Anindito Bhattacharya / Ishita Deb / Samarpita Roy
Artificial intelligence (AI) is transforming digital commerce by reshaping how consumers discover, evaluate, and purchase products. AI-driven recommendation systems have become a cornerstone of personalized online shopping, influencing purchase decisions, customer engagement, satisfaction, and brand loyalty. As intelligent technologies continue to evolve, understanding the factors that drive consumer adoption has become increasingly important for researchers, practitioners, and policymakers. AI-Driven Recommendation Systems in Online Shopping: Understanding Consumer Adoption and the Future of Intelligent Commerce examines the adoption of AI-powered recommendation systems through the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), with a primary focus on Generation Z while considering the emerging behaviours of Generation Alpha and Generation Beta. A key contribution of the book is the proposed AI-Commerce Adoption Framework (AICAF), which extends UTAUT2 by incorporating AI-specific constructs such as trust in AI, perceived personalization, recommendation quality, privacy concerns, algorithmic transparency, perceived risk, and social media validation. The framework explains how these factors influence behavioural intention, technology adoption, satisfaction, loyalty, and continued usage. The book also explores emerging trends including generative AI, explainable AI, agentic commerce, social commerce, ethical AI, digital trust, and the attention economy, highlighting their implications for the future of intelligent commerce. By integrating insights from information systems, marketing, consumer behaviour, and artificial intelligence, the book provides a comprehensive and practical resource for academics, postgraduate students, business leaders, technology developers, and marketing professionals seeking to understand the evolving role of AI in digital commerce.
