Research on the Impact of Algorithmic Bias in Different Categories on Consumers' Intention to Use

Abstract

The emergence of algorithmic bias leads to unfair decision outcomes for consumers. However, due to the complexity and opacity of algorithms, the phenomenon of algorithmic bias has become widespread, thus drawing attention to related issues in the field of intelligent services. Currently, most studies have discussed the causes and specific manifestations of algorithmic bias conceptually, but few scholars have investigated the different categories of algorithmic bias in intelligent service algorithms and their differential impacts on consumers' intention. Clarifying the reasons for the differences and their underlying mechanisms can supplement the theoretical basis of current research on algorithmic bias in intelligent services and provide guidance for enterprise services. Therefore, studying the impact of intelligent service algorithmic bias on consumers' intention to use is of great significance. This paper explores the differences in the impact of different categories of algorithmic bias on consumers' intention to use. This study proves that compared with the application bias, the contextual bias leads to lower intention to use among consumers. This is because the contextual bias induces consumers to have stronger suspicion, which results in a lower intention to use such intelligent services.