Making the right business decision: Forecasting the binary NPD strategy in Chinese automotive industry with machine learning methods

Abstract

The new product development (NPD) is crucial to firms’ survival and success. Tough decisions must be made between the binary NPD strategy (i.e. incremental NPD strategy and radical NPD strategy) to ensure that scarce resources are allocated efficiently. The inappropriate NPD strategy that does not meet the internal and external conditions may lead to resources waste and performance decline. The binary NPD strategy forecasting is helpful to guide the firms when to improve existing products and when to develop ‘really new’ products. Therefore, the primary purposes of this study are to construct an evaluating indicator system and to find the appropriate method for the binary NPD strategy forecasting. Here we obtain 1088 valid sample datasets from Chinese automotive industry, covering the period 2001–2014. The empirical results indicate that RS-MultiBoosting as a kind of hybrid ensemble machine learning (HEML) method demonstrate an outstanding forecasting performance in dealing with the small datasets by comparison with the other four ensemble machine learning (EML) methods and three individual machine learning (IML) methods. The findings can help firms to make the right business decision between incremental and radical NPD strategies so that they can avoid resources waste and improve the overall NPD performance.