基于cpls的客户满意度模型

Dawei Wang, Yin Bai, Xiangyang Ma
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引用次数: 1

摘要

传统的基于PLS路径建模的顾客满意度模型只能处理连续的或李克特尺度的变量。本研究提出一种分类偏最小二乘(cps)算法来处理顾客满意模型中的分类变量。分类变量和数值变量可以在同一模型中进行联合分析,该算法适用于模式A(反射测量模型)和模式B(形成性测量模型)。以某家电品牌顾客满意度研究为例,对该算法进行了实证验证。给出了cpl路径建模的完整过程,包括模型开发、模型评估和结果应用。最后,提出了研究的局限性和对未来研究的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A CPLS-based customer satisfaction model
Traditional PLS path modeling based customer satisfaction model could only handle continuous or Likert scale like variables. This study proposed an algorithm called categorical partial least squares (CPLS) to handling categorical variables in customer satisfaction model. Both categorical and numerical variables can be jointly analyzed in the same model and the proposed algorithm is suitable for both Mode A (reflective measurement model) and Mode B (formative measurement model). This algorithm was illustrated empirically in the context of a household appliance brand's customer satisfaction study. A complete process of using CPLS path modeling including model development, model assessment and the applications of the results was demonstrated. Moreover, the authors concluded with the limitations of their study and suggestions for future research.
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