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A MODELLING FRAMEWORK FOR DATA-DRIVEN DESIGN FOR SUSTAINABLE BEHAVIOUR IN HUMAN-MACHINE INTERACTIONS

Published online by Cambridge University Press:  27 July 2021

Tiziano Montecchi  and
Niccolo' Becattini
Tiziano Montecchi
Affiliation:
Universidad Técnica Federico Santa María, Chile
Niccolo' Becattini*
Affiliation:
Universidad Técnica Federico Santa María, Chile
*
Becattini, Niccolo, Politecnico di Milano, Mechanical Engineering, Italy, [email protected]

Abstract

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As the society is already permeated by data, a data-driven approach to inform design for sustainable behaviour can help to identify misbehaviours and target sustainable behaviours to achieve, as well as to select and implement the most suitable design strategies to promote a behavioural change and monitor their effectiveness. This work addresses the open challenge of providing designers with a model for Human-Machine Interactions (HMI) that helps to identify relevant data to collect for inferring user behaviour related to environmental sustainability during product use.

We propose a systematic modelling framework that combines constructs from existing representation techniques to identify the most critical variables for resources consumption, which are the determinants of potential misbehaviours related to HMI. The analysis is represented as a Behaviour-Inefficiency Model that graphically supports the analyst/designer to link user behaviours with a quantitative representation of resources consumption.

The paper describes the model through an example of the use of a kettle and an additional application of the same approach to a washing machine, in order to point out its versatility for modelling more complex interactions.

Keywords

Product modelling / modelsSustainabilityBig dataBehaviour ChangeProcess modelling
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 151 - 160
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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