Deep Learning for Information Systems Research

Sagar Samtani

doi:10.1080/07421222.2023.2172772

Deep Learning for Information Systems Research

2023-03-30

27

OA

AI

DOI：10.1080/07421222.2023.2172772

Original

Original request for help

Save

Abstract

En 中文

Modern artificial intelligence (AI) is heavily reliant on deep learning (DL), an emerging class of algorithms that can automatically detect non-trivial patterns from petabytes of rapidly evolving Big Data. Although the information systems (IS) discipline has embraced DL, questions remain about DL's interface with a domain and theory and DL contribution types. In this paper, we present a DL information systems research (DL-ISR) schematic that reviews DL while considering the role of the application environment and knowledge base, summarizes extant DL research in IS, a knowledge contribution framework (KCF) to position DL contributions, and ten guidelines to help IS scholars design, execute, and present DL for computational, behavioral, or economic IS research. We illustrate a research contribution to DL for cybersecurity. This article's contribution to theory resides in the conceptual DL-ISR schematic and KCF, while its contributions to practice are based on its practical guidelines for executing DL-based projects.

Keywords:

deep learning

artificial intelligence

knowledge-contribution framework

information-systems methodologies

research guidelines

design science research

behavioral research

economics of IS

Deep Learning for Information Systems Research

Abstract

Journal

Organization