Stafford Beer Medal

This award is named in memory of Stafford Beer, a world leader in the development of systems ideas, especially management cybernetics, and President of The OR Society 1970-71.

The Stafford Beer Medal is awarded in recognition of the most outstanding contribution to the philosophy, theory or practice of Information Systems published in the European Journal of Information Systems (EJIS) within the relevant year.

Citation for the Stafford Beer Medal 2025

Designing a wearable IoT-based bladder level monitoring system for neurogenic bladder patients

European Journal of Information Systems, Volume 33 (6), 993-1015

https://doi.org/10.1080/0960085x.2023.2283173

 
This year’s Stafford Beer Medal is presented to Claudius Jonas, Jannik Lockl, Maximilian Röglinger and Robin Weidlich, for their paper entitled “Designing a wearable IoT-based bladder level monitoring system for neurogenic bladder patients”.

Wearable IoT systems enable real-time monitoring of physiological parameters, which is particularly important for patients who lack sensation in parts of their bodies, such as those with neurogenic bladder dysfunctions. This paper describes the development and design principles for a new prototype bladder monitoring system using IoT sensors. The system aims to help neurogenic bladder patients regain control over their bladder management by monitoring their physiological parameters. The judges regarded this design science study as an excellent example of Information Systems researchers tackling a real-world problem and delivering a solution that can directly enhance patients’ health and well-being. They believe it establishes a foundation for future wearable IoT-based physiological monitoring systems and are pleased to recognise this achievement with the Stafford Beer medal.

 
The judges also highly commend the following paper for its original insights, practical implications and opportunities for future research in the field of data curation. 


Highly Commended 2025:

Data curation as anticipatory generification in data infrastructure


Elena Parmiggiani, Nana Kwame Amagyei, Steinar Kornelius Selebø Kollerud.

https://doi.org/10.1080/0960085x.2023.2232333