报告题目：Text-to-image AI and the Novelty of User Generated Content on Social Media

报 告 人：尚广志

报告时间：2025年06月10日（星期二），10:30-11:30

报告地点：线上直播腾讯会议：509-705-541（可于明哲楼517听取）

主办单位：东北财经大学现代供应链管理研究院

【报告人简介】

尚广志是佛罗里达州立大学（Florida State University）商业分析、信息系统和供应链系的Jim Moran运营管理副教授。他的研究成果已发表在Production and Operations Management、Journal of Operations Management、Management Information Systems Quarterly和Decision Sciences等期刊上，并获得了POM、JOM以及POM学会卓越运营卓越学院的最佳论文奖项的认可。他担任JOM期刊的实证研究方法部门和DS期刊的零售运营部门的部门编辑。他的审阅服务被认可为2019年DS杰出审阅员奖和2018年JOM最佳审阅员奖的获得者。他还被提名为POM最佳审阅员和JOM最佳副编辑。他与Mike Galbreth和Mark Ferguson合作在Reverse Logistics Magazine中制作专栏，名为“学界观点”，旨在将最新学术知识传播给处理消费者退货的行业专业人员。

尚广志目前的研究主题有三个：消费者退货管理、服务劳工问题以及创新技术管理。他从多个角度研究第一个主题，包括零售商如何制定最佳的退货政策、原始设备制造商或零售商如何更好地预测退货数量，以及零售商如何评估其退货政策的价值。对于第二个主题，他侧重于在线聊天联系中心的情境。研究问题包括顾客等待体验对聊天会话进展的影响，代理人从他们过去经验中学习的能力，以及顾客与代理人匹配问题。对于第三个主题，他研究新兴的金融科技，如加密货币和众筹平台。他热衷于进行实践驱动的研究。

【摘要】

Text-to-image AI tools enable social media users to transform their imaginative ideas into visual content in a wide range of visual styles, adding a new source of novelty for social media platforms. We argue that the AI tools also pose a spillover effect on the novelty of user-generated regular content on social media (i.e., content generated without an AI tool) because using AI tools could potentially change the reputation dynamics on social media platforms (i.e., reputation mechanism) and/or fundamentally reshape human cognitive processes and innovative thinking (i.e., AI-usage mechanism). Collaborating with an image-sharing social media platform, we employ a difference-in-differences (DID) approach to empirically demonstrate that using a text-to-image AI tool significantly reduces the novelty of user-generated regular images. That is, after using the text-to-image AI tool, AI users’ regular images become more similar to the platform-wide recent images than those of non-AI users. We do not find evidence supporting the reputation mechanism. However, empirical evidence consistently validates the AI-usage mechanism. Specifically, novelty reduction worsens with more frequent AI usage, and frequent AI generation induces a fixation effect evidenced by users’ conformity to AI, i.e., regular images from AI users become more similar to the AI-generated images than those from non-AI users. Delving into more granular image-level generation records, we conduct an instrumental variable analysis and find that repeatedly generating AI images in a specific style reinforces user perception of a strong prototype and intensifies the fixation effect, leading to greater conformity of their regular images to that AI style. Finally, prompt input plays a key role in shaping the AI-induced fixation effect on the novelty of regular content. AI users who consistently input prompts with broader informational scope and greater semantic variation are less subject to novelty reduction of regular content.

撰稿：王   戈

初审：冯月昕

复审：刘   旭

终审：张   颖