Design principles for a hybrid intelligence decision support system for business model validation
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RESEARCH PAPER
Design principles for a hybrid intelligence decision support system for business model validation Dominik Dellermann 1,2 & Nikolaus Lipusch 1,2 & Philipp Ebel 1,2 & Jan Marco Leimeister 1,2 Received: 15 September 2017 / Accepted: 17 July 2018 # Institute of Applied Informatics at University of Leipzig 2018
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. Keywords Collective intelligence . Machine learning . Decision support system . Hybrid intelligence . Business model . Decision making JEL classification D81
Introduction Responsible Editor: Alexander Mädche Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12525-018-0309-2) contains supplementary material, which is available to authorized users. * Dominik Dellermann [email protected] Nikolaus Lipusch [email protected] Philipp Ebel [email protected] Jan Marco Leimeister [email protected] 1
Research Center for IS Design (ITeG), Information Systems, University of Kassel, Pfannkuchstrasse 1, 34121 Kassel, Germany
2
Institute of Information Management, University of St. Gallen, Mueller-Friedberg-Strasse 8, 9000 St. Gallen, Switzerland
The rapid digital transformation of businesses and society generates great possibilities for developing novel business models that are highly successful in creating and capturing value. Many Internet startups such as Hybris, Snapchat, and Facebook are achieving major successes and quickly disrupting whole industries. Yet, most early-stage ventures fail. Nearly 90% of technology startups do not survive the first five years (Patel 2015). One reason for this is that entrepreneurs face tremendous uncertainties when creating their business models. Consequently, entrepreneurs must constantly reevaluate and continuously adapt their business models to succeed (Ojala 2016). This task is characterized by high levels of uncertainty concerning market and technological developments. In addition, entrepreneurs cannot be sure whether their competencies and internal resources are suitable to successfully run the new venture (Andr
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