ABSTRACT

The interaction between dysfunctional customers and other customers can cause the domino effect (i.e., the dissemination of a dysfunctional client’s behavior to other nearby clients). However, it is not understood if this effect will manifest under certain levels of damage to the company. This study thus aims to verify if the amount of damage from the dysfunctional behavior can affect the probability of the domino effect. Through five experimental studies, we prove that the amount of damage influences the likelihood of dysfunctional behavior replication. Moreover, we found that this effect is explained by the acceptability of the dysfunctional behavior. We also identify that a consumer’s ethics level, perception of risk in replicating the behavior, and social distance acts as boundary conditions of the amount of damage effect on the probability of replicating the dysfunctional behavior.

KEYWORDS:
Dysfunctional behavior; domino effect; ethics; perception of risk; contagious effect

RESUMEN

La interacción entre clientes disfuncionales y otros clientes puede causar un efecto dominó (es decir, la diseminación del comportamiento de un cliente disfuncional entre otros clientes cercanos). No obstante, no se entiende si este efecto se manifestará en diferentes proporciones de daño a la empresa. Por lo tanto, este estudio tiene como objetivo verificar si la proporción del daño del comportamiento disfuncional puede afectar la probabilidad del efecto dominó. A través de cinco estudios experimentales, verificamos que la proporción del daño influye en la probabilidad de replicación del comportamiento disfuncional. Además, descubrimos que ese efecto se explica por la aceptabilidad del comportamiento disfuncional. También identificamos que el nivel de ética del consumidor, la percepción del riesgo al replicar el comportamiento y la distancia social actúan como condicionantes del efecto de la proporción del daño en la probabilidad de replicar el comportamiento disfuncional.

PALABRAS CLAVE:
Comportamiento disfuncional; efecto dominó; ética; percepción del riesgo; efecto de contagio

RESUMO

A interação entre clientes disfuncionais e outros clientes pode causar o efeito dominó (ou seja, a disseminação do comportamento de um cliente disfuncional para outros clientes próximos). No entanto, não há uma compreensão sobre se esse efeito ocorrerá em tamanhos diferentes de danos à empresa. Nesse sentido, esta pesquisa tem como objetivo verificar se o tamanho do dano pode afetar a ocorrência de efeito dominó do comportamento disfuncional. Por meio de cinco estudos experimentais, verificamos que o tamanho do dano influencia a probabilidade de replicar um comportamento disfuncional. Além disso, descobrimos que esse efeito é explicado pela aceitabilidade de um comportamento disfuncional. Também identificamos que o nível de ética do consumidor, a percepção de risco ao replicar o comportamento e a distância social atuam como condicionantes do efeito do tamanho do dano na probabilidade de replicar o comportamento disfuncional.

PALAVRAS-CHAVE:
Comportamento disfuncional; efeito dominó; ética; percepção de risco; efeito de contágio

INTRODUCTION

In the retail context-offline or online-customers constantly interact and are influenced by other customers. These interactions and influences happen through conversations and observations (Libai et al., 2010Libai, B., Bolton, R., Bügel, M. S., Ruyter, K. Götz, O., Risselada, H., & Stephen, A. T. (2010). Customer-to-customer interactions: Broadening the scope of word of mouth research. Journal of Service Research, 13, 267-282. doi: 10.1177/1094670510375600
https://doi.org/10.1177/1094670510375600... ) and can be either positive for companies, as customers can influence each other to buy or spread positive word-of-mouth, or cause damage because there are customers who behave dysfunctionally (Wilkes, 1978Wilkes, R. E. (1978). Fraudulent behavior by consumers. Journal of Marketing, 42, 67-75. doi: 10.1177/002224297804200410
https://doi.org/10.1177/0022242978042004... ; Fullerton & Punj, 1993Fullerton, R. A., & Punj, G. (1993). Choosing to misbehave: A structural model of aberrant consumer behavior. Advances in Consumer Research, 20, 570-574.; Daunt & Greer, 2015Daunt, K. L., & Greer, D. A. (2015). Unpacking the perceived opportunity to misbehave: The influence of spatio-temporal and social dimensions on consumer misbehavior. European Journal of Marketing, 49, 1505-1526. doi: 10.1108/EJM-01-2014-0061
https://doi.org/10.1108/EJM-01-2014-0061... ).

Dysfunctional customer behavior (DCB), also called aberrant, unethical, or dishonest behavior, is understood as “actions by customers who intentionally or unintentionally, overtly or covertly, act in a manner that, in some way, disrupts otherwise functional service encounters” (Harris & Reynolds, 2003Harris, L., & Reynolds, K. L. (2003). The consequences of dysfunctional customer behavior. Journal of Service Research, 6, 144-161. doi: 10.1177/1094670503257044
https://doi.org/10.1177/1094670503257044... , p. 145). DCB has also been described as client behaviors that violate conduct rules and norms generally accepted in consumption situations (Fullerton & Punj, 1993Fullerton, R. A., & Punj, G. (1993). Choosing to misbehave: A structural model of aberrant consumer behavior. Advances in Consumer Research, 20, 570-574.). In these cases, customers break the rules established by the company or society and cause damage to companies, employees, themselves, and other customers. The types of DCB studied in marketing relate to behaviors such as shoplifting, purchasing counterfeit products, fraudulent return of merchandise, verbal or physical aggression, and vandalism (Fisk et al., 2010Fisk, R., Grove, S., Harris, L. C., Keeffe, D. A., Daunt, K. L., Russell-Bennett, R., & Wirtz, J. (2010). Customers behaving badly: A state of the art review, research agenda and implications for practitioners. Journal of Services Marketing, 24, 417-429. doi: 10.1108/08876041011072537
https://doi.org/10.1108/0887604101107253... ).

The interactions between dysfunctional customers and other customers can cause, among others, the domino effect, which is the dissemination of the dysfunctional behavior from a dysfunctional client to other nearby clients (Harris & Reynolds, 2003Harris, L., & Reynolds, K. L. (2003). The consequences of dysfunctional customer behavior. Journal of Service Research, 6, 144-161. doi: 10.1177/1094670503257044
https://doi.org/10.1177/1094670503257044... ). The possibility of the domino effect in the DCB has been explored (e.g., Harris & Reynolds, 2003; Wu, 2007Wu, C. H. J. (2007). The impact of customer-to-customer interaction and customer homogeneity on customer satisfaction in tourism service-the service encounter prospective. Tourism Management, 28(6), 1518-1528. doi: 10.1016/j.tourman.2007.02.002
https://doi.org/10.1016/j.tourman.2007.0... , 2008Wu, C. H. J. (2008). The influence of customer-to-customer interactions and role typology on customer reaction. The Service Industries Journal, 28(10), 1501-1513. doi: 10.1080/02642060802250310
https://doi.org/10.1080/0264206080225031... ; Liao, Chou, & Lin, 2015Liao, S., Chou, C. Y., & Lin, T. H. (2015). Adverse behavioral and relational consequences of service innovation failure. Journal of Business Research, 68(4), 834-839. doi: 10.1016/j.jbusres.2014.11.037
https://doi.org/10.1016/j.jbusres.2014.1... ). In general, previous studies assume and find that if a customer sees another one engaging in a DCB, he/she may replicate this behavior. However, these studies do not explore the role of the amount of damage to the company affected by the DCB. For example, will a customer replicate a DCB from another customer if the damage to the company is significant? What if the damage to the company is minimal?

