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

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Abstract

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.

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Acknowledgements

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5B5A16077452)

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Authors and Affiliations

  1. Technology Management, Economics and Policy Program, Seoul National University, Seoul, Republic of Korea

    Hosoo Cho

  2. Department of Interaction Science, Sungkyunkwan University, Seoul, Republic of Korea

    Daeho Lee

  3. Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, Republic of Korea

    Daeho Lee

  4. Media School, Hallym University, 1, Hallymdaehak-gil, Chuncheon-si, Gangwon-do, Republic of Korea

    Jae-Gil Lee

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Cho, H., Lee, D. & Lee, JG. User acceptance on content optimization algorithms: predicting filter bubbles in conversational AI services. Univ Access Inf Soc 22, 1325–1338 (2023). https://doi.org/10.1007/s10209-022-00913-8

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