Search: onr:"swepub:oai:DiVA.org:ltu-103988" >
Artificial intellig...
Artificial intelligence capabilities for circular business models: Research synthesis and future agenda
-
- Madanaguli, Arun (author)
- Luleå tekniska universitet,Industriell ekonomi
-
- Sjödin, David (author)
- Luleå tekniska universitet,Industriell ekonomi,USN Business School, University of South-Eastern Norway, Vestfold, Norway
-
- Parida, Vinit (author)
- Luleå tekniska universitet,Industriell ekonomi,USN Business School, University of South-Eastern Norway, Vestfold, Norway; Department of Management, University of Vaasa, Vaasa, Finland
-
show more...
-
- Mikalef, Patrick (author)
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway; SINTEF Digital, Department of Technology Management, Trondheim, Norway
-
show less...
-
(creator_code:org_t)
- Elsevier Inc. 2024
- 2024
- English.
-
In: Technological forecasting & social change. - : Elsevier Inc.. - 0040-1625 .- 1873-5509. ; 200
- Related links:
-
https://doi.org/10.1...
-
show more...
-
https://ltu.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- This study explores the interlink between AI capabilities and circular business models (CBMs) through a literature review. Extant literature reveals that AI can act as efficiency catalyst, empowering firms to implement CBM. However, the journey to harness AI for CBM is fraught with challenges as firms grapple with the lack of sophisticated processes and routines to tap into AI's potential. The fragmented literature leaves a void in understanding the barriers and development pathways for AI capabilities in CBM contexts. Bridging this gap, adopting a capabilities perspective, this review intricately brings together four pivotal capabilities: integrated intelligence capability, process automation and augmentation capability, AI infrastructure and platform capability, and ecosystem orchestration capability as drivers of AI-enabled CBM. These capabilities are vital to navigating the multi-level barriers to utilizing AI for CBM. The key contribution of the study is the synthesis of an AI-enabled CBM framework, which not only summarizes the results but also sets the stage for future explorations in this dynamic field.
Subject headings
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Företagsekonomi (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Business Administration (hsv//eng)
Keyword
- AI future research agenda
- Artificial intelligence
- Business model innovation
- Circular business models
- Entreprenörskap och innovation
- Entrepreneurship and Innovation
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database