A neural network-based approach of quantifying relative importance among various determinants toward organizational innovation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)13064-13072
Journal / PublicationExpert Systems with Applications
Volume38
Issue number10
Publication statusPublished - 15 Sep 2011

Abstract

Innovation management is the practice of managing new ideas and insights into business environment so as to increase market competitiveness. Therefore, it is vital to address the critical determinants of effective innovation management within an organizational context, hence called organizational innovation. Based on some existing frameworks of managing organizational innovation, several key determinants can be identified. Using industrial survey data, we then proposed and used a neural network-based approach to quantify the connectivity between each of these determinants and organizational innovation. First, the modeling accuracy of our proposed method was examined and benchmarked with multiple linear regression and non-linear least square fitting methods. Next, the relative importance among various key determinants toward organizational innovation can be computed using our method. We then compared the results with the qualitative judgment of field experts and professionals using the analytical hierarchy process. Based on the comparison outcomes, our proposed method is proved to be reasonably useful and practical in capturing the comparative influence among determinants toward organizational innovation. © 2010 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Analytical hierarchy process, Neural network, Organizational innovation, Relative importance