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Demystifying Disruption: A New Model for Understanding and Predicting Disruptive Technologies

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  • Ashish Sood

    (Goizueta School of Business, Emory University, Atlanta, Georgia 30322)

  • Gerard J. Tellis

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

Abstract

The failure of firms in the face of technological change has been a topic of intense research and debate, spawning the theory (among others) of disruptive technologies. However, the theory suffers from circular definitions, inadequate empirical evidence, and lack of a predictive model. We develop a new schema to address these limitations. The schema generates seven hypotheses and a testable model relating to platform technologies. We test this model and hypotheses with data on 36 technologies from seven markets. Contrary to extant theory, technologies that adopt a lower attack ("potentially disruptive technologies") (1) are introduced as frequently by incumbents as by entrants, (2) are not cheaper than older technologies, and (3) rarely disrupt firms; and (4) both entrants and lower attacks significantly reduce the hazard of disruption. Moreover, technology disruption is not permanent because of multiple crossings in technology performance and numerous rival technologies coexisting without one disrupting the other. The proposed predictive model of disruption shows good out-of-sample predictive accuracy. We discuss the implications of these findings.

Suggested Citation

  • Ashish Sood & Gerard J. Tellis, 2011. "Demystifying Disruption: A New Model for Understanding and Predicting Disruptive Technologies," Marketing Science, INFORMS, vol. 30(2), pages 339-354, 03-04.
    • Handle: RePEc:inm:ormksc:v:30:y:2011:i:2:p:339-354
    DOI: 10.1287/mksc.1100.0617
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    References listed on IDEAS

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