The present study aims to verify if the amount of damage can influence the occurrence of the dysfunctional behavior’s domino effect. We suppose that a customer, having interacted with a dysfunctional customer, is more likely to repeat this behavior if the damage is minimal than when it is great. This domino effect may be a consequence of DCB acceptability-that is, the greater the acceptability of a DCB, the greater the likelihood of imitation of the deviant behavior.

To better understand the damage amount effect, we investigated three conditions that could change the likelihood of the domino effect according to the level of damage to the company: the level of customer ethics, the perceived risk, and the social distance. Specifically, we expect that higher levels of customer ethics and perceived risk can mitigate the effects of the amount of damage on the likelihood of the DCB’s domino effect. Similarly, we expect that an in-group condition (vs. out-group condition from social distance) could reduce the contagion effect of the DCB.

Notably, explanations for these imitative behaviors are studied by psychologists through social learning theory (Bandura, 1977Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215. doi: 10.1016/0146-6402(78)90002-4
https://doi.org/10.1016/0146-6402(78)900... ) and by criminologists through broken windows theory (Schaefers, Wittkowski, Benoit, & Ferraro, 2016Schaefers, T., Wittkowski, K., Benoit, S., & Ferraro, R. (2016). Contagious effects of customer misbehavior in access-based services. Journal of Service Research, 19, 3-21. doi: 10.1177/1094670515595047
https://doi.org/10.1177/1094670515595047... ). Marketing studies also explore the mechanisms of observational learning, or how customers not only observe the behaviors of others but how they learn this behavior, and how these imitative effects influence consumer preferences and decisions (Libai et al., 2010Libai, B., Bolton, R., Bügel, M. S., Ruyter, K. Götz, O., Risselada, H., & Stephen, A. T. (2010). Customer-to-customer interactions: Broadening the scope of word of mouth research. Journal of Service Research, 13, 267-282. doi: 10.1177/1094670510375600
https://doi.org/10.1177/1094670510375600... ).

This paper first discusses the theoretical foundations of the domino effect of DCB and the hypotheses. Next, we carry out five experimental studies to test the hypotheses. Finally, we offer theoretical and practical implications.

THEORETICAL BACKGROUND AND HYPOTHESES

Bandura (1963)Bandura, A. (1963). The role of imitation in personality development. Journal of Nursery Education, 18(3), 1-9. argues that when a model is provided, behavioral patterns quickly emerge; unlike the longer instrumental training process, studies on imitation demonstrate that exposure to negative models (e.g., aggressive) implies an increase in subsequent responses. Imitation behavior refers to participating in an action after observing similar behavior by others (Akers & Sellers, 2004Akers, R. L., & Sellers, C. S. (2004). Criminological theory: Introduction and evolution, 2nd ed. Chicago: Routledge.). Whether the behavior modeled will be imitated depends on the characteristics of the models, the behavior observed, and the observed consequences of the behavior (Akers & Sellers, 2004Babcock, L., & Loewenstein, G. (1997). Explaining bargaining impasse: The role of self-serving biases. Journal of Economic perspectives, 11(1), 109-126. doi: 10.1257/jep.11.1.109
https://doi.org/10.1257/jep.11.1.109... ).

In the context of complaints, Kowalski (1996)Kowalski, R. M. (1996). Complaints and complaining: Functions, antecedents, and consequences. Psychological Bulletin, 119, 179-196. doi: 10.1037/0033-2909.119.2.179
https://doi.org/10.1037/0033-2909.119.2.... presents similar results on the domino effect. He calls it the “contagion of complaints” and concludes that, for various reasons, after an individual’s complaint, the incidents of people complaining tend to increase. Akers and Sellers (2004, p. 92)Akers, R. L., & Sellers, C. S. (2004). Criminological theory: Introduction and evolution, 2nd ed. Chicago: Routledge. relate that the relationship between social learning variables (i.e., social contagion or the domino effect) and criminal behavior (i.e., similar to DCB) is typically strong to moderate. Most customers may file modestly inflated claims that can still be justified by self-serving fairness perceptions to allow self-concept maintenance (Wirtz & McColl-Kennedy, 2010Wirtz, J., & McColl-Kennedy, J. R. (2010). Opportunistic customer claiming during service recovery. Journal of the Academy of Marketing Science, 38(5), 654-675. doi: 10.1007/s11747-009-0177-6
https://doi.org/10.1007/s11747-009-0177-... ). However, some may try to cheat for maximum benefit.

Imitation can be an influencer of this customer opportunistic complaint behavior (Baker, Magnini, & Perdue, 2012Baker, M. A., Magnini, V. P., & Perdue, R. R. (2012). Opportunistic customer complaining: Causes, consequences, and managerial alternatives. International Journal of Hospitality Management, 31, 295-303. doi: 10.1016/j.ijhm.2011.06.004
https://doi.org/10.1016/j.ijhm.2011.06.0... ). Gino, Gu, and Zhong (2009)Gino, F., Ayal, S., & Ariely, D. (2009). Contagion and differentiation in unethical behavior: The effect of one bad apple on the barrel. Psychological Science, 20(3), 393-398. doi: 10.1111/j.1467-9280.2009.02306.x
https://doi.org/10.1111/j.1467-9280.2009... investigated the imitative effect and found that observing colleagues who acted unethically, such as cheating in a test, increases the observer’s level of unethical behavior. A person’s behavior can be contagious if it induces other people to act similarly (Schaefers et al., 2016Schaefers, T., Wittkowski, K., Benoit, S., & Ferraro, R. (2016). Contagious effects of customer misbehavior in access-based services. Journal of Service Research, 19, 3-21. doi: 10.1177/1094670515595047
https://doi.org/10.1177/1094670515595047... ). Based on the broken windows theory of criminology, they studied the contagious effects of DCB and found that anonymity encourages dysfunctional behaviors in access-based car services. The broken windows theory proposes that urban deterioration signals previous misconduct and generates criminal activities, predicting the contagion of dysfunctional behavior; that is, for bad behavior to occur, it is not necessary for the person to witness past misbehavior (Schaefers et al., 2016Schaefers, T., Wittkowski, K., Benoit, S., & Ferraro, R. (2016). Contagious effects of customer misbehavior in access-based services. Journal of Service Research, 19, 3-21. doi: 10.1177/1094670515595047
https://doi.org/10.1177/1094670515595047... ) but only to realize that it has succeeded.

Moreover, the amount of damage caused may influence the probability of the domino effect. Specifically, behaviors that result in minor damages to businesses can be more easily accepted by people and, therefore, more easily imitated by other customers, thus increasing the likelihood of the domino effect.

