Theorizing the Data-AI Nexus

Tue, 11 Aug 2026 00:55:16 GMT

Cristina Alaimo, ESSEC Business School

Lauren Waardenburg, ESSEC Business School

Jonny Holmström, Umeå University

Lior Zalmanson, Tel Aviv University

Reza M. Baygi, VU Amsterdam

The special issue addresses a fundamental shift in how artificial intelligence operates: contemporary AI systems are built upon, learn from, and generate data at scale, making data rather than rules the core substrate of computational intelligence. This requires Information Systems scholars to develop new conceptual frameworks for understanding the data-AI nexus as a socio-technical phenomenon, moving beyond purely technical perspectives.

The call emphasizes that data are shaped by institutional practices and cognitive frames, while AI systems increasingly reshape the data they depend on, blurring boundaries between data and AI. Practitioners primarily engage with AI through data practices such as curation, labelling, and quality control, making it essential to understand the organizational routines and historical infrastructures that underpin these processes. Additionally, AI now generates synthetic data that feeds back into model training, raising institutional and epistemic challenges.

Appel publié par European Journal of Information Systems. Lire l'appel complet sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

Data and AI co-constitution: exploring where intelligence emerges in computational architectures, datasets, or their entanglement; how data selection, curation, labelling, and historical accumulation shape AI capabilities
AI as a data-maker: conceptualizing AI as an active producer of data; how classification systems, predictive models, and generative tools reorganize reality; AI agents autonomously generating and acting upon data; recursive dynamics of synthetic data training other AI systems
Data decoupled from practice: consequences of data production detached from institutionalized knowledge practices; governance and decision-making based on decontextualized data; risks when data-driven systems lack ties to local knowledge and professional judgment
The social life of the data-AI nexus: institutional, historical, and cognitive trajectories of data; how organizational memory, institutional logics, and collective sensemaking shape datasets and AI output interpretation; AI agents as new organizational actors
Theoretical and methodological discussions: analyses of co-constitution, entanglement, flow, recursion, performativity, and classification; reconceptualizing constructs like intelligence, data quality, ground truth, and model performance as relational and emergent
Cross-level insights: how assumptions about data and intelligence are embedded in AI systems; studies of data work (curation, labelling, cleaning), model development, synthetic data pipelines, generative AI integration
Data, AI and grand challenges: addressing climate change, public health, inequality, and geopolitical tensions; responsible AI design with incomplete, biased, or synthetic data; governance and accountability in distinctive contexts (scientific, environmental, planetary)

Timeline

May 28, 2026: Call launch at the Theorizing Data & AI Conference
January 15, 2027: Initial paper submission deadline
April 15, 2027: First round authors notification
July 31, 2027: Invited revisions deadline
October 31, 2027: Second round authors notification
January 15, 2028: Final revision deadline

Associate editors

Aleksi Aaltonen,
Ida Asadi Someh,
Ioanna Constantiou,
Domenico di Prisco,
Mayur Joshi,
Ekaterina Jussupow,
Jannis Kallinikos,
Tomislav Karačić,
Stan Karanasios,
Alexander Kempton,
Angelos Kostis,
Harris Kyriakou,
Christine Legner,
Kalle Lyytinen,
Eric Monteiro,
Jeff Parsons,
Mike Power,
Jan Recker,
Paavo Ritala,
Marta Stelmaszak Rosa,
Lauri Wesssel,

The future of creating and distributing value in digital health ecosystems

Thu, 14 Aug 2025 03:33:30 GMT

Lauri Wessel, European University Viadrina Frankfurt (Oder)

Melanie Reuter-Oppermann, Maastricht University

Roxana Ologeanu-Taddei, University of Montpellier

Hannes Rothe, University of Duisburg-Essen

Sirkka L. Jarvenpaa, The University of Texas at Austin

Digital health ecosystems involve diverse stakeholders—providers, patients, and laypersons—interacting to shape care delivery. While information systems research has advanced in understanding the technological foundations, significant research gaps remain regarding how value is created and distributed in these settings. The special issue seeks papers addressing two core trajectories: understanding diverse forms of value that digital technologies create in healthcare ecosystems, and examining how value should be distributed among participants over time.

The journal welcomes both cumulative research advancing understanding of value creation and distribution, and contrarian studies challenging prevailing assumptions. Research often assumes large data volumes will generate substantial benefits, yet in practice clinicians frequently lack data in the required format, quality, or volume. Submissions should offer fresh perspectives on creating and distributing value in digital health ecosystems, informed by both information systems and adjacent disciplines.

Appel publié par European Journal of Information Systems. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

The role of data management and data sharing in digital health ecosystems
Designing for measuring kinds of value arising from new digital technologies like XR, 5G, web 3.0, and machine or hybrid learning in digital health ecosystems
The role of digital health ecosystems during pandemics or natural disasters; specifically with an eye toward how data help to distribute value among ecosystem participants
(Data-driven) change of professional roles, identities, and institutions in digital health ecosystems
The difference between creating value for intervention vs. for prevention
Design of inclusive and responsible digital technologies for healthcare and well-being
Digital tools and use of digital health data to connect different participants of health service networks, to support decision making and to improve organizational processes
Negative consequences of digital technologies implementation in healthcare, such as health givers burnout and patients' anxiety
The role of digital tools like virtual coaching for autonomy of health care providers and patients

Timeline

March 1, 2026: Submission of extended abstracts/declaration of interest to submit papers
November 30, 2026: Full paper submission deadline
March 31, 2027: First round decisions due
September 30, 2027: Revisions due
December 15, 2027: Second round decisions due
February 28, 2028: Third round revisions due (if necessary)
May 31, 2028: Final decisions due

Associate editors

Aleksi Aaltonen, Stevens Institute of Technology
Saeed Akhlaghpour, University of Queensland
Aycan Aslan, IT University Copenhagen
Anna Essén, Stockholm School of Economics
Heiko Gewald, HS Neu-Ulm
Martin Gersch, Freie Universität Berlin
Camille Grangé, HEC Montréal
Maike Greve, Copenhagen Business School
Farkhondeh Hassan Doust, University of Auckland
Alexander Kempton, University of Oslo
Charlotte Koehler, European University Viadrina Frankfurt (Oder)
Johann Kranz, LMU
Jan-Marco Leimeister, University of St. Gallen
Wolfgang Maaß, Saarland University
Bogdan Negoita, HEC Montréal
Lemai Nguyen, Deakin University
Guy Paré, HEC Montréal
Scott Thiebes, Karlsruhe Institute of Technology
Manuel Trenz, University of Göttingen
Cristina Trocin, Católica Porto Business School
Marjolein van Offenbeek, University of Groningen
Polyxeni Vassilakopoulou, University of Agder
Till Winkler, Fernuniversität Hagen

kerostig | Tag : data governance

Theorizing the Data-AI Nexus

Potential topics

Timeline

Associate editors

The future of creating and distributing value in digital health ecosystems

Potential topics

Timeline

Associate editors