{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T05:18:32Z","timestamp":1787721512433,"version":"build-2784847793"},"reference-count":101,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>Promoting consumers\u2019 organic food purchases is an important way to drive the green transformation of the demand side of food consumption and achieve sustainable development. This study, through two studies, examines the interactive effects and psychological mechanisms of different types of AI shopping assistants (analytic AI vs. empathic AI) and algorithmic recommendation cues (item-referent cues vs. user-referent cues) on consumers\u2019 organic food purchase intention. The findings indicate that the two have an interaction effect, with analytic AI combined with item-referent cues being more effective, while empathic AI matched with user-referent cues is more effective. Further analysis revealed that perceived information validity and green trust each play a mediating role, forming a chain mediation path. Specifically, perceived information validity positively influences green trust, and green trust further positively influences purchase intention. This work reveals the matching mechanism between AI source characteristics and algorithmic recommendation cues, expands the application of Cue Utilization Theory and Signaling Theory in AI-driven organic food consumption contexts, and provides practical insights for companies to develop differentiated AI assistant configurations, recommendation information design, and green trust cultivation strategies.<\/jats:p>","DOI":"10.3390\/foods15172965","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T09:20:02Z","timestamp":1787563202000},"page":"2965","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["What Makes an AI Persuasive? The Interaction Effect of AI Shopping Assistants and Algorithmic Recommendation Cues on Consumers\u2019 Organic Food Purchase Intention"],"prefix":"10.3390","volume":"15","author":[{"given":"Sinan","family":"Li","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0396-7765","authenticated-orcid":false,"given":"Kai","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"ref_1","unstructured":"FAO, IFAD, UNICEF, WFP, and WHO (2023). 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