The level of moral development is an individual’s psychological characteristic, and moral restraints are a way to check if a behavior is right or wrong (Fullerton & Punj, 1993Fullerton, R. A., & Punj, G. (1993). Choosing to misbehave: A structural model of aberrant consumer behavior. Advances in Consumer Research, 20, 570-574.). These restrictions may play an important role in making each individual perceive a dysfunctional act as either moral or immoral.

Fullerton, Kerch, and Dodge (1996)Fullerton, S., Kerch, K. B., & Dodge, H. R. (1996). Consumer ethics: An assessment of individual behavior in the market place. Journal of Business Ethics, 15, 805-814. doi: 10.1007/BF00381744
https://doi.org/10.1007/BF00381744... indicate that the acceptance of unethical behavior by an individual is attributed more to the situation than to their attitude. Thus, although individuals perceive behaviors as unethical, depending on the situation, tolerance in the ethical judgment of certain behaviors may vary.

Following these observations, we believe that the customer ethics level plays an important role in the replication of dysfunctional behaviors. Specifically, for individuals with higher levels of ethics, an increase in company damage represents a lower DCB acceptability, or a decrease in the intention to perform a subsequent DCB.

Even with important motivational variables that influence individuals to repeat dysfunctional behaviors, the fear of being caught may deter them. Albers-Miller (1999)Albers-Miller, N. D. (1999). Consumer misbehavior: Why people buy illicit goods. Journal of Consumer Marketing, 16, 273-287. doi: doi/10.1108/07363769910271504
https://doi.org/10.1108/0736376991027150... points out that despite the financial advantages that dysfunctional behavior offers, criminal penalties can discourage such actions. That is, the lower the risk of detention, the greater the likelihood of dysfunctional behaviors.

Likewise, Daunt and Greer (2015)Daunt, K. L., & Greer, D. A. (2015). Unpacking the perceived opportunity to misbehave: The influence of spatio-temporal and social dimensions on consumer misbehavior. European Journal of Marketing, 49, 1505-1526. doi: 10.1108/EJM-01-2014-0061
https://doi.org/10.1108/EJM-01-2014-0061... found that the likelihood of a customer stealing a product in a store is lower when this store is under surveillance. Therefore, an opportunity to assume a dysfunctional behavior is the result of a set of favorable conditions that offer rewards and do not present punishments (Ferrell & Gresham, 1985Ferrell, O. C., & Gresham, L. G. (1985). Framework for contingency ethical decision understanding making in marketing. Journal of Marketing, 49, 87-96. doi: 10.1177/002224298504900308
https://doi.org/10.1177/0022242985049003... ). If the client perceives the opportunity, he will be more prone to act dysfunctionally.

The motivating or inhibiting factor of perceived risk is also influenced by previous client experiences. Daunt and Harris (2011)Daunt, K. L., & Harris, L. C. (2011). Customers acting badly: Evidence from the hospitality industry. Journal of Business Research, 64, 1034-1042. doi: 10.1016/j.jbusres.2010.10.010
https://doi.org/10.1016/j.jbusres.2010.1... point out that the behavior of deviant clients who were successful in the past is associated with performing the same acts in the future. When the customer realizes that they have a high chance of success (low perception of risk) after dysfunctional behavior, they merge past and future behaviors.

A consumer who engages in some type of DCB can be motivated by financial gain, revenge, dissatisfaction, or just taking advantage of a company’s failure (Wirtz & Kum, 2004Wirtz, J., & Kum, D. (2004). Consumer cheating on service guarantees. Journal of the Academy of Marketing Science, 32(2), 159-175. doi: 10.1177/0092070303261416
https://doi.org/10.1177/0092070303261416... ). Opportunistic consumer behavior can take several forms, including inflated and fake insurance claims (Wirtz & McColl-Kennedy, 2010). The findings of a study from Wirtz and McColl-Kennedy (2010) indicate that consumers tend to exaggerate claims when they complain about damage caused by a company.

Gino, Ayal, and Ariely (2009)Gino, F., Gu, J., & Zhong, C. (2009). Contagion or restitution? When bad apples can motivate ethical behavior. Journal of Experimental Social Psychology, 45, 1299-1302. found support for the idea that when the saliency of dishonesty increases, cheating decreases. Some claimants may try to maximize the compensation they seek to obtain; however, many opportunistic claims are likely to be less than the maximum amount possible, so that claimants maintain their positive self-concept (Mazar, Amir, & Ariely, 2008aMazar, N., Amir, O., & Ariely, D. (2008a). The dishonesty of honest people: The theory of self-concept maintenance. Journal of Marketing Research, 45(6), 633-644. doi: 10.1509/jmkr.45.6.633
https://doi.org/10.1509/jmkr.45.6.633... ). The ambiguity associated with what constitutes fair compensation enables a self-serving interpretation (Babcock & Loewenstein, 1997Babcock, L., & Loewenstein, G. (1997). Explaining bargaining impasse: The role of self-serving biases. Journal of Economic perspectives, 11(1), 109-126. doi: 10.1257/jep.11.1.109
https://doi.org/10.1257/jep.11.1.109... ) while allowing claimants to perceive themselves as not being dishonest (Mazar, Amir, & Ariely, 2008bMazar, N., Amir, O., & Ariely, D. (2008b). More ways to cheat-expanding the scope of dishonesty. Journal of Marketing Research, 45(6), 651-653. doi: 2010-01163-004
https://doi.org/2010-01163-004... ).

The social-norms theory (Tajfel, 1982Tajfel, H. (1982). Social psychology of intergroup relations. Annual Review of Psychology, 33(1), 1-39.) shows an important factor that might influence the degree to which people are affected by the unethical behavior of those around them: the degree to which they identify with them. The idea is that when the identification is strong, the behaviors of others will have a higher influence on observers’ social norms. When an in-group member is observed engaging in unethical behavior, other group members may make him or her the standard for the descriptive norm and, as a result, engage in unethical behavior themselves (Gino, Ayal, & Ariely, 2009Gino, F., Gu, J., & Zhong, C. (2009). Contagion or restitution? When bad apples can motivate ethical behavior. Journal of Experimental Social Psychology, 45, 1299-1302.).

