Entropy for Business Failure Prediction: An Improved Prediction Model for the Construction Industry

Entropy for Business Failure Prediction: An Improved Prediction Model for the Construction Industry

This paper examines empirically the effectiveness of entropy measures derived from information theory combined with discriminant analysis in the prediction of construction business failure. Such failure in modern complex supply chains is an extremely disruptive force, and its likelihood is a key fac...

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Main Authors: Jay Bal, Yen Cheung, Hsu-Che Wu
Format: Article
Language:English
Published: Asia University 2013-01-01
Series:Advances in Decision Sciences
Online Access:http://dx.doi.org/10.1155/2013/459751
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spelling doaj-cec8a581834948a6ba8f4da21b29cce02020-11-24T21:45:10ZengAsia UniversityAdvances in Decision Sciences2090-33592090-33672013-01-01201310.1155/2013/459751459751Entropy for Business Failure Prediction: An Improved Prediction Model for the Construction IndustryJay Bal0Yen Cheung1Hsu-Che Wu2International Digital Laboratory, University of Warwick, Coventry CV4 7AL, UKClayton School of IT, Monash University, Melbourne, Vic 3800, AustraliaDepartment of Accounting and Information Technology, National Chung Cheng University, 168 University Road, Min-Hsiung, Chia-Yi County 621, TaiwanThis paper examines empirically the effectiveness of entropy measures derived from information theory combined with discriminant analysis in the prediction of construction business failure. Such failure in modern complex supply chains is an extremely disruptive force, and its likelihood is a key factor in the prequalification appraisal of contractors. The work described, using financial data from the Taiwanese construction industry, extends the classical methods by applying Shannon's information theory to improve their prediction ability and provides an alternative to newer artificial-intelligence-based approaches.http://dx.doi.org/10.1155/2013/459751
collection DOAJ
language English
format Article
sources DOAJ
author Jay Bal
Yen Cheung
Hsu-Che Wu
spellingShingle Jay Bal
Yen Cheung
Hsu-Che Wu
Entropy for Business Failure Prediction: An Improved Prediction Model for the Construction Industry
Advances in Decision Sciences
author_facet Jay Bal
Yen Cheung
Hsu-Che Wu
author_sort Jay Bal
title Entropy for Business Failure Prediction: An Improved Prediction Model for the Construction Industry
title_short Entropy for Business Failure Prediction: An Improved Prediction Model for the Construction Industry
title_full Entropy for Business Failure Prediction: An Improved Prediction Model for the Construction Industry
title_fullStr Entropy for Business Failure Prediction: An Improved Prediction Model for the Construction Industry
title_full_unstemmed Entropy for Business Failure Prediction: An Improved Prediction Model for the Construction Industry
title_sort entropy for business failure prediction: an improved prediction model for the construction industry
publisher Asia University
series Advances in Decision Sciences
issn 2090-3359
2090-3367
publishDate 2013-01-01
description This paper examines empirically the effectiveness of entropy measures derived from information theory combined with discriminant analysis in the prediction of construction business failure. Such failure in modern complex supply chains is an extremely disruptive force, and its likelihood is a key factor in the prequalification appraisal of contractors. The work described, using financial data from the Taiwanese construction industry, extends the classical methods by applying Shannon's information theory to improve their prediction ability and provides an alternative to newer artificial-intelligence-based approaches.
url http://dx.doi.org/10.1155/2013/459751
work_keys_str_mv AT jaybal entropyforbusinessfailurepredictionanimprovedpredictionmodelfortheconstructionindustry
AT yencheung entropyforbusinessfailurepredictionanimprovedpredictionmodelfortheconstructionindustry
AT hsuchewu entropyforbusinessfailurepredictionanimprovedpredictionmodelfortheconstructionindustry
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