Skip to main content

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


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.

This is a preview of subscription content, access via your institution.

Fig. 1


  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.


  • Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27, 184–206.

    Article  Google Scholar 

  • Barrett, L. F. (1998). Discrete emotions or dimensions? The role of valence focus and arousal focus. Cognition & Emotion, 12, 579–599.

    Article  Google Scholar 

  • Berkey, C. S., Hoaglin, D. C., Mosteller, F., & Colditz, G. A. (1995). A random-effects regression model for meta-analysis. Statistics in Medicine, 14, 395–411.

    Article  Google Scholar 

  • Bijmolt, T. H., & Pieters, R. G. (2001). Meta-analysis in marketing when studies contain multiple measurements. Marketing Letters, 12, 157–169.

    Article  Google Scholar 

  • Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2010). A basic introduction to fixed effect and random effects models for meta-analysis. Research Synthesis Methods, 1, 97–111.

    Article  Google Scholar 

  • Brown, S. P., & Peterson, R. A. (1993). Antecedents and consequences of salesperson job satisfaction: meta-analysis and assessment of causal effects. Journal of Marketing Research, 30, 63–77.

    Article  Google Scholar 

  • Burnett, J. J., & Dunne, P. M. (1986). An appraisal of the use of student subjects in marketing research. Journal of Business Research, 14, 329–343.

    Article  Google Scholar 

  • Chebat, J. C., & Slusarczyk, W. (2005). How emotions mediate the effects of perceived justice on loyalty in service recovery situations: an empirical study. Journal of Business Research, 58, 664–673.

    Article  Google Scholar 

  • De Matos, C. A., Henrique, J. L., & Rossi, C. A. V. (2007). Service recovery paradox: a meta-analysis. Journal of Service Research, 10, 60–77.

    Article  Google Scholar 

  • Du, J., Fan, X., & Feng, T. (2011). Multiple emotional contagions in service encounters. Journal of the Academy of Marketing Science, 39, 449–466.

    Article  Google Scholar 

  • Farley, J. U., Lehmann, D. R., & Sawyer, A. (1995). Empirical marketing generalization using meta-analysis. Marketing Science, 14(3_supplement), G36–G46.

    Article  Google Scholar 

  • Folkes, V. S. (1984). Consumer reactions to product failure: an attributional approach. Journal of Consumer Research, 10, 398–409.

    Article  Google Scholar 

  • Frijda, N. H., & Zeelenberg, M. (2001). Appraisal: what is the dependent? In K. R. Scherer, A. Schorr, & T. Johnstone (Eds.), Appraisal processes in emotion: Theory, methods, research (pp. 141–155). Oxford University Press.

  • Gelbrich, K. (2010). Anger, frustration, and helplessness after service failure: coping strategies and effective informational support. Journal of the Academy of Marketing Science, 38, 567–585.

    Article  Google Scholar 

  • Gelbrich, K., & Roschk, H. (2011). A meta-analysis of organizational complaint handling and customer responses. Journal of Service Research, 14, 24–43.

    Article  Google Scholar 

  • Geyskens, I., Steenkamp, J. B. E., & Kumar, N. (1998). Generalizations about trust in marketing channel relationships using meta-analysis. International Journal of Research in Marketing, 15, 223–248.

    Article  Google Scholar 

  • Geyskens, I., Krishnan, R., Steenkamp, J. B. E., & Cunha, P. V. (2009). A review and evaluation of meta-analysis practices in management research. Journal of Management, 35, 393–419.

    Article  Google Scholar 

  • Grégoire, Y., Laufer, D., & Tripp, T. M. (2010). A comprehensive model of customer direct and indirect revenge: understanding the effects of perceived greed and customer power. Journal of the Academy of Marketing Science, 38, 738–758.

    Article  Google Scholar 

  • Harbord, R. M., & Higgins, J. P. (2008). Meta-regression in Stata. Meta, 8, 493–519.

