𝗧𝗵𝗲𝗼𝗿𝗶𝘇𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮-𝗔𝗜 𝗡𝗲𝘅𝘂𝘀

𝗖𝗔𝗟𝗟 𝗙𝗢𝗥 𝗣𝗔𝗣𝗘𝗥𝗦

𝗧𝗵𝗲𝗼𝗿𝗶𝘇𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮-𝗔𝗜 𝗡𝗲𝘅𝘂𝘀

𝗝𝗼𝘂𝗿𝗻𝗮𝗹:
European Journal of Information Systems

𝗣𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗿:
Taylor & Francis Group

𝗠𝗮𝗻𝘂𝘀𝗰𝗿𝗶𝗽𝘁 𝗗𝗲𝗮𝗱𝗹𝗶𝗻𝗲:
15 January 2027

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲

Artificial Intelligence is undergoing a profound transformation, shifting from rule-based systems toward data-driven computational intelligence powered by machine learning and deep learning technologies. This transition compels Information Systems scholars to rethink the foundations of AI and develop new theoretical perspectives to understand the evolving relationship between data and AI.

The Special Issue, “Theorizing the Data-AI Nexus,” seeks theoretically ambitious and empirically rich contributions that explore the complex socio-technical interplay between data, AI, and social practice. It aims to advance scholarly understanding of how data shapes AI systems, how AI systems reshape the data they consume and generate, and how these recursive dynamics transform organizations and societies.

The Special Issue encourages interdisciplinary research that moves beyond purely technical accounts of AI and examines the organizational, institutional, epistemic, and societal dimensions of data-driven intelligence. Contributions that challenge dominant narratives and propose innovative conceptualizations of intelligence, data quality, synthetic data, and AI governance are particularly welcome.

𝗧𝗼𝗽𝗶𝗰𝘀 𝗼𝗳 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁

Submissions may address, but are not limited to:

• Data and AI co-constitution and the emergence of computational intelligence
• AI as a producer and transformer of data
• Synthetic data generation and recursive AI training systems
• Data detached from social practice and institutional knowledge systems
• Organizational data work, including curation, labeling, cleaning, and evaluation
• AI agents and their role in organizational sensemaking and decision-making
• Data governance, accountability, and stewardship in AI systems
• Intelligence as a relational, temporal, and emergent phenomenon
• Methodological approaches for studying data and AI as socio-technical systems
• Trace data, ethnographic methods, computational methods, and archival analyses of AI systems
• AI and grand societal challenges, including climate change, public health, inequality, and digital governance
• Responsible AI design in contexts characterized by incomplete, biased, or synthetic data
• Novel applications of AI and data in scientific, environmental, and non-commercial settings

𝗞𝗲𝘆 𝗗𝗮𝘁𝗲𝘀

• Call Launch: 28 May 2026 (Theorizing Data & AI Conference)
• Initial Paper Submission Deadline: 15 January 2027
• First-Round Author Notification: 15 April 2027
• Authors’ Workshop: May 2027 (Theorizing Data & AI Conference)
• Invited Revisions Deadline: 31 July 2027
• Second-Round Notification: 31 October 2027
• Final Revision Deadline: 15 January 2028

𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀

• The Special Issue welcomes empirical and conceptual contributions.
• A broad range of paradigms and methodologies are encouraged, including interpretivist, critical, processual, positivist, computational, and design-science approaches.
• Contributions may include empirical studies, conceptual papers, philosophical analyses, methodological papers, and commentary pieces.
• Submissions should clearly foreground the Information Systems artefact and make an original theoretical contribution to understanding the data-AI nexus.
• Researchers uncertain about the suitability of their work are encouraged to contact the Special Issue Editors.

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗝𝗼𝘂𝗿𝗻𝗮𝗹

The European Journal of Information Systems (EJIS) is a leading international journal that publishes high-quality research on information systems, digital innovation, and socio-technical transformations. The journal is renowned for advancing interdisciplinary scholarship and developing novel theoretical perspectives on digital technologies and their impact on organizations and society.

𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲 𝗘𝗱𝗶𝘁𝗼𝗿𝘀

• Cristina Alaimo, ESSEC Business School
• Lauren Waardenburg, ESSEC Business School
• Jonny Holmström, Umeå University
• Lior Zalmanson, Tel Aviv University
• Reza M. Baygi, VU Amsterdam

𝗣𝗼𝘀𝘁𝗲𝗱 𝗼𝗻 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝗦𝗲𝘁𝘂 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰𝘀 — 𝗣𝗿𝗲𝗺𝗶𝗲𝗿 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗳𝗼𝗿 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 & 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻

Visit official website of the publisher