Investigating the Impacts of Customer Experience and Attribute Performances on Overall Ratings Using Online Review Data: Nonlinear Estimation and Visualization with a Neural Network
Investigating the Impacts of Customer Experience and Attribute Performances on Overall Ratings Using Online Review Data: Nonlinear Estimation and Visualization with a Neural Network
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使用在线评论数据研究客户体验和属性表现对总体评级的影响：使用神经网络进行非线性估计和可视化
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Sato Toshikuni
中科院分区：
文献类型：
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作者：
Sato Toshikuni
这项研究调查了用于营销和消费者行为研究的可解释神经网络，使用客户评论而不是测量量表来更好地了解客户体验。服务属性评分是用来衡量属性绩效，比较顾客体验和服务绩效对整体满意度的影响。虽然许多研究者已经研究了口碑评论及其实际应用，但这些评论的详细内容通常被忽视，可能是因为它们的高维性。为了解决这个问题，本研究提出了一些有用的神经网络方法来指定预期假设的基础上以前的知识或理论在消费者行为研究。由于神经网络有助于估计目标变量和预测变量之间的非线性关系，因此使用偏相关图来可视化估计的函数和边际效应。实证结果不仅提供了一个高度准确的神经网络模型，他们也创造了更好的营销意义。
This study investigates interpretable neural networks for marketing and consumer behavior research using customer reviews instead of measurement scales to better understand customer experiences. Service attribute ratings are used to measure attribute performances to compare the influence of customer experience and service performance on overall satisfaction. Although many researchers have investigated word-of-mouth reviews and their practical applications, the detailed contents of those reviews were generally disregarded, possibly because of their high dimensionality. To solve this problem, this study proposes some useful neural-network methods for specifying the expected assumptions based on previous knowledge or theories in consumer behavior research. Because neural networks help estimate nonlinear relationships between objective and predictive variables, a partial dependence plot is used to visualize the estimated functions and marginal effects. Empirical results not only provide a highly accurate neural-network model, they also create better marketing implications.
DOI：
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发表时间：
2017-02
期刊：
arXiv: Applications
影响因子：
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作者：
A. Crane-Droesch
通讯作者：
A. Crane-Droesch