Research on Incubator Screening (Incubation Assessment) Mechanisms: An Optimization Model to Improve the Probability of Successful Entrepreneurship

As China's innovation and entrepreneurship policy system continues to improve, technology business
incubators have been entrusted with the crucial functions of promoting regional economic development and enhancing startup success rates. However, against the backdrop of intensifying competition for incubation resources and rapidly evolving entrepreneurial  environments, how to conduct "incubatee selection" scientifically and accurately has become a key determinant of incubator operational efficiency and enterprise success rates. Currently, most incubators still adopt traditional expert review models, which suffer from strong subjectivity, inconsistent evaluation dimensions, insufficient data support, and difficulty in predicting success probability, leading to widespread resource misallocation. To address these issues, this paper constructs a "four-dimensional twelve-item" entry assessment model based on human capital theory, resource-based view, and signaling theory, establishes an indicator weighting system using the Analytic Hierarchy Process (AHP), and develops a Logit success probability prediction model, thereby forming an integrated "evaluation-prediction-decision" incubation selection optimization framework. The model is validated through a case study of X Technology Business Incubator, with results demonstrating significant improvements in one-year survival rates, financing success rates, and incubation resource utilization efficiency of admitted enterprises. This research provides valuable insights for the construction of scientific selection mechanisms in incubators, the implementation of innovation and entrepreneurship policies, and the optimization of incubation ecosystems.