Consumer Well-Being and Situational Self-Esteem in AI Algorithm Use: The Role of Consumer Stress

This study examined differences in consumer well-being and situational self-esteem among users of artificial intelligence (AI) algorithms across stress contexts. Consumer stress was conceptualized as three dimensions structurally likely to arise in algorithmic recommendation contexts and directly linked to the decision-making process: emotional exhaustion, social comparison stress, and uncertainty–regret anxiety. Cluster analysis identified three profiles: a social comparison stress group, an uncertainty–regret anxiety group, and a low-stress disengaged group. Results showed that the social comparison stress group and the uncertainty–regret anxiety group reported higher consumer well-being in AI recommendation environments than the disengaged group. They also experienced a greater perceived burden of choice. However, no significant differences emerged in situational self-esteem or perceived decision responsibility. These findings extend prior research on AI algorithms beyond cognitive and technical outcomes by incorporating the psychological dimensions of user experience. Practically, the results suggest that recommendation strategies should be tailored to consumers’ stress profiles, with implications for more psychologically informed marketing.