Key points are not available for this paper at this time.
• Integrates TOE framework and resource orchestration theory to explain green innovation. • Identifies multiple equifinal green innovation pathways using fsQCA. • Reveals strong causal asymmetry in green innovation formation mechanisms. • Shows digital–environmental governance synergy yields the highest sustainability performance. • Provides configuration-based insights for green logistics strategy and policy design. Against the backdrop of China’s dual-carbon goals and rapid digital economy development, logistics firms face increasing pressure to pursue green transformation. To identify effective green innovation (GI) pathways under complex technological (T), organizational (O), and environment (E), this study integrates the TOE framework with Resource Orchestration Theory (ROT) and develops a “technology–organization–environment–resource orchestration” analytical framework. Using data from Chinese A-share listed logistics firms, fuzzy-set Qualitative Comparative Analysis (fsQCA) uncovers multiple configuration paths—such as digitally empowered environmental governance and responsibility-driven models—that can lead to high-level GI, indicating evident causal asymmetry and multipath equivalence. Regression analyses further show that the pathway combining digitalization and governance synergy has the strongest effect on sustainable development performance (SDP), with other pathways also exerting positive but heterogeneous impacts. Robustness tests using HC3, White heteroskedasticity, VIF diagnostics, alternative dependent variables, and subgroup analyses consistently support these findings. This study contributes by integrating TOE and ROT to explain GI in digitally intensive service industries and by revealing the heterogeneous pathway–performance mechanisms. Managerially, firms should select suitable pathways based on resource conditions and enhance resource orchestration capabilities, while policymakers should promote digital–governance–GI synergies through differentiated policy support.
Yunlin Chen (Fri,) studied this question.