Customer Responses to Switching Costs: A Meta-Analytic Investigation of the Moderating Influence of Culture

Abstract

Relationship marketing effectiveness varies across different markets, but prior research has provided limited evidence on how cultural variations relate to the effects of relationship variables such as switching costs. The authors develop and test a theoretical framework that explains how culture moderates the relationship between perceived switching costs and key consequences. The findings of a meta-analysis, based on 451 effect sizes collected in 25 countries, show that similar components that refer to a match between the value essence of Hofstede's cultural dimensions and the motivators that steer particular consequences explain how individualism, power distance, uncertainty avoidance, and masculinity moderate the impact of switching costs on word of mouth of customers and loyalty types. Furthermore, the economic development of a country explains when switching costs either reduce or increase switching of customers. The findings of this study support international companies in making decisions about the generation of switching costs in particular markets and the formation of international relationship marketing and referral programs.

The impact of quality of services and satisfaction on customer loyalty: The moderate role of switching costs

“…The dataset confirming our assumption about the moderating role was played by the switching cost and the results were consistent with previous studies related to the interplay between switching cost and customer loyalty (e.g. Blut et al, 2015;El-Manstrly, 2016;Kacen & Lee, 2002;Kaura et al, 2015;Pick & Eisend, 2016). On another hand, the high cost of changing the service provider such as penalties and other charges are preventing customers from changing the service provider if the quality of service and the customers are satisfied, which will be reflected by the high-level customers loyalty.…”

Explaining Digital Piracy: A Meta-Analysis

“…636-664, © 2019 and created new categories for those variables that appeared in at least three studies. To code these variables and to assign them to broader categories, the procedure applied in other meta-analyses was followed (e.g., Pick and Eisend 2016): highly related variables were combined into composite constructs referring to one type of antecedent with a consistent influence on user response toward digital piracy. To that end, the definitions and measurement models of the variables were carefully examined to ensure that only variables from the same nomological network were combined.…”

Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage

“…Without considering country differences, we implicitly assume that our theory is generalizable across countries (Burgess and Steenkamp 2006). We therefore consider the moderating influence of economic country variables, because economic differences (e.g., income level) are known to influence customer behavior in various countries (Pick and Eisend 2016). More specifically, the international marketing literature has discussed GDP per capita to be related to customers' purchasing power and preferences (Berry et al 2010).…”

“…Hox (2010) recommended this approach as it is unlikely that samples reporting multiple measurements are independent of one another. Similar to Eisend (2016) andBabić Rosario et al (2016), we used hierarchical linear modeling software to calculate a random-effects model that differentiates between effect size level (level 1) and study level (level 2). We treated the reliability-adjusted correlations as dependent variables and regressed them on the moderators on levels 1 and 2.…”