The purpose of this paper is to identify and evaluate the critical success factors which make the dimensions for measuring customer experience (CE) in banking organizations more effective and purposeful. These critical success factors have been prioritized in order to find out which is more important for the five threshold dimensions to achieve favourable CE.
The paper examines the critical success factors of CE which have been identified from the literature survey and through expert's opinion have finally confirmed in context of this study on banking organizations. Further, analytic hierarchy process (AHP) has been used in order to evaluate the identified 14 critical factors of CE and to find their priorities for success in banking organizations.
On the basis of the proposed hierarchical model, the findings suggest that the factors such as convenience, employees, online functional elements, and servicescape are critical for measuring CE in banking organizations.
In the literature, it is assumed that the criteria for measuring CE are mutually exclusive. But it has been found that in the real world these criteria may sometimes depend on one other, particularly in banking organizations. Therefore, in order to resolve such issues it is advised that future research may apply analytic network process (ANP).
The findings suggest that the managers in banking organizations must ameliorate these critical factors according to their weights, in order to attain favourable CE, sustainable competitive advantage and customer delight.
Because of the subjective nature of CE, AHP serves as an empirical approach in prioritization of critical success factors of CE, which has been a major lacuna in available literature. The results presented in this paper are objective, reliable, and may be generalized for measuring CE in banking organizations.
Garg, R., Rahman, Z., Qureshi, M.N. and Kumar, I. (2012), "Identifying and ranking critical success factors of customer experience in banks: An analytic hierarchy process (AHP) approach", Journal of Modelling in Management, Vol. 7 No. 2, pp. 201-220. https://doi.org/10.1108/17465661211242813
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