Design principles for a hybrid intelligence decision support system for business model validation
Journal
Electronic Markets
ISSN
1019-6781
ISSN-Digital
1422-8890
Type
journal article
Date Issued
2018
Author(s)
Research Team
IWI 6
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 early-stage 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.
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 early-stage 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.
Language
English
Keywords
Business_model
Collective intelligence
Decision making
Decision support system
Hybrid intelligence
Machine learning
HSG Classification
contribution to practical use / society
Refereed
Yes
Publisher
Routledge
Volume
29
Number
3
Start page
423
End page
441
Division(s)
Eprints ID
254798
File(s)![Thumbnail Image]()
open.access
Name
JML_697.pdf
Size
1.62 MB
Format
Adobe PDF
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