Introduction

The relationship between organizations and clients is founded on the trust that is established between the two parties (Sirdeshmukh, Singh, & Sabol, 2002). In a service context, trust is important because in most circumstances, the clients buy promises. Services are typically manufactured and consumed in real time, leaving no chance for inspection (risk reduction) prior to purchase. Moreover, on the basis of the commitment–trust theory of relationship marketing (Morgan & Hunt, 1994), trust is a key factor in relationships and, unlike satisfaction, is a future-oriented component that can influence company performance through the maintenance of the client base.

Although organizations develop strategies to increase the trust of their clients, this can be violated by various shortcomings, including service failure (Xie & Peng, 2009; Laer & de Ruyter, 2010; Basso & Pizzutti, 2016). Reservations for restaurants and hotels that have been confirmed but are not honored by service providers are examples of failures in service that may violate a client’s trust. During such times, the company that caused the failure should take action to recover the trust of its clients (Basso & Pizzutti, 2016).

Previous research has discussed issues, such as trust violation and recovery, in the fields of marketing (Xie & Peng, 2009; Basso & Pizzutti, 2016; Gasparotto et al., 2018) and organizational behavior (Kim et al., 2004; Kim et al., 2006; Elangovan, Auer-Rizzi, & Szabo, 2007). However, they consider individuals alone to test how trust is violated and recovered, without taking into account, for instance, the consumption situations that may occur in restaurants or hotels where the clients are commonly in groups. The presence of other clients in such situations can modify how individual clients make their judgment and may influence their choices (Bearden & Etzel, 1982; Childers & Rao, 1992; Du, Fan, & Feng, 2014). Thus, a gap exists in the service literature’s understanding of how the presence of a group can influence clients’ trust violation and recovery. It should be noted that in the present study, a group is defined as two or more people who know each other and who act in the same service consumption situation.

The presence of other people can influence both the violation of clients’ trust and its recovery (Childers & Rao, 1992; Du et al., 2014) because social pressure can affect their choices (Bearden & Etzel, 1982). Williams and Karau (1991) state that differences in people’s behavior occur because people in groups expend less effort in making decisions than people do when they are alone. Cacioppo and Gardner (1999) argue that people have a certain susceptibility to other people’s emotions; therefore, the mere presence of a group during a consumption situation creates a condition of contagion in which individuals change their behavior according to the behavior of the group. Some studies have examined the effects of groups on consumer behavior (e.g., Bagozzi, 2000; Grier & Deshpandé, 2001; Dholakia, Bagozzi, & Pearo, 2004; Zhou et al., 2013); however, a gap exists in verifying how a group can modify consumer trust violation and recovery.

When individuals are in a group, their decisions can be polarized (Hinsz & Davis, 1984). In social psychology, the group polarization effect produces a tendency for individuals to be more extreme in their judgments when they are in groups than when they are isolated (Hogg, 2001). The effect of this group dynamic, in which small groups of individuals tend to adopt more extreme (polarized) positions than when these individuals alone, can be a useful approach to explain how the reaction to a trust violation and recovery of a client in a group can differ from that of a client in isolation.

When a client is in a group, the group polarization effect may influence the extent to which a client’s trust is violated and recovered. This study aims to examine the group polarization effect as the mechanism underlying the effect of the group on trust violation and recovery.

Some studies in the services marketing literature (Zhou et al., 2013; Du et al., 2014; Van Vaerenbergh, Vermeir, & Larivière, 2013) have considered the presence of other individuals in service failure and recovery situations. Specifically, Zhou et al. (2013) show how economic and social tactics for service recovery operate in public (group) or private (individual) recovery contexts, whereas Du et al. (2014) analyze the effect of the group on complaint intentions, and Van Vaerenbergh et al. (2013) demonstrate how an observing client is influenced by the service failure and recovery experience of another client in a queue. Although these studies consider a group (or the “presence of other[s],” such as in the work of Van Vaerenbergh et al. (2013)), they use a transaction marketing paradigm (i.e., the quality–satisfaction–loyalty paradigm) and do not focus on trust or the relationship with clients. Using a relationship paradigm (i.e., the satisfaction–trust–commitment paradigm), the present research proposes that a group can also interfere in clients’ trust.

