Korean companies pitch AI as a business tool rather than a social concern, but AI posts draw less engagement than other corporate messages; embedding AI within CSR narratives narrows this public-engagement gap, especially in conventional industries where AI is novel.

Main Finding

Korean corporate AI communication is predominantly instrumental—framed around business value, operational enablement, and brand positioning—while giving limited attention to societal or ethical implications. Public responses are generally positive but show lower engagement and fewer positive comments for AI-related posts compared with non-AI posts; framing AI communication within CSR mitigates this engagement penalty. Reception also varies by industry: AI messaging is better received in conventional industries (where AI is novel) than in high-tech sectors (where it is expected). Interviews with practitioners confirm awareness of ethical issues but reveal continued prioritization of instrumental messaging and caution about integrating CSR or normative framing into AI communication in Korea’s high-tech, state-guided context.

Key Points

• Dominant framing: Companies present AI instrumentally—as a source of business value, efficiency, and brand differentiation—rather than emphasizing social/ethical impacts.
• Public sentiment vs. engagement: Although sentiment toward AI posts is positive on average, such posts attract lower engagement (likes, shares, comments) and fewer expressly positive comments than comparable non-AI posts.
• CSR as a moderator: When AI is framed within corporate social responsibility narratives, the negative engagement gap narrows—CSR framing partially offsets audience reluctance.
• Industry heterogeneity: Conventional industries (e.g., manufacturing, finance) gain more favorable reception from AI messaging than high-tech industries, likely due to novelty effects and differing expectations.
• Practitioner stance: Corporate practitioners are aware of ethical concerns but still prioritize instrumental messaging for strategic reasons and are cautious about explicitly adopting CSR or normative framings around AI.
• Context matters: Findings emerge in Korea’s institutional setting—high-tech, with active state guidance—shaping organizational incentives and public expectations.

Data & Methods

• Design: Mixed-methods study combining computational social media analysis (Study 1) and qualitative interviews (Study 2).
• Study 1 (computational/social media):
  • Corpus: Corporate social media posts from Korean companies (platforms and exact sample size/timeframe unspecified in the summary).
  • Analyses: Automated content classification to identify AI-related posts and classify frames (instrumental vs. societal/ethical/CSR); sentiment analysis of public comments; engagement metrics (likes, shares, comment counts); industry-level comparisons.
  • Key measures: Framing prevalence, sentiment polarity, engagement volume, effect of CSR framing, industry differences.
  • Limitations (inferred): Potential platform- and sample-selection bias; measurement limits of automated sentiment and frame classification; correlational—not causal—relationships.
• Study 2 (qualitative interviews):
  • Method: In-depth interviews with corporate practitioners involved in AI strategy/communication (participant roles and number not specified in the summary).
  • Analysis: Thematic coding to extract perceptions of AI adoption, ethical concerns, communication strategies, and governance preferences.
  • Contribution: Provides interpretive context explaining why instrumental messaging persists and why CSR/normative framing is cautiously applied.
  • Limitations: Qualitative sample may not be representative; findings shaped by Korea’s institutional context.

Implications for AI Economics

• Market signaling and adoption:
  • Corporate AI messaging conveys signals about readiness, capability, and expected value; instrumental framing emphasizes private returns and may accelerate investment but could under-communicate social risks.
  • Lower engagement for AI posts suggests communication frictions that could slow consumer adoption or acceptance, particularly in industries where AI is expected.
• Value of CSR framing:
  • Integrating CSR into AI narratives can reduce engagement penalties, implying that normative framing may improve consumer acceptance and reduce reputational risk—potentially lowering adoption costs.
  • Firms weighing communication strategies should consider CSR framing as a tool to unlock broader social license and demand.
• Industry heterogeneity and diffusion:
  • Novelty in conventional industries makes AI communication more effective there; policy and firm strategies should be industry-specific when promoting diffusion or designing incentives.
• Corporate governance and investment:
  • Practitioner caution about normative communication points to governance gaps: investors and regulators may need clearer standards and reporting expectations so firms can credibly communicate both capability and safeguards.
• Policy and regulatory design:
  • Regulators in state-guided contexts (like Korea) can influence both adoption and public trust by encouraging transparency, standardized disclosures about AI safeguards, and incentives for CSR-oriented AI deployment.
  • Public-sector procurement and industry guidance that reward ethical practices could change firms’ communication incentives and internal governance investments.
• Research and measurement needs:
  • Quantify economic impacts of communication strategies on sales, adoption rates, stock market reactions, and long-term reputational costs.
  • Comparative and experimental studies to establish causal effects of framing (instrumental vs. CSR vs. ethical transparency) on consumer and investor behavior across countries and industries.

Taken together, the study shows that how firms talk about AI matters economically: messaging shapes public engagement and acceptance, which interacts with industry context and governance incentives to influence adoption dynamics and welfare-relevant outcomes.