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Ex-Ante Prediction of Disruptive Innovation: The Case of Battery Technologies

Author

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  • Julian Marius Müller

    (Business Management, Salzburg University of Applied Sciences, Puch, 5412 Salzburg, Austria)

  • Raphael Kunderer

    (School of Business, Economics and Society, Friedrich-Alexander-University Erlangen-Nürnberg, 91054 Erlangen, Germany)

Abstract

Battery technologies represent a highly relevant field that is undergoing conversions in the context of, for instance, battery electric vehicles or stationary power storage for renewable energies. Currently, lithium-ion batteries represent the predominant technology that has, however, a considerable environmental impact that could hinder the emergence of sustainable energy systems. Driven by these conversions, several authors claim that potentially disruptive technologies could occur. The concept of disruptive innovation has been highly regarded in research and practice, but has only been successfully regarded from an ex-post perspective. However, without the possibility to establish ex-ante predictions of disruptive innovation, several authors disregard the concept of having significant relevance for practice. In response to this research gap, the present paper attempts to establish an ex-ante prediction of potential disruptive innovation. The method is based on the disruption hazard model by Sood and Tellis, testing seven hypotheses regarding a potential disruption hazard of redox-flow batteries towards lithium-ion batteries. The paper finds that redox-flow batteries could represent a disruptive technology, but this evaluation is limited to an expert evaluation. The authors discuss this finding, as the technical characteristics of redox-flow batteries support its role as a potential disruptive innovation, concluding with implications, limitations as well as suggestions for future research.

Suggested Citation

  • Julian Marius Müller & Raphael Kunderer, 2019. "Ex-Ante Prediction of Disruptive Innovation: The Case of Battery Technologies," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5229-:d:270184
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    2. Minhao Xiang & Dian Fu & Kun Lv, 2023. "Identifying and Predicting Trends of Disruptive Technologies: An Empirical Study Based on Text Mining and Time Series Forecasting," Sustainability, MDPI, vol. 15(6), pages 1-22, March.

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