We propose a new spatial modeling approach to calibrate the potential impact of spatial dependency and heterogeneity on the underlying drivers of customer service and/or satisfaction measurement. The newly proposed procedure derives regionally varying coefficients, provides more flexible fitting, improves calibration fit and predictive validation, and can potentially result in augmented managerial implications compared to existing procedures by utilizing a hierarchical Bayes framework with geographical boundary effects. Using synthetic datasets, we illustrate how the proposed model outperforms four relevant benchmark models including ordinary linear regression, a Spatially Dependent Segmentation model (Govind, Rabikar, and Mittal 2018), classic Geographically Weighted Regression, and Bayesian Geographically Weighted Regression. The improved performance is most prominent when there exist significant differences between geographic boundaries and/or irregular patterns of observation locations. In our automobile customer satisfaction application study, the proposed approach also demonstrates favorable performance compared to these benchmark models. We find a dramatically heterogeneous pattern regarding two covariates in the Mountain U.S. geographic division: dealership service is more important in urban areas (e.g., Phoenix, Salt Lake City and Denver) than in rural areas, but vice-versa concerning vehicle quality.

我们提出了一种新的空间建模方法来校准空间依赖性和异质性对客户服务和/或满意度测量的潜在驱动因素的潜在影响。新提出的程序推导出区域变化系数，提供更灵活的拟合，改进校准拟合和预测验证，并且通过利用具有地理边界效应的分层贝叶斯框架，与现有程序相比，可能会增加管理影响。使用合成数据集，我们说明了所提出的模型如何优于四个相关的基准模型，包括普通线性回归、空间相关分割模型（Govind、Rabikar 和 Mittal 2018）、经典地理加权回归和贝叶斯地理加权回归。当地理边界和/或观测位置的不规则模式之间存在显着差异时，改进的性能最为显着。在我们的汽车客户满意度应用研究中，与这些基准模型相比，所提出的方法还展示了良好的性能。我们发现美国山区地理划分中关于两个协变量的显着异质模式：经销商服务在城市地区（例如，凤凰城、盐湖城和丹佛）比在农村地区更重要，但在车辆质量方面反之亦然。与这些基准模型相比，所提出的方法还表现出良好的性能。我们发现美国山区地理划分中关于两个协变量的显着异质模式：经销商服务在城市地区（例如，凤凰城、盐湖城和丹佛）比在农村地区更重要，但在车辆质量方面反之亦然。与这些基准模型相比，所提出的方法还表现出良好的性能。我们发现美国山区地理划分中关于两个协变量的显着异质模式：经销商服务在城市地区（例如，凤凰城、盐湖城和丹佛）比在农村地区更重要，但在车辆质量方面反之亦然。