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Dynamic commercialization strategies for disruptive technologies : evidence from the Speech Recognition Industry

Auteur : Matt Marx; Joshua Gans; David H Hsu; National Bureau of Economic Research.
Éditeur : Cambridge, Mass. : National Bureau of Economic Research, 2013.
Collection : Working paper series (National Bureau of Economic Research), no. 19764.
Édition/format :   Livre électronique : Document : EnglishVoir toutes les éditions et tous les formats
Base de données :WorldCat
Résumé :
When startup innovation involves a potentially disruptive technology -- initially lagging in the predominant performance metric, but with a potentially favorable trajectory of improvement -- incumbents may be wary of engaging in cooperative commercialization with the startup. While the prevailing theory of disruptive innovation suggests that this will lead to (exclusively) competitive commercialization and the  Lire la suite...
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Détails

Type d’ouvrage : Document, Ressource Internet
Format : Internet Resource, Computer File
Tous les auteurs / collaborateurs : Matt Marx; Joshua Gans; David H Hsu; National Bureau of Economic Research.
Numéro OCLC : 868158921
Notes : Title from http://www.nber.org/papers/19764 viewed January 15, 2014.
"December 2013."
Description : 1 online resource (36 pages) : illustrations.
Titre de collection : Working paper series (National Bureau of Economic Research), no. 19764.
Responsabilité : Matt Marx, Joshua S. Gans, David H. Hsu.

Résumé :

When startup innovation involves a potentially disruptive technology -- initially lagging in the predominant performance metric, but with a potentially favorable trajectory of improvement -- incumbents may be wary of engaging in cooperative commercialization with the startup. While the prevailing theory of disruptive innovation suggests that this will lead to (exclusively) competitive commercialization and the eventual replacement of incumbents, we consider a dynamic strategy involving product market entry before switching to a cooperative commercialization strategy. Empirical evidence from the automated speech recognition industry from 1952-2010 confirms the main prediction of the model.

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