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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Bibliographic Details
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 Paper
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
Description
Summary: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.

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