Valuing Consumer Preferences with the CUB Model: A Case Study of Fairtrade Coffee
Valuing Consumer Preferences with the CUB Model: A Case Study of Fairtrade Coffee
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使用 CUB 模型评估消费者偏好：公平贸易咖啡案例研究
DOI：
10.18461/ijfsd.v1i1.119
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发表时间：
2010
影响因子：
--
通讯作者：
D. Piccolo
中科院分区：
文献类型：
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作者：
G. Cicia;Marcella Corduas;T. Giudice;D. Piccolo
D'Elia和Piccolo（2005）最近提出了一个混合分布，命名为CUB，用于序数数据。使用这种混合分布进行建模评级是合理的，原因如下：一个主体所表达的判断是两个组成部分的结果，即不确定性和选择性。将CUB模型的参数与协变量联系起来的可能性使得该公式在实际应用中很有趣。在本案例研究中，224名公平贸易咖啡消费者在商店接受了采访。有了这个数据集，CUB模型根据消费者的偏好将他们分为两个不同的部分：一个显示高价格弹性，一个显示低价格弹性。就CUB模型的潜力而言，它与随机实用模型（即潜类模型）具有相当大的整合能力。事实上，通过使用CUB中出现的分割因素作为潜在类别模型中分割的协变量，并设置与CUB中出现的类别数量相等的类别数量，可以估计出一个模型，该模型不仅验证了CUB的发现，而且可以估计出不同群体中公平贸易特征的WTP。
D'Elia and Piccolo (2005) have recently proposed a mixture distribution, named CUB, for ordinal data. The use of such a mixture distribution for modelling ratings is justified by the following consideration: the judgment that a subject expresses is the result of two components, uncertainty and selectiveness. The possibility of relating the parameters of CUB models to covariates makes the formulation interesting for practical applications In this case study, a sample of 224 fair‐trade coffee consumers were interviewed at stores. With this data‐set, CUB model split consumers, according to their preferences, in two different segments: one showing high price elasticity, and one with a low price elasticity. As regards the potential of the CUB model, it showed a considerable integration capacity with stochastic utility models, namely latent class models. Indeed, by using the segmentation factors emerging from the CUB as covariates of segmentation in a latent class model and setting the number of classes equal to those emerging from the CUB, it was possible to estimate a model which not only validated the findings of the CUB but also allowed estimation of the WTP for the fair trade characteristic in the different groups.