Paper Number
ICIS2026-1633
Paper Type
Panel
Abstract
Data are central to Information Systems research, yet the field increasingly studies different kinds of data under a single, undifferentiated label. From government data, through biometric data, personal data, synthetic data, natural world data, to trace data, researchers work across distinct data domains, defined as socio-technical contexts in which data are produced, governed, interpreted, and used, with implications for organizational and societal outcomes. This panel opens a debate among scholars working across distinct data domains to examine what in data constitutes a unified phenomenon and what requires domain-specific theorizing, whether core data studies constructs travel across domains, and whether methods from one data domain can be productively applied to others. By making both convergence and divergence visible, the panel invites a rethinking of core assumptions about data in Information Systems research, with implications for research questions, theory building, and methods.
Recommended Citation
Stelmaszak, Marta and Gualdi, Francesco, "Not All Data Are Alike: Rethinking Data Domains in Information Systems Research" (2026). ICIS 2026 Proceedings. 1.
https://aisel.aisnet.org/icis2026/panels/Panels/1
Not All Data Are Alike: Rethinking Data Domains in Information Systems Research
Data are central to Information Systems research, yet the field increasingly studies different kinds of data under a single, undifferentiated label. From government data, through biometric data, personal data, synthetic data, natural world data, to trace data, researchers work across distinct data domains, defined as socio-technical contexts in which data are produced, governed, interpreted, and used, with implications for organizational and societal outcomes. This panel opens a debate among scholars working across distinct data domains to examine what in data constitutes a unified phenomenon and what requires domain-specific theorizing, whether core data studies constructs travel across domains, and whether methods from one data domain can be productively applied to others. By making both convergence and divergence visible, the panel invites a rethinking of core assumptions about data in Information Systems research, with implications for research questions, theory building, and methods.
Comments
25_Panels