Augmenting Organizational Decision-Making with Deep Learning Algorithms: Principles, Promises, and Challenges
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Abstract (arXiv)
The current expansion of theory and research on artificial intelligence in management and organization studies has revitalized the theory and research on decision-making in organizations. In particular, recent advances in deep learning (DL) algorithms promise benefits for decision-making within organizations, such as assisting employees with information processing, thereby augment their analytical capabilities and perhaps help their transition to more creative work.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
- 2021Machine learning and deep learningcited by 26
- 2024AdapFair: Ensuring Adaptive Fairness for Machine Learning Operationscited by 1
- 2026A categorical error sensitivity index (ISEC): A preventive ordinal decision-support measure for irrecoverable errors in manual data entry systemscited by 0
- 2026A categorical error sensitivity index (ISEC): A preventive ordinal decision-support measure for irrecoverable errors in manual data entry systems12 May 2026
- 2024AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations23 Sep 2024
- 2021Multi-Center Federated Learning: Clients Clustering for Better Personalization19 Aug 2021
- 2021Machine learning and deep learning12 Apr 2021