Price Perception Fairness in the Era of Robo-Advisory

This thesis investigates the impact of advisor type and banking environment on customers' perceptions of price fairness.

Price Perception Fairness in the Era of Robo-Advisory
Customer interacting with robo-advisory services (created by DALL.E)


My Master Thesis investigates the perceptions of price fairness between robo-advisors and human advisors in both online and traditional in-person banking environments. The study aims to understand how customers perceive price increases for these services and the impact of these perceptions on their intentions to repurchase and recommend banking services.


This topic is highly relevant for practitioners because it provides insights into customer attitudes towards innovative financial technologies like robo-advisors. Understanding these perceptions helps financial institutions develop effective pricing strategies, enhance customer satisfaction, and foster long-term engagement. This is crucial as the adoption of financial technology continues to grow.


The research revealed that customers perceive price increases for robo-advisor services as less fair compared to human advisor services, regardless of the environment (online or in-person). Perceived price fairness significantly influences customers' intentions to repurchase and recommend banking services. The study found no significant difference in price fairness perceptions between online and in-person environments, indicating that context does not affect fairness perceptions.

Implications for Practitioners

  • Develop transparent pricing strategies that communicate value to customers.
  • Implement customer-centric pricing policies considering perceived fairness.
  • Ensure uniform pricing strategies across different service delivery environments.
  • Enhance customer education on the benefits of robo-advisors to justify pricing.
  • Focus on building trust and long-term relationships through fair pricing practices.


The methodology involved an experimental design with four conditions: online banking with robo-advisors, online banking with human advisors, traditional banking with robo-advisors, and traditional banking with human advisors. A total of 160 participants were randomly assigned to these conditions. Data were collected through a survey on Qualtrics, measuring perceptions of price fairness, repurchase intention, recommendation intention, and general attitudes towards financial technology. Statistical analyses, including ANOVA and ANCOVA, were conducted using SPSS to test the hypotheses and analyze the data .