However, another stream of research found that a proximal identification between customers could mitigate the contagion effects from the DCB (Schaefers et al., 2016Schaefers, T., Wittkowski, K., Benoit, S., & Ferraro, R. (2016). Contagious effects of customer misbehavior in access-based services. Journal of Service Research, 19, 3-21. doi: 10.1177/1094670515595047
https://doi.org/10.1177/1094670515595047... ). This research is supported by the notion of community, in which a member of the community tries to reestablish the order after another person’s misbehavior in the group. Sampson, Morenoff, and Gannon-Rowley (2002)Sampson, R. J., Morenoff, J. D., & Gannon-Rowley, T. (2002). Assessing “neighborhood effects”: Social processes and new directions in research. Annual Review of Sociology, 28(1), 443-478. doi: 10.1146/annurev.soc.28.110601.141114
https://doi.org/10.1146/annurev.soc.28.1... found that social ties can be an informal control mechanism for misbehavior in a group. Therefore, individuals evaluate in-group members (socially close ones) that misbehave more negatively than out-group members with the same behavior (Gino, Gu, & Zhong, 2009Gino, F., Gu, J., & Zhong, C. (2009). Contagion or restitution? When bad apples can motivate ethical behavior. Journal of Experimental Social Psychology, 45, 1299-1302.). Based on this evidence, we propose that:

To test our hypotheses, we conducted five experimental studies. Studies 1A and 1B tested the effect of the amount of damage on the likelihood of replicating a DCB and examined the mediator role of DCB acceptability and the ethics level moderation. Studies 2A and 2B replicated the domino effect and evaluated how the perception of risk influences the relationship between the amount of damage and the likelihood of replicating a DCB. In Studies 1A and 2A, we tested as a dependent variable the likelihood of replicating a DCB, and in Studies 1B and 2B, we tested as a dependent variable the amount of money that the participant requested after being exposed to a successful DCB. Study 3 replicated the domino effect and evaluated how the perception of risk influences the relationship between the amount of damage and the likelihood of replicating a DCB and examined the moderator effect of social distance (in-group vs. out-group).

STUDY 1A

The goal of Study 1A was to test the effect of the amount of damage (minimal vs. great) on the likelihood of replicating a dysfunctional behavior using an insurance claim scenario. We theorized that when a customer reports a “padded” insurance claim, he/she will exhibit more dysfunctional behavior when the amount of damage is low (vs. high). In addition, we examined the mediator role of dysfunctional behavior acceptability (Neale & Fullerton, 2010Neale, L., & Fullerton, S. (2010). The international search for ethics norms: Which consumer behaviors do consumers consider (un)acceptable? Journal of Services Marketing, 24(6), 476-486. doi: 10.1108/08876041011072591
https://doi.org/10.1108/0887604101107259... ) and the moderator role of the ethical level (Muncy & Vitell, 2005Muncy, J., & Vitell, S. J. (2005). The Muncy-Vitell consumer ethics scale: A modification and application. Journal of Business Ethics, 62, 267-275. doi: 10.1007/s10551-005-7058-9
https://doi.org/10.1007/s10551-005-7058-... ).

Study design and method

The study manipulated a between-subject factor (damage: low advantage vs. high advantage) and used the continuous measure of the probability of the domino effect. Seventy-nine participants (_Mage = 41.74 years old; SD = 13.38; 53.2% female) were recruited from Amazon’s Mechanical Turk with monetary compensation.

Procedures and stimulus. The participants read a scenario about dysfunctional behavior in an insurance claim based on Miyazaki (2009)Miyazaki, A. D. (2009). Perceived ethicality of insurance claim fraud: Do higher deductibles lead to lower ethical standards? Journal of Business Ethics, 87, 589-598. doi: 10.1007/s10551-008-9960-4
https://doi.org/10.1007/s10551-008-9960-... . The stimulus was based on a third consumer (Bill) who reported a dysfunctional behavior regarding an insurance claim to his friend (Sam). Subjects had heard a report of a “padded” insurance claim made weeks earlier. This is an important fact in the scenario because it allowed the occurrence of the domino effect to stem from listening to the report of another dysfunctional customer.

Inside the scenario, two conditions were presented between subjects: low damage vs. high damage. Then, subjects were presented with the following situation: “On a recent trip, Sam had his suitcase stolen. While completing the claim for the insurance company, Sam remembered that the same case happened to his friend Bill a couple of weeks ago. Bill told Sam that he made a claim for $750 (vs. the maximum $3,000) instead of the actual loss of about $500. Because of that, Sam felt comfortable ‘padding’ the insurance claim, as well. He believed it was acceptable to increase a claim for a low (vs. high) amount to make up for the deduction (vs. insurance premium) of $250. The insurance company had accepted Bill’s claim and he received $750.” The values used for the claims were based on real data extracted from Miyazaki (2009)Miyazaki, A. D. (2009). Perceived ethicality of insurance claim fraud: Do higher deductibles lead to lower ethical standards? Journal of Business Ethics, 87, 589-598. doi: 10.1007/s10551-008-9960-4
https://doi.org/10.1007/s10551-008-9960-... .

Next, they were asked about the probability of the domino effect, measured by a 7-point scale (1 = not at all likely to 7 = extremely likely) for the two situations. The first measured the likelihood of another person (Sam) replicating a dysfunctional behavior through the question “What is the likelihood that Sam would repeat Bill’s behavior?” The second item, “How likely are you to repeat Bill’s behavior?” measured the probability of the subject replicating Bill’s dysfunctional behavior. These measures were based on Daunt and Greer (2015)Daunt, K. L., & Greer, D. A. (2015). Unpacking the perceived opportunity to misbehave: The influence of spatio-temporal and social dimensions on consumer misbehavior. European Journal of Marketing, 49, 1505-1526. doi: 10.1108/EJM-01-2014-0061
https://doi.org/10.1108/EJM-01-2014-0061... . To perform the manipulation check, we asked, “How much do you think Bill took advantage of the value of the lost items?” (1 = a little to 7 = a lot), followed by the mediation variable “How acceptable do you think his behavior was in the situation?” (1 = completely unacceptable to 7 = completely acceptable) based on Neale and Fullerton (2010)Neale, L., & Fullerton, S. (2010). The international search for ethics norms: Which consumer behaviors do consumers consider (un)acceptable? Journal of Services Marketing, 24(6), 476-486. doi: 10.1108/08876041011072591
https://doi.org/10.1108/0887604101107259... . After that, we applied the consumer ethics scale (CES), “defined as the moral principles and standards that guide the behavior of individuals or groups as they obtain, use, and dispose of goods and services” by Muncy and Vitell (2005, p. 268)Muncy, J., & Vitell, S. J. (2005). The Muncy-Vitell consumer ethics scale: A modification and application. Journal of Business Ethics, 62, 267-275. doi: 10.1007/s10551-005-7058-9
https://doi.org/10.1007/s10551-005-7058-... to verify the moderating effect of individual ethical behavior. This was on a 7-point scale with 31 items (1 = strongly believe that it is wrong and 7 = strongly believe it is not wrong) (α = .916). At the end, participants answered a few demographic questions and were extensively debriefed.

Pretest. We pretested our stimuli to ensure that the dysfunctional behaviors acceptability would have different perceptions in relation to the damage amount. The participants read the scenario about dysfunctional behavior in an insurance claim based on Miyazaki (2009)Miyazaki, A. D. (2009). Perceived ethicality of insurance claim fraud: Do higher deductibles lead to lower ethical standards? Journal of Business Ethics, 87, 589-598. doi: 10.1007/s10551-008-9960-4
https://doi.org/10.1007/s10551-008-9960-... . In this scenario, participants read about a situation in which one customer had their suitcase stolen. The manipulation check was the same as the main study. Participants indicated on a 7-point scale “How much do you think he took advantage of the value of the lost items?” (1 = a little to 7 = a lot) and “How acceptable do you think his behavior was in the situation?” (1 = completely unacceptable to 7 = completely acceptable) based on Neale and Fullerton (2010)Neale, L., & Fullerton, S. (2010). The international search for ethics norms: Which consumer behaviors do consumers consider (un)acceptable? Journal of Services Marketing, 24(6), 476-486. doi: 10.1108/08876041011072591
https://doi.org/10.1108/0887604101107259... .

