Overview
We want to implement an innovative approach that transforms dynamic pricing into a transparent and trustworthy practice by using AI chatbots as adaptive mediators. Customers often perceive dynamic pricing as a “black box,” which harms fairness perceptions and weakens loyalty. Our idea is to test adaptive transparency features that explain price changes interactively and make transparency relevant for different target groups.
Economic Innovation: The project empowers Pricenow to enrich its product offering and provide SMEs with empirically validated guidance. By addressing fairness perceptions—key drivers of satisfaction and loyalty—adaptive transparency increases acceptance while aligning transparency with revenue performance.
Technological Innovation: The chatbot leverages adaptive AI to tailor explanations to customer expectations, levels of detail, and communication styles. This goes beyond static disclosures and enables personalized, dynamic communication that fosters fairness and trust.
Research & UX Innovation: The project ensures the highest possible user-friendliness. Transparency avoids information overload and is seamlessly integrated into existing price presentations. With creative and interactive UX methods, we identify how transparency enhances trust without friction and define the factors driving customer acceptance in B2C contexts.
How does it differ from existing solutions or products?
So far, AI has primarily been applied to adjust pricing algorithms, while its potential to ensure the necessary transparency has received little attention (Chenavaz & Dimitrov, 2025). As a result, both the final price and its underlying rationale often remain a black box — a situation that is not conducive to customer trust (Neubert, 2022).
Our research expertise and previous work connect directly to this gap: we can draw on our experience in analyzing trust-building communication through chatbots (Basel et al., 2023, see figure below), while also bringing the necessary expertise in measuring customer trust and distrust in technological settings (e.g. Basel, 2024; Basel & Rubin, 2021).
Building on this foundation, the proposed project will develop and test innovative approaches to integrate AI not only as a pricing tool, but as an enabler of transparency and trust. In doing so, it will contribute to both scientific advancement and practical solutions for fairer, more customer-oriented pricing systems.
