This study has incorporated many determinants and outcomes of e-trust into one model. These determinants
and outcomes have all been investigated in previous studies [Hwang & Kim 2007, Pennington et al. 2004].
However, they had not been tested simultaneously in any previous e-marketing or e-commerce investigations.
Additionally, the design of this study, which broke down the antecedents of e-trust into technical and social bonds, is
an approach that has not previously garnered much attention in the online auction literature. The results of this
model should provide more in-depth information about how online auction companies can better foster e-trust. Also,
this study has distinguished two types of e-loyalty that are influenced by e-trust in online auctions. The results
suggest that the e-trust model is a good representative of the sample data, with the exception of the construct of
learning capability. All of the other hypotheses were supported by the data.
4.1. The antecedents of e-trust: technical bonds
Henczel [2004] proposed the idea that by incorporating user profiling, an online company is providing a higher
quality of information to its customers. Furthermore, an online auction site offering helpful information applications,
which place the interactions with the customer at the center of an effective information management system,
provides a competitive advantage. In addition to the results of the present investigation, other research [Liu & Arnett
2000] has supported the relationship between information quality- that is the real time availability, correctness, and
comprehensiveness of information- to online users and e-trust. Thus, information quality has an active influence on
e-trust.