To the best of our knowledge, the only study that used a group to develop an understanding of how trust can be repaired was that by Kim et al. (2013), who examine the topic in the organizational behavior context. Their study illustrated how an initial trust violation and recovery are different for groups and isolated individuals. The present study builds on Kim et al.’s (2013) findings by showing how trust in a pre-existing relationship can be violated and recovered in a different way, depending on whether the client is affected in isolation or as a part of a group (with a focus on individuals and not on groups).

Additionally, the present study advances understandings of the effect of groups in service contexts (Zhou et al., 2013; Du et al., 2014) and in trust violation and recovery (Kim et al., 2013) by showing that the effect of a group on a client’s trust violation and recovery is explained by the group polarization effect. This underlying mechanism has not been used and tested in previous studies.

Social Influence

Social influence refers to how individuals are affected by other people in a social structure to conform their behaviors to that accepted and practiced in the social structure (Abrams & Hogg, 1990). According to Wood (2000), when people consider themselves as members of a group, they adopt the attitudes and behaviors of the group as their own. Turner (1991) mentions that groups exert an influence on individual behavior through an informational process; being a member of a group gives the individual the subjective certainty that his/her attitudes reflect the external reality and then can be converted to accepted behaviors.

Cialdini and Trost (1998), using social norms, describe that the individuals in groups or in a social environment seek conformity to the attitudes and behaviors of others to be a part of the group. For Cialdini and Goldstein (2004), conformity refers “to the act of changing one’s behavior to match the responses of others” (p. 606). According to the authors, individuals seek conformity because of two main reasons: first, to validate and make their self-judgments more accurate and, second, to seek social approval or social harmony with other participants of the group. Using normative social influence, or else, the necessity for approval, Schultz, Khazian, and Zaleski (2008) find evidence that hotel guests are better prompted to save hotel resources (e.g., water) when other guests do the same.

Regarding the goal of affiliation in a group, Cialdini and Goldstein (2004) mention that individuals can use two different strategies. First, they can mimicry the attitudes and behaviors of the other people in the group. In this strategy, individuals make their attitudes and behaviors conform to those of the other people in the group to integrate themselves in the group, as a chameleon effect. Second, different from mimicry, which could be more nonconscious, individuals constantly engage in more conscious actions to gain the approval of and make bonds with the other people in the group. These actions could be represented by choosing a product selected by the majority instead of a product chosen by the minority.

Regarding the difficulty to verify the expression of conformity to group attitudes and behaviors, some studies have been conducted to test the effects of groups on individual behavior. For example, Bond et al. (2012), through an observational study with a large sample, provide evidence that the messages coming from other members of a group have a power to influence the attitudes and behaviors of people. The authors also support the idea that when these messages come from close friends (stronger ties), these have a greater capacity to influence individuals’ attitudes and behaviors. In other words, when the messages come from an easily identified group, the effect of social influence is more intense.

Trust Violation

Trust can be defined as the “expectations held by the consumer that the service provider is dependable and can be relied on to deliver on its promises” (Sirdeshmukh et al., 2002, p. 17). Thus, while trust building has several positive effects on companies and service providers (Terres, Santos, & Basso, 2015; Terres & Santos, 2013), negative events, such as service failures, may adversely affect clients’ expectations that a company will honor its agreement, which may result in a violation of trust (Basso & Pizzutti, 2016). In addition to the direct effects of such failures on trust violation, negative indirect effects are involved, such as public complaints, negative word-of-mouth, and boycott (Huefner & Hunt, 2000; Funches, Markley, & Davis, 2009; Chang et al., 2015).

Although organizations are continually confronted by the challenge of building trust in hopes of expanding their client relationships, failures are common and hinder trust creation and maintenance. Generally, when a person’s trust-related expectations are not met because of failure or non-fulfillment of a promise, a violation of trust occurs (Elangovan et al., 2007; Rao & Lee, 2007). In marketing, specifically in the context of exchanges between companies and clients, a violation of trust occurs when a company disappoints its clients by acting contrary to or less than what was promised (Basso & Pizzutti, 2016).

