hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Ideation and Machine Learning: Problem Finding in Disruptive Innovation
Halmstad University, School of Business, Innovation and Sustainability.ORCID iD: 0000-0003-1390-1820
Halmstad University, School of Business, Innovation and Sustainability.ORCID iD: 0000-0002-0560-7392
2022 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

Disruptive innovation is widely recognised as a bold enterprise. Risks and uncertainties drive incumbent firms to seek alternative solutions to find disruptive ideas. Machine learning emerges as a powerful tool to reduce uncertainties while processing vast amounts and types of information. However, incumbents encounter immense difficulty in codifying tacit knowledge into effective algorithms and often end up with incremental or tactical outcomes despite bold aspirations. Using the literature on problem finding, we explore the development process of machine learning for ideation. Our action research conducted on a healthcare firm provides theoretical and managerial contributions. First, this study suggests that ideation for disruptive innovation benefits from machine learning by facilitating a heuristic search in which a group of actors evaluate plausible hypotheses rather than seek logically accurate conclusions. Previous studies on ideation stress directional search. Second, we propose an ideation process centred on problem formulation to identify disruptive innovation based on its inherent characteristics (e.g., radical functionality and discontinuous technical standard). Third, we discuss the challenges of adopting algorithm-based systems in the ideation — a process well known for being fuzzy.

Place, publisher, year, edition, pages
2022.
Keywords [en]
disruptive innovation, ideation, machine learning, artificial intelligence, problem finding
National Category
Information Systems, Social aspects
Research subject
Health Innovation
Identifiers
URN: urn:nbn:se:hh:diva-47176OAI: oai:DiVA.org:hh-47176DiVA, id: diva2:1671785
Conference
R&D 2022 Management Conference, June 9-13, 2022, Trento, Italy
Funder
Knowledge Foundation, 220023Available from: 2022-06-17 Created: 2022-06-17 Last updated: 2024-05-02Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records

Gama, FábioHolmén, Magnus

Search in DiVA

By author/editor
Gama, FábioHolmén, Magnus
By organisation
School of Business, Innovation and Sustainability
Information Systems, Social aspects

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 548 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf