Abstract
This research explores the transformative role of advanced technologies, particularly AI, machine learning (ML) and block chain, in the development of smart e-commerce solutions that enhance user experiences and operational efficiencies. It also explores how customized algorithms can improve customer engagement, boost conversion rates and encourage sustainable shopping practices. Additionally, the research investigates AI-driven solutions to encourage sustainable shopping behaviors and optimize shopping post-purchase experiences. Specifically, it aims to assess the impact of AI-based personalization algorithms, examine the trade-offs between data privacy and user experience and analyze AI's influence on consumer trust and decision-making processes. Primary data from surveys and interviews are supplemented with secondary data on customer behavior and sales metrics to evaluate the effectiveness of technologies like AI, machine learning and block chain in driving personalized, ethical and efficient e-commerce. A mixed-method research design, combining qualitative and quantitative approaches, employed to capture insights from key stakeholders (e.g., consumers, developers and e-commerce managers) and assess the impact of smart e-commerce platforms on shopping behavior, trust and loyalty. Implementing the Agile Model, the research iteratively addresses system requirements, testing and deployment challenges to create a flexible, scalable solution. Key findings underscore the role of predictive modeling in inventory management, the ethical potential of sustainable product recommendations and the positive consumer responses to transparency initiatives. The study contributes valuable insights into the development of secure, personalized and ethical e-commerce platforms that align with modern consumer expectations for convenience and responsibility.
Published Version
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