Integration of Importance - Performance Analysis and Fuzzy Dematel

Abstract

The traditional Importance-Performance Analysis (IPA) assumes that quality attributes are independent variables, and presupposes that explicit customers' response data is used for assessing the importance and performance of quality attributes. Under this supposition, when the quality attribute has explicit causation data, the traditional IPA cannot correctly provide importance and priority of improvement. Moreover, the influential degree of the traditional quality attributes is emphasized as maximum degree. This study employs regression analysis and the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL), which consider the fuzziness of human thinking, to calculate the causal relationship and the influential degree among each quality attribute, and then proposes a new methodology of decision analysis, which modifies the traditional IPA and obtains the accurate importance and the improvement the quality attributes. In this study, a Taiwanese bank is an empirical case study, which illustrates the application and the effectiveness of integrating fuzzy DEMATEL and IPA.