Deep Learning for Information Systems Research

Sagar Samtani, Hongyi Zhu, Balaji Padmanabhan, Yidong Chai, Hsinchun Chen, Jay F. Nunamaker

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)271-301
Number of pages31
JournalJournal of Management Information Systems
Issue number1
StatePublished - 2023
Externally publishedYes


  • artificial intelligence
  • behavioral research
  • deep learning
  • design science research
  • economics of IS
  • information-systems methodologies
  • knowledge-contribution framework
  • research guidelines

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management


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