# Value-dependent and empathy-mediated: how artificial intelligence-generated marketing content influences customer engagement, and when to disclose its origin

**Authors:** Xuan Gao, Weiwei Li, Yanli Zhao

PMC · DOI: 10.3389/fpsyg.2025.1701085 · Frontiers in Psychology · 2026-01-02

## TL;DR

This paper explores how AI-generated marketing content affects customer engagement through value and empathy, and when disclosing its AI origin is effective.

## Contribution

The study introduces a novel framework linking AI-generated content value, credibility, empathy, and disclosure effects on consumer engagement.

## Key findings

- Hedonic content increases emotional and behavioral engagement more than functional content.
- Content value and credibility interact to influence engagement outcomes.
- AI disclosure paradoxically strengthens cognitive empathy for functional content but weakens affective empathy for hedonic content.

## Abstract

The rapid adoption of artificial intelligence-generated marketing content in recent years raises a need for a deeper understanding of its impact on consumer engagement. Moving beyond the traditional focus on technological capabilities, this study examines how artificial intelligence-generated marketing content value (functional vs. hedonic) and credibility jointly influence customer engagement, as well as the psychological pathways involved. Using the Elaboration Likelihood Model and the Cognitive-Affective Processing System (CAPS) framework, two between-subjects experiments (Study 1: N = 152; Study 2: N = 186) were conducted. The results show that: (1) hedonic value elicited higher emotional and behavioral engagement than functional value; (2) a significant interaction emerged between content value and credibility; (3) functional value exerted its indirect effect on engagement through cognitive empathy, whereas hedonic value did so through affective empathy; and (4) crucially, AI disclosure exerted a paradoxical moderating effect, amplifying the cognitive empathy path for functional content but attenuating the affective empathy path for hedonic content. These findings offer a nuanced theoretical framework for understanding the effects of AIGMC and provide managers with actionable insights for implementing value-congruent AI disclosure strategies.

## Full-text entities

- **Diseases:** AI (MESH:C538142), pain (MESH:D010146), MGC (MESH:D063466)
- **Chemicals:** AIGMC (-)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

114 references — full list in the complete paper: https://tomesphere.com/paper/PMC12829478/full.md

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Source: https://tomesphere.com/paper/PMC12829478