This paper reconceptualizes persuasion in hospitality marketing in response to the growing role of artificial intelligence (AI) agents that curate, filter and increasingly autonomously book hospitality services. Traditional persuasion models assume a human cognitive target; however, AI agents now function as parallel decision-makers whose algorithmic evaluations shape customer choices. This study aims to articulate how persuasion operates when influence must target both human customers and AI agents.
This conceptual study synthesizes research from hospitality marketing, AI-enabled customer decision-making and agentic systems. Drawing on interdisciplinary evidence, it develops the Dual-Target Persuasion Framework, which differentiates between AI-assisted and AI-autonomous pathways and integrates key mediators (i.e. trust, cognitive load, preference alignment and perceived agency) and moderators (i.e. service type, technological fluency and emotional salience).
The framework proposes that persuasion in AI-mediated hospitality environments is bifurcated: one pathway targets human psychology through AI-assisted decision support, while the other targets algorithmic evaluation through machine-readable signals and structured data. The effectiveness of the influence depends on how mediating mechanisms operate across these two pathways and on boundary conditions that determine when human judgment or agentic optimization dominates. Customer loyalty, satisfaction and decision quality increasingly emerge from a hybrid interplay between human sentiment and agentic logic.
This study offers guidance to hospitality managers designing content for two distinct persuasion targets. Effective strategies must combine emotionally resonant narratives for customers with structurally optimized, verifiable and machine-readable information for AI agents. The framework highlights how data quality, transparency and preference alignment influence an agent’s selection processes and how experiential framing and agency preservation support human acceptance of AI-mediated recommendations.
This paper advances persuasion theory by identifying AI agents as legitimate persuasion targets and offering the first hospitality-specific account of dual-path influence in AI-mediated decisions. It reframes persuasion as a continuum from AI assistance to autonomy and outlines a forward-looking research agenda on customer–AI–brand interactions in environments where loyalty may be shaped as much by algorithmic selection as by human cognition.
