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2005). Indeed it has been reported that users are finding it difficult to identify specific information on
Websites (Center for the Digital Future, 2004). In addition, we do recognize that users may not be able
to make proper use of the collected information. For this purpose, we developed tools to evaluate the
trustworthiness of an EC Website based on the collected information. Two models have been developed
in (Meziane & Kasiran, 2005, Meziane & Kasiran, 2008) for evaluating the trust factor; the linear model
and the parameterized model. More details about these two models will be provided in the comparison
section.
However, for both models, we do recognize that this is not the natural way customers use to evaluate
their trust towards online merchants or make the decision to buy or not to buy. As with any other business
transaction, customers develop in their mind some sort of ambiguity and uncertainties when purchasing
online (Mohanty & Bhasker, 2005). Customers may wish to classify merchants using different prefer-
ences or take into account other parameters such as the cost or the brand of a product. The decision to
complete an online transaction is often based on the customer's human intuitions, common sense, and
experience rather than on the availability of clear, concise, and accurate data (Akhter, Hobbs, & Maamar,
2005). In this article, we develop a new trust evaluation model using fuzzy reasoning to evaluate the
trust factor as it allows the encoding of the information available on the merchant's Website in a form
that can be used to reflect the way customers reach the decision to engage in an EC transaction.
The remaining of the article is organized as follows. In the second section we review some related
work on trust and trust modelling in EC. In the third section, we describe the fuzzy inference and fuzzy
logic system, we justify the use of fuzzy logic in the fourth section and we construct the rules base in
the fifth section. We evaluate the newly developed fuzzy model in the sixth section and we compare
it with the linear and parameterized models and underline the advantages of our fuzzy system in the
seventh section. We conclude in the final section.
rela ted work
New technologies have deeply modified traditional forms of social relations and communications, in
particular norms, social rules, hierarchies, familiarity, reputation, delegation and trust (Castelfranchi
& Pedone, 2003). This is certainly true for B2C EC, one of the areas that benefited the most from the
development of the Internet and the WWW. EC applications have created a new global market (Cohan,
2000) where businesses and consumers are no longer restricted by physical boundaries such as geo-
graphical or time differences (Guo & Sun, 2004). Today, EC influences business in a major way and
shapes the future of the B2C segment (Li, Kuo, & Russell, 1999; Schmitz & Latzerb, 2002). In reality,
EC has redefined several business processes (Hoffman, Novak, & Chatterjee, 1995) such as marketing
(Hoffman, Novak, & Peralta, 1997), customer services (Romano, Nicholas, & Fjermestad, 2003), pay-
ment (Ranganathan & Ganapathy, 2002) and fulfilment (Bayles, 2001).
Most people have an understanding of EC based on their experience as shoppers and buyers in a
traditional brick and mortar environment, and they bring this experience with them when they start
shopping online. EC sites play their role of seller by trying to broadcast two messages to potential buy-
ers: “buy from us” and “trust us”. The impact of these explicit messages, though, is often corrupted
by contradictory or distracting messages implicit in the site's implementation of the navigation flow,
page layout, visual continuity and information space (Nah & Davis, 2002). In the next subsection, we
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