住宿
内容(测量理论)
点对点
功率(物理)
同行评审
业务
心理学
计算机科学
政治学
万维网
神经科学
数学
法学
量子力学
物理
数学分析
作者
Ningyuan Fan,Xiang Li,Chao Liu,Zhi‐Ping Fan
标识
DOI:10.1177/00472875251332951
摘要
As a technological breakthrough, large language models stand to revolutionize operations in many industries, including the tourism sector. Despite the transformative potential of artificial intelligence (AI)-generated content (AIGC), its role in marketing performance remains unclear. This research used a prediction–interpretation–inference machine learning framework to evaluate how AIGC affects Airbnb listings’ performance. Specifically, we scrutinized AIGC’s efficacy under two conditions (i.e., with an AIGC tool serving as a language assistant vs. a content creator) and examined the impacts of prompt design strategies. Three key findings emerged. First, the prompt design strategy greatly influenced AIGC’s effectiveness. Second, AIGC did not uniformly enhance listing performance: although it boosted revenue and bookings for underperforming listings, its impact on low-performing listings was limited and not as strong as anticipated. AIGC may even negatively influence high-performing listings. Third, the AIGC tool appeared more effective as a language assistant (vs. a content creator) for underperforming listings.
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