Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing
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ISSN:2168-1007
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Answering the research questions on human-AI collaboration in innovation will often best be achieved through inductive and abductive rather than deductive reasoning.
“Hence, answering these questions will often best be achieved through inductive and abductive, rather than deductive reasoning (Amabile, 2020; Bamberger, 2018; von Krogh, 2018).”
From Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models (2023) · cites this paper as von Krogh, 2018
In “Making Decisions with Intelligent Machines” (p. 4), the paper says AI decision-making questions are particularly suitable for abductive reasoning; “Solving Problems with Intelligent Machines” (p. 5) also proposes an abductive research program. It does not establish that inductive reasoning is preferable to deductive reasoning for questions about human–AI collaboration in innovation. (LLM, conf=0.86)
Experimental — accuracy not yet measuredBoth traditional and non-traditional work settings will be increasingly shaped by the interaction of human and machine authority regimes.
“As von Krogh (2018) explains, both tradi-tional and non-traditional work settings will be increas-ingly shaped by the “interaction of human and machine authority regimes” (p.406).”
From Algorithmic management in a work context (2021) · cites this paper as von Krogh, 2018
In “Making Decisions with Intelligent Machines” (p. 4), von Krogh anticipates research into the emergence and interaction of human and machine authority regimes in organizations. The passage does not distinguish traditional from non-traditional work settings or assert that both will increasingly be shaped by that interaction; it frames the interaction as something researchers may explain. (LLM, conf=0.85)
Experimental — accuracy not yet measuredExisting research has identified that collective intelligence-a symbiotic partnership between AI and human intelligence-increases the capability of human workers, leading to higher employee productivity, better psychological outcomes, low turnover, and improved quality of outputs.
“In this context, existing research has identified the importance of collective intelligence, i.e., symbiotic partnership between AI and HI, and how this will also increase the capability of human workers, which will lead to higher employee productivity, better psychological outcomes, low turn-over and improved quality of outputs (Glikson and Wooley, 2020;Murray et al., 2020;von Krogh, 2018).”
From Unlocking the value of artificial intelligence in human resource management through AI capability framework (2023) · cites this paper as von Krogh, 2018
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredRallying support within and outside an organization remains a profoundly human capability.
“Rallying support within and outside an organization remains a profoundly human capability (von Krogh, 2018).”
From Artificial intelligence and knowledge management: A partnership between human and AI (2023) · cites this paper as von Krogh, 2018
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“The layered nature of the potential disruptions in the introduction of AI in the public sector calls for a renewed research agenda, and new theorisation e ff orts (von Krogh, 2018).”
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“Researchers emphasize that AI requires well-defined objectives, as it struggles with managing uncertainty and processing complex inputs [102, 119, 122].”
Influential
- Authors
- von Krogh, G.
- Affiliations
- ETH Zurich
- Venue
- Academy of Management Discoveries
- Publisher
- Academy of Management
- Published
- 2018-12
- Volume
- 4
- Issue
- 4
- Pages
- 404-409
- ISSN
- 2168-1007 (ISSN-L)
- OpenAlex
- W2906095734
- Cited by
- 641
- References
- 26
- Citations by year
- 2026: 153 · 2025: 128 · 2024: 101 · 2023: 74 · 2022: 81 · 2021: 62 · 2020: 31 · 2019: 9 · 2018: 2
- FWCI
- 14.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Academy of Management Discoveries (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Keywords
- artificial intelligence in organizations, artificial intelligence adoption, organizational theory
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