Online community supporting trading functions in an online auction website
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Authors
Elian, Ryan
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Degree
Master of Computing Systems
Grantor
Unitec Institute of Technology
Date
2007
Supervisors
Joyce, Donald
Muller, Logan
Muller, Logan
Type
Masters Dissertation
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
online auctions
online communities
online communities
ANZSRC Field of Research Code (2020)
Citation
Elian, R. (2007). Online community supporting trading functions in an online auction website. Unpublished thesis submitted in partial fulfillment of the degree of Master of Computing Systems, Unitec New Zealand, New Zealand.
Abstract
Online auctions have been taking place along the lines of traditional auctions. This has been made possible by the internet and web technology. Online communities associated with the online auction websites are thought to be a critical success factor. It is therefore important to identify how an online community supports an online auction website.
The objectives of this research are to identify the value of the different activities and needs of the online community in online auction websites, and also the facilities provided by the online auction website to support the needs of the community.
This dissertation looks at Trade Me as a local example of an online auction website. An initial observation of this New Zealand online auction website was first conducted to find out the extent of Trade Me’s community and the facilities provided by Trade Me to support its community. This information, together with the results from a literature search, was then used to create research questions for an online survey posted on Trade Me’s discussion board. There were 224 responses to the online survey.
The dataset shows that about three quarters of the respondents are females and more than half (56%) are from the younger age group (34 or less). The dataset also shows that respondents have a high need to interact within Trade Me. Further, the dataset finds that various activities are performed by users before and after a trade and that the attitudes of users affect their feedback as traders.
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