## 1 Introduction
Similarly, social robots can show human-like sensory abilities through artificial perception, which raises the question of whether they, too, should be granted a certain degree of moral consideration. Embedding appropriate values into robot programming to guide their interactions with users is crucial, ensuring that these robots pursue moral objectives that support human development and social harmony [20]. To be considered a moral agent, whether human or artificial, an entity is generally expected to possess autonomy, which refers to a certain degree of independence in decision-making [21]. However, while these robots may appear to provide autonomous and naturally fluid responses that seem tailored to meet users' personalized needs, their actions are shaped by programming created by human developers and accumulated data. This means that their behavioral patterns are governed by pre-coded instructions. For example, robots trained with large language models (LLMs) can engage in conversations with users in a way that appears more natural. Additionally, machine learning (ML) algorithms are employed to analyze user data, with the goal of customizing robot behavior based on individual feedback and preferences [17]. This reliance on pre-programmed instructions and processed data reveals the limitations of social robots. Even though these robots may seem independent and autonomous in their decision-making, they are, in fact, bound to external inputs, much like autonomous agents, exposing their underlying dependency [22]. One such social robot that embodies these features is MOCCA, the central focus of this research. MOCCA, a social robot integrating advanced episodic memory and anthropomorphic traits, reflects the evolution of human-robot interaction (HRI). It expresses human-like emotions and intentions through movements of its head, arms, facial expressions, and synthesized voice. Its built-in intelligent functions enable contextual memory and information processing, allowing it to accurately recognize users' intentions and respond naturally, rather than being limited to pre-programmed dialogues [23]. This dynamic and personalized interaction illustrates the growing trend of social robots evolving beyond mere tools, gradually becoming companions that offer emotional value and foster deeper, more trustworthy relationships. Unlike prior research [24; 25], this study applies the Stimulus-Organism-Response (SOR) theory to examine how MOCCA's features influence perceived trust and parasocial interaction, which serve as two core psychological mechanisms within the SOR framework [26]. By integrating both affective and cognitive processes, these internal states ultimately shape users' intentions to continue using the social robot. This study also empirically validates a structured model that incorporates both positive (intimacy and morality) and negative (dependency and privacy risk) features, offering a balanced perspective on how users cognitively and emotionally make behavioral decisions regarding social robot usage. The findings seek to inform the design of future social robots by guiding developers in optimizing the balance between morality, autonomy, and privacy protection. These insights contribute to a deeper theoretical understanding of HRI while also offering practical implications for promoting ethical and user-centered design in social robotics. Thus, as social robots become increasingly integrated into everyday life, understanding the factors that influence users' long-term continuance intentions becomes essential. Accordingly, this study aims to clarify how various features of MOCCA, a social robot equipped with episodic memory, affect users' continuance intentions through perceived trust and parasocial interaction within the framework of the SOR theory.