The Impact of Personalisation Algorithms on Consumer Engagement and Purchase Behaviour in AI-Enhanced Virtual Shopping Assistants

Abstract Algorithms are increasingly used in consumer-oriented decision-making, and understanding how customers respond to them is crucial. The research is grounded in self-determination theory and aims to identify AI algorithm variables that affect consumers' decision-making. These can improve consumers' pleasure, and engagement and boost revenue by increasing customer loyalty. Online shopping involves making purchases through the internet, following a five-phase process. Artificial intelligence is revolutionizing customer engagement by providing tailored experiences and insights. Generative and conversational AI can generate product recommendations, while AI-driven systems offer advantages for both businesses and consumers, boosting sales, and customer satisfaction, and optimizing the shopping process. The study uses Social Exchange Theory (SET) and Service-Dominant Logic (SDL) to study how AI-powered technology can benefit consumers by offering personalized recommendations and quick service. According to the study, different roles played by algorithmic agents have different impacts on consumers' purchasing decisions. This is consistent with the inverted U-shaped hypothesis. Purchase decisions made by customers have the most influence when algorithmic decision-making autonomy is at a medium degree. The psychological processes and behavioral attitudes of customers towards AI services and buying decisions must be understood. It recommends businesses prioritize personalized algorithm design and raise users' self-efficacy to maintain control over the purchasing process. Understanding customer engagement and balancing AI and human interaction can improve customer engagement strategies and satisfaction.