Percepção dos consumidores em relação a sistemas de recomendação baseados em inteligência artificial

Abstract

Among the applications of artificial intelligence in the digital environment, recommendation systems that personalize offers and content based on user behavior stand out. Despite their widespread use on streaming platforms, e-commerce, and social networks, evidence on how consumers perceive these systems and the extent to which they trust the recommendations remains limited. This research aimed to evaluate consumer perception of AI-based recommendation systems. This is a quantitative study with data collected through a questionnaire administered to 300 participants in the state of Amazonas. The respondents are predominantly young adults who are frequent users of these systems. It was observed that recommendations are generally perceived as useful and relevant, as they facilitate searches and reduce decision-making effort. However, a fragile level of trust was found, with discomfort regarding the repetitiveness and predictability of suggestions, as well as doubts about the veracity and quality of the recommended products. It is concluded that trust in recommendation systems is not stable, but depends on the perceived quality, accuracy, and transparency of the interactions. The findings provide insights for organizations to improve the use of AI in personalization strategies, focusing on building trust in digital consumption.