Studies on services failures (e.g., Tax, Brown, & Chandrashekaran, 1998; Smith, Bolton, & Wagner, 1999; McCollough, Berry, & Yadav, 2000; Prasongsukarn & Patterson, 2012) have investigated client behavior after a violation and focused on provider–consumer interactions drawn from situations in which the client is alone. Research into the perceived severity of a failure (Maxham III & Netemeyer, 2002), the perceived intentionality of a failure (Iglesias, Varela-Neira, & Vázquez-Casielles, 2015), the perception of justice after a failure (Hocutt, Goutam, & Mowen, 1997; Prasongsukarn & Patterson, 2012), and the intention of client complaints (Andreassen, 2000) has been conducted from the perspective of a client in isolation. However, evidence shows that trust can be violated even for those who were not directly harmed by a failure (Ferrin et al., 2007). Ferrin et al. (2007) confirm the existence of a gap in previous research by arguing that trust violation can occur not only to a single individual but also to groups of people, as well as to those who are a part of a group at the time of a failure. Abrams et al. (2003) propose that the interactions between clients who belong to such a group can change an individual client’s attitudes, beliefs, and behaviors. Diener et al. (1980) also mention that this type of group interaction can cause customers to become more emotional and more impulsive, which can negatively affect the relationship between clients and the company. Failures in service can negatively affect a client in a group of consumers differently from when a client is alone (Du et al., 2014), and a client’s trust as a part of a group is possibly violated in different proportions than it would be if the client were alone.

Du, Fan, and Feng (2011) conduct experiments by manipulating the number of people exposed to the same failure. They show that anger and intention to complain are higher when a failure occurs to a group of consumers than when it is experienced by an isolated consumer. However, their study does not address client trust or a relational paradigm in terms of emotional contagion within groups or the effect of groups on emotions.

Mattila, Hanks, and Wang (2014) and Van Vaerenbergh et al. (2013) also focus on other people in service failure and recovery encounters. Their findings suggest that observing a service recovery (directed toward a complaining customer) affects the observer’s perceptions of that particular service provider. The present study examines the opposite situation, in which complainers behave differently in the presence of other clients.

The attitudes of individuals in a group tend to be more extreme than if these individuals are isolated (Isenberg, 1986). In social psychology, this phenomenon is called group polarization and suggests that the responses of a group tend to be more extreme in the same direction as the response that an individual would experience in isolation. Therefore, individual negative responses tend to become more negative in a group, and positive responses tend to become more positive (Myers & Lamm, 1976). Individuals change their attitudes and emotions toward more extreme positions to stand out in the group (Cooper, Kelly, & Weaver, 2001).

Therefore, a client in a group tends to perceive a greater trust violation than a client in isolation. On the basis of group polarization effect, the emotional reactions (the consequences of a failure) are hypothesized to be greater for a client in a group than for a client in isolation. Furthermore, the group polarization effect can fully mediate the relationship between the clients in a group or in isolation and trust violation. The first hypotheses of this study are as follows:

1. H1: The perceived violation of trust is greater when the violation is experienced by an individual in a group than when it is experienced by a client in isolation.

2. H2: The relationship between the clients in a group or in isolation and trust violation is mediated by the group polarization effect.

A violation of trust causes a reduction in the trust that existed before the failure (Elangovan et al., 2007). Trust recovery is needed to prevent damage that may go beyond client loss, such as the damage caused by complaints, boycotts, and public offenses (Tripp & Grégoire, 2011).

Trust Recovery

After a failure, service recovery actions can be conducted to reverse the negative effects that influence the client and restore the trust that was violated through the failure (Basso & Pizzutti, 2016). In this context, Kim et al. (2004) define trust recovery as “activities directed at making a trustor’s trusting beliefs and trusting intentions more positive after a violation is perceived to have occurred” (p. 105).

