This proposed study explores how chatbot design can enhance dialogical information seeking by guiding users to articulate latent or unexpressed information needs. It builds on the literature in information systems (IS), search intermediaries, and human–chatbot interactions, introducing anchored instructions (generalized vs. specified) and verbal embodiment (human-like dialogue cues) as key design elements. Drawing on mindset theory, the authors argue that specified instructions align with a deliberative mindset activated by embodied chatbots, whereas generalized instructions align with an implemental mindset triggered by non-embodied chatbots. A field experiment on an electric vehicle (EV) retail platform will test these hypotheses, with outcomes measured in terms of service evaluation, conversion likelihood, and search behavior. This study contributes by identifying how chatbot design features interact with users’ cognitive orientations to influence information-seeking outcomes, revealing a moderated mediation model.