Abstract
This article develops and tests a comprehensive model of customer revenge that contributes to the literature in three manners. First, we identify the key role played by the customer’s perception of a firm’s greed—that is, an inferred negative motive about a firm’s opportunistic intent—that dangerously energizes customer revenge. Perceived greed is found as the most influential cognition that leads to a customer desire for revenge, even after accounting for well studied cognitions (i.e., fairness and blame) in the service literature. Second, we make a critical distinction between direct and indirect acts of revenge because these sets of behaviors have different repercussions—in “face-to-face” vs. “behind a firm’s back”—that call for different interventions. Third, our extended model specifies the role of customer perceived power in predicting these types of behaviors. We find that power is instrumental—both as main and moderation effects—only in the case of direct acts of revenge (i.e., aggression and vindictive complaining). Power does not influence indirect revenge, however. Our model is tested with two field studies: (1) a study examining online public complaining, and (2) a multi-stage study performed after a service failure.
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Notes
Revenge and vengeance are viewed as synonymous. For simplicity’s sake, we use the label “revenge” hereafter.
We validate the sequence of our extended model by relying on a comparison of fit between our model and rival structures (e.g., Cronin et al. 2000).
Power is distinct from the concept of self-efficacy (Bandura 1977), which is defined as a belief that an individual can successfully perform a particular action (i.e., a perceived competence). Unlike self-efficacy, power does not rely only on one’s perceived competence, but it also explicitly takes into consideration factors in the environment—for instance, a customer’s perception of dependency toward the firm.
We recruited undergraduate students from the summer session. We contacted all the instructors and provided them with an invitation slide. In addition, we personally visited the larger classrooms with more than 40 students. Overall, 114 students contacted us to be part of this study, and 103 students completed all the stages.
We examined the convergent validity of a DR scale by comparing it with the vengeance scale of Bechwati and Morrin (hereafter BM). We performed a study in which 49 students had to read a scenario about a failed recovery, and then answer questions about the two scales. As expected, the DR scale (α = .93; M = 3.56; SD = 1.55) was highly correlated at .82 (p < .001) with BM’s scale (α = .81; M = 3.27; SD = 1.30). A principal component analysis revealed two factors, however. All the items of the DR scale (loadings between .80 and .93) and the three positive items of BM (between .64 and .85) loaded on the first factor, whereas the two negatively worded item of BM (between −.78 and −.88) loaded on the second factor. In sum, these two scales converge, although they somewhat differ by the use of the negatively worded items.
This construct is measured with a three-item scale including “The seller needed to continue business with me more than I needed to continue business with it,” and “The seller needed my continuing business.”
We used the four-item scale developed by Sirdeshmukh et al. (2002), which includes four semantic differential items, such as “The firm was undependable vs. dependable” (M = 4.84; SD = 1.53; α = .93).
This research uses bootstrapping (300 runs) to assess the significance of the parameters.
PLS is less sensitive to sample size and has greater statistical power (Reinartz et al. 2009). As a result, it could estimate our large models that included 159 parameters (Fig. 2a) and 149 parameters (Fig. 2b). Given the guideline suggested by Bentler and Chou (1987), these numbers of parameters were too high for a covariance-based approach, given our sample size. Accordingly, we had to simplify our models to focus only on the structural paths. Although this approach has limitations, it has also been regularly used for similar reasons (see McQuitty 2004).
The details of these CFA models are available by contacting the first author. Most of these results were consistent with the psychometric tests already presented in the measurement sections and for our PLS models (Appendix A).
The path “procedural fairness → greed” was not found to be different across samples (∆χ 2 [1] = 2.60; p = .11).
References
Aquino, K., Tripp, T. M., & Bies, R. J. (2001). How employees respond to personal offense: the effects of blame attribution, victim status, and offender status on revenge in the workplace. Journal of Applied Psychology, 86, 52–59.
Aquino, K., Tripp, T. M., & Bies, R. J. (2006). Getting even or moving on? Power, procedural justice, and types of offense as predictors of revenge, forgiveness, reconciliation, and avoidance in organizations. Journal of Applied Psychology, 91, 653–658.
