An uncertain Kansei Engineering methodology for behavioral service design

An uncertain Kansei Engineering methodology for behavioral service design
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行为服务设计的不确定感性工程方法

DOI:
10.1080/24725854.2020.1766727
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发表时间:
2020-06
期刊:
影响因子:
2.6
通讯作者:
Li Ming
Li Ming
中科院分区:
工程技术3区
文献类型:
--
作者:
Yan Hong-Bin;Li Ming

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为了完善服务,服务提供者必须了解服务可能引发的基本情感效应。感性工学(KE)近年来已被应用于服务行业,以实现服务设计元素和客户的情感感知之间的关系。然而,基于顾客体验的有效服务设计仍然受到顾客情绪的不确定性和行为偏差的严重挑战。本文试图提出一个不确定KE方法的行为服务设计。为此,首先提出了一个整合框架,将设计属性,情感需求和整体满意度联系起来,以设计最能满足客户情感需求的服务。其次,使用多项逻辑回归建立设计属性与情感属性之间的不确定关系。第三,提出了一个定量的Kano模型来模拟反映积极情绪和消极情绪的“得失”效应的非对称和非线性满意度函数。其次,运用前景理论,通过区分"得与失",得出顾客总体满意度。最后,将所提出的方法应用于中国校园快递服务的案例研究。一项独立的跟踪研究表明,结果与服务接受度一致,并提供了有价值的见解。
Abstract To perfect a service, service providers must understand the fundamental emotional effects that a service may invoke. Kansei Engineering (KE) has been recently adapted to service industries to realize the relationships between service design elements and customers’ emotional perceptions. However, effective service design based on KE is still seriously challenged by the uncertainty and behavioral biases of customers’ emotions. This article tries to propose an uncertain KE methodology for behavioral service design. To do so, an integrative framework is first proposed by linking design attributes, emotional needs, and overall satisfaction, so as to design services best satisfying customers’ emotional needs. Second, multinomial logistic regression is used to build the uncertain relationships between design attributes and emotional attributes. Third, a quantitative Kano model is proposed to model the asymmetric and nonlinear satisfaction functions reflecting the “gains and losses” effect of positive emotions and negative emotions. Next, the Prospect Theory is used to derive customer overall satisfaction by distinguishing the “gains and losses”. Finally, the proposed methodology is applied to a case study of the campus express delivery service in China. An independent tracking study shows that the results are consistent with service acceptance and provide valuable insights.
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