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A longitudinal examination of customer commitment and loyalty

机译:纵向检查客户的承诺和忠诚度

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Purpose - This study aims to provide the first longitudinal examination of the relationship between affective, calculative, normative commitment and customer loyalty by using longitudinal panel survey data. Design/methodology/approach - Repeated measures for 269 customers of a large financial services provider are employed. Two types of segmentation methods are compared: predefined classes and latent class models and predictive power of different models contrasted. Findings - The results reveal that the impact that different dimensions of commitment have on share development varies across segments. A two-segment latent class model and a managerially relevant predefined two-segment customer model are identified. In addition, the results demonstrate the benefits of using panel survey data in models that are designed to study how loyalty develops over time. Practical implications - This study illustrates the benefits of including both baseline level information and changes in the dimensions of commitment in models that try to understand how loyalty unfolds over time. It also demonstrates how managers can be misled by assuming that everyone will react to commitment improvement efforts similarly. This study also shows how different segmentation schemes can be employed and reveals that the most sophisticated ones are not necessarily the best. Originality/value - This research provides the first examination of models for change in customer loyalty by employing survey panel data on the three-component model of customer commitment (affective, calculative, and normative) and considers alternative segmentation methods.
机译:目的-本研究旨在通过纵向面板调查数据,对情感,计算,规范承诺与客户忠诚度之间的关系进行首次纵向检验。设计/方法/方法-对大型金融服务提供商的269个客户采用了重复措施。比较了两种类型的分割方法:预定义类和潜在类模型,并对不同模型的预测能力进行了对比。调查结果-结果表明,不同承诺程度对股票发展的影响在各个细分市场之间是不同的。确定了两段潜在类模型和与管理相关的预定义的两段客户模型。此外,结果证明了在旨在研究忠诚度如何随着时间发展的模型中使用面板调查数据的好处。实际意义-这项研究说明了在模型中包含基线水平信息和承诺维度变化的好处,这些模型试图了解忠诚度如何随着时间而发展。它还通过假设每个人都会对承诺改进工作做出类似反应来演示如何误导管理人员。这项研究还显示了如何使用不同的分割方案,并揭示出最复杂的分割方案不一定是最好的。原创性/价值-这项研究通过使用关于客户承诺的三要素模型(情感,计算和规范)的调查小组数据,首次检查了客户忠诚度变化的模型,并考虑了其他细分方法。

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