Corporate AI communication: Public responses, organizational practice, and governance in Korea
This mixed-method study examines (1) how Korean companies communicate AI in strategic communication, (2) how publics respond to such communication, and (3) how practitioners perceive AI adoption, related challenges, and governance mechanisms. This study combines computational analysis of corporate social media posts (Study 1) with in-depth interviews with practitioners (Study 2). Study 1 shows that corporate AI communication is predominantly instrumental, framing AI primarily as business value, an operational enabler, or a means of brand positioning, while giving limited emphasis to its societal and ethical implications. Although AI communication generally receives positive public sentiment, it attracts less engagement and fewer positive comments than non-AI communication. Yet framing AI within the context of CSR mitigates this engagement penalty. AI communication is better received in conventional industries where AI remains relatively novel than in high-tech industries where it is more expected. Study 2 reveals that, despite practitioners’ awareness of AI’s ethical implications, corporate communication continues to prioritize instrumental messaging and remains cautious about integrating CSR into AI communication. These findings advance understanding of strategic AI communication by highlighting the interplay among instrumental and normative communication, public responses, and organizational practice in the high-tech, state-guided context of Korea.
Authors
- Wanjiang Jacob Zhang (ORCID: https://orcid.org/0000-0002-6806-8218)
- Yingru Ji (ORCID: https://orcid.org/0000-0003-2969-2947)
- Chen Silvia Zhang (ORCID: https://orcid.org/0009-0003-2499-7247)
- Sora Kim (ORCID: https://orcid.org/0000-0003-0557-6538)
Institutions
- Zhejiang International Studies University (CN)
- Hong Kong Polytechnic University (HK)
- Chinese University of Hong Kong (HK)
Publication Details
- Journal
- Public Relations Review
- Published
- 2026-09-05
- DOI
- https://doi.org/10.1016/j.pubrev.2026.102764
- Primary Topic
- Computational and Text Analysis Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00