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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/25/11/1560 |
| _version_ | 1797459436095143936 |
|---|---|
| author | Liping Zhang Hanhui Qiu Jinyi Chen Wenhao Zhou Hailin Li |
| author_facet | Liping Zhang Hanhui Qiu Jinyi Chen Wenhao Zhou Hailin Li |
| author_sort | Liping Zhang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T16:51:19Z |
| format | Article |
| id | doaj.art-e77f7d1882ca483a91dbdf554e9cd283 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T16:51:19Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e77f7d1882ca483a91dbdf554e9cd2832023-11-24T14:41:07ZengMDPI AGEntropy1099-43002023-11-012511156010.3390/e25111560How Do Heterogeneous Networks Affect a Firm’s Innovation Performance? A Research Analysis Based on Clustering and ClassificationLiping Zhang0Hanhui Qiu1Jinyi Chen2Wenhao Zhou3Hailin Li4College of Business Administration, Huaqiao University, Quanzhou 362021, ChinaCollege of Business Administration, Huaqiao University, Quanzhou 362021, ChinaCollege of Business Administration, Huaqiao University, Quanzhou 362021, ChinaCollege of Business Administration, Huaqiao University, Quanzhou 362021, ChinaCollege of Business Administration, Huaqiao University, Quanzhou 362021, ChinaBased 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.https://www.mdpi.com/1099-4300/25/11/1560innovation performanceIUR collaboration networkIF collaboration networkdecision rulesmachine learning algorithmsentropy weight |
| spellingShingle | Liping Zhang Hanhui Qiu Jinyi Chen Wenhao Zhou Hailin Li How Do Heterogeneous Networks Affect a Firm’s Innovation Performance? A Research Analysis Based on Clustering and Classification Entropy innovation performance IUR collaboration network IF collaboration network decision rules machine learning algorithms entropy weight |
| 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 | innovation performance IUR collaboration network IF collaboration network decision rules machine learning algorithms entropy weight |
| url | https://www.mdpi.com/1099-4300/25/11/1560 |
| work_keys_str_mv | AT lipingzhang howdoheterogeneousnetworksaffectafirmsinnovationperformancearesearchanalysisbasedonclusteringandclassification AT hanhuiqiu howdoheterogeneousnetworksaffectafirmsinnovationperformancearesearchanalysisbasedonclusteringandclassification AT jinyichen howdoheterogeneousnetworksaffectafirmsinnovationperformancearesearchanalysisbasedonclusteringandclassification AT wenhaozhou howdoheterogeneousnetworksaffectafirmsinnovationperformancearesearchanalysisbasedonclusteringandclassification AT hailinli howdoheterogeneousnetworksaffectafirmsinnovationperformancearesearchanalysisbasedonclusteringandclassification |