A Survey of the Comparison Shopping Agent-Based Decision Support Systems

Abstract

ABSTRACT The web-based comparison shopping agents (CSAs) or shopbots have emerged as important business intermediaries that provide decision support to both the shoppers and the merchants. The basic idea is to provide an easy access to both the price and non-price based competitive features to shoppers. The CSAs do not have an equivalent counterpart in the offline world and they have generated a significant amount of interest among researchers in economics, marketing, and information systems fields. There have been numerous studies on the CSAs in the contexts of price dispersion, consumer behavior, search costs, and recommender systems. The focus of this paper is to study the contemporary literature about the CSAs to analyze them in the context of decision support systems (DSS). In order to provide comprehensive decision support, a typical DSS should have four components: data, models, interfaces, and user specific customization. In this paper, this four component framework is used to synthesize the current research work in the context of DSS and to explore contemporary CSAs. The paper provides suggestions for improving the decision support aspect of the CSAs and proposes a research agenda for the CSA-based decision support systems. Keywords: comparison shopping agents, CSA, shopbots, comparison shopping, decision support systems 1. Introduction The advent of the Internet has created new avenues for the merchants to sell their products and has also reduced the barriers for entering into retail business. This, along with the expanded reach of the Internet retailing and widely discussed 'long-tail' phenomenon, has generated intense competition in the online retail sector. In the online domain, the oligopolistic nature of the brick and mortar (B&M) retailing has virtually become open-for-all type of retailing. This has increased the number of available options for the shopper's purchasing decisions and merchant's marketing decisions. As the optimal decision-making involves thorough comparison and analysis of all alternatives on hand, the increase in number of available options has made users' decision problems more complex. Moreover, such complexity is compounded by the fact that as compared to their B&M counterparts, online merchants are more diverse. They are not only heterogeneous in terms of the services that they provide, but also differ in other areas such as the type of products that they stock (e.g. regular, refurbished, used, outdated, or long-tail) and the channel-mix of their operations (e.g. pure-play, click and mortar (C&M), and manufacturer-owned). It is no wonder that in the days of the Internet, intermediaries have occupied a prominent space in facilitating the decision making process of both the merchants and the shoppers. Majority of these Web-based intermediaries bring merchants and shoppers together and facilitate a successful sales transaction. One of the most popular forms of the Web-based intermediaries is the comparison shopping agents (CSA) or shopbots. CSAs provide decision support tools to shoppers for comparing their purchase alternatives based on both the price and non-price (e.g. product, merchant reputation) based factors. They are increasingly becoming popular among shoppers. The report from a leading Web-analytics firm, Compete, shows that each of the top two major CSAs, Nextag.com and Bizrate.com, has attracted more than two million unique visitors during January 2009. It is not surprising to see that major Internet companies like Microsoft, Yahoo, and Google have integrated comparison shopping in their search-based solutions. The CSA-based decision support systems are also proactively integrated by some merchants like Buy.com. CSAs do not have an equivalent counterpart in the offline world and they have generated a significant amount of interest among researchers in economics, marketing, and information systems fields. There have been numerous studies about the CSAs in the contexts of the price dispersion [Brynjolfsson et al. …

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