Elsevier

Journal of Retailing

Volume 90, Issue 4, December 2014, Pages 463-480
Journal of Retailing

Contrasting the Drivers of Switching Intent and Switching Behavior in Contractual Service Settings

https://doi.org/10.1016/j.jretai.2014.07.002Get rights and content

Highlights

  • We contrast the drivers of switching intent with those of actual switching behavior.

  • Consistent with construal level theory, our findings show that switching intent is explained by only a handful of desirability- or outcome-related variables.

  • Actual switching behavior, in contrast, is driven by a large number of variables.

  • We show for the first time that construal level theory has applications in the switching context.

Abstract

We examine consumer switching decisions in contractual service settings and contrast the drivers of actual switching with those of switching intent. We surveyed a panel of subscribers to all cell phone service providers in a market and recorded key marketing mix data. At four months intervals, we asked panel members about their switching intentions and then subsequently observed actual switching behavior. Consistent with construal level theory, our findings show that switching intent is explained by only a handful of desirability- or outcome-related variables (i.e., overall satisfaction, performance perceptions of important attributes, and monetary switching costs). In contrast, the results show that many more variable categories contributed to explain actual switching behavior. These findings confirm that switching intent is driven by a qualitatively different set of variables than switching behavior. Implications for theory, research and practice are discussed.

Graphical abstract

Conceptual framework explaining switching behavior and switching intent.

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Introduction

In this paper, we investigate and contrast the drivers of customer switching behavior and switching intent in contractual service settings.3 Customer defection is a fact of life in the business world. However, it is more serious for contractual services as a firm often loses all future cash flow from that customer relationship until the customer potentially switches back. Furthermore, there is a convex relationship between the churn rate and customer profitability (Blattberg, Kim, and Neslin 2008), and sizable customer defections can have a particularly devastating effect on the bottom line of contractual service providers (Keaveney and Parthasarathy 2001). It is thus especially important for a contractual service firm to develop effective and proactive policies for targeted retention efforts (Lovelock and Wirtz 2011). To achieve this, marketers must first understand the driving forces behind customer defection and ideally improve their ability to predict who is going to defect at any given time.

Consumers’ self-reported intentions are widely used in academic and applied research because they are considered easy-to-collect proxies of behavior (Chandon, Morwitz, and Reinartz 2005). However, customers routinely provide inaccurate predictions about their future behavior (Seiders et al. 2005). We collected consumers’ information on both switching intention and actual switching, which allows us to explore potential systematic biases that customers may have when predicting their own likelihood of switching. Specifically, we test the construal level theory (Liberman, Trope, and Stephan 2007) for the first time in a switching context, whereby when expressing switching intent consumers are predicted to systematically over- and under-weight decision-relevant outcome and process variables, respectively. Furthermore, we contrast the effects of the often hard-to-collect marketing mix variables in the switching literature (e.g., advertising spent and distribution network density) which we expect to have a stronger impact on switching behavior than on switching intent.

Section snippets

Switching Intent Versus Actual Switching Behavior

Switching intent represents the customer's self-reported likelihood of terminating a current service relationship, whereas actual switching is the objectively observed act of switching to another provider. A number of studies show a strong link between satisfaction and loyalty intentions, but do not establish a relationship with actual repeat purchase behavior (Gupta and Zeithaml 2006). Despite the claim that customer switching is linked to dissatisfaction (or loyalty to satisfaction), few

Modeling Consumer Switching in Contractual Services

To be able to contrast models of switching behavior and switching intent, a sufficiently comprehensive set of potential switching drivers needs to be included in these models. To provide a more comprehensive picture of switching drivers in a contractual service context, we identified the following key categories of drivers through a broad review of the marketing and economics literature: (1) overall customer satisfaction; (2) customers’ perceptions of important attributes of the performance of

Method

To allow for a robust comparison of the drivers of switching behavior and switching intent, we built one of the most integrated models of explaining switching behavior and intent in the context of contractual services published to date. First, switching behavior (in contrast to intent) has hardly been studied in the context of contractual services, and our dataset has unique features which help us examine switching behavior comprehensively. Specifically, in contrast to many studies that use

Empirical Analyses

We first present and discuss the model that best explains switching behavior as a base model. We then contrast it with the best fitting model for explaining switching intent to test our hypotheses.

Contrasting Drivers of Switching Intent and Switching Behavior

Consumers’ self-reported intentions are widely used in applied and academic research because they are considered easy-to-collect proxies of behavior (Chandon, Morwitz, and Reinartz 2005). However, customers routinely provide inaccurate predictions about their future behavior (Seiders et al. 2005), and researchers have emphasized the importance of examining actual behavioral responses rather than attitudes or intentions (Gupta and Zeithaml 2006). To heed the call, we contrast the drivers of

Acknowledgements

The authors thank Yuxin Chen, Yu-Chen Hung, Wagner A. Kamakura, and Catherine W.Y. Yeung for helpful comments and suggestions.

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