Dynamic interplay of operations and R&D capabilities in U.S. high-tech firms: Predictive impact analysis

Abstract

This study presents a unique analytic process in measuring two complementary capabilities (operations and R&D) and assessing their comparative impact on firm performance, represented by Return on Sales (ROS) and Tobin's Q. First, as a new approach, the combined data envelopment analysis (DEA)-artificial neural network (ANN) measures both capabilities by using data from U.S. high-tech firms. Then, the joint OLS multiple regression (MR)-ANN model investigates the individual effect of these capabilities and further explores their relative influence and the synergistic interplay on ROS and Tobin's Q. At its core, this study not only proposes an innovative approach to quantifying capabilities, but also examines the complementary aspect of both capabilities. We found that both capabilities positively affect two temporal performance dimensions. In addition, the impact of the operations capabilities was larger for ROS, whereas R&D capabilities had a greater impact on Tobin's Q. Moreover, the integrative effect of R&D capabilities in association with operations capabilities signifies the importance of a firm balancing efficient operations and technological innovations in pursuit of a sustainable competitive advantage. Through the predictive analytic process, this study further affirms that operational excellence is a main driver of R&D effectiveness.