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Predicting e-commerce company success by mining the text of its publicly-accessible website

 
: Thorleuchter, Dirk; Van den Poel, Dirk

:
Preprint urn:nbn:de:0011-n-2138960 (229 KByte PDF)
MD5 Fingerprint: 7fa6e31fe28b2eee50fc238c85e85688
Created on: 11.9.2012


Expert Systems with Applications 39 (2012), No.17, pp.13026-13034
ISSN: 0957-4174
English
Journal Article, Electronic Publication
Fraunhofer INT ()
success factor; E-Commerce; LSI; classification; text mining; Website

Abstract
We analyze the impact of textual information from e-commerce companies' websites on their commercial success. The textual information is extracted from web content of e-commerce companies divided into the Top 100 worldwide most successful companies and into the Top 101 to 500 worldwide most successful companies. It is shown that latent semantic concepts extracted from the analysis of textual information can be adopted as success factors for a Top 100 e-commerce company classification. This contributes to the existing literature concerning e-commerce success factors. As evaluation, a regression model based on these concepts is built that is successful in predicting the commercial success of the Top 100 companies. These findings are valuable for e-commerce websites creation.

: http://publica.fraunhofer.de/documents/N-213896.html