How well do shopbots represent online markets? A study of shopbots' vendor coverage strategy

Consumers often use shopbots to search for information when making purchase decisions in Internet markets. Although they have varying sensitivity to shopbot bias, consumers generally prefer accurate market representation. However, in choosing the accuracy of market representation, shopbots must balance the desires of consumers with the costs of providing their services and with the desires of the vendors, who are often the largest source of their revenue. In this paper, we study how accurately shopbots represent a market and analyze the strategies shopbots adopt to achieve market representativeness. We theoretically identify two important drivers in shopbot vendor coverage strategy – how many vendors it covers (shopbot size) and which vendors it covers (shopbot affiliation) – and analytically show how the drivers affect shopbot market representativeness. We report the results of a large-scale study in which we collected 2.2 million vendor price listings from eight shopbots and develop metrics for measuring shopbot size, shopbot affiliation, and shopbot market representativeness. We found that (1) shopbots do not represent markets equally well; (2) size drives a shopbot's market representativeness positively whereas affiliation drives a shopbot's market representativeness negatively; (3) shopbots follow differnet vendor representative strategies to pursue market representativeness.