The Influence of AI-Enabled Smart Recommendations on Consumer Satisfaction and Choice Architecture

Author

Mr. Sachin Vasant Chaugule, Dr. Bhalchandra Bite

Abstract

 Purpose: This study examines how AI-enabled smart recommendation systems (AI-SR) affect consumer satisfaction and the way digital platform’s structure consumer choices. It focuses on four perceived AI-SR characteristics—personalisation accuracy, algorithmic transparency, contextual relevance, and recommendation diversity—and examines consumer trust and perceived autonomy preservation as explanatory mechanisms. Privacy risk perception and digital literacy are considered as boundary conditions. The study proposes and tests the AI Recommendation Satisfaction and Choice Architecture Model (ARSCAM).

Design/methodology/approach: A quantitative, cross-sectional survey was conducted among 412 adult users of AI recommendation systems in India. Respondents were drawn from e-commerce, streaming/entertainment, and financial-technology contexts. Data were analysed using SPSS 27 and SmartPLS 4.1. 

Findings: Personalisation accuracy showed significant positive relationships with utilitarian, hedonic, and relational satisfaction. Transparency was positively related to trust and satisfaction. Contextual relevance predicted satisfaction, while recommendation diversity predicted hedonic satisfaction and consideration-set breadth. AI-SR significantly affected option framing, default structuring, and consideration-set composition. Trust and perceived autonomy preservation played mediating roles. Privacy risk weakened the personalisation–satisfaction relationship, whereas digital literacy strengthened the transparency–trust relationship. The model explained 71.2% of the variance in consumer satisfaction and 64.8% in choice architecture.

Practical implications: Recommendation platforms can improve consumer experience by combining accurate personalisation with understandable explanations, privacy controls, diverse recommendations, and mechanisms that allow users to expand or modify the algorithmically curated option space.

Keywords

AI recommendation systems; consumer satisfaction; choice architecture; personalisation; transparency; trust; autonomy; privacy risk; digital literacy; PLS-SEM.

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