Seventy M-Turk panelists completed the pretest. Ten participants whose native language was not English were excluded from the final sample. Thus, the final sample included 60 participants (_Mage = 36.23; 50% male). An independent t-test was performed to compare the mean for the small advantage (_{Msmall_advantage} = 5.93, SD = 1.484), which was significantly lower than the large advantage (_{Mlarge_advantege} = 6.80, SD = 0.487, t(58) = 3.041, p = .004), and the high damage of DCB’s acceptability (_{Mhigh_damage} = 1.363; SD = 2.080), which was significantly lower than for low damage (_{Mlow_damage} = 2.87; SD =1.73, t(58) = 2.496, p = .015). Thus, the pretest indicated that the scenarios about the insurance claim were perceived as different among the participants.

Results

Manipulation check. We verified that subjects in the large amount of damage condition (or higher advantage) (M = 6.59) perceived that Bill took more advantage than subjects in the low amount of damage condition (or lower advantage) (M = 5.30; F(1, 77) = 16.880; p < .001)

Domino effect. Subjects exposed to the high amount of damage condition (M = 4.56) indicated a lower probability that Sam would repeat Bill’s behavior than subjects exposed to the low amount of damage condition (M = 5.45; F(1, 77) = 7.389; p < .01). Additionally, the subjects in the high amount of damage condition (M = 2.25) indicated a lower likelihood of themselves replicating Sam`s behavior than participants in the low amount of damage condition (M = 3.52; F(1, 77) = 7.858; p < .01).

Moderated-mediation. To verify if the acceptability of the DCB explains why the domino effect happens, and how the participants’ ethics level influences this relationship, we ran a moderated-mediation analysis following Hayes’ (2013)Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York and London: Guilford Press. recommendations. First, we dummy coded the amount of damage (0 = low and 1 = high) for the independent variable. The mediator was the acceptability of the DCB, and the dependent variable was the likelihood that Sam would replicate Bill’s behavior. We also used the subjects’ ethics level as the moderator of the relationship between the amount of damage and the mediator.

There is a significant effect of the interaction between the amount of damage and ethics level on acceptability (a = -.36; t = -2.45; p < .05). This result indicates that the acceptability of the DCB depends on the amount of damage moderated by the ethics level. On the other side, the mediation effect of the likelihood that Sam would replicate Bill’s dysfunctional behavior was positive (b = .18; t = 2.03; p < .05). The indirect effect is significant only in the high levels of ethics (one SD above the mean). In this condition, through 5,000 resamples from bootstrapping, the indirect effect (a x b = -.11) is found to be negative and significant because the confidence interval (95%) did not present a null effect (-.31 to -.01). The significance of the direct effect of the amount of damage on the likelihood of the domino effect (c’ = -.33; t = -1.98; p = .05) indicates that there is a partial mediation.

Likewise, we tested the mediation role of the acceptability in the relationship between the amount of damage and the likelihood of the subject to replicate Sam’s behavior. As in the first test, we also considered the level of ethics as the moderator of the relationship between the amount of damage and the acceptability.

For the moderated-mediation model, the effect of the interaction between the amount of damage and ethics level on acceptability (a = -.36; t = -2.45; p < .05) is the same as in the first analysis. The effect of the mediator on the likelihood that a subject would replicate Sam`s dysfunctional behavior was positive (b = .92; t = 11.65; p < .001). The indirect effect is also significant but only in high levels of ethics (one SD above the mean). Specifically, through 5,000 resamples from bootstrapping, the indirect effect (a x b = -.57) is found to be negative and significant because the confidence interval (95%) did not present a null effect (-.96 to -.15). Unlike the first moderated-mediation model, the direct effect of the amount of damage on the likelihood of the domino effect (c’ = -.10; t = -.72; p = .47) is not significant, which indicates that the effect of the amount of damage on the domino effect is fully explained by the acceptability of the DCB.

To improve the understanding of the levels of ethics moderation in the relationship between the amount of damage and the acceptability of the customer dysfunctional behavior, we performed a Johnson-Neyman test to determine the location of the effect of damage significance on the acceptability, considering the level of ethics as a moderator, as shown in Graphic 1.

Discussion

Study 1A, supporting H1, identified that the likelihood of replicating a DCB is higher in conditions that result in a lower amount of damage to the company. Specifically, the intention to replicate the behavior of another client is more pronounced when the subjects perceive that this behavior has minimal negative consequences to the company.

As predicted by H2, we also verified that the acceptability of the previous DCB mediated the influence of the amount of damage on the domino effect. The negative path between the amount of damage and the acceptability indicates that the higher the amount of damage to the company, the less acceptable the behavior, and, consequently, the probability that the client will replicate this behavior is lower.

In addition to the mediation effect, the level of ethics modifies how the amount of damage influences the acceptability, supporting H3. Specifically, for subjects with higher levels of ethics, the effect of the amount of damage on the acceptability of the DCB is negative, indicating that for these subjects, ethics reduce the acceptability of the DCB with the amount of damage increase more than for subjects with lower levels of ethics.

STUDY 1B

Study 1B was designed to be similar to Study 1A but used a different dependent variable measure. We measured it with a metrical scale from $500 to $3,000. Using this measure, we can verify how much money customers would claim from their insurance and if they are influenced by other customers. We show that this is influenced by the customer ethics level and the acceptability of the DCB. The aim of this study is to test hypotheses 1 and 5.

Study design and method

In this study, one factor, on two different levels, was manipulated (damage: low advantage vs. high advantage) in a between-subject design measuring the amount of the claim (domino effect). Ninety-three participants were recruited from an online panel (Amazon Mechanical Turk, _Mage = 34.38 years old; SD = 9.90; 55.9% male) in exchange for monetary compensation.

Procedures and stimulus. The procedure was identical to Study 1A with one important modification-damage manipulation participants answered only one question related to the dependent variable: “How about you? How much money would you ask for from the insurance company in a similar situation?” It was measured with a metrical scale from $500 to $3,000. The acceptability of the DCB mediation and CES (α = .942) moderation followed the same procedures as in Study 1A. At the end, participants answered a few demographic questions and were extensively debriefed.

Results

Manipulation check. Participants in the high amount of damage condition (or higher advantage) (M = 5.63) perceived that Bill took more advantage than subjects in the low amount of damage condition (or lower advantage) (M = 4.67; F(1, 91) = 8.032; p < .01).

