1. Introduction to XAI in E-Commerce -- 2. Explainable Artificial Intelligence (XAI) for Managing Customer Needs in E-Commerce: a Systematic Review -- 3. Decoding the Recommender System: A Comprehensive Guide to Explainable AI in E-commerce -- 4. The AI Revolution in E-Commerce: Personalization and Predictive Analytics -- 5. Impact of Artificial Intelligence on Purchase Intention: A bibliometric analysis -- 6. Chatbot-XAI – The New Age Artificial Intelligence Communication tool for E-Commerce -- 7. Demystifying Applications of Explainable Artificial Intelligence (XAI) in e-Commerce -- 8. From Algorithms to Ethics: XAI's Impact on E-Commerce.

The technological boom has provided consumers with endless choices, removing the hindrance of time and place. Understanding the dynamic and competitive business environment, marketers know they need to reinforce indestructible customer experience with the support of algorithmic configurations to minimize human intrusion. World Wide Web (WWW) and online marketing have changed the way of conducting business; with artificial intelligence (AI), business houses can furnish a customized experience to fulfil the perceived expectation of the customer. Artificial intelligence bridges the gap between business and prospective clients, provides enormous amounts of information, prompts grievance redressal system, and further complements the client’s preference. The opportunities online marketing offers with the blend of artificial intelligence tools like chatbots, recommenders, virtual assistance, and interactive voice recognition create improved brand awareness, better customer relationship marketing, and personalized product modification. Explainable AI provides the subsequent arena of human–machine collaboration, which will complement and support marketers and people so that they can make better, faster, and more accurate decisions. According to PwC’s report on Explainable AI(XAI), AI will have $15.7 trillion of opportunity by 2030. However, as AI tools become more advanced, more computations are done in a “black box” that humans can hardly comprehend. But the rise of AI in business for actionable insights also poses the following questions: How can marketers know and trust the reasoning behind why an AI system is making recommendations for action? What are the root causes and steering factors? Thus, transparency, trust, and a good understanding of expected business outcomes are increasingly demanded.