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

Fingerprint

Neural networks
Backpropagation
Innovation

All Science Journal Classification (ASJC) codes

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

Cite this

@article{484a773bc884408c8d753792010383da,
title = "Exploring the relative impact of R&D and operational efficiency on performance: A sequential regression-neural network approach",
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.",
author = "Jooh Lee and Kwon, {He Boong} and Niranjan Pati",
year = "2019",
month = "12",
day = "15",
doi = "https://doi.org/10.1016/j.eswa.2019.07.026",
language = "English (US)",
volume = "137",
pages = "420--431",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",

}

Exploring the relative impact of R&D and operational efficiency on performance : A sequential regression-neural network approach. / Lee, Jooh; Kwon, He Boong; Pati, Niranjan.

In: Expert Systems With Applications, Vol. 137, 15.12.2019, p. 420-431.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Exploring the relative impact of R&D and operational efficiency on performance

T2 - A sequential regression-neural network approach

AU - Lee, Jooh

AU - Kwon, He Boong

AU - Pati, Niranjan

PY - 2019/12/15

Y1 - 2019/12/15

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85068888684&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068888684&partnerID=8YFLogxK

U2 - https://doi.org/10.1016/j.eswa.2019.07.026

DO - https://doi.org/10.1016/j.eswa.2019.07.026

M3 - Article

VL - 137

SP - 420

EP - 431

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -