and it can also facilitate fast across-industrial-chain investment and improve the innovation performance of AI enterprises.
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