Domino effect. The participants in the large amount of damage condition (or higher advantage) (M = 1,457.10) indicated that they would ask for more money from the insurance company than participants in the low amount of damage condition (or lower advantage) (M = 833.45; F(1, 91) = 16.634; p < .001).

Moderated-mediation. To verify how the interaction between the amount of damage and ethics of the participant influence the acceptability of the DCB and how this acceptability influences the domino effect, we used a moderated-mediation model, as in Study 1A. However, the dependent variable in this study was the amount of money requested by the participant in the insurance claim.

The path between the interaction and acceptability was significant (a = -.71; t = -2.09; p < .05), indicating that the amount of damage and ethics interaction influences the acceptability of the DCB. The acceptability positively influences the domino effect (b = 207.66; t = 5.74; p < .001), suggesting that an increase in the acceptability leads to higher amounts of money asked for from the insurance company by the participant.

The indirect effect was only significant in the higher ethics levels (one SD above the mean); through 5,000 resamples from bootstrapping, this effect (a x b = -237.68) is shown to be negative and significant because the confidence interval (95%) did not present a null effect (-508.21 to -43.43). The significance of the direct influence of the amount of damage on the domino effect (c’ = 699.62; t = 5.29; p < .001) indicates that there is a partial mediation, or the acceptability partially mediates the influence of the amount of damage on the domino effect, considering the level of ethics as a moderator, as shown in Graphic 2.

To specify how the level of ethics of the participant modifies their acceptability of DCB, we used the Johnson-Neyman test. Regarding the significance of the effect of damage on the acceptability, as a moderator, levels of ethics above 4.77 (7-point scale) indicate a negative and significant effect of the amount of damage on the acceptability. However, there is no relationship between the amount of damage and acceptability for levels of ethics below 4.77.

Discussion

The findings of this second study support the assumption of H5. Specifically, subjects exposed to a DCB in which the previous customer asked for a high amount from the company adjusted their claim and also asked for a higher amount than subjects who were exposed to a previous customer asking for a low amount. This finding supports the logic that customers anchor and adjust their future behavior according to previous behavior from another customer.

Moreover, we support the findings from Study 1A for the underlying mechanism, showing that acceptability is the mechanism that explains how previous DCB influences the domino effect. We also support the role of customer ethics, which moderates the relationship between previous behavior and the likelihood of the domino effect.

STUDY 2A

The goal of Study 2A was to replicate the domino effect and evaluate how risk perception (low vs. high) influences the relationship between the amount of damage and the likelihood of replicating a dysfunctional behavior. We predicted that when the subject perceives a high (vs. low) risk, it means there is a higher probability that the behavior can be discovered and punished by the company, thereby reducing the intention to replicate a dysfunctional behavior in the high amount of damage condition. The aim of this study is to test hypothesis 4.

Study design and method

Study 2A was a 2 (damage: low advantage vs. high advantage) × 2 (risk perception: low vs. high) between-subjects design. One hundred and twenty-one participants were recruited online (Amazon Mechanical Turk, _Mage = 38.78 years old; SD = 12.20; 58.7% female) in exchange for monetary compensation.

Procedures and stimuli. Study 2A had the same procedures as Study 1A, with some important modifications, such as the risk perception stimuli between scenarios. In the low damage scenario (vs. high), the low risk perception was manipulated through the following information that participants received: “There is a clause in their contracts that do not allow the insurance company to use airports’ x-ray videos for examining the application of the customer. The insurance company accepted Bill’s claim, and he received $750 (vs. the maximum $3,000).” In the high damage scenario (vs. low), the high risk perception was manipulated as follows: “There is a clause in their contracts that allows the insurance company to use airports’ x-ray videos for examining the application of the customer; in case cheating is detected, the insurance company is not obligated to indemnify the customer. The insurance company accepted Bill’s claim, and he received $3,000 (vs. $750).” The manipulation check question regarding the amount of advantage was measured on a 7-point scale: “How much do you think he took advantage of the value of the lost items?” (1 = a little to 7 = a lot). Risk perception was measured through two items from Blais and Weber (2006)Blais, A. R., & Weber, E. U. (2006). A domain-specific risk-taking (DOSPERT) scale for adult populations. Judgement and Decision Making, 1, 33-47. on a 7-point scale. At the end, participants answered a few demographic questions and were extensively debriefed.

Results

Manipulation check. We verified that participants in the high amount of damage condition (or higher advantage) (M = 5.89) perceived that Bill took more advantage than subjects in the low amount of damage condition (or lower advantage) (M = 5.38; F(1, 119) = 4.122; p < .05). Likewise, subjects exposed to the lower risk condition (M = 3.76) perceived that the risk of Sam following Bill’s behavior is lower than subjects exposed to the higher risk condition (M = 4.21; F(1, 119) = 4.282; p < .05).

Perceived risk moderation. We found a significant effect of the interaction between the amount of damage/advantage and perceived risk on the probability of Sam repeating Bill’s behavior (F(1, 117) = 4.329; p < .05). Specifically, there is no difference in the domino effect along the low damage condition (_{Mlower_risk} = 5.13; _{Mhigher_risk} = 5.18; F(1, 117) = .022; p = .883), but there is a difference in the domino effect along the high damage condition (F(1, 117) = 7.638; p < .01). Therefore, subjects exposed to the higher risk indicated a lower probability (M = 4.23) of Sam replicating Bill’s behavior than subjects exposed to the lower risk condition (M = 5.27).

To complete the model, we ran a pairwise comparison of the amount of damage effect along the risk perception conditions. The participants exposed to the lower risk condition did not present a significant difference in the perceptions related to Sam following Bill’s behavior (e.g., domino effect) (_{Mhigh_damage} = 5.27; _{Mlow_damage} = 5.13; F(1, 117) = .143; p = .706). However, participants exposed to the higher risk condition had different judgments about Sam’s behavior (F(1, 117) = 6.718; p < .01). In the high damage condition, respondents indicated a lower probability of Sam replicating Bill’s behavior (M = 4.23) than in the low damage condition (M = 5.18). These findings are presented in Graphic 3.

We also verified how the amount of damage and the risk perception influence the behavioral intentions of the respondent. There is a significant effect of the interaction on the probability of the respondent to replicate Bill’s behavior (F(1, 117) = 4.640; p < .05). Specifically, there is a marginal difference in the likelihood of the respondent to repeat Bill’s behavior in the low amount of damage condition (F(1, 117) = 3.365; p = .06) because subjects exposed to higher risk indicate a higher probability (M = 3.25) of repeating Bill’s behavior than subjects exposed to a lower risk condition (M = 2.43). There is no difference in the probability along the high amount of damage condition (F(1, 117) = 1.487; p = .22).

Nevertheless, there is a significant difference in the probability of repeating Bill’s behavior in the higher risk condition (F(1, 117) = 6.292; p < .05). In this condition, subjects exposed to a low amount of damage presented a higher probability (M = 3.25) of engaging in the domino effect than subjects in the high amount of damage (M = 2.13). It is important to note that there is no difference along the lower risk condition (F(1, 117) = .316; p = .57). Graphic 4 presents these findings.

