File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Predicting the survival or failure of click-and-mortar corporations

TitlePredicting the survival or failure of click-and-mortar corporations
Authors
Issue Date2005
PublisherIEEE, Computer Society.
Citation
IEEE International Conference on e-Technology, e-Commerce and e-Service Proceedings, Hong Kong, China, 29 March - 1 April 2005, p. 692-697 How to Cite?
AbstractWith the boom in e-business, several corporations have emerged in the late nineties that have primarily conducted their business through the Internet and the Web. They have come to be known as the dotcoms or click-and-mortar corporations. The success of these companies has been short lived and many of these companies have failed rapidly in a short span of 4-5 years. This research is an investigation of the burst of the dotcom bubble from a financial perspective. Data from the financial statements of several survived and failed dotcom companies is used to compute financial ratios, which are analyzed using two data mining techniques - discriminant analysis (DA) and neural networks (NN) to find out whether they can predict the financial fate of companies. Neural networks perform better than discriminant analysis in predicting survival or failure of click-and-mortar corporations. The key financial ratios that play a major role in the process of prediction are identified. © 2005 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/48481
References

 

DC FieldValueLanguage
dc.contributor.authorBose, Ien_HK
dc.contributor.authorPal, Ren_HK
dc.date.accessioned2008-05-22T04:14:34Z-
dc.date.available2008-05-22T04:14:34Z-
dc.date.issued2005en_HK
dc.identifier.citationIEEE International Conference on e-Technology, e-Commerce and e-Service Proceedings, Hong Kong, China, 29 March - 1 April 2005, p. 692-697en_HK
dc.identifier.urihttp://hdl.handle.net/10722/48481-
dc.description.abstractWith the boom in e-business, several corporations have emerged in the late nineties that have primarily conducted their business through the Internet and the Web. They have come to be known as the dotcoms or click-and-mortar corporations. The success of these companies has been short lived and many of these companies have failed rapidly in a short span of 4-5 years. This research is an investigation of the burst of the dotcom bubble from a financial perspective. Data from the financial statements of several survived and failed dotcom companies is used to compute financial ratios, which are analyzed using two data mining techniques - discriminant analysis (DA) and neural networks (NN) to find out whether they can predict the financial fate of companies. Neural networks perform better than discriminant analysis in predicting survival or failure of click-and-mortar corporations. The key financial ratios that play a major role in the process of prediction are identified. © 2005 IEEE.en_HK
dc.format.extent234488 bytes-
dc.format.extent1820 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE, Computer Society.en_HK
dc.relation.ispartofProceedings - 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service, EEE-05en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.titlePredicting the survival or failure of click-and-mortar corporationsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailBose, I: bose@business.hku.hken_HK
dc.identifier.authorityBose, I=rp01041en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/EEE.2005.104en_HK
dc.identifier.scopuseid_2-s2.0-30944440265en_HK
dc.identifier.hkuros104129-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-30944440265&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage692en_HK
dc.identifier.epage697en_HK
dc.identifier.scopusauthoridBose, I=7003751502en_HK
dc.identifier.scopusauthoridPal, R=55435080500en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats