User acceptance on content optimization algorithms: predicting filter bubbles in conversational AI services

As the role of artificial intelligence (AI) agents in information curation has emerged with recent advancements in AI technologies, the present study explored which users would potentially be susceptible to the filter bubble phenomenon. First, a large-scale analysis of conversational agent users in South Korea (N = 2808) was conducted to investigate the relative importance of content optimization algorithms in shaping positive user experience. Five user clusters were identified based on their information technology proficiency and demographics, and a multiple-group path analysis was performed to compare the influences of content optimization algorithms across the user groups. The results indicated that the personalization algorithm generally exhibited a stronger impact on evaluations of an AI agent's usefulness than the diversity algorithm. In addition, increased user age and greater Internet usage were found to decrease the importance of objectivity in shaping trust in AI agents. This study improves the understanding of the social influence of AI technology and suggests the necessity of segmented approaches in the development of AI technology.