The utilization of hybrid approaches to classify venture capital decisions and predicting the ruling factors that influencing their granting fund to Egyptian start-up and SMEs

This study aims to improve the classification accuracy of venture capital granting decisions to startups and SMEs in Egypt, predict the ruling factors that affect granting venture capital financing to these companies, and present a decision-making model that can be used as critical standards for venture capital granting decisions to startups and SMEs. To achieve these objectives, we conducted a questionnaire survey that targeted 28 Egyptian venture capital firms' investment committees, which have 224 members, including startup and SMEs support institution personnel and startup and SMEs consultants. Hybrid approaches, such as discriminant analysis (DA) integrated with both factor analysis (FA) and medoid partitioning (MP)were used to examine various factors (12 items) considered in venture capital grant decisions based on previous significant studies. The results have shown that the legal environment, the economic environment, business model, sector, location, firm size, net revenue, VC fund stage, and finally working experiences, respectively, all played a role in influencing venture capital grant decisions. According to the comparison results, the DA-MP model has the highest classification accuracy of 97.2%. The fitted model also accurately predicts group membership (granting or not granting financing) in new venture capital granting decisions to startups and SMEs in Egypt.