A Decision Support System for Trust Formalization - Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications - page 60

values (0, 0.5, 1) this would certainly affect the precision of the results of the trust factor compared to
those obtained by the fuzzy model
By developing the fuzzy model, there is now no need for the use of the parameterized model as the
expertise, on a general scale rather then chosen by individual users, is incorporated in the evaluation
process. Users experience is also subjective and the use of the fuzzy model will provide a stronger
and a more objective tool to all users regardless of their expertise and experience. It is clear from this
experiment that the linear model should be discarded as the results produced do not reflect the true
evaluation of trust in EC.

conclusion and future work

In this article, we presented a system based on fuzzy logic to support the evaluation and the quantifica-
tion of trust in EC. Although, the system has addressed many issues that other systems did not such as
taking into account the fuzzy nature of trust and using a substantial number of variables, we believe that
the system can be improved in many ways. As stated in many trust models, there are other aspects that
contribute to the completion of online transactions. This include the price, the rarity of the item and the
experience of the customer. In order to develop an effective decision support system, future development
should include some if not all of these aspects. The price of the item is certainly an important variable
as it is shown in many studies that if the price is reasonably low, customers are ready to take the highest
risk to purchase the item. Online transactions also depend on customer's experience and personality.
Some customers may value some variables more then others. Hence we believe that future systems
should allow customers to rank trust variables according to their own perception and experience.

references

Akhter, F., Hobbs, D., & Maamar, Z. (2005). A fuzzy logic-based system for assessing the level of business-
to-consumer (B2C) trust in electronic commerce. Expert Systems with Applications, 28 ,623-628.

Araujo, I., & Araujo I. (2003). Developing trust in internet commerce. In proceedings of the IBM confer-
ence of the Centre for Advanced Studies on Collaborative Research , (pp. 1-15). Toronto, Canada.

Basso, A., Goldberg, D., Greenspan, S., & Weimer, D. (2001). First impressions: Emotional and cogni-
tive factors underlying judgments of trust E-Commerce. In Proceedings of the 3rd ACM conference on
Electronic Commerce, (pp.137-143). Tampa, FL.

Bayles, D. L. (2001). E-commerce logistics and fulfillment: Delivering the goods. New Jersey:Prentice
Hall.

Castelfranchi, C., & Pedone, R. (2003). A review of trust in information technology . The ALFEBIITE
Project, http://alfebiite.ee.ic.ac.uk/docs/papers/D1/ab-d1-cas+ped-trust.pdf

Cazier, J. A., Shao, B. B. M., & Louis, R. D. St. (2006). E-business differentiation through value-based
trust. Information & Management , 43 (6), 718-727.