Bandura, A. (1977). Self efficacy: toward a unifying theory of behavioral change. Psychological Review, 84, 191–215.
Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: a review. International Journal of Research in Marketing, 13, 139–161.
Bechwati, N. N., & Morrin, M. (2003). Outraged consumers: getting even at the expense of getting a good deal. Journal of Consumer Psychology, 13, 440–453.
Bechwati, N. N., & Morrin, M. (2007). Understanding voter vengeance. Journal of Consumer Psychology, 17, 277–291.
Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16, 78–117.
Beverland, M. B., Kates S.M., Lindgreen A. & Chung E. (in press). Exploring consumer conflict management in service encounters. Journal of the Academy of Marketing Science.
Bies, R. J., & Tripp, T. M. (1996). Beyond distrust: ‘Getting even’ and the need for revenge. In R. M. Kramer & T. Tyler (Eds.), Trust in organizations (pp. 246–260). Thousand Oaks: Sage.
Bies, R. J., & Tripp, T. M. (2009). Righteous Anger:‘Mad as Hell’ at ‘Greed is Good.’ Retrieved April 8, 2009 from www.ChangeThis.com.
Bitner, M., Booms, B. H., & Tetreault, M. S. (1990). The service encounter: diagnosing favorable/unfavorable incidents. Journal of Marketing, 54, 71–84.
Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: a structural equation perspective. Psychological Bulletin, 110, 305–14.
Bolton, R. N., & Lemon, K. N. (1999). A Dynamic model of customers’ usage of services: usage as an antecedent and consequence of satisfaction. Journal of Marketing Research, 36, 171–186.
Bonifield, C., & Cole, C. (2007). Affective responses to service failure: anger, regret, and retaliatory and conciliatory responses. Marketing Letters, 18, 85–99.
Bougie, R., Pieters, R., & Zeelenberg, M. (2003). Angry customers don’t come back, they get back: the experience and behavioral implications of anger and dissatisfaction. Journal of the Academy of Marketing Science, 31, 377–393.
Byrne, B., Shavelson, R. J., & Muthen, B. (1989). Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance. Psychological Bulletin, 105, 456–466.
Campbell, M. C. (1999). Perceptions of price unfairness: antecedents and consequences. Journal of Marketing Research, 36, 187–199.
Chebat, J. C., & Slusarcsyk, W. (2005). How emotions mediate the effects of perceived justice on loyalty in service recovery situations: an empirical study. Journal of Business Research, 12, 664–673.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp. 295–336). Mahwah: Lawrence Erlbaum Associates.
Cronin, J. J., Brady, M. K., & Hult, G. T. (2000). Assessing the effect of quality, value, and customer satisfaction on consumer behavioral intentions in service environment. Journal of Retailing, 76, 193–218.
Crossley, C. D. (2009). Emotional and behavioral reactions to social undermining: a closer look at perceived offender motives. Organizational Behavior and Human Decision Processes, 108, 14–24.
Dahl, R. A. (1957). The concept of power. Behavioral Science, 2, 201–215.
De Wulf, K., Odekerken-Schroder, G., & Iacobucci, D. (2001). Investments in consumer relationships: a cross-country and cross-industry exploration. Journal of Marketing, 65, 33–50.
Douglas, S. C., & Martinko, M. J. (2001). Exploring the role of individual differences in the prediction of workplace aggression. Journal of Applied Psychology, 86, 547–560.
Folkes, V. S. (1984). Consumer reactions to product failures: an attributional approach. Journal of Consumer Research, 10, 398–409.
Fornell, C., & Bookstein, F. L. (1982). Two structural equations models: Lisrel and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
Frazier, G. L. (1999). Organizing and managing channels of distribution. Journal of the Academy of Marketing Science, 27, 226–240.
French, J., & Raven, B. (1959). The bases of social power. In D. Cartwright (Ed.), Studies in Social Power. Ann Arbor: Institute for Social Research.
Gelbrich, K. (in press). Anger, frustration, and helplessness after service failure: coping strategies and effective informational support. Journal of the Academy of Marketing Science.
