Exploring the relative impact of R&D and operational efficiency on performance: A sequential regression-neural network approach

Jooh Lee, He Boong Kwon, Niranjan Pati

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)420-431
Number of pages12
JournalExpert Systems with Applications
Volume137
DOIs
StatePublished - Dec 15 2019

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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