Discussion

There is evidence that the level of perceived risk influences the relationship between the amount of damage and the domino effect. This finding supports H4. When the subject perceives a high risk, it means there is a higher probability that the behavior can be discovered and punished by the company. In this case, the customer reduces the intention to replicate a DCB in the high amount of damage condition. However, it is interesting to note that the perceived risk did not influence the domino effect in the low amount of damage condition. This should be explored in future studies, as it is possible that in the low amount of damage condition, the domino effect can be easily justifiable when compared to the high amount of damage, in which the advantage taken by the client is larger.

Study 2B

Study 2B was designed to replicate Study 2A, using a dependent variable measure from Study 1B. We used a metrical scale from $500 to $3,000. We replicated the same mechanism evaluated in Studies 1A and 1B and showed the relationship between the customer ethics level and the acceptability of the DCB.

Study design and method

Study 2B was a 2 (damage: low advantage vs. high advantage) × 2 (risk perception: low vs. high) between-subjects design. The 181 participants were recruited online (Amazon Mechanical Turk, _Mage = 33.72 years old; SD = 10.06; 61.9% male) in exchange for monetary compensation.

Procedures and stimulus. The procedure was identical to Study 2A. However, . participants answered the same dependent variable question from Study 1B, using a metrical scale from $500 to $3000. The manipulation check and acceptability of the DCB mediation were the same as Studies 1B and 2A. At the end, participants answered a few demographic questions and were extensively debriefed.

Results

Manipulation check. The subjects in the high amount of damage condition (or higher advantage) (M = 5.78) perceived that Bill took more advantage than subjects in the low amount of damage condition (or lower advantage) (M = 5.12; F(1, 179) = 11.003; p < .05). We also verified that subjects exposed to the lower risk condition (M = 3.76) perceived that the risk of Sam following Bill’s behavior was lower than subjects exposed to the higher risk condition (M = 4.21; F(1, 119) = 4.282; p < .05).

Perceived risk moderation. We analyzed how the amount of damage and the perception of risk influence the amount of money requested by the respondents. There is a significant effect of the interaction between the amount of damage and the perception of risk on the probability of Sam repeating Bill’s behavior (F(1, 177) = 8.838; p < .01).

Along pairwise comparisons, subjects in the low amount of damage condition do not present differences in the risk perceptions conditions (F(1, 177) = .145; p = .70). However, in the high amount of damage condition, subjects exposed to the lower risk perception (M = 1,559.06) indicate a higher value than subjects exposed to the higher risk perception (M = 1,021.77; F(1, 177) = 15,324; p < .001).

In the risk perception conditions, there is a significant difference in the amount of money that the respondent would ask for in the lower risk perception (F(1, 177) = 25.407; p < .001). In this condition, subjects exposed to a low amount of damage would ask for a lower amount (M = 838.61) than subjects exposed to the high amount of damage condition (M = 1,559.06). There is no difference regarding the higher risk condition (F(1, 177) = .853; p = .35). Graphic 5 presents these findings.

Moderated-mediation. Considering that the acceptability of the DCB was tested as the underlying mechanism in the first study, in Study 2B, we also included this variable in the relationship between the interaction among damage and risk and the amount of money that the participant requested.

The path between the interaction and the behavior’s acceptability is significant (a = -1.21; t = -2.05; p < .05), indicating that the amount of damage and risk perception interaction influence the acceptability of the DCB. The acceptability positively influences the domino effect (b = 171.05; t = 7.61; p < .001).

The indirect effect is only significant in the higher levels of risk perception; through 5,000 resamples from bootstrapping, this effect (a x b = -231.39) is shown to be negative and significant because the confidence interval (95%) did not present a null effect (-410.01 to -88.22). Additionally, the significance of the direct effect of the amount of damage on the domino effect (c’ = 549,58; t = 6.00; p < .001) indicates a partial mediation.

Discussion

This study provides additional support to H2, H3, H4, and H5. Specifically, the results of Study 2B indicate that the acceptability of the DCB mediates the effect of the amount of damage on the domino effect (measured by the value claimed to the company); the level of ethics and the perception of risk act as boundary conditions of the occurrence of the domino effect.

STUDY 3

The goal of Study 3 was to test the domino effect and evaluate how risk perception (low vs. high) and social distance (in-group vs. out-group) influence the relationship between the amount of damage and the likelihood of replicating a dysfunctional behavior. The aim of this study was to test hypothesis 6.

Study design and method

Study 3 had a 2 (damage: low advantage vs. high advantage) × 2 (risk perception: low vs. high) x 2 (social distance: in-group vs. out-group) between-subjects design. Participants in the experiment were 167 undergraduate students from a Brazilian university (_Mage = 29.28 years old; _SDage = 8.77; 51.5% female). They did not receive any compensation.

Procedures and stimulus. The procedure was identical to that of Study 2B with one important modification-the social distance stimuli between scenarios. In the in-group condition (vs. out-group), participants received the following extra information: "While completing the claim for the insurance company, he remembered that the same case happened to his brother a few days ago. His brother told him that he had applied for compensation.” The out-group scenario was manipulated as follows: “While filling out the claim form, he remembered that this same situation had occurred with a passenger sitting in the back row during his one-way flight. He listened to the passenger telling the person on the side about his claim for compensation.” Later, the scenario manipulation participants answered the following dependent variable question: “How much money would you ask for from the insurance company in a similar situation?” This question was measured with a metrical scale of R$375 to R$4,520. This value was adapted to the Brazilian participants; we used the proportional value based on the maximum value (R$4,520) proposed by the Montreal convention (Morsello, 2004Morsello, M. F. (2004). Responsabilidade civil no transporte aéreo. São Paulo: Universidade de São Paulo.). The manipulation check question about social distance was measured on a 7-point scale: “How close am I to the person who made the claim before him?” (1 = too far to 7 = too near). At the end, participants answered a few demographic questions and were extensively debriefed.

Results

Manipulation check. We verified that subjects in the high amount of damage condition (or higher advantage) (M = 5.26; SD = 1.90) perceived that Bill took more advantage than subjects in the low amount of damage condition (or lower advantage) (M = 4.31; SD = 1.85 F(1, 165) = 10.579; p < .001). Subjects exposed to the lower risk condition (M = 3.07; SD = 1.81) also perceived that the risk of Sam following the domino behavior was lower than subjects exposed to the higher risk condition (M = 4.05; SD = 1.77; F(1, 165) = 12.472; p < .001). Likewise, subjects exposed to the in-group condition (M = 5.68; SD = 1.69) perceived that the social distance from the other passenger was lower than subjects exposed to the out-group condition (M = 2.29; SD = 1.66; F(1, 164) = 169.63; p < .000).

Domino effect. Subjects exposed to the high amount of damage condition (or higher advantage) (M = 2,105.58) indicated that they would ask for more money from the insurance company than subjects exposed to the low amount of damage condition (or lower advantage) (M = 1,183.27; F(1, 165) = 22.550; p < .001).

