Paper Number
ECIS2026-2377
Paper Type
SP
Abstract
This paper examines how hope, as a collective orientation toward desirable futures, shapes data-driven development in a large European police organization. Drawing on immersive fieldwork across operational, investigative, and IT units, the paper explores how efforts to become data-driven reshape everyday work as data is increasingly produced, shared, and used across the organization. Guided by affect theory and the concept of sociotechnical imaginaries, the study investigates how growing data use intersects with collective expectations of what data-driven development might make possible. The findings show that employees in data-intensive contexts navigate competing visions of the future, creating uncertainty and practical challenges. Nevertheless, hope remains central to sustaining momentum, helping employees imagine improved ways of working even when progress is uncertain. The study contributes to emerging research on data-driven development by highlighting its intertwined affective and temporal dimensions, showing how organizational data ambitions are sustained in practice through expectations of desirable futures.
Recommended Citation
Barbala, Astri and Conboy, Kieran, "Data-Driven Imaginaries: Exploring The Temporal-Affective Dimensions Of Data-Driven Development" (2026). ECIS 2026 Proceedings. 14.
https://aisel.aisnet.org/ecis2026/datasc_isresearch/datasc_isresearch/14
Data-Driven Imaginaries: Exploring The Temporal-Affective Dimensions Of Data-Driven Development
This paper examines how hope, as a collective orientation toward desirable futures, shapes data-driven development in a large European police organization. Drawing on immersive fieldwork across operational, investigative, and IT units, the paper explores how efforts to become data-driven reshape everyday work as data is increasingly produced, shared, and used across the organization. Guided by affect theory and the concept of sociotechnical imaginaries, the study investigates how growing data use intersects with collective expectations of what data-driven development might make possible. The findings show that employees in data-intensive contexts navigate competing visions of the future, creating uncertainty and practical challenges. Nevertheless, hope remains central to sustaining momentum, helping employees imagine improved ways of working even when progress is uncertain. The study contributes to emerging research on data-driven development by highlighting its intertwined affective and temporal dimensions, showing how organizational data ambitions are sustained in practice through expectations of desirable futures.