人工智能生成人像的真实性如何影响消费者的分享意愿？

谢毅¹　张艳红^2,3　常蕴哲³　张曦元⁴

（1.北京师范大学；2.山东女子学院；3.对外经济贸易大学；4.北京三快在线科技有限公司）

摘要：生成式人工智能（GenAI）技术正快速地在营销实践中得到越来越广泛的应用。然而，已有文献对图像生成，尤其是对人像生成的探讨仍较为匮乏。本文从自我动机与调节定向的理论视角出发，探讨AI生成人像的真实性对消费者分享意愿的影响及其边界条件。通过三项实验研究发现,AI生成人像的真实性对消费者分享意愿具有明显的正向影响。其中，自我增强动机与自我验证动机在二者关系中起中介作用。调节定向会对上述关系起调节作用：相较于促进定向个体，真实性对防御定向个体分享意愿的正向影响更强。且该调节作用仅出现在真实性经由自我增强动机影响分享意愿的间接路径，对自我验证动机的间接路径无明显影响。本文的研究拓展了真实性研究在生成式人工智能情境下的应用范围，为品牌在设计与传播人工智能生成人像产品的实践提供了理论依据和管理启示。

关键词：AI生成人像；真实性；分享意愿；自我增强动机；自我验证动机；调节定向

作者简介：谢毅，北京师范大学湾区国际商学院教授、博士生导师，珠海，519087；张艳红，山东女子学院工商管理学院讲师，济南，250300；对外经济贸易大学国际商学院博士研究生，北京，100029；常蕴哲，对外经济贸易大学国际商学院博士研究生，通信作者；张曦元，北京三快在线科技有限公司职员，北京，100102。

引用格式：谢毅，张艳红，常蕴哲，等. 人工智能生成人像的真实性如何影响消费者的分享意愿？[J]. 经济与管理研究，2026，47（8）：152-164.

How Does the Authenticity of AI-Generated Portraits Affect Consumers’ Sharing Intention？

XIE Yi¹, ZHANG Yanhong^2,3, CHANG Yunzhe³, ZHANG Xiyuan⁴

(1. Beijing Normal University, Zhuhai 519087;

2. Shandong Women’s University, Jinan 250300;

3. University of International Business and Economics, Beijing 100029;

4. Beijing Sankuai Online Technology Co., Ltd., Beijing 100102)

Abstract: Consumers increasingly use artificial intelligence-generated content (AIGC) tools to create portraits for online self-presentation, social interaction, and brand engagement. AIGC portraits can beautify, stylize, and reconstruct personal images at low cost, but consumers may not always want to share them publicly. Some consumers may prefer portraits that appear authentic and recognizable, whereas others may value idealized or creative visual effects. This tension raises an important marketing question about the factors motivating consumers to share AIGC portraits on social platforms.

This paper examines the impact of the authenticity of AIGC portraits on consumers' sharing intention. Such authenticity refers to the extent to which consumers perceive an AI-generated portrait as being consistent with their actual appearance. It proposes that higher authenticity increases sharing intention and then further identifies two psychological mechanisms. First, authentic AIGC portraits activate consumers' self-enhancement motivation by enabling them to present a positive, natural, and socially acceptable image. Second, authentic AIGC portraits activate consumers' self-verification motivation because such portraits affirm and express a stable self-concept. This paper also examines regulatory focus as a boundary condition. Three experiments were conducted to test hypotheses. Study 1 used a single-factor between-subjects design and found that AIGC portraits with high authenticity generated stronger sharing intention. Study 2 replicated this main effect with a different manipulation. It also revealed that self-enhancement motivation and self-verification motivation mediate this positive effect. Study 3 adopted a 2 × 2 between-subjects design and indicated a significant interaction between portrait authenticity and regulatory focus. High authenticity had a stronger effect on sharing intention for prevention-focused (vs. promotion-focused) consumers. In addition, moderated mediation analyses further showed that regulatory focus strengthened the indirect effect of authenticity on sharing intention via self-enhancement motivation.

This paper makes three contributions. First, it extends authenticity research to AIGC portraits. Second, it explains why authentic AIGC portraits increase consumers' sharing intention. Third, it enriches regulatory focus theory by showing that prevention-focused consumers depend more on authenticity cues. The findings also offer practical implications. Firms that are providing or planning to provide AIGC portrait services should emphasize image authenticity, preserve users' recognizable personal features, and design communication strategies that align with the motivations activated among consumers. Messages for prevention-focused consumers should emphasize accurate representation, natural appearance, and safe sharing, while messages for promotion-focused consumers can emphasize creativity, idealized self-expression, and personalized style.

Keywords: AI-generated portrait; authenticity; sharing intention; self-enhancement motivation; self-verification motivation; regulatory focus

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