    Google Scholar 

  • Hedges, L., & Olkin, I. (1985). Statistical models for meta-analysis. Academic Press.

  • Hofstede, G. (1991). Cultures and Organizations: Software of the mind London/New York: McGrawHill

  • Hofstede, G. (2011). Dimensionalizing cultures: the Hofstede model in context. Online Readings in Psychology and Culture, 2(1), 8.

    Article  Google Scholar 

  • Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings. Sage.

  • Lazarus, R. S. (1966). Psychological stress and the coping process. McGraw-Hill.

  • Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.

  • Lench, H. C., Flores, S. A., & Bench, S. W. (2011). Discrete emotions predict changes in cognition, judgment, experience, behavior, and physiology: a meta-analysis of experimental emotion elicitations. Psychological Bulletin, 137, 834–855.

    Article  Google Scholar 

  • Markus, H. R., & Kitayama, S. (1991). Culture and the self: implications for cognition, emotion, and motivation. Psychological Review, 98, 224–253.

    Article  Google Scholar 

  • Matsumoto, D. (1989). Cultural influences on the perception of emotion. Journal of Cross-Cultural Psychology, 20(1), 92–105.

    Article  Google Scholar 

  • Matsumoto, D. (2006). Culture and cultural worldviews: do verbal descriptions about culture reflect anything other than verbal descriptions of culture? Culture & Psychology, 12, 33–62.

    Article  Google Scholar 

  • Orsingher, C., Valentini, S., & De Angelis, M. (2010). A meta-analysis of satisfaction with complaint handling in services. Journal of the Academy of Marketing Science, 38(169-186), 2010.

    Google Scholar 

  • Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal of Applied Psychology, 90, 175–181.

    Article  Google Scholar 

  • Robinson, D. T., Smith-Lovin, L., & Wisecup, A. K. (2006). Affect control theory. In J. E. Stets & J. H. Turner (Eds.), Handbook of the sociology of emotions (pp. 179–202). Springer.

  • Roseman, I. J., Wiest, C., & Swartz, T. S. (1994). Phenomenology, behaviors, and goals differentiate discrete emotions. Journal of Personality and Social Psychology, 67, 206–221.

    Article  Google Scholar 

  • Rosenthal, R. (1991). Meta-analytic procedures for social research. Sage.

  • Rosenthal, R. (1995). Writing meta-analytic reviews. Psychological Bulletin, 118, 183–192.

    Article  Google Scholar 

  • Sagie, A., & Koslowsky, M. (1993). Detecting moderators with meta-analysis: an evaluation and comparison of techniques. Personnel Psychology, 46, 629–640.

    Article  Google Scholar 

  • Schoefer, K., & Diamantopoulos, A. (2008). The role of emotions in translating perceptions of (in)justice into postcomplaint behavioral responses. Journal of Service Research, 11, 91–103.

    Article  Google Scholar 

  • Schoefer, K., & Diamantopoulos, A. (2009). A typology of consumers’ emotional response styles during service recovery encounters. British Journal of Management, 20, 292–308.

    Article  Google Scholar 

  • Smith, A. K., & Bolton, R. N. (2002). The effect of customers’ emotional responses to service failures on their recovery effort evaluations and satisfaction judgments. Journal of the Academy of Marketing Science, 30(1), 5–23.

    Article  Google Scholar 

  • Szymanski, D. M., & Henard, D. H. (2001). Customer satisfaction: a meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29(1), 16–35.

    Article  Google Scholar 

  • Triandis, H. C. (1995). Individualism & collectivism. Westview.

  • Van Vaerenbergh, Y., Orsingher, C., Vermeir, I., & Larivière, B. (2014). A meta-analysis of relationships linking service failure attributions to customer outcomes. Journal of Service Research, 17, 381–398.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Sara Valentini.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material


(DOCX 42 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI:


  • Meta-analysis
  • Complaint handling
  • Emotions
  • Service failure
  • Service recovery
  • Satisfaction with complaint handling