No Face, No Effect: AI-Acceptance, Not “Human vs. Robo,” Moves the Money
(A2026-133901)

AUTHORS

MAGDALENA BEKK, Seeburg Castle University; Johannes Zaunschirm, Seeburg Castle University; Matthias Spörrle, Seeburg Castle University

ABSTRACT

We examine persuasion in faceless advisory interfaces that strip anthropomorphic cues and isolate advisor identity (human vs. robo), differentiating how advisor type, trust, and AI-acceptance shape willingness to follow advice versus willingness to invest in the option. In a randomized experiment (N = 262), advisor type shows no main effects on following advice or willingness to invest. Instead, AI-acceptance governs responses. AI-acceptance produces a robust crossover for willingness to invest (high AI-acceptance investors prefer robo advise; AI-skeptics prefer human avice). Across all models, trust explains the most variance, while age does not condition advisor effects. The study exposes a hidden fault line in digital advice and shifts the focus from who gives advice to Ai-acceptance, and from following advice to investment intention: trust drives following advice, while AI-acceptance moves investment intention. For managers: build trust architecture, segment by AI-acceptance, and stop paying for “more human” labels.