Artificial intelligence recommendations evoke dissonance? The impact of contextual bias on service satisfaction

Despite artificial intelligence recommendations being widely used, there are still many problems behind the algorithms. One of the critical problems is contextual bias in AI recommendations. Contextual bias refers to systematic algorithmic bias that leads to unfair outcomes for specific individuals or groups at the levels of demographics, behavioral history, sociocultural background, or other contextual factors. Across four experimental studies and a field experiment, this research finds that contextual bias reduces service satisfaction, an effect mediated by cognitive dissonance. This negative effect is moderated by consumer motivation and consumer innovativeness. The research contributes to deepening the understanding of how contextual bias influences consumer cognitive processes and further enriches the application of relevant theories in the field of algorithmic bias. Managerially, this research provides insights into how different consumer motivations and levels of consumer innovativeness strategically affect the cognitive processes of consumers regarding contextual bias.