Looking for potential service quality gaps to improve customer satisfaction by using a new GA approach

Abstract

Gap analysis (GA) is one of the most popular techniques which has been widely used in service marketing to help businesses make customer satisfaction (CS) decisions through a service quality perspective. However, an assumption of a linear relationship between attribute performance and CS underlies the application of the GA approach. And this assumption is challenged by Kano's two-dimensional model which indicates the possible phenomenon of a non-linear relationship between attribute performance and CS. Therefore, the main purpose of this study is to propose a novel GA approach through Kano's two-dimensional conception with the application of the neural network technique in order to extract the actual contribution offered by each attribute for CS improvement. A comparative analysis with the traditional GA approach and our proposed approach was performed by using an empirical study on a Taiwanese Human Resource service online agency. Results showed that the new proposed GA approach has higher effectiveness in reflecting the impact of gap reduced on CS improvement. This implies that the new GA approach can be considered as a helpful decision-making technique for CS decision making. More implications from empirical research were discussed.

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