Imitation is a vital skill that humans leverage in various situations. Humans achieve imitation by observing others with apparent ease. Yet, in reality, it is computationally expensive to model on artificial agents (e.g., social robots) to acquire new skills by imitating an expert agent. Although learning through imitation has been extensively addressed in the robotic literature, most studies focus on answering the following questions: what to imitate and how to imitate. In this conceptual paper, we focus on one of the overlooked questions of imitation through observation: who to imitate. We present possible answers to the who-to-imitate question by exploring motivational factors documented in psychological research and their possible implementation in robotics. To this end, we focus on two critical instances of the who-to-imitate question that guide agents to prioritize one demonstrator over another: outcome expectancies, viewed as the anticipated learning gains, and efficacy expectations, viewed as the anticipated costs of performing actions, respectively.