Finding the unicorn: Predicting early stage startup success through a hybrid intelligence method
This addresses the challenge of making accurate predictions in high-risk, uncertain environments like early-stage startups, offering a novel approach that could benefit investors and entrepreneurs.
The paper tackles the problem of predicting startup success under extreme uncertainty by combining human intuition with machine intelligence, resulting in a hybrid method that demonstrates utility for such predictions.
Artificial intelligence is an emerging topic and will soon be able to perform decisions better than humans. In more complex and creative contexts such as innovation, however, the question remains whether machines are superior to humans. Machines fail in two kinds of situations: processing and interpreting soft information (information that cannot be quantified) and making predictions in unknowable risk situations of extreme uncertainty. In such situations, the machine does not have representative information for a certain outcome. Thereby, humans are still the gold standard for assessing soft signals and make use of intuition. To predict the success of startups, we, thus, combine the complementary capabilities of humans and machines in a Hybrid Intelligence method. To reach our aim, we follow a design science research approach to develop a Hybrid Intelligence method that combines the strength of both machine and collective intelligence to demonstrate its utility for predictions under extreme uncertainty.