Design principles for a hybrid intelligence decision support system for business model validation
This work addresses the challenge of business model validation for entrepreneurs, but it is incremental as it builds on existing decision support and hybrid intelligence concepts.
The paper tackles the problem of validating business models for early-stage startups, which is highly uncertain and complex, by developing design principles for a Hybrid Intelligence decision support system (HI-DSS) that combines human and machine intelligence. It provides prescriptive knowledge and contributes to decision support systems for uncertain contexts.
One of the most critical tasks for startups is to validate their business model. Therefore, entrepreneurs try to collect information such as feedback from other actors to assess the validity of their assumptions and make decisions. However, previous work on decisional guidance for business model validation provides no solution for the highly uncertain and complex context of earlystage startups. The purpose of this paper is, thus, to develop design principles for a Hybrid Intelligence decision support system (HI-DSS) that combines the complementary capabilities of human and machine intelligence. We follow a design science research approach to design a prototype artifact and a set of design principles. Our study provides prescriptive knowledge for HI-DSS and contributes to previous work on decision support for business models, the applications of complementary strengths of humans and machines for making decisions, and support systems for extremely uncertain decision-making problems.