Green innovation is a crucial aspect of an enterprise’s core competitiveness and long-term sustainable development, garnering significant attention from both academic scholars and industry practitioners. However, while existing research has primarily focused on green innovation at the organizational level, the mechanisms driving green innovation behaviors at the individual level have not been thoroughly explored in the literature. This study is grounded in the classic Job Demands–Resources (JD-R) theoretical framework and highlights the interplay between job demands (such as environmental ethics and corporate environmental strategies) and job resources (such as green human resource management practices and green transformational leadership). It also integrates individual-level characteristics, specifically green mindfulness and connectedness to nature, to construct a multidimensional interactive model aimed at uncovering the complex mechanisms driving employees’ green innovation. To achieve this, the study employs fuzzy-set qualitative comparative analysis (fsQCA). The findings suggest that no single condition is necessary for employee green innovation. However, connectedness to nature consistently appears across all core configurations, indicating a prominent “enabling” effect. This suggests that employee green innovation is an active and proactive form of environmentally responsible behavior, largely driven by individuals’ emotional affinity with nature. Additionally, connectedness to nature serves as a foundational source of intrinsic motivation for environmental awareness and acts as a catalyst across multiple pathways. Configurational analysis reveals an equifinal pattern, identifying three distinct motivational pathways: (1) Self-motivation Combined with Resource Support; (2) Self-motivation Combined with Job Demands; and (3) Triple Interaction of Demand, Resources, and Individuals. This study possesses both theoretical and practical significance in systematically examining green innovation behaviors at the individual level.
Hua Wu (Thu,) studied this question.