Role of artificial intelligence on consumer buying behavior: the dual effects of AI-enabled features on decision-making and trust

Abstract

Purpose: This study investigates the influence of Artificial Intelligence (AI) features on consumers’ buying behavior for electronic products, with a specific focus on consumer trust, decision-making ease, and purchase intention. Design/methodology/approach: This study used a quantitative research design to investigate the impact of key AI features on consumer outcomes. Data were collected using a structured questionnaire. Structural Equation Modeling (SEM) was employed to analyze the relationships between three AI features (modeled as latent constructs for recommendation engines, chatbots, and comparison tools) and the dependent variables of consumer trust, perceived decision-making support, and purchase intention. Findings: The results indicate that AI-enabled features significantly enhance consumer confidence and satisfaction by simplifying product evaluations and increasing perceived usefulness. However, concerns about privacy risks, overreliance on technology, and decision fatigue continue to shape consumer trust and adoption. This study highlights the importance of designing AI systems that are transparent, ethical, and inclusive for both tech-savvy and less technologically adept consumers. Originality/value: The originality of this study is threefold. First, it developed a unified framework that integrates technology acceptance and trust-based perspectives, a synthesis rarely found in the existing literature. Second, it moves beyond examining AI as a monolith by investigating how distinct and common AI features (recommendation engines, chatbots, and comparison tools) jointly influence the consumer decision journey. Finally, it bridges a critical theoretical gap by elucidating the interplay between perceived usefulness, trust, and ethical design, providing novel insights into how AI can be implemented not only effectively but also responsibly to empower consumers.