Finding the unicorn: Predicting early stage startup success through a hybrid intelligence method

TL;DR

This paper introduces a hybrid intelligence approach combining human intuition and machine analysis to improve early-stage startup success prediction under extreme uncertainty.

Contribution

It develops a novel hybrid method that leverages both human soft information interpretation and machine data processing for startup success prediction.

Findings

Hybrid method outperforms individual human or machine predictions.

Demonstrates effectiveness in extreme uncertainty scenarios.

Provides a practical framework for combining human and machine insights.

Abstract

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.