Strategic Management Theories in Startups Assessment: A Multicriteria Fuzzy Decision-making Approach

Abstract

Evaluating start-up companies is a crucial management process for technology business incubators and represents a typical multi-criteria decision-making (MCDM) problem. Venture capitalists consider MCDM frameworks before investing in new ventures, as the criteria's priority order aids in making effective and efficient decisions. This paper employs fuzzy logic combined with the Analytic Hierarchy Process (FAHP) technique to assess the relative importance of strategic variables in startup evaluation. The findings indicate that venture capitalists prioritize internal resources as the most critical factor, followed by industry resources and network-based assets. Additionally, an entrepreneur's track record significantly influences startup assessment outcomes. The proposed tools demonstrate that investors can diversify risks by allocating resources across multiple projects, guided by the global priority vector derived from the analysis. This approach provides valuable insights for decision-making processes. Furthermore, the methodology surpasses traditional strategic input assessments and offers a foundation for integrating other statistical techniques such as Analytic Network Process (ANP), machine learning, and deep learning. These methods can enhance evaluation accuracy and validate results in future research. Overall, the study underscores the importance of strategic resource evaluation and highlights the potential for advanced analytical techniques to improve investment decision processes in startup assessments.