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dc.contributor.advisorΧατζηνικόλας, Γιώργος
dc.contributor.authorΛάζου, Χριστοθέα
dc.contributor.otherLazou, Christothea
dc.coverage.spatialΚύπροςel_GR
dc.date.accessioned2013-07-29
dc.date.accessioned2013-09-02T05:55:40Z
dc.date.available2013-09-02T05:55:40Z
dc.date.copyright2013-05
dc.date.issued2013-09-02
dc.identifier.otherΤΡΑ-ΧΡΗ/2013/00116el_GR
dc.identifier.urihttp://hdl.handle.net/11128/1375
dc.descriptionΠεριέχει βιβλιογραφικές παραπομπές.el_GR
dc.description.abstract------el_GR
dc.format.extent131 σ. πιν., 30 εκ.el_GR
dc.languagegrel_GR
dc.language.isogrel_GR
dc.subjectΗλεκτρονικό εμπόριοel_GR
dc.subjectElectronic commerceel_GR
dc.subjecte-businessel_GR
dc.titleΠαράγοντες που επηρεάζουν το επιτυχημένο ηλεκτρονικό εμπόριοel_GR
dc.typeΜεταπτυχιακή Διατριβήel_GR
dc.description.translatedabstractThe internet revolution has led many companies to the creation of electronic shops and to the growth of electronic commerce. One of the e-commerce advantages is the accessibility that provides. An online shop can be accessed 24/7, which is very important for the consumers. his research, will focus in other factors influencing the electronic commerce. For this purpose data from ebay.co.uk were used. Data came from sales completed in a period of 90 days and the seller was located in Cyprus. After the creation of the database there was a categorization based on product type. Some of the categories had a significant amount of data and were used for further analysis. After the categorization, the final categories and the variables which were suitable for analysis were chosen. These selected variables were product condition, seller type, sales method, product price and shipping cost. Data analysis was based in two analysis methods, hypothesis testing and binary logistic regression. Both methods were applied separately in each category. Looking at hypothesis testing for factors influencing the sale of a product were searched based on the following: Whether sold products percentage was higher when the products were new or used (ProductCondition). Whether sold products percentage was higher when the seller was business or private (SellerType). Whether sold products percentage was higher when the sale was listed as bid or «buy it now» (BuyMethod). Whether the consumers prefer buying products with low shipping cost (ShippingCost). At binary logistic regression product condition, seller type, sale method, price and shipping cost were used as independent variables and sold (yes/no) was used as dependent variable. Product condition, seller type, sale method and sold were binary values so they were transformed into 1/0. Price and shipping cost were continuous variables and they were used as is. After the data preparation, the 10 / 131 binary regression model for each category separately was exported, and found the regression equation that predicts when an item can be sold and what factors are important in order to get sold. Finally in the last chapter, the results of this research are presented. The results are different in each category and the conclusion is that there are different factors influencing sales in each category. Some of the factors though, are affecting most of the categories. One of these factors is the sales method (Bid/Buy it Now). Based on the research it can be said, that most consumers prefer buying from auction because of the lower prices they might get. Similarly, it can be said that, when it comes to antiques or high valued items, consumers prefer buying from business than private sellers due to the authentication of items.el_GR
dc.format.typepdfel_GR


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