Deep Learning for Information Systems Research

• 成果类型: Article
• 署名作者: Samtani, Sagar; Zhu, Hongyi; Padmanabhan, Balaji; Chai, Yidong; Chen, Hsinchun; Nunamaker, Jay F. F.
• 署名单位: Indiana University System; Indiana University Bloomington; IU Kelley School of Business; University of Texas System; University of Texas at San Antonio; State University System of Florida; University of South Florida; Hefei University of Technology; University of Arizona; University of Arizona
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2023.2172772
• 发表日期: 2023
• 页码: 271-301
• 关键词: design science; Social media; IMPACT; identification; analytics; FRAMEWORK; traits

摘要

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.