TY - JOUR
T1 - Deep Learning for Information Systems Research
AU - Samtani, Sagar
AU - Zhu, Hongyi
AU - Padmanabhan, Balaji
AU - Chai, Yidong
AU - Chen, Hsinchun
AU - Nunamaker, Jay F.
N1 - Funding Information:
There is significant focus across the academic landscape on attaining external grant funding from government (e.g., National Science Foundation) or industry. However, attaining funding requires clear articulations of the practical utility and value of a DL-based work (irrespective of IS paradigm). Detailed discussions of how the environment and model outcomes together influence business and societal outcomes can help articulate the translational process.
Funding Information:
This material is based upon work supported by the National Science Foundation under Grant Numbers OAC-1917117 (CICI), CNS-1936370 (SaTC CORE), CNS-1850362 (CRII SaTC), and DGE-2038483 (SaTC-EDU). Y. Chai was also partially supported by the National Natural Science Foundation of China (91846201, 72101079,71722010) and the Shanghai Data Exchange Cooperative Program (W2021JSZX0052).
Publisher Copyright:
© 2023 Taylor & Francis Group, LLC.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - behavioral research
KW - deep learning
KW - design science research
KW - economics of IS
KW - information-systems methodologies
KW - knowledge-contribution framework
KW - research guidelines
UR - http://www.scopus.com/inward/record.url?scp=85152469080&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85152469080&partnerID=8YFLogxK
U2 - 10.1080/07421222.2023.2172772
DO - 10.1080/07421222.2023.2172772
M3 - Article
AN - SCOPUS:85152469080
SN - 0742-1222
VL - 40
SP - 271
EP - 301
JO - Journal of Management Information Systems
JF - Journal of Management Information Systems
IS - 1
ER -