制造企业绿色创新韧性提升的多元驱动路径研究:基于PLS—ANN—fsQCA的混合方法分析OA

Against the dual backdrop of China's"dual-carbon"goals and accelerating digi-tal transformation,green innovation resilience has emerged as a critical organizational capability enabling manufacturing enterprises to navigate environmental uncertainties and policy volatility.While existing research has predominantly examined green innovation through linear analytical lenses,this study breaks new ground by systematically investigating the configurational mecha-nisms that underpin green innovation resilience development.Leveraging the Technology-Organization-Environment(TOE)framework as our theoretical foundation,we analyze survey data from 319 Chinese manufacturing enterprises using an innovative multi-method approach that integrates partial least squares structural equation modeling(PLS-SEM),artificial neural networks(ANN),and fuzzy-set qualitative comparative analysis(fsQCA).This methodological triangulation allows us to comprehensively examine how digital technology accumulation and variation affordances,top management's environmental cognition,big data analytics capabilities,and both command-and-control and market-based environmental regulations interactively con-tribute to green innovation resilience.Our findings yield three key insights:①While all six ante-cedent conditions demonstrate statistically significant positive effects on green innovation resil-ience,their relative importance and operational mechanisms show substantial variation.②ANN analysis reveals significant nonlinearity in green innovation resilience,particularly highlighting the context-dependent roles of market-based environmental regulations and big data analytics capabilities,whose importance rankings fluctuate across different organizational and environ-mental conditions.③Through fsQCA,we identify four distinct yet equally effective configura-tions that lead to high green innovation resilience:the technology-cognition synergistic pathway,the cognition-data-regulation tripartite driven model,the digital accumulation with dual-regulation compensation approach,and the cognition-centered regulation-driven pattern.These findings robustly confirm the presence of both conjunctural causation and causal asymmetry in green innovation resilience.This study makes significant theoretical contributions by advancing a configurational perspective on green innovation resilience that complements and extends exist-ing linear paradigms.Practically,our findings provide manufacturing enterprises with actionable pathways to strategically align their resource endowments and build resilient green innovation ca-pabilities amidst complex,uncertain environments,while offering policymakers nuanced insights for designing more effective environmental governance frameworks.