Characterisation of e-commerce website structures using webometrics and social network analysis methods
The hyperlinks among Web pages of intra- and inter- Websites, are believed to have valuable information that is hidden in the structure. Exploitation of these hyperlinks using Web structure mining methods is expected to increase the understanding about how Website’s internal and external hyperlink s...
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my-mmu-ep.52202014-02-17T01:44:57Z Characterisation of e-commerce website structures using webometrics and social network analysis methods 2012-12 Kannan, Rathimala HF Commerce The hyperlinks among Web pages of intra- and inter- Websites, are believed to have valuable information that is hidden in the structure. Exploitation of these hyperlinks using Web structure mining methods is expected to increase the understanding about how Website’s internal and external hyperlink structures are interlinked and what information they hold. This research aims to characterise the commercial Website structure of a company and investigate its relationship with the company’s Web traffic and business performance. It attempts to extract the overall structure of commercial Websites and identify the optimal internal structural design. This structure represents the Website page organisation that ensures easy navigation and improved usability which subsequently increases the Web traffic. Data for this study comes from the top five hundred Asia Pacific retail companies as they are the leaders in the retail industry of Asia Pacific region and have well established Websites. The retail companies are divided into seven subgroups based on their retail sector. 2012-12 Thesis http://shdl.mmu.edu.my/5220/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php phd doctoral Multimedia University Graduate School of Management |
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Multimedia University |
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MMU Institutional Repository |
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HF Commerce |
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HF Commerce Kannan, Rathimala Characterisation of e-commerce website structures using webometrics and social network analysis methods |
description |
The hyperlinks among Web pages of intra- and inter- Websites, are believed to have valuable information that is hidden in the structure. Exploitation of these hyperlinks using Web structure mining methods is expected to increase the understanding about how Website’s internal and external hyperlink structures are interlinked and what information they hold. This research aims to characterise the commercial Website structure of a company and investigate its relationship with the company’s Web traffic and business performance. It attempts to extract the overall structure of commercial Websites and identify the optimal internal structural design. This structure represents the Website page organisation that ensures easy navigation and improved usability which subsequently increases the Web traffic. Data for this study comes from the top five hundred Asia Pacific retail companies as they are the leaders in the retail industry of Asia Pacific region and have well established Websites. The retail companies are divided into seven subgroups based on their retail sector. |
format |
Thesis |
qualification_name |
Doctor of Philosophy (PhD.) |
qualification_level |
Doctorate |
author |
Kannan, Rathimala |
author_facet |
Kannan, Rathimala |
author_sort |
Kannan, Rathimala |
title |
Characterisation of e-commerce website structures using webometrics and social network analysis methods |
title_short |
Characterisation of e-commerce website structures using webometrics and social network analysis methods |
title_full |
Characterisation of e-commerce website structures using webometrics and social network analysis methods |
title_fullStr |
Characterisation of e-commerce website structures using webometrics and social network analysis methods |
title_full_unstemmed |
Characterisation of e-commerce website structures using webometrics and social network analysis methods |
title_sort |
characterisation of e-commerce website structures using webometrics and social network analysis methods |
granting_institution |
Multimedia University |
granting_department |
Graduate School of Management |
publishDate |
2012 |
_version_ |
1747829564969582592 |