The Impact of Algorithmic Price Discrimination on Consumers' Perceived Betrayal

With the development of artificial intelligence technology, data support is increasing in importance, as are problems such as information disclosure, algorithmic discrimination and the digital divide. Algorithmic price discrimination occurs when online retailers or platforms charge experienced consumers who are purchasing products on their online platforms higher prices than those charged to new consumers for the same products at the same time. The purpose of this paper is to investigate the impact of algorithmic price discrimination on consumers' perceived betrayal. This paper employed a field experimental method involving two studies. In total, 696 questionnaires were distributed to consumers: 310 for Study 1 and 386 for Study 2. The collected data were analyzed using variance analysis and process analysis methods and SPSS software. Our findings suggest (1) Increased algorithmic price discrimination leads to increased perceived betrayal. (2) Increased algorithmic price discrimination leads to lower perceived price fairness and therefore to increased perceived betrayal among consumers. (3) Higher perceived ease of use of online retailers decreases the impact of algorithmic price discrimination on consumers' perceived betrayal. We are a small group of researchers focusing on algorithmic price discrimination and integrating algorithmic discrimination into the consumer research field. Our research introduces the concept of consumer perceived betrayal to the field of artificial intelligence. We adopt a field experimental study to examine the impact of algorithmic price discrimination on consumers' perceived betrayal by introducing variables of perceived price fairness and perceived ease of use.