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

•Decision-making in organizations can be augmented with Deep Learning algorithms.•Deep Learning algorithms find application in i) targeting ii) monitoring and iii) scheduling within organizations.•Deep Learning also feature various economic and organizational challenges. The current expansion of the...

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Bibliographic Details
Published in:Journal of business research Vol. 123; pp. 588 - 603
Main Authors: Shrestha, Yash Raj, Krishna, Vaibhav, von Krogh, Georg
Format: Journal Article
Language:English
Published: Elsevier Inc 01.02.2021
Elsevier B.V
Subjects:
Algorithms
Analysis
Artificial intelligence
Case studies
Data mining
Decision-making
Deep learning
Employee assistance programs
Case studies
Decision-making
Deep learning
Artificial intelligence
ISSN:0148-2963, 1873-7978
Online Access:Get full text
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Summary:•Decision-making in organizations can be augmented with Deep Learning algorithms.•Deep Learning algorithms find application in i) targeting ii) monitoring and iii) scheduling within organizations.•Deep Learning also feature various economic and organizational challenges. 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.
ISSN:0148-2963
1873-7978
DOI:10.1016/j.jbusres.2020.09.068