Understanding the interplay of artificial intelligence and strategic management: four decades of research in review
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
If an organization has demonstrated historical biases in hiring, firing, pay, or other managerial decisions, these biases will be learned by the algorithm.
“If an organization has demonstrated historical biases in hiring, firing, pay, or other managerial decisions, these biases will be “learned” by the algorithm (Keding, 2021).”
From Algorithmic management in a work context (2021) · cites this paper as Keding, 2021
The citation lists haven’t been fetched yet
References 124 · Cited by 231
- Authors
- Keding, C.
- Affiliations
- ESCP Business School (DE)
- Corresponding author
- Keding, Christoph
- ORCID
- Keding, Christoph
- Venue
- Management Review Quarterly
- Published
- 2020-02-24
- Volume
- 71
- Issue
- 1
- Pages
- 91-134
- ISSN
- 2198-1620 (ISSN-L)
- 2198-1639
- OpenAlex
- W3007320742
- Cited by
- 231
- References
- 124
- Citations by year
- 2026: 52 · 2025: 52 · 2024: 38 · 2023: 34 · 2022: 32 · 2021: 20 · 2020: 3
- FWCI
- 8.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Management Review Quarterly (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- 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 › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Keywords
- artificial intelligence, strategic management, organizational impact of AI, individual effects, research framework, research agenda
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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