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Cited by 331Artificial Intelligence ApplicationsImpact of AI and Big Data on Business and SocietyAI and HR Technologies

Augmenting organizational decision-making with deep learning algorithms: principles, promises, and challenges

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Shrestha, Yash Raj, Krishna, Vaibhav, von Krogh, Georg·2021·Journal of Business Research
DOI 10.1016/j.jbusres.2020.09.068
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Abstract

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. We conceptualize the decision-making process in organizations augmented with DL algorithm outcomes (such as predictions or robust patterns from unstructured data) as deep learning–augmented decision-making (DLADM). We contribute to the understanding and application of DL for decision-making in organizations by (a) providing an accessible tutorial on DL algorithms and (b) illustrating DLADM with two case studies drawing on image recognition and sentiment analysis tasks performed on datasets from Zalando, a European e-commerce firm, and Rotten Tomatoes, a review aggregation website for movies, respectively. Finally, promises and challenges of DLADM as well as recommendations for managers in attending to these challenges are also discussed.

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  • By using AI and ML algorithms, robots can make informed decisions based on data analysis, resulting in better overall performance.

    “Improved Decision-making: By using AI and ML algorithms, robots can make informed decisions based on data analysis, resulting in better overall performance [55] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [55]

    The Introduction says AI and ML can use data to augment organizational decision-making, and §3.1 says autonomous robots draw on reinforcement learning to select actions. The paper does not establish that robots using these algorithms achieve better overall performance; its decision-making and performance examples concern organizational applications. (LLM, conf=0.84)

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Cited by

Sorted by citations

Authors
Shrestha, Y. R., Krishna, V., & von Krogh, G.
Affiliations
ETH Zurich (CH)
Corresponding author
Krishna, Vaibhav
ORCID
Venue
Journal of Business Research
Publisher
Elsevier BV
Published
2021-02
Volume
123
Pages
588-603
ISSN
0148-2963 (ISSN-L)
DOI
10.1016/j.jbusres.2020.09.068
OpenAlex
W3094605956
Cited by
331
References
21
Citations by year
2026: 76 · 2025: 89 · 2024: 75 · 2023: 38 · 2022: 35 · 2021: 18
FWCI
8.80
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Business Research (publishedVersion, cc-by)
  • SSRN Electronic Journal (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • IRIS (submittedVersion)
  • SERVAL (Université de Lausanne) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
  • ETH Zürich Research Collection (cc-by)
Funders
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
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 › Organizational Behavior and Human Resource Management › AI and HR Technologies
Keywords
deep learning, organizational decision-making, artificial intelligence in management, image recognition, sentiment analysis, e-commerce, creative work

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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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