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Who Wants to Try AI? Profiling AI Adopters and AI‐Trusting Publics in South Korea

Author

Listed:
  • Hyelim Lee

    (College of Media & Communication, Korea University, Republic of Korea)

  • Changyoung Jung

    (Data Science Group, INTERX, Republic of Korea)

  • Nayoung Koo

    (Sejong University, Republic of Korea)

  • Seongbeom Seo

    (Sejong University, Republic of Korea)

  • Sangbong Yoo

    (AI, Information and Reasoning Laboratory, Korea Institute of Science and Technology, Republic of Korea)

  • Hyein Hong

    (Miridih, Republic of Korea)

  • Yun Jang

    (Sejong University, South Korea)

Abstract

The study introduces new approaches that integrate public relations concepts to identify adopters of AI technologies. It seeks to discover novel methods for identifying target groups likely to adopt AI technologies, even in contexts where these technologies are not yet familiar. The study achieves this by employing latent profile analysis, regression, and structural equation modeling using data from a South Korean online panel survey ( N = 625). The results identify five distinct public profiles based on their relationship assessments, problem recognition, and general trust in AI: the Cautious, Balanced, Uninterested, Confident, and Enthusiastic. Notably, the Enthusiastic group—characterized by high trust and strong relationships with the service provider—showed the strongest interest in adopting AI, highlighting their potential as key publics for introducing new AI services in business contexts. Additionally, the article contributes to public segmentation theory through the use of the situational theory of problem-solving and enhances the applicability of established public relations frameworks to the evolving field of AI.

Suggested Citation

  • Hyelim Lee & Changyoung Jung & Nayoung Koo & Seongbeom Seo & Sangbong Yoo & Hyein Hong & Yun Jang, 2025. "Who Wants to Try AI? Profiling AI Adopters and AI‐Trusting Publics in South Korea," Media and Communication, Cogitatio Press, vol. 13.
    • Handle: RePEc:cog:meanco:v13:y:2025:a:9639
    DOI: 10.17645/mac.9639
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