Grégoire, Y., & Fisher, R. J. (2006). The effects of relationship quality on customer retaliation. Marketing Letters, 17, 31–46.
Grégoire, Y., & Fisher, R. J. (2008). Customer betrayal and retaliation: when your best customers become your worst enemies. Journal of the Academy of Marketing Science, 36, 247–261.
Grégoire, Y., Tripp, T., & Legoux, R. (2009). When customer love turns into lasting hate: the effects of relationship strength and time on customer revenge and avoidance. Journal of Marketing, 73, 18–32.
Harris, L., & Reynolds, K. L. (2003). The consequences of dysfunctional customer behavior. Journal of Service Research, 6, 144–161.
Huefner, J. C., & Hunt, H. K. (2000). Consumer retaliation as a response to dissatisfaction. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 13, 61–82.
Hulland, J. S. (1999). Use of partial least square in strategic management research: a review of four recent studies. Strategic Management Journal, 20, 194–204.
Mackenzie, S. B. (2001). Opportunities for improving consumer research through latent variable structural equation modeling. Journal of Consumer Research, 28, 159–166.
Maxham, J. G., III, & Netemeyer, R. G. (2002a). Modeling customer perceptions of complaint handling over time: the effects of perceived justice on satisfaction and intent. Journal of Retailing, 78, 239–252.
Maxham, J. G., III, & Netemeyer, R. G. (2002b). A longitudinal study of complaining customers’ evaluation of multiple service failures and recovery efforts. Journal of Marketing, 66, 57–71.
McColl-Kennedy, J. R., Patterson, P., Smith, A. K., & Brady, M. K. (2009). Customer rage episodes: emotions, expressions and behaviors. Journal of Retailing, 85, 222–237.
McCullough, M. E., Bellah, C. G., Kilpatrick, S. D., & Johnson, J. L. (2001). Vengefulness: relationships with forgiveness, rumination, well-being, and the big five. Personality and Social Psychology Bulletin, 27, 601–610.
McGovern, G., & Moon, Y. (2007). Companies and the customers who hate them. Harvard Business Review, 85, 78–84.
McQuitty, S. (2004). Statistical power and structural equation models in business research. Journal of Business Research, 57, 175–183.
Menon, K., & Bansal, H. (2006). Exploring consumer experience of social power during service consumption. International Journal of Service Industry Management, 18, 89–104.
Ping, R. A. (1995). A parsimonious estimating technique for interaction and quadratic latent variables. Journal of Marketing Research, 32, 336–347.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903.
Povinelli, D. J. (2001). On the possibilities of detecting intentions prior to understanding them. In B. Malle, D. Baldwin, & L. Moses (Eds.), Intentionality: A Key to Human Understanding (pp. 225–248). Cambridge: MIT Press.
Reeder, G. D., Kumar, S., Hesson-McInnis, M. S., & Trafimow, D. (2002). Inferences about the morality of an aggressor: the role of perceived motive. Journal of Personality and Social Psychology, 83, 789–803.
Reeder, G. D., Pryor, M., Wohl, J. A., & Griswell, M. L. (2005). On attributing negative motives to others who disagree with our opinions. Personality and Social Psychology Bulletin, 31, 1498–1510.
Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26, 332–344.
Reynolds, K. L., & Harris, L. C. (2009). Dysfunctional customer behavior severity: an empirical examination. Journal of Retailing, 85, 321–335.
Ringberg, T., Odekerken-Schroder, G., & Christensen, G. L. (2007). A cultural models approach to service recovery. Journal of Marketing, 71, 194–214.
Shaver, P., Schwartz, J., Kirson, D., & O’Connor, C. (1987). Emotion knowledge: further exploration of a prototype approach. Journal of Personality and Social Psychology, 52, 1061–1086.
Singh, J. (1988). Consumer complaint intentions and behavior: definitional and taxonomical issues. Journal of Marketing, 52, 93–107.
Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value, and loyalty in relational exchanges. Journal of Marketing, 66, 15–37.
