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Perceived social agency-the perception of a robot as an autonomous and intelligent social other-is important for fostering meaningful and engaging human-robot interactions. While end-user programming (EUP) enables users to customize robot behavior, enhancing usability and acceptance, it can also potentially undermine the robot's perceived social agency. This study explores the trade-offs between user control over robot behavior and preserving the robot's perceived social agency, and how these factors jointly impact user experience. We conducted a between-subjects study (N = 57) where participants customized the robot's behavior using either a High-Granularity Interface with detailed block-based programming, a Low-Granularity Interface with broader input-form customizations, or no EUP at all. Results show that while both EUP interfaces improved alignment with user preferences, the Low-Granularity Interface better preserved the robot's perceived social agency and led to a more engaging interaction. These findings highlight the need to balance user control with perceived social agency, suggesting that moderate customization without excessive granularity may enhance the overall satisfaction and acceptance of robot products. 

As human-robot interactions become more social, a robot's personality plays an increasingly vital role in shaping user experience and its overall effectiveness. In this study, we examine the impact of three distinct robot personalities on user experiences during well-being exercises: a Baseline Personality that aligns with user expectations, a High Extraversion Personality, and a High Neuroticism Personality. These personalities were manifested through the robot's dialogue, which were generated using a large language model (LLM) guided by key behavioral characteristics from the Big 5 personality traits. In a between-subjects user study (N = 66), where each participant interacted with one distinct robot personality, we found that both the High Extraversion and High Neuroticism Robot Personalities significantly enhanced participants' emotional states (arousal, control, and valence). The High Extraversion Robot Personality was also rated as the most enjoyable to interact with. Additionally, evidence suggested that participants' personality traits moderated the effectiveness of specific robot personalities in eliciting positive outcomes from well-being exercises. Our findings highlight the potential benefits of designing robot personalities that deviate from users' expectations, thereby enriching human-robot interactions. 

Robots, particularly in service and companionship roles, must develop positive relationships with people they interact with regularly to be successful. These positive human-robot relationships can be characterized as establishing “rapport,” which indicates mutual understanding and interpersonal connection that form the groundwork for successful long-term human-robot interaction. However, the human-robot interaction research literature lacks scale instruments to assess human-robot rapport in a variety of situations. In this work, we developed the 18-item Connection-Coordination Rapport (CCR) Scale to measure human-robot rapport. We first ran Study 1 (N = 288) where online participants rated videos of human-robot interactions using a set of candidate items. Our Study 1 results showed the discovery of two factors in our scale, which we named “Connection” and “Coordination.” We then evaluated this scale by running Study 2 (N = 201) where online participants rated a new set of human-robot interaction videos with our scale and an existing rapport scale from virtual agents research for comparison. We also validated our scale by replicating a prior in-person human-robot interaction study, Study 3 (N = 44), and found that rapport is rated significantly greater when participants interacted with a responsive robot (responsive condition) as opposed to an unresponsive robot (unresponsive condition). Results from these studies demonstrate high reliability and validity for the CCR scale, which can be used to measure rapport in both first-person and third-person perspectives. We encourage the adoption of this scale in future studies to measure rapport in a variety of human-robot interactions.