Integrating Algorithmic Precision and Emotive AI Design: Two Pathways to Consumer Behavior and Brand Loyalty in Automotive Marketing

Abstract

The AI-driven transformation of automotive marketing models is a critical area. This study investigates how traditional automakers use AI to influence consumer behavior and brand loyalty. We propose a conceptual model integrating deep learning and NLP services to examine two impact pathways: efficiency resonance and emotional resonance, which function as mediating variables. A quantitative survey (N=140 intelligent vehicle consumers) tested the model using Structural Equation Modeling (SEM). Results show AI marketing significantly impacts consumer behavior and brand loyalty. Real-time data processing for personalized recommendations enhances efficiency resonance, driving rational consumer behavior. Concurrently, AI emotive design in customer interaction fosters emotional resonance, which builds brand loyalty. Theoretically, this work integrates these two pathways into a single framework, supporting Interaction Design Theory in the context of user experience. Practically, findings guide the development of robust AI marketing systems by showing the necessity of balancing computational precision (for efficiency) and interaction design (for emotional connection) to gain a lasting brand advantage.