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Grey clustering evaluation based on triangular whitenization weight function of enterprise's management innovation performance

Grey clustering evaluation based on triangular whitenization weight function of enterprise's... Purpose – The purpose of this paper is to attempt to establish a grey clustering evaluation model of center-point triangular whitenization weight function to measure the performance of enterprise's management innovation (MI). The author intends to provide some theory basis and tool support for enterprise's managers and other researchers who are engaged in performance measuring. Design/methodology/approach – The study uses questionnaire survey and expert interviews to determine the index weight of enterprise's MI performance (MIP), classifies the grey clusters based on center-point triangular whitenization weight function, calculates the membership of performance criteria and ranks the performance level of all dimensions. Findings – The survey data of case company shows that production performance is in superior level, employee and society influence performance are in satisfied level, finance and market performance are in intermediate level, total MIP is in satisfied level. Practical implications – MI is the fundamental way to keep enterprise's core competitiveness and achieve its strategic objectives. Performance is an effective tool to measure the MI. Therefore, based on the practices of MI in China, the study systematically clarifies the performance level of MI from five dimensions. Originality/value – The evaluation index of enterprise's MIP is built with five dimensions which contain production, market, finance, employee and social influence. The grey clustering evaluation model based on triangular whitenization weight function is applied to assess the performance criteria. This paper presents a new research idea for enterprise's performance evaluation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Grey Systems: Theory and Application Emerald Publishing

Grey clustering evaluation based on triangular whitenization weight function of enterprise's management innovation performance

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2043-9377
DOI
10.1108/GS-05-2014-0017
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to attempt to establish a grey clustering evaluation model of center-point triangular whitenization weight function to measure the performance of enterprise's management innovation (MI). The author intends to provide some theory basis and tool support for enterprise's managers and other researchers who are engaged in performance measuring. Design/methodology/approach – The study uses questionnaire survey and expert interviews to determine the index weight of enterprise's MI performance (MIP), classifies the grey clusters based on center-point triangular whitenization weight function, calculates the membership of performance criteria and ranks the performance level of all dimensions. Findings – The survey data of case company shows that production performance is in superior level, employee and society influence performance are in satisfied level, finance and market performance are in intermediate level, total MIP is in satisfied level. Practical implications – MI is the fundamental way to keep enterprise's core competitiveness and achieve its strategic objectives. Performance is an effective tool to measure the MI. Therefore, based on the practices of MI in China, the study systematically clarifies the performance level of MI from five dimensions. Originality/value – The evaluation index of enterprise's MIP is built with five dimensions which contain production, market, finance, employee and social influence. The grey clustering evaluation model based on triangular whitenization weight function is applied to assess the performance criteria. This paper presents a new research idea for enterprise's performance evaluation.

Journal

Grey Systems: Theory and ApplicationEmerald Publishing

Published: Oct 28, 2014

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