Web Systems Design and Online Consumer Behavior [Electronic resources] نسخه متنی

Issues and Problems

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.

Competitive Prices

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.

Product Review

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.

Merchant Reputation Systems

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.

Fraud Risk Management

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.

Rating 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.

Biased Rating

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.