Does culture matter? Corporate reputation and sustainable satisfaction in the Chinese and German banking sector

IF 4 Q2 BUSINESS
Svenja Damberg, Yide Liu, Christian M. Ringle
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引用次数: 0

Abstract

Abstract Corporate reputation is important for all types of banks across the world, despite these countries differing culturally. Building on an extended corporate reputation model, we identify the key drivers of customer-based reputation and sustainable customer satisfaction in two culturally different countries, namely China and Germany. We also consider two reputation dimensions—perceived competence and likeability—and their effects on the target construct. Empirical data from 625 German and 734 Chinese commercial bank customers allow us to estimate the corporate reputation model with the partial least squares structural equation modeling (PLS-SEM) method, and by substantiating the relationships by means of a necessary condition analysis (NCA) and a predictive power analysis. By comparing the two countries’ results, we identify their cultural differences. Overall, we confirm the model’s relevance for the two cultures, finding that banks’ perceived attractiveness is the most important driver of both cultures’ customer-perceived bank reputation. By means of an importance-performance map analysis, we identify a large overlap between the two cultures’ set of important constructs, likeability’s much greater importance in Germany, and the perceived quality construct’s relevance in both countries. We contribute to research and scientific knowledge about corporate reputation models by identifying the similarities in and differences between two countries’ markets with respect to the banking sector, all of which have implications for international banks’ management.
文化重要吗?中国和德国银行业的企业声誉和持续满意度
企业声誉对世界各地所有类型的银行都很重要,尽管这些国家的文化不同。在扩展的企业声誉模型的基础上,我们确定了两个文化不同的国家,即中国和德国,以客户为基础的声誉和可持续的客户满意度的关键驱动因素。我们还考虑了两个声誉维度——感知能力和喜爱度——以及它们对目标构念的影响。来自625家德国商业银行客户和734家中国商业银行客户的实证数据允许我们使用偏最小二乘结构方程模型(PLS-SEM)方法,并通过必要条件分析(NCA)和预测能力分析来证实关系,来估计企业声誉模型。通过比较两国的结果,我们发现了他们的文化差异。总体而言,我们证实了该模型与两种文化的相关性,发现银行的感知吸引力是两种文化中客户感知银行声誉的最重要驱动因素。通过重要性-绩效图分析,我们发现两种文化的重要构式之间存在很大的重叠,在德国,受欢迎度的重要性要大得多,而在两国,感知质量构式的相关性都很高。我们通过识别两国市场在银行业方面的异同,为企业声誉模型的研究和科学知识做出贡献,所有这些都对国际银行的管理产生影响。
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来源期刊
CiteScore
5.40
自引率
16.70%
发文量
46
期刊介绍: Data has become the new ore in today’s knowledge economy. However, merely storing and reporting are not enough to thrive in today’s increasingly competitive markets. What is called for is the ability to make sense of all these oceans of data, and to apply those insights to the way companies approach their markets, adjust to changing market conditions, and respond to new competitors. Marketing analytics lies at the heart of this contemporary wave of data driven decision-making. Companies can no longer survive when they rely on gut instinct to make decisions. Strategic leverage of data is one of the few remaining sources of sustainable competitive advantage. New products can be copied faster than ever before. Staff are becoming less loyal as well as more mobile, and business centers themselves are moving across the globe in a world that is getting flatter and flatter. The Journal of Marketing Analytics brings together applied research and practice papers in this blossoming field. A unique blend of applied academic research, combined with insights from commercial best practices makes the Journal of Marketing Analytics a perfect companion for academics and practitioners alike. Academics can stay in touch with the latest developments in this field. Marketing analytics professionals can read about the latest trends, and cutting edge academic research in this discipline. The Journal of Marketing Analytics will feature applied research papers on topics like targeting, segmentation, big data, customer loyalty and lifecycle management, cross-selling, CRM, data quality management, multi-channel marketing, and marketing strategy. The Journal of Marketing Analytics aims to combine the rigor of carefully controlled scientific research methods with applicability of real world case studies. Our double blind review process ensures that papers are selected on their content and merits alone, selecting the best possible papers in this field.
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