The use for the competition theory of the industrial investment decisions–a case study of the Taiwan IC assembly industry

This study empirically analyzes model accuracy, and applies grey forecasting to handle non‐linear problems, insufficient data resources and forecasting involving small samples, and to construct the co‐opetition diffusion model for the Lotka–Volterra (L.V.) system. Furthermore, this study examines historical data comprising revenue trends in the Taiwanese IC assembly industry during the past ten years and selects from a range of forecasting models. Empirical study uses MAPE to precisely analyze revenue trends in the L.V. dynamic co‐opetition diffusion model relation to the IC assembly industry. The nine companies will be selected from 4 to 11 of the modeling, the results of the LV model 64 accuracy test, its accuracy is higher than 95% accounted for 59 times, five times better than the grey prediction, showing LV competing diffusion model not only with grey prediction, and better than the traditional grey forecasting model to make a higher accuracy of the predicted value. Like grey forecasting, MAPE can promptly respond even given insufficient data. Additionally, MAPE is able to provide more accurate forecasting values than the traditional Grey forecasting model. This study demonstrates the applicability of the dynamic co‐opetition theory forecasting model to the Taiwanese IC assembly industry and provides management with a reference for use in decisions aimed to increase managerial competitiveness.