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Customers’ emotions in service failure and recovery: a meta-analysis

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Customers’ emotions have emerged as a dominant dimension in the complaint-handling domain. This research provides a quantitative synthesis of the role of emotions triggered by service failure and recovery situations. First, we develop a unifying conceptual framework that considers emotional reactions triggered by both service failure and recovery and explains why customers are likely to get “emotional twice.” Second, we show that studies conceptualize emotions using different underlying theoretical assumptions (discrete versus dimensional models). Our results show that this distinction significantly affects the strength of the relationship between emotions and their correlates. Third, our meta-analysis highlights what recovery actions managers should consider when they need to address customers’ negative emotions or want to enhance positive emotions. Monetary compensations are the only tool that can attenuate the strength of negative emotions. Clear communication of the waiting time and procedures required to complete the recovery process can strengthen positive emotions.

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  1. The firm can proceed with a proactive recovery strategy in at least two different situations. First, if the company detects the failure before the customer is aware of it, immediately activates the recovery process, and informs the customer (the dashed arrow in Fig. 1 represents this situation). Second, when the customer chooses to complain to others (e.g., through online forums), or simply decides not to complain at all.

  2. Although CAT is the prevailing emotion theory in service failure/recovery research, other theories are used: notably, affect control theory (Chebat and Slusarczyk 2005), affect-balance theory (Schoefer and Diamantopoulos 2008, 2009), and emotional contagion theory (Du et al. 2011).

  3. Three different papers displayed the same context, sample size, and sociodemographic characteristics. There was abundant evidence that these papers employed the same sample, albeit investigating partially different variables. Thus, using the correlation matrix provided by the authors, we included their study only once, to avoid a fictitious increase in the number of retrieved studies.

  4. We used the Cronbach’s alpha value of each construct involved to indicate the reliability of dependent and independent variables. When alphas were unavailable or a study used a single-item measure (11% of the retrieved effect sizes), we used the average reliability for that construct across all studies.

  5. We used a combination of alternative approaches to assess homogeneity (Q test, 75% rule, credibility interval, and residual standard deviation). We report only a portion of these heterogeneity tests in Table 2.

  6. We estimated three alternative moderator models. First was an overall moderator model including all retrieved effect sizes. Because we have both positive and negative emotions, we included the absolute value of rEC as dependent variable as well as a dummy controlling for the valence. Second, we estimated two separate moderator models distinct for positive and negative emotions. The purpose of this additional analysis is to investigate the moderating impact of different recovery actions on the strength of the observed relationships. Distinct models for positive and negative emotions are more appropriate in this situation, as they contribute to gaining managerial insight.


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Correspondence to Sara Valentini.

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Valentini, S., Orsingher, C. & Polyakova, A. Customers’ emotions in service failure and recovery: a meta-analysis. Mark Lett 31, 199–216 (2020).

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