An extension of importance-performance analysis method: integrated with fuzzy MICMAC

Abstract

Importance-performance analysis (IPA) has attracted the interest of various academics and practitioners from different fields. However, many scholars have put forward some shortcomings of IPA as well as some methods to improve it. This study focuses on a new perspective which the interaction between attribute shall be considered and proposes a new approach that integrating IPA into another complex problem analysis technique - impact matrix cross-reference multiplication applied to a classification - to achieve more convenient calculation and application. Its contribution and innovation are mainly measuring the interaction between attribute and effectively quantifying the integration of IPA model; may modify the importance and the performance of attribute by influence degree and dependence degree; and integrating the Fuzzy theory to make the assessment results more accurate. Through an empirical study, this method should have certain usability.