Influence of Device Performance and Agent Advice on User Trust and Behaviour in a Care-taking Scenario

Monitoring systems have become increasingly prevalent in order to increase the safety of elderly people who live alone. These systems are designed to raise alerts when adverse events are detected, which in turn enables family and carers to take action in a timely manner. However, monitoring systems typically suffer from two problems: they may generate false alerts or miss true adverse events. This motivates the two user studies presented in this paper: (1) in the first study, we investigate the effect of the performance of different monitoring systems, in terms of accuracy and error type, on users’ trust in these systems and behaviour; and (2) in the second study, we examine the effect of recommendations made by an advisor agent on users’ behaviour. Our user studies take the form of a web-based game set in a retirement village, where elderly residents live in smart homes equipped with monitoring systems. Players, who “work” in the village, perform a primary task whereby they must ensure the welfare of the residents by attending to adverse events in a timely manner, and a secondary routine task that demands their attention. These conditions are typical of a retirement setting, where workers perform various duties in addition to keeping an eye on a monitoring system. Our main findings pertain to: (1) the identification of user types that shed light on users’ trust in automation and aspects of their behaviour; (2) the effect of monitoring-system accuracy and error type on users’ trust and behaviour; (3) the effect of the recommendations made by an advisor agent on users’ behaviour; and (4) the identification of influential factors in models that predict users’ trust and behaviour. The studies that yield these findings are enabled by two methodological contributions: (5) the game itself, which supports experimentation with various factors, and a version of the game augmented with an advisor agent; and (6) techniques for calibrating the parameters of the game and determining the recommendations of the advisor agent.