AI source disclosure in sustainable advertising: felt obligation, competence framing and decision-making orientation
Ying Yang, Di WuPurpose
This research examines when and why artificial intelligence (AI) source disclosure reduces participation in sustainable advertising. It proposes that AI source disclosure is associated with lower felt obligation toward the campaign organization and that this negative effect depends on appeal type and decision-making orientation.
Design/methodology/approach
Five experiments tested the proposed model. Study 1 used an interactive webpage and a consequential sharing task to examine actual participation. Study 2 examined felt obligation as the proposed mediator while testing perceived sincerity as an alternative explanation. Studies 3 and 4 tested the moderating role of warmth versus competence appeals in Chinese and US samples, respectively. Study 5 examined instrumental versus normative decision-making as an additional boundary condition.
Findings
Campaigns attributed to AI sources generated lower participation than campaigns attributed to human sources. This effect was associated with reduced felt obligation toward the organization, while sincerity and authenticity were not supported as mediators. The negative effect was stronger for warmth appeals and normative decision-making, but weaker for competence appeals and instrumental decision-making.
Originality/value
This research identifies felt obligation toward the campaign organization as a pathway associated with responses to AI source disclosure in sustainable advertising. It extends the stereotype content model by demonstrating that appeal type conditions the AI source-disclosure effect and contributes to stakeholder and AI decision-making research by identifying decision-making orientation as an additional boundary condition.