Research and development (R&D) metrics influence research decisions, research efforts, resources allocation, performance control, and eventually innovation. Increasing concerns have emerged regarding how characteristic differences can significantly affect output as well as regarding the relative performance and productivity associated within the high tech industry. The primary objective of this study is to apply a new variant of the data envelopment analysis (DEA) model to examine the influence of specific organizational factors on high tech firmperformance through system-DEA (SDEA) models, summarizing the insights in the process. Such insights are obtained using a new procedure for measuring high tech firm efficiency in R&D innovation productivity that takes into account specific organizational factors and variety. The SDEA divides decision-making units into homogenous and non-homogenous efficiency frontier systems and then employs the rank-sum-statistics-test to identify different systems, comparing them to the basic BCC DEA model among various high tech firms. As such, this study advances the understanding of specific organizational factors by exploring a largely ignored aspect in extant literature-namely, the performance measurement of R&D innovation. The empirical results strongly demonstrate that applying the SDEA to the high tech industry can find that firms operating within specific organizational factors are less efficient than those within non-specific organizational factors. The key findings verify the mutual learning effects that occur in R&D innovation processes of homogenous and non-homogenous high-tech firms applying the different systems