AI Recommendation Systems and Consumer Price and Value Perception in Digital Markets

Artificial intelligence increasingly shapes how consumers search for products, evaluate alternatives, and make purchasing decisions in digital marketplaces. Online platforms such as e-commerce sites, travel services, and streaming platforms rely heavily on AI recommendation systems to personalise search results, rank products, and highlight price information. As a result, algorithmic systems play a central role in shaping how consumers interpret price, quality, and value during the decision process.

A growing body of research shows that digital environments fundamentally influence how consumers gather and process information when making choices online (Huang, Lurie and Mitra, 2009). At the same time, behavioural research suggests that consumers respond differently when decisions or recommendations are generated by algorithms rather than humans (Yalcin et al., 2022). While AI systems can enhance decision efficiency and personalisation, they may also introduce uncertainty or scepticism when consumers do not understand how recommendations are generated or when algorithms appear to make mistakes (Shaker and Purmehdi, 2025; Srinivasan and Sarial-Abi, 2021).

This PhD project will investigate how the design of AI recommendation systems influences consumer perceptions of price and value during online product searches. In particular, the research will examine how algorithmic features such as adaptivity, recommendation framing, ranking strategies, and transparency cues shape consumer evaluations of products and willingness to pay.

The project builds on behavioural pricing research demonstrating that consumers interpret price in multiple ways. Depending on the decision context, price may function both as a signal of product quality and as a representation of monetary sacrifice (Bornemann and Homburg, 2011). However, little is known about how these interpretations evolve when product recommendations are generated dynamically by AI systems.

Using a quantitative research approach, the project will employ behavioural experiments to examine how consumers interact with AI-generated recommendations and how their perceptions of price and value evolve throughout the decision journey. By uncovering how algorithmic design influences consumer evaluations and purchasing decisions, the research will contribute to the development of more transparent and responsible digital marketplaces. In doing so, the project supports broader societal goals aligned with the United Nations Sustainable Development Goals, particularly SDG 9 (Industry, Innovation and Infrastructure) by advancing knowledge on responsible digital innovation, and SDG 12 (Responsible Consumption and Production) by generating insights that can help design AI systems that support more informed and value-conscious consumer choices. The findings will therefore be relevant not only to academics but also to digital platforms and organisations seeking to design AI-enabled consumer experiences that promote transparency, trust, and responsible consumption.

Supervisors

Hamid Shaker

Anish Yousaf