The recent failure of major PC and smartphone makers in launching new generations of high-tech products in time shows that analyzing and capturing the timing of technology disruption is an important yet less explored research area. This paper conducts theoretical and empirical analyses for ex-ante quantitative evaluation of the timing of technology disruption. We conceptualize the ease and network factors as key determinants of performance improvement for a disruptive technology. A dynamic consumer model is developed to identify two critical times, termed D-Day and V-Day, of technology disruption. We also show that, if the network factor dominates the performance improvement process, there may exist some “bleak days” during which a firm would discontinue a “promising” technology that will eventually disrupt. Empirical tests are conducted with data of hard disk drives, semiconductor technologies, and CPU performance for mobile devices to verify key model assumptions and to show how to estimate the ease and network factors. We also perform a numerical experiment to demonstrate how to forecast the timing of technology disruption. A decision tree and a systematic framework are also developed to operationalize key model parameters and analytical results from a decision-support perspective. This paper contributes to the literature by presenting a novel analytical tool and new insights for high-tech companies to forecast and manage the timing of technology disruption. 
The D-Day, V-Day, and bleak days of a disruptive technology: A new model for ex-ante evaluation of the timing of technology disruption