Beyond the code: How algorithmic creativity enhances tolerance to recommendation bias?

While algorithms drive transformative changes in marketing practices, they concurrently generate recommendation biases that result in unequal consumer outcomes and adverse reactions. This research examines the issue of recommendation bias stemming from recommendation algorithms. Grounded in intentionality theory, the study elucidates the underlying mechanism through which algorithmic creativity (high vs. low) influences consumer responses. Across six studies (NÂ =Â 2686), the results demonstrate that under conditions of recommendation bias, consumers exhibit less negative responses toward high creativity algorithms than toward low creativity ones. This effect is serially mediated by intentionality inference and perceived unfairness. Furthermore, the impact of algorithmic creativity is amplified in the context of experience goods, whereas no significant difference emerges for search goods. These findings offer novel insights into consumer reactions to algorithmic decision-making and provide practical implications for firms seeking to refine their personalized recommendation strategies.