Smith, A. K., Bolton, R. N., & Wagner, J. (1999). A model of customer satisfaction with service encounters involving failure and recovery. Journal of Marketing Research, 36, 356–372.
Steenkamp, J. B., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25, 78–90.
Tax, S. S., Brown, S. W., & Chandrashekaran, M. (1998). Customer evaluations of service complaint experiences: implications for relationship marketing. Journal of Marketing, 62, 60–76.
Tripp, T. M., Bies, R. J., & Aquino, K. (2007). A vigilante model of justice: revenge, reconciliation, forgiveness, and avoidance. Social Justice Research, 19, 10–34.
Tucker, N. (2007). Taking a whack against comcast; Mona shaw reached her breaking point, then for her hammer. The Washington Post, October 18, p. C1.
Wade, S. H. (1989). The development of a scale to measure forgiveness. Unpublished Doctoral Dissertation, Fuller Theological Seminar, Pasadena, California.
Ward, J. C., & Ostrom, A. L. (2006). Complaining to the masses: the role of protest framing in customer-created complaint web sites. Journal of Consumer Research, 33, 220–230.
Weiner, B. (2000). Attributional thoughts about consumer behavior. Journal of Consumer Research, 27, 382–387.
Wetzer, I. M., Zeelenberg, M., & Pieters, R. (2007). Never eat in that restaurant, I did! Exploring why people engage in negative word-of-mouth communication. Psychology & Marketing, 24, 661–680.
White, J. C., Varadarajan, P. R., & Dacin, P. A. (2003). Market situation interpretation and response: the role of cognitive style, organizational culture, and information use. Journal of Marketing, 67, 63–79.
Wooten, D. B. (2009). Say the right thing: apologies, reputability, and punishment. Journal of Consumer Psychology, 19, 225–235.
Zourrig, H., Chebat, J.-C., & Toffolic, R. (2009). Consumer revenge behavior: a cross-cultural perspective. Journal of Business Research, 62, 995–1001.
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Appendices
Appendix A: Measures and Loadings (PLS Models)
Item | Study 1 | Study 2 |
Blame attribution (Study 1: α = .86; AVE1 = .68) (Study 2: α = .89; AVE = .72) | ||
Overall, the firm was “not at all” (1) vs. “totally” (7) responsible for the poor recovery. | .65 | .76 |
The service failure episode was in “no way” (1) vs. “completely” (7) the firm’s fault. | .92 | .87 |
To what extent do you blame the firm for what happened? Not at all (1) – completely (7). | .89 | .91 |
| ||
A firm’s greed (Study 1: α = .90; AVE = .70) (Study 2: α = .92; AVE = .74) | ||
The firm did not intend to take advantage of me – ...intended to take advantage of me (7). | .89 | .91 |
The firm was primarily motivated by my interest (1) – ...its own interest (7). | .87 | .89 |
The firm did not try to abuse me (1) – ...tried to abuse me (7). | .67 | .84 |
The firm had good intentions (1) – ...had bad intentions (7). | .89 | .79 |
| ||
Anger (Study 1: α = .92; AVE = .74) (Study 2: α = .92; AVE = .75) | ||
-I felt 1) outraged, 2) resentful, 3) indignation, and 4) angry. | .85–.93 | .81–.90 |
| ||
Desire for revenge (Study 1: α = .97; AVE = .87) (Study 2: α = .97; AVE = .86) | ||
-Indicate to which extent you wanted to: | ||
... take actions to get the firm in trouble. | .92 | .89 |
... punish the firm in some way. | .94 | .95 |
... cause inconvenience to the firm. | .94 | .94 |
... get even with the service firm. | .93 | .93 |
... make the service firm get what it deserved. | .93 | .92 |
| ||
Perceived customer power (Study 1: α = .91; AVE = .73) (Study 2: α = .94; AVE = .78) | ||
Thinking of the way you felt through the recovery episode, indicate your agreement with the following statement: | ||
Through this service recovery, I had leverage over the service firm. | .72 | .77 |
I had the ability to influence the decisions made by the firm. | .90 | .90 |
The stronger my conviction, the more I was able to get my way with the firm. | .91 | .94 |
