单次多阶段服务下顾客累积满意度动态变化的测量模型

Measurement model for dynamic cumulative satisfaction in a single multi-stage service

为了反映单次服务中顾客的实际心理变化,顾客满意度的测量应当充分考虑顾客的需求权重以及双重期望的动态变化。首先,将单次服务按流程拆分,揭示顾客满意度在不同阶段的变化规律。然后,通过质量功能展开将顾客需求映射为不同服务特性并确定对应权重,从而反映出顾客需求对满意度的影响。随后,考虑顾客的双重期望,建立分段函数讨论顾客感知参照不同期望值对顾客满意度的影响,进而构建结合顾客需求和双重期望、反映顾客累积满意度动态变化的测量模型。最后,将模型应用于B2C电子商务,优化得到最佳的资源配置方案。结果表明,基于当前的B2C电子商务服务流程,改善物流环节的服务质量对提升顾客满意度有显著作用。同时,在资源有限的环境下,企业可以运用服务补救悖论在顾客需求分散的环节制造轻微的失误,而在顾客需求集中的环节进行高质量的补救,从而大幅提升顾客的整体满意度。

Service is a process and customer satisfaction needs to be measured in the entire process. It is an overall evaluation of consumer experience over a period of time. Customer needs will likely change at different stages of the service process. So measuring customer satisfaction needs to consider the diversity and their weights of customer needs. Furthermore, customer expectations are important factors which influence customer satisfactions. Customer expectation is not unique but with multi-level feature. In satisfaction measurement process, in order to describe customer’s actual psychological variations, the weights of customers’ needs and the dynamics of dual customer expectations should be fully considered. Hence, we propose a model combining the customer needs and dual-expectations for measuring dynamic cumulative satisfaction in a single multi-stage service. We provide our approach in detail as follows. First, we divide the whole service process into sub-processes for revealing the variation of customer satisfactions at different stages. The entire service process can be split based on customer behavior using the service blueprint. We take B2 C electronic commerce as an example. The entire process can be divided into 6 sub-services, which include decision, access, transaction, payment, logistics and after-sale service. Secondly, we map customer needs to different service characteristics and determine the corresponding weights through Quality Function Deployment, reflecting the influence of customer requirement on satisfaction. We identify a total of 35 customer needs（or called service characteristics） and determine each of their weights. Our model contains three variables which includes Perception, Disappointment and Regret. And the service characteristics will be represented by these variables in the model. Thirdly, considering the dual customer expectations, we establish piecewise functions to discuss the influence of customer perception with reference to different expectations on customer satisfaction. And then a measurement model combining customer needs and dual expectations for dynamic cumulative satisfaction is constructed. We build the model based on Weber-Fechner’s law and our measurement model effectively expresses the nonlinear relationship between customer perception with reference to different expectations and customer satisfaction. Moreover, a zone of tolerance exists in services between will-expectation and should-expectation（called dual expectations）. The measurement model must be a piecewise function for describing three different situations containing below the zone, in the zone and above the zone. Finally, we apply the model to B2 C e-commerce and obtain the best solution of the allocation of resources. We use Matlab R2012 a to solve the optimization with non-linear constraints and compute the solution of the allocation of resources at the maximum customer satisfaction. Sensitivity analysis of the optimal solution of customer satisfaction and initial value of service expectation zone of tolerance is performed. The result of sensitivity analysis shows that the fluctuation range is no more than 5%. It means that the initial value of service expectation zone of tolerance has little effect on the optimal solution of customer satisfaction. The results show that according to the current B2 C electronic business processes, improving service quality of logistics chain can significantly improve customer satisfaction. At the same time, in the situation of limited resources, companies can make minor mistakes in scattered links of customers’ needs and implement high quality recovery in centralized links using service recovery paradox, thereby significantly enhancing the overall customer satisfaction.