Prerequisites for the Innovation Performance of Artificial Intelligence Laboratory: A Fuzzy-Set Qualitative Comparative Analysis

Artificial intelligence (AI) is widely adopted as a general-purpose technology, bringing about disruptive innovative changes. R&D laboratories (labs) from universities, enterprises, and public institutions drive AI innovation. However, research on the factors affecting AI innovation in R&D labs is rarely discussed. To address this gap, we constructed an adjusted technology-organization-environment framework to analyze different configurations that influence AI basic research and engineering breakthroughs. This article uses fuzzy set qualitative comparative analysis for analysis aimed at 43 international typical AI labs. The results indicate that technological, organizational, and environmental conditions jointly impact AI labs' innovation. Specifically, AI basic research depends on strong computing resources and a high-quality innovation ecology, and it is moving from academia to industry. AI Engineering breakthroughs rely on public R&D institutions and leading firms, and high-quality data has a significant impact on applications. The findings highlight the equivalent effect of different configurations in AI innovation. In addition, this study provides implications for the government's AI innovation policies and the technological management of AI labs.