Abstract
Purpose. This study explores the dynamic evolution of entrepreneurship in environmental enterprises across their life cycles, reveals the interaction mechanism among institutional environment, enterprise life cycle and entrepreneurship, and addresses gaps in contextual analysis and nonlinear evolution explanations in existing research.
Theoretical Framework. Integrating entrepreneurship theory, enterprise life cycle theory and institutional theory, this study constructs a three-dimensional framework of “institutional pressure–enterprise life cycle–entrepreneurship”, focusing on the phasic transformation of innovation, risk-taking and proactivity, as well as their organizational embedded paths.
Methodology. An exploratory single-case study of Company A is conducted with multi-source data: in-depth interviews with five managers, internal and public document analysis, participant observation and supplementary questionnaires for triangulation. Data are coded and qualitatively analyzed via Nvivo 11.0, with a third party verifying reliability to ensure objectivity.
Findings. First, Environmental enterprises show nonlinear “policy shock–stage leap” life cycles, deviating from linear assumptions, with project-based business models aggravating stage mismatches. Second, Entrepreneurship evolves dynamically: proactivity in the startup stage, risk-taking in growth, and innovation in maturity, shifting from individual-led to organization-embedded via decentralization, institutionalized leadership and systematic learning. Third, Institutional pressures and internal factors form a two-way interaction; entrepreneurship can reshape the industry through institutional entrepreneurship.
Conclusions and Recommendations. Entrepreneurship evolution is the result of the interaction between internal and external factors. It is recommended that enterprises should cultivate entrepreneurship according to the characteristics of different development stages, while policymakers should design phased support policies. Future research may conduct multiple case studies to enhance the generalizability of the findings.