JOURNAL ARTICLE

Call for Papers— INFORMS Journal on Data Science Virtual Special Issue on Generative AI, Foundation Models, and Deep Learning with Applications to Business Analytics.

  • Published In: INFORMS Journal on Data Science, 2025, v. 4, n. 2. P. v 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Abbasi, Ahmed; Chen, Ningyuan; Li, Xiaocheng; Liu, Xiao 3 of 3

Abstract

The article focuses on the transformative potential of generative AI (GenAI), foundation models, and deep learning in data science, emphasizing their impact on decision-making and analytics. It outlines three key areas for exploration: innovative applications in data science, practical understanding of these technologies, and their societal impact and policy implications. The article invites submissions for a virtual special issue that seeks interdisciplinary research on these topics, encouraging contributions from various fields such as business, engineering, and social sciences. The submission deadline is September 1, 2025, with a structured review process outlined for accepted manuscripts. [Extracted from the article]

Additional Information

  • Source:INFORMS Journal on Data Science. 2025/04, Vol. 4, Issue 2, pv
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:2694-4022
  • DOI:10.1287/ijds.2025.cfp.v03.n3
  • Accession Number:187724890
  • Copyright Statement:Copyright of INFORMS Journal on Data Science is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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