Importance performance analysis based fuzzy neural for determining critical service attributes

It presents the IPAFN (Importance performance analysis based fuzzy neural) approach that integrates fuzzy set theory, BPNN (back propagation neural network) and three-factor theory. The importance of service attributes is implicitly derived via the BPNN that used defuzzification crisp number data. The weights between input neurons and output neurons in the BPNN represent the actual importance of attributes that had considered the attribute category in three factor theory and the nature of fuzziness in human perception. Applying the BPNN enables practitioners accommodate non-normal, multicollinearity, and non-linear what in practical systems. Furthermore, from the perspective of workload in conducting a questionnaire-based survey, the IPAFN approach eliminates the need to measure the pre-consuming importance of attributes. This unnecessary process is time-consuming for analysts and respondents. Consequently, an appropriate, effective and reliable action plan for each critical service attribute can be acquired by applying the IPAFN approach to service quality management or customer satisfaction management, thereby providing managers with a competitive advantage.