Because I had a strong conviction of being right, I was able to convince the firm. | .87 | .92 |
| ||
Marketplace aggression (Formative constructs) | ||
I have damaged property belonging to the service firm. | – | – |
I have deliberately bent or broken the policies of the firm. | – | – |
I have showed signs of impatience and frustration to someone from the firm. | – | – |
I have hit something or slammed a door in front of (an) employee(s). | – | – |
| ||
Relationship commitment (Study 1: α = .94; AVE = .83) (Study 2: α = .90; AVE = .76) | ||
I was very committed to my relationship with the firm. | .91 | .91 |
The relationship was something I intended to maintain for a long time. | .93 | .92 |
I put the efforts into maintaining this relationship for a long time. | .89 | .77 |
| ||
Negative WOM (Study 1: α = .91; AVE = .77) (Study 2: α = .96; AVE = .88) | ||
I spread negative word-of-mouth about the company or service firm. | .94 | .92 |
I denigrated the service firm to my friends. | .93 | .96 |
When my friends were looking for a similar service, I told them not to buy from the firm. | .75 | .93 |
| ||
Vindictive complaining (Study 1:α = .88; AVE = .71) (Study 2: α = .96; AVE = .85) | ||
-I complained to the firm to... | ||
... give a hard time to the representatives. | .89 | .95 |
... be unpleasant with the representatives of the company. | .93 | .94 |
... make someone from the organization pay for their services. | .88 | .92 |
| ||
Online complaining for negative publicity (Study 1: α = .95; AVE = .82) | ||
-complained to consumeraffairs.com... | – | – |
... to make public the behaviors and practices of the firm. | .93 | – |
... to report my experience to other consumers. | .86 | – |
... to spread the word about my misadventure. | .91 | – |
| ||
Interactional fairness (Study 1: α = .91; AVE = .73) (Study 2: α = .95; AVE = .83) | ||
The employee(s) who interacted with me ... | ||
... treated me in a polite manner. | .86 | .93 |
... gave me detailed explanations and relevant advice. | .77 | .84 |
... treated me with respect. | .92 | .95 |
... treated me with empathy. | .86 | .92 |
| ||
Distributive fairness (Study 1: α = .93; AVE = .83) (Study 2: α = .98; AVE = .94) | ||
Overall, the outcomes I received from the service firm were fair. | .95 | .97 |
Given the time, money and hassle, I got fair outcomes. | .94 | .98 |
I got what I deserved. | .84 | .95 |
| ||
Procedural fairness (Study 1: α = .93; AVE = .76) (Study 2: α = .96; AVE = .85) | ||
Despite the hassle caused by the problem, the firm responded fairly and quickly. | .89 | .95 |
I feel the firm responded in a timely fashion to the problem. | .81 | .93 |
I believe the firm has fair policies and practices to handle problems. | .87 | .86 |
With respect to its policies and procedures, the firm handled the problem in a fair manner. | .90 | .95 |
| ||
Failure severity (Study 1: α = .92; AVE = .79) (Study 2: α = .93; AVE = .82) | ||
The poor recovery caused me... | ||
... minor problems (1). — ... major problems (7). | .88 | .90 |
... small inconveniences (1). — ... big inconveniences (7). | .92 | .94 |
... minor aggravation (1). — ... major aggravation (7). | .86 | .88 |
| ||
Perceived alternatives (Study 1: α = .86; AVE = .75) (Study 2: α = .88; AVE = .79) | ||
There were many alternatives for this product and service. | .90 | .95 |
I could take my business elsewhere. | .83 | .90 |
Appendix B: Subgroup Analysis with Covariance-Based SEM
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Grégoire, Y., Laufer, D. & Tripp, T.M. A comprehensive model of customer direct and indirect revenge: understanding the effects of perceived greed and customer power. J. of the Acad. Mark. Sci. 38, 738–758 (2010). https://doi.org/10.1007/s11747-009-0186-5
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DOI: https://doi.org/10.1007/s11747-009-0186-5