Value Creation of Intellectual Capital in Intelligent Manufacturing Enterprises From the Perspective of System Configuration: A Dynamic Qualitative Comparative Analysis

In the era of digital intelligence, intelligent manufacturing enterprises expect to leverage the power of knowledge to reduce costs and enhance efficiency. Therefore, exploring the logic of intellectual capital value creation within intelligent manufacturing systems is of great significance. Drawing on complex systems theory and employing dynamic qualitative comparative analysis, this study analyses the panel data of listed intelligent manufacturing enterprises from 2015 to 2023 to examine the multi‐factor synergistic effects of technological, organizational and environmental factors in driving high‐level intellectual capital value creation from temporal and cross‐case perspectives. The study finds that (1) individual elements within the intelligent manufacturing system do not constitute necessary conditions for high‐level intellectual capital value creation. (2) There are four configurations to drive high‐level intellectual capital value creation in intelligent manufacturing enterprises, namely, production configuration assistance, technical management drive, strategic change support and resource–ecology synergy. (3) In the time dimension, the consistency of the four configurations collectively declined during 2019–2020 and significantly increased in 2021. (4) In the case dimension, the technology management‐driven configuration exhibits stronger explanatory power for state‐owned enterprises. The strategy change–supported configuration exhibits stronger explanatory power for intelligent manufacturing enterprises located in intellectual property demonstration cities. (5) The configurations driving high‐level intellectual capital value creation demonstrate asymmetry in configuration patterns with those for non‐high‐level intellectual capital value creation. The research content helps deepen the understanding of intellectual capital value creation, and the conclusions provide empirical references for the high‐quality development of intelligent manufacturing enterprises.