Moderated-mediation. To verify how the interaction between the amount of damage and social distance influence the acceptability of the DCB and how this acceptability influences the domino effect, we used a moderated-mediation model, as in Study 1A. The dependent variable in this study was the amount of money asked for in the insurance claim by the participant (similar results were found with the projective measure as the dependent variable).

The path between the interaction and acceptability is significant (a = -.41; t = -3.11; p < .01), indicating that the amount of damage and social distance interaction influences the acceptability of the DCB. However, as social distance conditions the effect from the amount of damage, the effect is significant when the participant was exposed to the in-group condition (a = -.61; t = -3.25; p < .01) and non-significant when the participant was exposed to the out-group condition (a = .21; t = 1.14; p = .25). Following the model, the acceptability positively influences the domino effect (b = 372.33; t = 7.87; p < .001), indicating that an increase in the acceptability leads to higher amounts of money asked for from the insurance company by the participant.

The indirect effect is only significant in the in-group condition; through 5,000 resamples from bootstrapping, this effect (a x b = -228.91) is shown to be negative and significant because the confidence interval (95%) did not present a null effect (-373.72 to -88.47). The significance of the direct effect of the amount of damage on the domino effect (c’ = 532.56; t = 6.38; p < .001) indicates that there is a partial mediation, or the acceptability partially mediates the effect of the amount of damage on the domino effect, considering the social distance as a moderator of this relationship.

Three-way moderation. We also tested a three-way moderation using the amount of damage, the social distance, and the risk perception. We verify that the social distance conditions the effect from the interaction between the amount of damage and risk perception on the domino effect (F(1, 159) = 7.810; p < .01). Specifically, the effect presented in Study 2A happens when the participant was exposed to the in-group condition (F(1, 79) = 4.569; p < .05). Subjects exposed to the low risk perception presented a higher claim amount when the amount of damage was higher (M = 2,509.08) than when the amount of damage was lower (M = 1,165.19; F(1, 79) = 12.772; p < .01). In the higher risk perception, there is no difference between the amount of damage conditions (F(1, 79) = .146; p = .703). In the same sense, when exposed to the out-group condition, the effect from the interaction on the domino effect was not significant (F(1, 80) = 3.252; p = .075).

Discussion

Study 3 provides additional support for H1, H2, H4, and H5. Specifically, the results of Study 3 indicate that social distance is an important variable in the DCB context. The social distance is capable of reducing the domino effect, supporting H6, when the subject feels that it is low (in-group). This result is aligned with the notion of community and the social control and balance established between members to keep social cohesion. Moreover, when the subject is exposed to the in-group, the risk perception conditions the effect of the amount of damage on the DCB.

GENERAL DISCUSSION

We respond to calls to investigate the processes underlying DCB (Fisk et al., 2010Fisk, R., Grove, S., Harris, L. C., Keeffe, D. A., Daunt, K. L., Russell-Bennett, R., & Wirtz, J. (2010). Customers behaving badly: A state of the art review, research agenda and implications for practitioners. Journal of Services Marketing, 24, 417-429. doi: 10.1108/08876041011072537
https://doi.org/10.1108/0887604101107253... ; Harris & Reynolds, 2003Harris, L., & Reynolds, K. L. (2003). The consequences of dysfunctional customer behavior. Journal of Service Research, 6, 144-161. doi: 10.1177/1094670503257044
https://doi.org/10.1177/1094670503257044... ). As previously discussed, the domino effect has not been adequately investigated empirically (e.g., Gino, Gu, & Zhong, 2009Gino, F., Gu, J., & Zhong, C. (2009). Contagion or restitution? When bad apples can motivate ethical behavior. Journal of Experimental Social Psychology, 45, 1299-1302.; Schaefers, Wittkowski, & Ferraro, 2016Schaefers, T., Wittkowski, K., Benoit, S., & Ferraro, R. (2016). Contagious effects of customer misbehavior in access-based services. Journal of Service Research, 19, 3-21. doi: 10.1177/1094670515595047
https://doi.org/10.1177/1094670515595047... ), and the main role of the amount of damage remained uncovered by the previous literature. We address this gap using four experimental studies that investigated both the amount of money the participant requested and the likelihood of replicating a dysfunctional behavior, going beyond common approaches (Fisk et al., 2010). We also investigated three distinct underlying mechanisms for these behaviors.

Our studies, supporting H1, identify that the likelihood of replicating a DCB is higher in a condition in which the DCB results in a lower amount of damage to the company. Specifically, the intention to replicate the behavior of another client is stronger when the subject realizes that this behavior has minimal negative consequences to the company than when he/she perceives that the consequences to the company are greater. This evidence seems aligned with the capacity of justification of the customer (Wirtz & McColl-Kennedy, 2010Wirtz, J., & McColl-Kennedy, J. R. (2010). Opportunistic customer claiming during service recovery. Journal of the Academy of Marketing Science, 38(5), 654-675. doi: 10.1007/s11747-009-0177-6
https://doi.org/10.1007/s11747-009-0177-... ; Dootson, Neale, & Fullerton, 2014Dootson, P., Neale, L., & Fullerton, S. (2014). When size matters: Exploring perceptions and justifications of deviant consumer behavior. Advances in Consumer Research, 42, 298-301.). It is easier to justify a DCB when it causes less damage to the company than when it causes significant damage. Future studies could explore this underlying mechanism; however, it makes sense that great damage is considered more wrong than a small amount of damage.

As predicted by H2, we also found that the acceptability of the previous DCB mediates the effect of the amount of damage on the domino effect. The negative path between the amount of damage and the acceptability indicates that the larger the amount of damage to the company, the less acceptable the behavior, and, consequently, it is less likely that the client will replicate it. Although the acceptability was not a justification, it seems that a more acceptable behavior is easier to justify. Therefore, the evidence that the acceptability mediates the influence of the amount of damage on the domino effect supports the idea that the lower the damage to the company, the more justifiable the behavior, and, consequently, the more likely it is to be replicated.

Additionally, the level of ethics modifies how the amount of damage influences the acceptability, supporting H3. Specifically, for subjects with higher levels of ethics, the effect of the amount of damage on the acceptability of the DCB is negative, indicating that for these subjects, ethics reduces the acceptability of the DCB with the amount of damage increase more than for subjects with lower levels of ethics.

Finally, we showed that the level of perceived risk and the social link influences the relationship between the amount of damage and the domino effect. This finding supports H4 and H6. When the subject perceives a high risk or when there is more probability of discovering and punishing the behavior, the customer reduces his intention to replicate a DCB in the high amount of damage condition. However, it is interesting that the perceived risk does not influence the domino effect in the low amount of damage condition. This result should be explored in future studies, as it is possible that in the low amount of damage condition, the domino effect can be easily justifiable, compared to a high amount of damage, in which the advantage taken by the client is clearer. In the same way, when the person is a group insider, the social link reduces the likelihood of replicating the DCB, supporting the hypotheses that the social proximity acts as an informal control mechanism to prevent an imbalance in the group.

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