The evolution of academic discourse on integrative innovation talent: a configurational analysis using fsQCA

Cultivating integrative innovation talent is a critical imperative for national competitive advantage in an era of complex innovation ecosystems. However, academic literature lacks a systematic, configuration-based understanding of the combinatorial pathways that foster such talent, often focusing on single factors rather than synergistic configurations. This study addresses this gap by uniquely treating academic discourse itself as the unit of analysis to map the evolution of this key knowledge domain. To this end, the study is explicitly positioned as a context-specific mapping of the Chinese academic discourse, aiming to reveal how the scholarly community of a major innovation-driven nation constructs successful narratives of talent development. We employ a novel methodological framework integrating scientometrics, fuzzy-set Qualitative Comparative Analysis (fsQCA), and Latent Dirichlet Allocation (LDA) on a corpus of 152 Chinese academic articles (2003–2023). Our fsQCA identifies multiple, equifinal configurations that are discursively constructed as successful, while our LDA excavates the core thematic substance underpinning this positive discourse. The study’s core finding reveals a significant temporal evolution in these discursive models: earlier literature (2003–2018) emphasizes “Demand for Talent” configurations, whereas recent discourse (2019-2023) converges on the centrality of “Interdisciplinary Training” as a core, almost self-sufficient, “capability-centered” configuration. This research contributes a structured, configurational map of a key knowledge domain’s cognitive evolution, offering an evidence-based framework that serves as a nuanced reference for policymakers and educators to understand discursive shifts within the scholarly community.