Please use this identifier to cite or link to this item: http://hdl.handle.net/1901/115
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| contributor.advisor | Diane Kelly | en |
| creator | Rachel L Farrell | en |
| date.accessioned | 2004-11-22T15:59:19Z | - |
| date.available | 2004-11-22 | en |
| date.issued | 2004-11-22T15:59:19Z | - |
| date.submitted | November 22, 2004 | en |
| identifier.uri | http://hdl.handle.net/1901/115 | - |
| description.abstract | This is an exploratory study that investigates desirable characteristics for a seller to possess in an on-line auction setting based on buyer’s written feedback comments and ratings from positive to negative on the online auction site eBay. It was conducted to determine the common characteristics that buyers write about and to expand the traditional feedback rating system for sellers from one-dimensional to a multi-dimensional. Five hundred comments written by unique buyers about a hundred sellers were gathered from eBay on September 9, 2004. Content Analysis was used to analyze the comments and ratings once they were transferred to an excel spreadsheet. In these online auctions buyer’s comments on feedback ratings systems are what other buyers use to help determine if they are going to purchase an item from the seller. The current types of feedback rating systems do not offer the ability to rank a seller on multiple characteristics that are important to the buyer. This lack of consistency in determining the seller’s expected performance is a major factor in the public’s reluctance to adopt electronic commerce as a means to purchase items. | en |
| format | application/pdf | en |
| format.extent | 527944 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en_US | en |
| publisher | School of Information and Library Science | en |
| rights | Attribution-NonCommercial 1.0 | en |
| subject | Auctions Electronic Commerce Trust Relevance Feedback | en |
| title | An Analysis of Feedback Provided by Buyers in an Online Setting | en |
| type | Electronic Theses and Dissertations | en |
| degree.discipline | Information Science | en |
| degree.grantor | University of North Carolina at Chapel Hill | en |
| degree.level | Master | en |
| degree.name | Master of Science | en |
| Appears in Collections: | SILS Master's Papers |
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