Multiple regression models for electronic product success prediction

• 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
• Institution: Nanyang Technological University

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.