Exploratory and Exploitative Innovation Performance in the Artificial Intelligence Industry in China from the Perspective of a Collaboration Network: A Data-Driven Analysis.

Liping Zhang, Hailin Li, Wenhao Zhou, Hanhui Qiu, Yenchun Jim Wu

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Liping ZhangCollege of Business Administration, Huaqiao University, Quanzhou 362021, Fujian, China.

Hailin LiCollege of Business Administration, Huaqiao University, Quanzhou 362021, Fujian, China.ORCID 0000-0001-6924-9689

Wenhao ZhouCollege of Business Administration, Huaqiao University, Quanzhou 362021, Fujian, China.

Hanhui QiuCollege of Business Administration, Huaqiao University, Quanzhou 362021, Fujian, China.

Yenchun Jim WuGraduate Institute of Global Business and Strategy, National Taiwan Normal University, Taipei 106, Taiwan.ORCID 0000-0001-5479-2873

Funding

Huaqiao University's Academic Project Supported by the Fundamental Research Funds for the Central Universities 2024HQYJ15Social Science Foundation Project of Fujian Province of China FJ2024BF039

6 · The paper itself

Abstract

Identifying the structural characteristics of a collaboration network that influence firms' exploratory and exploitative innovation performance can help these firms enhance the output of innovation achievements and their core competitiveness. Based on 14,790 issued patents of 281 firms in the artificial intelligence industry in China, this study explores the complex nonlinear relationship between the structural characteristics of inter-organizational collaboration networks and firms' exploratory and exploitative innovation performance by using clustering algorithms and classifications based on information entropy or the Gini index. The results indicate the following: (1) The four characteristics of degree centrality, closeness centrality, the local clustering coefficient, and structural holes affect the exploratory and exploitative innovation performance of firms. (2) In different firm clusters, there are different characteristic combinations that provide firms with various development strategies to improve this performance. (3) There are different paths that firms can take to improve this performance, which should be comprehensively considered along with the development goals of firms.

Indexed as

collaboration networkexploitative innovation performanceexploratory innovation performanceinformation entropymachine learning algorithms

Identifiers

PMID40566164

DOI10.3390/e27060577

PMCPMC12191727

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.