Innovative products can be highly prospective and apt to disrupt usages profoundly. They can lead to multiple long-term social impacts influencing people's way of life and behaviour. So it is necessary to anticipate them without delay. Due to high uncertainty, designers may face the problem that conventional user-centred methods, which assess design performances from today's users, are not adapted. We think sociologists can help characterise the likely social impacts of future products. So we propose an original framework called the Representation-Usage-Impact (RUI) method to stimulate sociologists' projection and capture relevant knowledge about probable social impacts. The method includes a database structure encoding the knowledge of sociologists for further use in the design process. Its goal is to help designers avoid making choices today that may be regretted in decades. We illustrate the method and its process with the design of autonomous vehicle scenarios, as it will likely bring many new usages in the future. As the method is still under construction, we present an intermediate validation step involving sociologists. The first results suggest that the method might be a safeguard for the design of disruptive products.

A refrigerant system (like that of a supermarket) is a complex system if we consider all the stakeholders throughout its lifecycle phases (use, maintenance, technological update, end of life). The lack of stakeholders' interaction during the design and other lifecycle stages of such a system generates issues and leads to sub-optimal system performances. We used the RID methodology to identify the main areas for improvement for these activities related to the refrigerant system. It is precisely designed to analyze, within the scope of activity, the major stakeholders' problems (user profiles) during lifecycle phases (use situations) to deduce areas for improvement (value buckets). Therefore, we built a process of interviews and data collection on existing practices to feed into a RID model. The first results are an archetypal description of the actors and problems encountered according to the lifecycle phases. The second part is a prioritized mapping of the areas to improve despite a certain number of known available solutions but proven insufficient.