Quantifying uncertainty in applying importance-performance analysis

Quantifying uncertainty in applying importance-performance analysis In a traditional importance-performance analysis (IPA), the information of uncertainty for importance and performance does not normally take into account from the survey but does commonly exist in practice. To further consider the uncertainty for each item from the survey results, the IPA under uncertainty method is proposed in this study. The traditional IPA possesses the ease of calculation and can be further viewed as a special case of the proposed IPA under uncertainty method, which is more complicated for computation but provides much more information for a decision maker to judge the strengths and weaknesses effectively by considering sampling, instrument effects, measurement conditions, sample effects, computational effects, random effects, etc. Finally, a brief case is illustrated to show how the proposed IPA under uncertainty method works and what the differences are between the traditional IPA and proposed methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Quantifying uncertainty in applying importance-performance analysis

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Publisher
Springer Netherlands
Copyright
Copyright © 2009 by Springer Science+Business Media B.V.
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-009-9245-8
Publisher site
See Article on Publisher Site

Abstract

In a traditional importance-performance analysis (IPA), the information of uncertainty for importance and performance does not normally take into account from the survey but does commonly exist in practice. To further consider the uncertainty for each item from the survey results, the IPA under uncertainty method is proposed in this study. The traditional IPA possesses the ease of calculation and can be further viewed as a special case of the proposed IPA under uncertainty method, which is more complicated for computation but provides much more information for a decision maker to judge the strengths and weaknesses effectively by considering sampling, instrument effects, measurement conditions, sample effects, computational effects, random effects, etc. Finally, a brief case is illustrated to show how the proposed IPA under uncertainty method works and what the differences are between the traditional IPA and proposed methods.

Journal

Quality & QuantitySpringer Journals

Published: May 12, 2009

References

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