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How Do Heterogeneous Networks Affect a Firm’s Innovation Performance? A Research Analysis Based on Clustering and Classification

Based on authorized patents of China’s artificial intelligence industry from 2013 to 2022, this paper constructs an Industry–University–Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quali...

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Detalles Bibliográficos
Autores principales: Zhang, Liping, Qiu, Hanhui, Chen, Jinyi, Zhou, Wenhao, Li, Hailin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670113/
https://www.ncbi.nlm.nih.gov/pubmed/37998252
http://dx.doi.org/10.3390/e25111560
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author Zhang, Liping
Qiu, Hanhui
Chen, Jinyi
Zhou, Wenhao
Li, Hailin
author_facet Zhang, Liping
Qiu, Hanhui
Chen, Jinyi
Zhou, Wenhao
Li, Hailin
author_sort Zhang, Liping
collection PubMed
description Based on authorized patents of China’s artificial intelligence industry from 2013 to 2022, this paper constructs an Industry–University–Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm’s innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers.
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spelling pubmed-106701132023-11-19 How Do Heterogeneous Networks Affect a Firm’s Innovation Performance? A Research Analysis Based on Clustering and Classification Zhang, Liping Qiu, Hanhui Chen, Jinyi Zhou, Wenhao Li, Hailin Entropy (Basel) Article Based on authorized patents of China’s artificial intelligence industry from 2013 to 2022, this paper constructs an Industry–University–Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm’s innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers. MDPI 2023-11-19 /pmc/articles/PMC10670113/ /pubmed/37998252 http://dx.doi.org/10.3390/e25111560 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Liping
Qiu, Hanhui
Chen, Jinyi
Zhou, Wenhao
Li, Hailin
How Do Heterogeneous Networks Affect a Firm’s Innovation Performance? A Research Analysis Based on Clustering and Classification
title How Do Heterogeneous Networks Affect a Firm’s Innovation Performance? A Research Analysis Based on Clustering and Classification
title_full How Do Heterogeneous Networks Affect a Firm’s Innovation Performance? A Research Analysis Based on Clustering and Classification
title_fullStr How Do Heterogeneous Networks Affect a Firm’s Innovation Performance? A Research Analysis Based on Clustering and Classification
title_full_unstemmed How Do Heterogeneous Networks Affect a Firm’s Innovation Performance? A Research Analysis Based on Clustering and Classification
title_short How Do Heterogeneous Networks Affect a Firm’s Innovation Performance? A Research Analysis Based on Clustering and Classification
title_sort how do heterogeneous networks affect a firm’s innovation performance? a research analysis based on clustering and classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670113/
https://www.ncbi.nlm.nih.gov/pubmed/37998252
http://dx.doi.org/10.3390/e25111560
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