Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges
dc.contributor.author
Shrestha, Yash R.
dc.contributor.author
Krishna, Vaibhav
dc.contributor.author
von Krogh, Georg
dc.date.accessioned
2020-10-27T10:36:10Z
dc.date.available
2020-10-27T00:33:17Z
dc.date.available
2020-10-27T10:36:10Z
dc.date.issued
2021-02
dc.identifier.issn
0148-2963
dc.identifier.issn
1873-7978
dc.identifier.other
10.1016/j.jbusres.2020.09.068
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/448059
dc.identifier.doi
10.3929/ethz-b-000448059
dc.description.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. © 2020 The Author(s)
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Case studies
en_US
dc.subject
Decision-making
en_US
dc.subject
Deep learning
en_US
dc.subject
Artificial intelligence
en_US
dc.title
Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-10-21
ethz.journal.title
Journal of Business Research
ethz.journal.volume
123
en_US
ethz.journal.abbreviated
J. bus. res
ethz.pages.start
588
en_US
ethz.pages.end
603
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Leading and Coordinating Knowledge Creation in Online Communities
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03719 - von Krogh, Georg / von Krogh, Georg
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03719 - von Krogh, Georg / von Krogh, Georg
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03719 - von Krogh, Georg / von Krogh, Georg
ethz.grant.agreementno
169441
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projekte GSW
ethz.date.deposited
2020-10-27T00:33:22Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-10-27T10:36:23Z
ethz.rosetta.lastUpdated
2022-03-29T03:50:09Z
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true
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