The Algorithmic Nudge: Balancing Efficiency and Autonomy in AI-Driven Choice Architecture
Year: 2026
Type: article
Abstract: This research investigates the psychological impact of AI-driven "nudges" on consumer decision-making. While automated defaults improve transaction efficiency, I theorize that excessive opacity triggers Psychological Reactance, a motivational state where users reject suggestions to restore their sense of freedom. I proposed the Autonomy-Efficiency Trade-off model, which posits that long-term brand trust is negatively correlated with perceived loss of agency unless mitigated by transparency. To address this, I developed the "Transparent Nudging" framework and the principle of "Friction Symmetry," advocating that opting out of an AI's influence should be as frictionless as accepting it. This paper offers a strategic roadmap for firms to balance algorithmic precision with human autonomy, ensuring sustainable customer loyalty in an automated economy.
Author: Kojima, Shuzo
Language: English
Topic: AI in Service Interactions
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7130992051
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