A multi-perspective framework for research on (sustainable) autonomous systems
Open accessAbstract
The ongoing digital transformation is challenging the way in which business is conducted and value is created and captured (Vial 2019 ). While prior digitalization waves focused on replacing paper as physical carrier of information, leveraging the Internet as global communication infrastructure, and developing reactive, partly automated business processes and systems (e.g. Legner et al. 2017 ), the next wave will be about transforming these processes/systems into proactive autonomous systems (AS). Such systems represent complex “systems of systems” with different maturities, qualities, reliabilities, and performances, which may develop their own dynamics (Boardman and Sauser 2006 ; Maier 1999 ). In the information systems (IS) context, a common characteristic of AS is their reliance on large amounts of data, along with the use of advanced technologies—such as the Internet of Things, Artificial Intelligence (AI), Machine Learning, or Blockchain—that allow for gathering and processing ‘big’ data with limited, or even no, human involvement.
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Standard AI struggles with novel or rare events (e.g., extreme weather, cyber-physical attacks), necessitating advanced FDD and adaptive learning, though ensuring the safety of such adaptive systems is a research and operational challenge.
“Standard AI struggles with novel or rare events (e.g., extreme weather, cyber-physical attacks as in Section 3.1.3),necessitating advanced FDD and adaptive learning, though ensuring the safety of such adaptive systems is a research and operational challenge (Section 3.3; [10,49]).”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [49]
Embracing experimentation through tools like regulatory sandboxes and committing to continuous learning from real-world feedback are the essence of sustainable innovation and Collaborative Adaptation.
“Embracing experimentation through tools like regulatory sandboxes and committing to continuous learning from real-world feedback are the essence of sustainable innovation and Collaborative Adaptation in a complex field [49,52].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [49]
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- Authors
- Beck, R., Dibbern, J., & Wiener, M.
- Affiliations
- IT University of Copenhagen (DK)
- University of Bern (CH)
- Technische Universität Dresden (DE)
- Corresponding author
- Dibbern, Jens
- Venue
- Business & Information Systems Engineering
- Published
- 2022-05-03
- Volume
- 64
- Issue
- 3
- Pages
- 265-273
- ISSN
- 1867-0202 (ISSN-L)
- 2363-7005
- OpenAlex
- W4225312580
- Cited by
- 23
- References
- 48
- Citations by year
- 2026: 10 · 2025: 4 · 2024: 4 · 2023: 5
- FWCI
- 4.68
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-02-23)
- Open access
- hybrid
- doi.org
- Locations
- Business & Information Systems Engineering (publishedVersion, cc-by)
- IT University Of Copenhagen (IT University of Copenhagen) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Journal of the Association for Information Systems (submittedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- autonomous systems, digital transformation, systems of systems, Internet of Things, artificial intelligence, machine learning, blockchain, big data, research framework
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