• 沒有找到結果。

Conclusion

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similar situation. We decided to follow these precedents to solve the non-linear problem between attribute-level performance and the overall satisfaction by formulating piecewise regression whose solution presumably gives a near-optimal solution to the original problem. In piecewise regression, each phase is characterized by a different equation and it can simplified non-linear question. This essay will base on major quality classification description of Kano’s model to apply piecewise regression graphing relationship curve of quality performance and customer satisfaction of different quality attribute of bank service. The quality attribute in case study will be revealed without pair-wise questionnaire.

In social science research, questionnaire devising and analyzing has always been in significance instrument. Researchers often use quantity method of Likert Scale (LS) to measure the recognition level of study object. Nevertheless, the advantage of LS scale is with definite structure and easy to apply, there is still error while transferring into quantity. It will limit the stability of evaluation result. Kano’s functional and dysfunctional questionnaire is to ask about the psychological response of respondents. Customers have various responses to the same linguistic term due to the quality performance and satisfaction of different quality attribution.

Therefore, this essay will conduct Kano’s model on different non-linear association of each quality attribute performance and satisfaction. It is to combine the conceptual work of Tan and Shen (2000) and Matzler and Hinterhuber (1998) to innovate evaluation application. The scale value on options to 5-points or 7-points Likert scale will be revised. We used the simple rules to explain the relationship between quality performance and customer satisfaction by the Kano categorization and the concept of hierarchical structure to sum among the effectiveness of each one.

Comparing CS and CS’, it shows that there will be different scores, different gap and different sequences between three hospitals under the same weight. CS’ is based on typical ranging from 0~100, and the central point is 50. However, this essay uses Kano’s two dimensional model to explain the divergence of each quality element. It reasonably transfers the connection between quality performance and customer satisfaction. By this definition, CS ranges from -100~100, and the central point is 0.

The numeric in CS can closely state and response if there is any exceed expectation of functional dysfunctional result.

The final contribution of the essay is to apply Kano’s model in related issue of

Likert Scale. In the study, the 5 or 7 points scale in Likert scale can not be taken as scores of satisfaction. The typical Likert scale is a one-dimensional concept and Fornell (2002) once precisely pointed that CS can not be assessed or measured straightforward. This study based on quantified Kano’s quality classification which can be applied directly when Tam and Shan, in addition with Shahin, addressed on quality performance and satisfaction issues. In traditional investigative research, the performance degree for the service attribute used the 5-points Likert scale. In this paper, we used the simple rules to explain the relationship between quality performance and customer satisfaction by the Kano categorization and the concept of hierarchical structure to sum among the effectiveness of each one.

This essay conducts multivariate analysis to revise the principle of Kano classification within the above new two dimensional model classification and evaluation application. It aims to deliver reasonable analysis on quality and satisfaction. The Kano’s model is a powerful way to classify a set of customer requirements, the way of categorization, however, probably is arbitrary. Therefore, this essay chose different and new methodology to reflect the concept of nonlinear and subgroups. By identifying attributes that should be emphasized or de-emphasized, Kano’s model guides the development of action plans to minimize mismatches between satisfaction and attribute-level performance, resulting in improved operational efficiencies through resource redeployment recommendation.

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