Exploring the relative impact of R&D and operational efficiency on performance: A sequential regression-neural network approach. (15th December 2019)

• Record Type: Journal Article
• Authors: Lee, Jooh; Kwon, He-Boong; Pati, Niranjan
• Is Part Of: Expert systems with applications. Volume 137(2019)
• Page Start: 420
• Page End: 431
• Publication Date: 2019-12-15
• Subjects: Market value -- Neural network -- Operational efficiency -- R&D intensity -- Tobin's Q; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.07.026
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A combined OLSMR-BPNN is a promising paradigm for explorative analysis. R&D and efficiency are significant factors for manufacturing firms. R&D and efficiency are even more influential for the above-average performers. Tech. innovation and operational excellence are crucial performance drivers. Abstract: This study explores the potential strategic determinants of firm performance, with an emphasis on R&D investment and operational efficiency in leading U.S. manufacturing firms. In particular, it investigates R&D as a driver of technological innovation, and operational efficiency and as an indicator of the best-practice operations, for their impact relative to Tobin's Q and Market value. The study jointly uses ordinary least square multiple regression (OLSMR) and backpropagation neural network (BPNN), not only to measure the statistical significance of factors, but also to explore new insights into their relative importance, and to determine the differential impact of each factor following the varying performance levels. A major finding is that proactive R&D investments and operational excellence are the most impactful factors on both metrics of performance as compared to other conventional factors used in this study. Another encouraging finding is that both R&D intensity and operational efficiency are even more influential in the above-average performers and yield higher returns in market valuation. Through a combined OLSMR-BPNN approach, this study presents insightful findings on this intriguing subject and highlights prospective research opportunities.