Most agent shopping Web sites currently offer three features: competitive prices for a searched product, product recommendations, and merchant reputation ratings. These features help customers search for a product with the lowest price and choose merchants they can trust and feel comfortable with.
Most shopping agents claim to eliminate the search necessary to identify the right product at the best price. They take a query, visit their member e-tail stores that may have the product sought, bring back the results, and present them in a consolidated and compact format that allows for comparative shopping at a glance. The impact of this comparative price system lowers the search cost not only for consumers but also for suppliers who wish to find out what prices their rivals are charging. This makes it easier for merchants to operate price-trigger strategies that may enable them to sustain high prices. This also makes it more difficult for sellers to undercut each other secretly (Vulkan, 1999).
Recent research shows that price and promotion are no longer the main reasons for a customer’s decision to make a purchase. More and more sophisticated online customers prefer to pay higher prices to merchants who provide high quality e-service (Schneider, 2002). The most experienced and successful merchants are beginning to realize that the key determinants of success or failure are not merely Web presence or low price, but e-service quality (Zeithaml, 2002). The customer’s e-satisfaction rating is an important measure of e-service quality.
The product reviews on an agent Web site are contributed by customers shopping on different e-tailers’ Web sites linked to the agent Web site. Thus, the product review system is able to provide a large volume of product reviews from customers. Avery et al. (1999) studied the potential of a market for evaluations and indicated that personal experience with products is enormously powerful in forming the customers’ decisions. These personal recommendation networks enable consumers to learn from the experience of others.
Ganesh and Amit (2003) focused their research on how product review systems affect customer preferences of unfamiliar products since recommendations of unfamiliar products represent an important new source of business. Behavioral research suggests that consumers view recommendations of unfamiliar products negatively (e.g., Park and Lessig, 1981). The result demonstrates that positive contextual recommendations based on a customer’s prior purchase interest do not always produce positive effects on the sale of new products. It is important that designers of electronic agents create contexts judiciously. The designers offer tentative guidelines for product review systems. Contextual recommendations may be beneficial when they are known to be attractive to the customer and are likely to be perceived as similar to the target item. The context can be provided in a manner that makes it salient when customers first encounter the unfamiliar recommendations. Conversely, new recommendations should be presented when little is known about the shopper and when familiar recommendations are likely to be perceived as different from the unfamiliar recommendations.
In addition to obtaining competitive prices and product recommendations, many shopping agent Web sites have developed systems to keep track of merchant reputation ratings as well as service quality simply because consumers want to know that the merchant with whom they are transacting an order is reliable, and will deliver the product as specified in the delivery schedule, will maintain confidentiality, and will have appropriate product packaging and handling arrangements. Resnick et al. (2000) have defined a reputation system as one which collects, distributes, and aggregates feedback about participants’ past behavior. Though few producers or consumers of the ratings know one another, these systems help people decide whom to trust and also encourage trustworthy behavior. So far, the merchant reputation system is best known as a technology for building trust and encouraging trustworthiness in e-commerce transactions by taking past behavior as a publicly available predictor of likely future behavior (Dellarocas, 2003).
Merchant reputation mechanisms initially attracted attention as a mechanism for building trust. Trust is an essential concept that an e-business should attend to since trust has been considered a building block that strengthens relationships between customers and merchants (Siebel and Hous, 1999). In the B2C e-commerce environment, trust is more difficult to establish and even more critical for success than in traditional business. The retailers down the block will likely be there tomorrow, but the merchant that exists in cyberspace is often not real in the customer’s eyes (Head and Hassanein, 2002). The customers’ lack of inherent trust in “strangers” in the e-stores is logical and to be expected. If an e-tail store wants to do business, it has to prove its trustworthiness by satisfying customers for many years as it grows (Schneider, 2002).
The merchants’ reputation is built with customers’ shopping experiences and comments. The comments from an e-tail store’s previous customers could be a valuable asset to other prospective customers. The goal for online reputation systems is to encourage trustworthiness in e-commerce transactions by using past behavior as an accessible predictor of likely future behavior. Taylor (1974) found that consumers tend to regard information obtained by “word of mouth” as more objective and possibly more accurate. A satisfied customer will tell three people about his or her experience, but a dissatisfied customer will complain to thirty people. Therefore, consumer comments can be a powerful influence on the purchasing decisions of others (McGaughey and Mason, 1998). The merchant reputation has significant impact on customers’ trust and on their intentions towards adopting e-services (Ruyter et al., 2001). In February 2003, Jeff Bezos, the CEO of Amazon.com, decided to cancel all plans for any television or general print advertising because he believed that his company was better served though word-of-mouth generated through the Internet than by paid advertising. Despite its undeniable importance and widespread adoption, the current merchant reputation system still encounters some problems.
