An Empirical Analysis of Seller Advertising Strategies in an Online Marketplace

• 成果类型: Article
• 署名作者: Sun, Haoyan; Fan, Ming; Tan, Yong
• 署名单位: Lehigh University; University of Washington; University of Washington Seattle
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2019.0874
• 发表日期: 2020
• 页码: 37-56
• 关键词: word-of-mouth; MODEL; sales; reputation; DYNAMICS; IMPACT; persuasion; reviews; price

摘要

Online marketplaces are increasingly adopting innovative business models such as paid advertising as a major revenue source. We study the effectiveness of two popular advertising tools, sponsored search and social media endorsement, in increasing traffic and sales for online sellers at a retail e-commerce platform. We find that, controlling for sellers' self-selection behavior in choosing their strategies, both sponsored search and social media endorsement can significantly increase traffic for sellers, with sponsored search being more effective than social media endorsement. In contrast, only sponsored search has a positive and significant impact on sales. In examining the differential effects for sellers with low and high reputations, we find that sponsored search is more effective in increasing traffic for low-reputation sellers, but its effect on sales is larger for high-reputation sellers. Moreover, although social media endorsement increases traffic for sellers regardless of their reputation, it is effective in increasing sales for only high-reputation sellers. Our study provides important managerial implications to sellers as well as e-commerce platforms.