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Science & Technology Management and Innovation Management

Multiple Pathways to Enhancing Green Innovation Resilience in  Manufacturing Enterprises: A PLS—ANN—fsQCA Hybrid  Analysis 

Guo Min1 , Zhai He2 , Wang Jingbei3 , Liu Hui4 

(1.School of Economics and Management, Xi'an University of Posts and Telecommunications, Xi'an 710000, China; 2.School of Management, Northwestern Polytechnical University, Xi'an 710129, China; 3.School of Man⁃ agement, Shandong University, Jinan 250100, China; 4.Business School, Central South University, Changsha  410083, China)

Abstract: 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 TechnologyOrganization-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 dualregulation 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. 

Key words: green innovation resilience; TOE framework; configuration analysis; PLS-SEM; ANN; manufacturing enterprises; affordance of digital technology; digital transformation