Key facets of the usefulness and successful adoption of emerging reputation systems are their accuracy, consistency, and reliability. Can, should, and indeed, to what extent, do consumers rely on the information on the agent Web site and use it for their decision-making processes? One party could blackmail another by threatening to post negative feedback unrelated to actual performance. A group of academic scholars also conducted research on the risk management of merchant reputation systems on the agent Web sites. Kollock (1999) states that online rating systems have emerged as an important risk management mechanism in the e-commerce community. Dellarocas (2000) identified several scenarios (“ballot stuffing,” “bad-mouthing,” positive seller discrimination, negative seller discrimination and unfair ratings “flooding”) in which buyers and sellers can attempt to “rig” an online rating to their advantage. Some important management mechanisms have been developed. Friedman and Resnick (1998) discuss risks related to the ease with which online community participants can change their identity. They conclude that the assignment of a lowest possible reputation value to newcomers of agent Web sites is an effective mechanism that discourages participants from misbehaving and, subsequently, changing their identity.
Most agent Web sites have developed countermeasures to counteract the above potential threats in various ways. Some have created registration systems to restrict online evaluation writers only to its member customers. Member customers have to log on to their accounts before they evaluate their merchants. Some agents have claimed that they are able to detect and eliminate fraudulent ratings by using a combination of sophisticated mathematical algorithms and a large number of reviews from a variety of sources that have been checked for consistency.
Most online reputation systems have adopted an ordinal rating system to rate merchants as illustrated in Figure 13-1.
Figure 13-1: Merchant overall rating system
As Zacharia et al. (2000) indicate, individual standings are developed through social interactions among a loosely connected group that shares the same interest. Through this interaction, the users of online communities form subjective opinions of each other. These opinions may differ greatly among different users, and their variance is large enough most of the time to make the average opinion a rather unreliable prediction.
On the other hand, as an increasing number of online reputation systems become available in the shopping agent Web sites, one customer may be exposed to multiple online merchant reputation systems for the same merchant. Since different reputation systems may provide different ratings for the same merchant, customers may get confused. Thus, questions are raised on the consistency of the ratings provided by different reputation systems. Are online ratings on merchants consistent between different reputation systems? Are online ratings consistent on merchants on the same reputation system? Do they prove to be misleading to customers to a certain extent? If not, to what extent do consumers rely on the ratings and use them for their decision-making processes?
It is crucial for online customers to get to know the reliability ratings of merchant service quality from merchant reputation systems before they make a purchase decision. Consistent ratings are able to provide more reliable evaluations of a merchant. Wang and Christopher (2003) conducted an empirical study on the rating consistency of reputation systems using three sets of data collected online from two popular merchant reputation systems. The results of the study showed that individual customer ratings are not consistent for the same set of merchants across reputation systems. However, the averaged customer ratings are consistent on the same merchant across different merchant reputation systems. The averaged customer ratings on different occasions in the same reputation system are consistent, too.
The solution to the rating discrepancy problem across different reputation systems is to develop a new agent Web site listing the averaged rating for each merchant on different reputation systems as a comparison at a glance reference resource for customers.
Customers with exceptionally positive or negative views of e-tail stores are more likely to respond than the general customer population. The implication is that those customers who wrote an online comment are strongly opinionated. This results in a biased response that would be more likely to identify current quality problems than a controlled survey of equal sample size. The customers who remained neutral might not be motivated to rate the online store.
People may not bother to provide feedback at all since there is little incentive to spend another few minutes filling out the form. Most people do it because of their gratitude or their desire to take revenge. People could be paid for providing feedback. Further research needs to be done on the portion of customers who did not choose to either express or share their post-shopping satisfaction.
Table 13-1 presents a frequency distribution of 106 overall star ratings classified by customers’ post-shopping satisfaction level. This exhibit lists the frequency of occurrence of each classification of rate. The total number of given ratings was 106. The total percent was computed by dividing the number in each level by the number of ratings.
|
Level of Satisfaction (Overall Rating) |
Number of Overall Rates at Satisfaction Level |
Percent of Total |
|---|---|---|
|
Very Satisfied |
53 |
50.0 |
|
Somewhat Satisfied |
9 |
8.5 |
|
Neutral |
0 |
0.0 |
|
Somewhat Dissatisfied |
3 |
2.8 |
|
Very dissatisfied |
41 |
38.7 |
|
Total Reviews |
106 |
100.0 |