Augmenting Organizational Decision-Making with Deep Learning Algorithms: Principles, Promises, and Challenges

86 Pages Posted: 17 Nov 2020

Date Written: September 29, 2020

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.

Keywords: case studies, tutorial, deep learning, decision-making, artificial intelligence

Suggested Citation

Shrestha, Yash Raj and Krishna, Vaibhav and von Krogh, Georg, Augmenting Organizational Decision-Making with Deep Learning Algorithms: Principles, Promises, and Challenges (September 29, 2020). Journal of Business Research, 2020, Available at SSRN: https://ssrn.com/abstract=3701592 or http://dx.doi.org/10.2139/ssrn.3701592

Yash Raj Shrestha (Contact Author)

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Vaibhav Krishna

ETH Zürich ( email )

Zürichbergstrasse 18
8092 Zurich, CH-1015
Switzerland

Georg Von Krogh

ETH Zurich ( email )

D-MTEC, SMI, WEV J 411
Weinbergstrasse 56/58
Zurich, 8092
Switzerland
+41 44 632 88 50 (Phone)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
14
Abstract Views
107
PlumX Metrics