This paper presents a multi-dimensional taxonomy of levels of automation and reparation specifically adapted to Virtual Assistants (VAs) in the context of Human-Human-Interaction (HHI). Building from this framework, the main output of this study provides a method of calculation which helps to generate a trust rating by which this score can be used to optimise users’ engagement. This tool may be critical for the optimisation of energy management and consumption. Based on the research findings, the relevance of contextual events and dynamism in trust could be enhanced, such as trust formation as a dynamic process that starts before a user’s first contact with the system and continues long thereafter. Furthermore, following the continuously evolving of the system, factor-affecting trust during user interactions change together with the system and over time; thus, systems need to be able to adapt and evolve as well. Present work is being dedicated to further understanding of how contexts and its derivative unintended consequences affect trust in highly automated VAs in the area of energy consumption.
Keywords trust, energy management, engagement, system design, calibration system.