Abstract: The orderly operation of the science and technology (S&T) talent policy system constitutes a fundamental guarantee for the construction and governance of high-level talent hubs. However, the intrinsic interaction mechanisms among the constituent elements within this system remain inadequately explored and require further revelation. To address this gap, this study leverages panel data from the Mainland Chinese city cluster within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) spanning the period 2007–2021. Employing an integrated methodological approach that combines dynamic fuzzy-set Qualitative Comparative Analysis (fsQCA) and BERTopic machine learning, the research investigates the synergistic effects of key factors—namely innovation motivation, innovation capability, innovation emotion, and institutional environment—on enhancing the level of orderly operation within the S&T talent system. This investigation is grounded in an "individual-organizational" interaction perspective and systematically explores these dynamics across both temporal and spatial dimensions. The analysis yields several significant findings. Firstly, the orderly operation of the S&T talent policy system manifests through multiple pathways underpinned by complex mechanisms. Five distinct configurations were identified: a "motivation"-dominated synergy-driven type; an "emotion"-dominated regulation-driven type; a "motivation-emotion"-dominated synergy-driven type; a "motivation-capability"-dominated synergy-regulation-driven type; and a multi-factor synergy regulation-driven type. Crucially, within these five pathways, none of the individual or organizational factors alone constitutes a necessary condition for achieving a high degree of system orderliness. Instead, the research reveals the existence of equifinality—equivalent substitutability—between specific condition combinations. Notably, substitutable relationships exist between "motivation-driven" mechanisms and "regulatory intensity," between "emotion-driven" mechanisms and "interdepartmental coordination," and between the combination of "capability cultivation + interdepartmental coordination + regulatory intensity" and "emotion-driven" mechanisms. Among these, motivation-driven mechanisms consistently emerge as a core condition across multiple pathways, playing a pivotal role in achieving high system orderliness.Secondly, weak innovation motivation coupled with low regulatory intensity is identified as a primary constraint negatively impacting the orderly operation of the S&T talent policy system. Thirdly, the development trajectory of this system within the GBA's Mainland city cluster exhibits significant time effects, with 2013 and 2017 serving as critical inflection points. Heterogeneous pathways observed prior to these nodes consistently converged towards states characterized by high consistency and stability thereafter. Fourthly, distinct regions demonstrate differentiated realization pathways for achieving system orderliness, highlighting the significant influence of specific contextual conditions on the effective configuration of factors. From a theoretical perspective, this research offers novel contributions. By adopting the "individual-organizational" interaction lens, it focuses on micro-level elements influencing S&T talents—specifically individual innovation motivation, capability, and emotion—thereby revealing the synergistic mechanisms operating between conditions at the individual and organizational levels. Furthermore, grounded in the theory of policy attention allocation, the study clarifies the equifinal relationships among key elements, systematically delineating both the core conditions and the substitutable pathways essential for realizing the orderly operation of the S&T talent policy system.From a pactical perspective, the findings provide actionable insights for policymakers. Decision-makers should prioritize "motivation-driven" mechanisms while also flexibly utilizing the identified equivalent substitutability relationships to optimize the allocation of policy attention. Crucially, capability cultivation is not a universally applicable key mechanism, its effectiveness is contingent upon regional economic, social, and cultural environmental factors. During policy design and implementation, policymakers must simultaneously address individual talent needs and organizational synergy mechanisms to ensure the policy system operates sustainably and efficiently.Consequently, the conclusions provide robust theoretical support and concrete policy recommendations aimed at enhancing the operational effectiveness and governance of S&T talent policy systems.

摘要： 科技人才政策系统的有序运行是高水平人才高地建设和治理的重要保障，然而科技人才政策系统中各要素的内在互动机制尚有待揭示。本研究采用2007—2021年粤港澳大湾区面板数据，运用动态模糊集定性比较分析和BERTopic机器学习相结合的方法，基于“个体-组织”互动视角，从时间和空间的双重维度探究创新动机、创新能力、创新情感和制度环境等要素，在提升科技人才系统运行有序度水平中的联动效应。研究发现：（1）科技人才政策系统有序运行存在多元路径和复杂机制，包括“动机”主导的协同驱动型、“情感”主导的规制驱动型、“动机-情感”主导的协同驱动型、“动机-能力”主导的协同规制驱动型和多元协同的规制驱动型五类；（2）创新动机弱和规制力度低是影响科技人才政策系统运行有序度的主要制约；（3）粤港澳大湾区科技人才政策系统发展以2013年和2017年为时间节点，存在明显的时间效应；（4）不同区域在各自情境下呈现出系统有序运行的差异化实现路径。研究结论为提升科技人才政策系统运行效能提供了理论支持和政策建议。