Back-propagation neural network based importance-performance analysis for determining critical service attributes

Authors:
Wei-Jaw Deng;Wen-Chin Chen;Wen Pei
Affiliations:
Graduate School of Business Administration, Chung Hua University, No. 707, Sec. 2, WuFu Road, 300 Hsinchu, Taiwan;Graduate Institute of Management of Technology, Chung Hua University, No. 707, Sec. 2, WuFu Road, 300 Hsinchu, Taiwan;Graduate School of Business Administration, Chung Hua University, No. 707, Sec. 2, WuFu Road, 300 Hsinchu, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1

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Abstract

Importance-performance analysis (IPA) is a simple but effective means of assisting practitioners in prioritizing service attributes when attempting to enhance service quality and customer satisfaction. As numerous studies have demonstrated, attribute performance and overall satisfaction have a non-linear relationship, attribute importance and attribute performance have a causal relationship and the customer's self-stated importance is not the actual importance of service attribute. These findings raise questions regarding the applicability of conventional IPA. Therefore, this study presents a revised IPA which integrates back-propagation neural network and three-factor theory to effectively assist practitioners in determining critical service attributes. Finally, a customer satisfaction improvement case is presented to demonstrate the implementation of the proposed Back-Propagation Neural Network based Importance-Performance Analysis (BPNN-IPA) approach.