Smart Water Conservation: A Behaviourally-Grounded Recommender System for Demand Management Programs

Water utilities are increasingly turning to digital solutions to promote conservation behaviours among households; however, traditional campaigns often suffer from limited personalisation, low interactivity, and modest long-term impact. Though computer-tailored and recommender systems (RSs) may offer personalisation, these systems lack a generalised framework that integrates behavioural theory with system design. This study addresses this research gap by introducing a novel framework that unites behavioural science, user experience (UX) design, and adaptive digital feedback to foster water-conscious practices at the residential level. The model draws on established behavioural theories, including the Theory of Planned Behaviour, the Transtheoretical Model, and Intervention Mapping, to ensure that tailored recommendations align with users’ psychological drivers, behavioural readiness, and daily routines. An industry-first prototype RS was developed and evaluated through an online survey (N = 300), assessing user perceptions of relevance, motivation, ease of use, and likelihood of action. The results reveal strong support for personalised suggestions, with 82% of respondents agreeing that personalised recommendations would help conserve water, and 76% indicating incentives would motivate adoption. This evidence indicates early acceptance and high potential impact. This study also addresses a critical research gap: no generic model previously existed to guide the integration of RSs with behaviour change interventions in water demand management. Broader implications are also discussed for applying the model to other sustainability domains such as energy use, waste reduction, and climate adaptation.