Multiple regression models for electronic product success prediction

Multiple regression models for electronic product success prediction

As the cost of failure in new product development is very high, product developers are looking for good product success/failure prediction models. The general direction of search is towards Knowledge Based Systems (KBS) that incorporate the wisdom of experienced developers and extracts from data of...

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Main Authors: Lo, Frank Cheong Wah, Foo, Say Wei, Bauly, John A.
Other Authors: IEEE International Conference on Management of Innovation and Technology (1st : 2000 : Singapore)
Format: Conference or Workshop Item
Language:English
Published: 2009
Online Access:https://hdl.handle.net/10356/91312
http://hdl.handle.net/10220/4587
http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:EVII&id=doi:10.1109/ICMIT.2000.917374&genre=&isbn=0 7803 6652 2&issn=&date=2000&volume=&issue=&spage=419&epage=22&aulast=Lo&aufirst=%20F%20C%20%2DW&auinit=&title=Proceedings%20of%20the%202000%20IEEE%20International%20Conference%20on%20Management%20of%20Innovation%20and%20Technology%2E%20ICMIT%202000%2E%20%60Management%20in%20the%2021st%20Century%27%20%28Cat%2E%20No%2E00EX457%29&atitle=Multiple%20regression%20models%20for%20electronic%20product%20success%20prediction
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spelling sg-ntu-dr.10356-913122020-03-07T13:24:46Z Multiple regression models for electronic product success prediction Lo, Frank Cheong Wah Foo, Say Wei Bauly, John A. IEEE International Conference on Management of Innovation and Technology (1st : 2000 : Singapore) As the cost of failure in new product development is very high, product developers are looking for good product success/failure prediction models. The general direction of search is towards Knowledge Based Systems (KBS) that incorporate the wisdom of experienced developers and extracts from data of past projects. In this paper, results of investigation using multiple regression models are reported. It is found that 90% accuracy may be achieved in success/failure prediction of electronic product development using a multiple regression model based on six critical factors. Accepted version 2009-04-28T03:25:29Z 2019-12-06T18:03:25Z 2009-04-28T03:25:29Z 2019-12-06T18:03:25Z 2000 2000 Conference Paper Lo, F.C.W., Foo, S.W. & Bauly, J.A. (2000). Multiple regression models for electronic product success prediction. IEEE International Conference on Management of Innovation and Technology 2000: (pp. 419-422). Singapore: Singapore Polytechnic. https://hdl.handle.net/10356/91312 http://hdl.handle.net/10220/4587 http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:EVII&id=doi:10.1109/ICMIT.2000.917374&genre=&isbn=0 7803 6652 2&issn=&date=2000&volume=&issue=&spage=419&epage=22&aulast=Lo&aufirst=%20F%20C%20%2DW&auinit=&title=Proceedings%20of%20the%202000%20IEEE%20International%20Conference%20on%20Management%20of%20Innovation%20and%20Technology%2E%20ICMIT%202000%2E%20%60Management%20in%20the%2021st%20Century%27%20%28Cat%2E%20No%2E00EX457%29&atitle=Multiple%20regression%20models%20for%20electronic%20product%20success%20prediction 10.1109/ICMIT.2000.917374 en © 2000 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. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. 4 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
description As the cost of failure in new product development is very high, product developers are looking for good product success/failure prediction models. The general direction of search is towards Knowledge Based Systems (KBS) that incorporate the wisdom of experienced developers and extracts from data of past projects. In this paper, results of investigation using multiple regression models are reported. It is found that 90% accuracy may be achieved in success/failure prediction of electronic product development using a multiple regression model based on six critical factors.
author2 IEEE International Conference on Management of Innovation and Technology (1st : 2000 : Singapore)
author_facet IEEE International Conference on Management of Innovation and Technology (1st : 2000 : Singapore)
Lo, Frank Cheong Wah
Foo, Say Wei
Bauly, John A.
format Conference or Workshop Item
author Lo, Frank Cheong Wah
Foo, Say Wei
Bauly, John A.
spellingShingle Lo, Frank Cheong Wah
Foo, Say Wei
Bauly, John A.
Multiple regression models for electronic product success prediction
author_sort Lo, Frank Cheong Wah
title Multiple regression models for electronic product success prediction
title_short Multiple regression models for electronic product success prediction
title_full Multiple regression models for electronic product success prediction
title_fullStr Multiple regression models for electronic product success prediction
title_full_unstemmed Multiple regression models for electronic product success prediction
title_sort multiple regression models for electronic product success prediction
publishDate 2009
url https://hdl.handle.net/10356/91312
http://hdl.handle.net/10220/4587
http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:EVII&id=doi:10.1109/ICMIT.2000.917374&genre=&isbn=0 7803 6652 2&issn=&date=2000&volume=&issue=&spage=419&epage=22&aulast=Lo&aufirst=%20F%20C%20%2DW&auinit=&title=Proceedings%20of%20the%202000%20IEEE%20International%20Conference%20on%20Management%20of%20Innovation%20and%20Technology%2E%20ICMIT%202000%2E%20%60Management%20in%20the%2021st%20Century%27%20%28Cat%2E%20No%2E00EX457%29&atitle=Multiple%20regression%20models%20for%20electronic%20product%20success%20prediction
_version_ 1681034538992533504

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