Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue

• 成果类型: Article
• 署名作者: Ghose, Anindya; Ipeirotis, Panagiotis G.; Li, Beibei
• 署名单位: New York University; New York University; Carnegie Mellon University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2013.1828
• 发表日期: 2014
• 页码: 1632-1654
• 关键词: travel search engine; randomized experiments; hierarchical Bayesian methods; information systems; IT policy and management; electronic commerce

摘要

In this paper, we study the effects of three different kinds of search engine rankings on consumer behavior and search engine revenues: direct ranking effect, interaction effect between ranking and product ratings, and personalized ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments using a real-world hotel search engine application. Our archival data analysis and randomized experiments are consistent in demonstrating the following: (1) A consumer-utility-based ranking mechanism can lead to a significant increase in overall search engine revenue. (2) Significant interplay occurs between search engine ranking and product ratings. An inferior position on the search engine affects higher-class hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from social media into their ranking algorithms. (3) Our randomized experiments also reveal that an active personalized ranking system (wherein users can interact with and customize the ranking algorithm) leads to higher clicks but lower purchase propensities and lower search engine revenue compared with a passive personalized ranking system (wherein users cannot interact with the ranking algorithm). This result suggests that providing more information during the decision-making process may lead to fewer consumer purchases because of information overload. Therefore, product search engines should not adopt personalized ranking systems by default. Overall, our study unravels the economic impact of ranking and its interaction with social media on product search engines.