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Win, lose, or draw? Forecasting the outcome of a race toward a dominant formal standard with machine learning

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  • Dai, Haiwen
  • Qualls, William J.
  • Zhu, You

Abstract

Accurate forecasting of the outcome of a race toward a dominant formal standard can help companies make informed decisions and develop appropriate strategies to achieve market leadership. This paper focuses on factors contributing to formal standards dominance and applies machine learning (ML) methods to predict which standard supporters will win in formal standard battles. The empirical context is the de jure standard-setting process in the Chinese solid-state lighting (SSL) industry. We employ 1011 valid sample datasets from 1998 to 2016. Our findings reveal that the random subspace (R.S.)-MultiBoosting approach outperforms the other three approaches regarding forecasting outcomes, especially when dealing with small datasets. Strong tie strength in standard supporting alliances, prior experience in patent application, and an appropriate level of marketization can enhance firms' chances of winning dominant formal standards.

Suggested Citation

  • Dai, Haiwen & Qualls, William J. & Zhu, You, 2024. "Win, lose, or draw? Forecasting the outcome of a race toward a dominant formal standard with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:tefoso:v:205:y:2024:i:c:s0040162524002956
    DOI: 10.1016/j.techfore.2024.123499
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