The interactive effects of knowledge elements and collaboration networks on exploratory innovation performance

Evidence from the Chinese artificial intelligence industry

Bibliographic Data

ID17832258
AuthorsLiping Zhang (0000-0003-1903-6612, Huaqiao University), Jinyi Chen (Huaqiao University), Hanhui Qiu (Huaqiao University), Hailin Li (0000-0001-6924-9689, Huaqiao University), Y J Wu (0000-0001-5479-2873, National Taiwan Normal University, corresponding author)
Year2026
Volume13
Issue1
Publication date2026-02-09
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueHumanities and Social Sciences Communications (JOURNAL)
Journal identifiersISSN: 2662-9992 • E-ISSN: 2662-9992
PublisherPalgrave Macmillan (PUBLISHER • GB)
DOI10.1057/s41599-026-06637-x
OpenAlexW7128443272
LanguageEN
References cited81

Knowledge elements and collaboration networks are essential internal and external factors that affect firms’ exploratory innovation. This study discusses the combined effect of internal knowledge element characteristics and external collaboration network characteristics on firms’ exploratory innovation performance. From the perspective of interaction between knowledge elements and collaboration networks, this study based on authorized patents in the Chinese artificial intelligence industry, firms are divided into different types using a hierarchical clustering algorithm. The classification and regression tree (CART) algorithm is then used to deeply explore how the internal knowledge element and external collaboration network characteristics of different types of firms influence exploratory innovation performance. The study reveals the following findings: (1) Based on knowledge element and collaboration network characteristics, firms can be classified into three types: Collaboration-oriented, Knowledge-oriented, and Balanced, the pathways to enhance exploratory innovation performance differ across these types of firms. (2) The characteristics of internal knowledge elements mainly determine firms’ exploratory innovation performance, with the decision rules exhibiting a support degree of 63.46% and an average confidence degree of 81.9%. For other firms, however, external collaboration network characteristics can mitigate the negative impact of unreasonable combinations of knowledge elements. (3) Knowledge element and collaboration network characteristics have complex nonlinear effects on most firms’ exploratory innovation performance, with the decision rules achieving an average confidence degree of 73.28%. Previous research has focused only on the influence of knowledge elements or collaboration networks on knowledge creation while ignoring the combined effects of internal and external factors. In this study, firms’ knowledge element and collaboration network characteristics, both internally and externally, are comprehensively considered, thereby revealing differences between them across various types of firms, providing new ideas and perspectives for future research

Cluster analysis · Exploratory analysis · Exploratory research · Knowledge sharing · Innovation and Knowledge Management · Intellectual Property and Patents · Open Source Software Innovations