The purpose of this study is to answer two questions: "Are R & D value-added expen ses just wasted expenditures in the pharmaceutical and semiconductor industries? " and "Is there a non-linear relationship between R & D intensity and corporate fi nancial performance?" The research methodology utilizes a hierarchical regression to capture the incre ase in R square due to each variable alone. The addition of squared terms for R & D Intensity is performed to examine the non-linear relationship between research and development intensity and corporate financial performance (CFP). The main findings of the study are as follows: There's a significant non-linear relationship between R & D Intensity and CFP. For the pharmaceutical industry, the optimal level of R & D Intensity is around 28%. For the semiconductor industry, t he optimal level of R & D Intensity is greater than the one corresponding to the p harmaceutical industry with values being 179% and 82% depending on the years of study. The research results suggest that more investment in R & D is not always value-add ed to the organization; firms should be aware the optimal level of R & D depends o n the industry and thus, they should allocate their R & D resources accordingly. This study extends prior research's viewpoints by investigating both the pharmac eutical and semiconductor industries in the U.S and implementing hierarchical re gression and non-linear relationship analysis. The results can provide the refer ence for further research on the relationship between R & D and corporate financia l performance. Future research should concentrate on (1)Use of a longer time lag of 5 to 10 yea rs between R & D and the rest of the data, (2)Test the effect of the interaction b etween R & D and other factors on the financial performance of firms in the 2 indu stries, (3)Research new industries such as the chemical industry or other indust ries with intangible products (services) such as insurance, advertising, consume r services; add more control variables that can reliably increase R square, use other proxies for financial performance, and study markets outside the U.S.