Deep Learning for Information Systems Research

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.

• 저널Journal of Management Information Systems · 40(1) · 271–301
• 토픽Big Data and Business Intelligence · Management Information Systems
• DOI10.1080/07421222.2023.2172772
• 저자Sagar Samtani, Hongyi Zhu, Balaji Padmanabhan, Yidong Chai, Hsinchun Chen, Jay F. Nunamaker