A promising application of social robots highlighted by the ongoing labor shortage is to deploy them as service robots at organizational frontlines. As the face of the firms, service robots are expected to provide cognitive and affective supports in response to customer inquiries. However, one question remains unanswered: Would having a robot with a high level of affective support be helpful when such a robot cannot provide a satisfactory level of cognitive support to users? In this study, we aim to address this question by showing that empathetic service robots can be beneficial, although the extent of such benefits depends on the quality of services they provide. Our in-person human–robot interaction study (n = 55) shows that when a service robot can only provide a partial solution, it is preferable for it to express more empathetic behaviors, as users will perceive it to be more useful and will have a better customer experience. However, when a service robot is able to provide a full solution, the level of empathy displayed by it does not result in significant differences on perceived usefulness and customer experience. These findings are further validated in an online experimental study performed in another country (n = 395).

Facial expression recognition (FER) has emerged as a promising approach to the development of emotion-aware intelligent systems. The performance of FER in multiple domains is continuously being improved, especially through advancements in data-driven learning approaches. However, a key challenge remains in utilizing FER in real-world contexts, namely ensuring user understanding of these systems and establishing a suitable level of user trust towards this technology. We conducted an empirical user study to investigate how explanations of FER can improve trust, understanding and performance in a human-computer interaction task that uses FER to trigger helpful hints during a navigation game. Our results showed that users provided with explanations of the FER system demonstrated improved control in using the system to their advantage, leading to a significant improvement in their understanding of the system, reduced collisions in the navigation game, as well as increased trust towards the system.