Deep Learning for Information Systems Research. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Samtani, Sagar; Zhu, Hongyi; Padmanabhan, Balaji; Chai, Yidong; Chen, Hsinchun; Nunamaker, Jay F.
• Is Part Of: Journal of management information systems. Volume 40:Issue 1(2023)
• Page Start: 271
• Page End: 301
• Publication Date: 2023-01-02
• Subjects: deep learning -- artificial intelligence -- knowledge-contribution framework -- information-systems methodologies -- research guidelines -- design science research -- behavioral research -- economics of IS; Management information systems -- Periodicals; Management information systems; Periodicals; 658.4038011
• DOI: 10.1080/07421222.2023.2172772
• Languages: English
• ISSNs: 0742-1222

Abstract

ABSTRACT: 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.