Design principles for a hybrid intelligence decision support system for business model validation

TL;DR

This paper develops design principles for a Hybrid Intelligence decision support system to aid early-stage startups in validating their business models amid high uncertainty, combining human and machine intelligence.

Contribution

It introduces a set of design principles and a prototype for a HI-DSS tailored for business model validation in uncertain startup environments.

Findings

Provides prescriptive design principles for HI-DSS

Develops a prototype artifact demonstrating the principles

Contributes to decision support systems for uncertain environments

Abstract

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.