Compared with traditional e-commerce, the advanced technology of live streaming e-commerce provides more dynamic information cues to enable viewers to make better decisions. Drawing on the information foraging theory (IFT) and elaboration likelihood model (ELM), we construct a synthetic model by considering how live streaming information cues influence viewers’ decision making via two distinct routes (central and peripheral). Using the technology of web crawling and text mining, Douyin’s live streaming data were collected, transformed, and then analyzed by fixed-effect regression. The results indicated that product interpretation duration, popularity cue, and herding information are significantly associated with sales performance in live streaming e-commerce. This study enriches our knowledge about live streaming e-commerce, extends the application of IFT and ELM in the individuals’ information foraging and processing by using objective data in the live streaming e-commerce context, and offers practical suggestions to live streaming e-commerce practitioners.

与传统电商相比，直播电商的先进技术提供了更加动态的信息线索，使观看者能够做出更好的决策。借鉴信息搜寻理论（IFT）和精化似然模型（ELM），我们通过考虑直播信息线索如何通过两种不同的途径（中心和外围）影响观众的决策，构建了一个综合模型。利用网络爬虫和文本挖掘技术，对抖音直播数据进行采集、转换，然后进行固定效应回归分析。结果表明，产品解读时长、流行线索和羊群信息与直播电商的销售业绩显着相关。本研究丰富了我们对直播电商的认识，利用直播电商背景下的客观数据，拓展了IFT和ELM在个人信息采集和处理中的应用，为直播电商提供了切实可行的建议。从业者。