Although some service recovery studies do not focus on trust recovery, the studies that do address it show how service recovery influences trust after service failure (e.g., Choi & La, 2013; DeWitt, Nguyen, & Marshall, 2008; Kau & Loh, 2006; Weun, Beatty, & Jones, 2004; Tax et al., 1998). Many of these studies show that trust is influenced by satisfaction with the service recovery (e.g., Tax et al., 1998; Weun et al., 2004; Kau & Loh, 2006; Choi & La, 2013), whereas others discuss the influence of justice perceptions on trust (DeWitt et al., 2008). In general, these studies support the contention that service recovery can positively influence trust. Unlike these studies, Xie and Peng (2009), Laer and de Ruyter (2010), and Basso and Pizzutti (2016) focus specifically on understanding how trust can be recovered after a service failure. Xie and Peng (2009) propose three types of trust recovery: affective recovery, functional recovery, and informational recovery. These three types positively influence the recovery of trust through an increase in the levels of perceived integrity, competence, and benevolence of the company. Laer and de Ruyter (2010) find that a narrative apology can improve the perceived integrity of the service provider more than an analytic apology can.

Basso and Pizzutti (2016) suggest that apology and promise can recover trust after a double deviation. Specifically, they argue that promise is more efficient in trust recovery when the failure is competence based, whereas apology is more efficient when the failure is based on a company’s perceived lack of integrity.

These studies (Xie & Peng, 2009; Laer & de Ruyter, 2010; Basso & Pizzutti, 2016) provide evidence that recovering a client’s trust is possible after a service failure, especially by giving an apology; however, they confine their discussion to isolated clients and do not focus on understanding how trust recovery tactics are performed when the client is in a group. An apology for a violation has a beneficial effect on the offender because recognizing a violation is also a way of expressing a sincere feeling of regret (Kim et al., 2004; Ferrin et al., 2007). Grebe (2013) show the importance of making an apology in a genuine and sincere way when the aim is to restore trust. Ohbuchi, Kameda, and Agarie (1989) find that an apology can mitigate the expression of negative feelings, such as anger, which a violation can engender. Similarly, an apology can decrease the desire for customer retaliation (Tripp & Grégoire, 2011). Although the studies mentioned above provide a substantial theoretical basis for understanding the recovery of trust, they all focus on isolated individuals and their response to trust recovery efforts. However, when failures and recovery efforts occur in consumption situations, they often involve more than one person or group. Based on evidence that social interactions change the way people make decisions (Bearden & Etzel, 1982), the effectiveness of trust recovery actions may trigger different responses when the individual is a part of a group.

Consistent with the logic of trust violation, trust recovery is expected to be stronger when an individual is a part of a group than when he or she is isolated. This assumption is associated with the group polarization effect and the fact that individuals may take more extreme positions when they receive information about a group of which they consider themselves to be a part (Mackie, 1986).

On the basis of this assumption, when a trust recovery action is targeted on a client in a group, its effect is hypothesized to be greater than when the target is a single client. Furthermore, trust recovery will be greater for the client in a group as a result of the group polarization effect. This theoretical background leads to the following hypotheses:

1. H3: The effect of trust recovery is higher when it is attempted for a client in a group than for a client in isolation.

2. H4: The response of a client in a group or in isolation to trust recovery is mediated by the group polarization effect.

Study 1

Study 1 tested for differences in a client’s trust after a violation (H1) and recovery actions (H3) when the client is a part of a group or is in isolation.

Design and Participants

This study used a mixed design (Hernandez, Basso, & Brandão, 2014). Specifically, the experimental treatments used a single-factor between-subjects design with random assignment, manipulating the number of individuals exposed to the trust violation and recovery (two conditions: client alone and client in a group). Additionally, a within-subjects design was used in the trust measures, which were measured at three points in time.

The participants included 115 MBA students who were invited to complete a paper-and- pencil questionnaire in a consumer behavior laboratory (57 in the client-alone condition and 58 in the client-in-a-group condition). The average age of the participants in the sample was 29.14 years (σ = 7.49), and 57% of them were females.

Procedures

The participants should have stayed in a hotel in the last 12 months (each participant should have booked the hotel to guarantee that a certain level of trust existed between the respondent and the selected hotel). It should be noted that the participants indicated they had stayed at the hotel (M = 2.43 times) in the last year. They were then asked to recall the hotel, and the level of trust in the hotel (T1) of each participant was surveyed. In this sequence, all the control and demographic variables were measured.

In the second part of the study, the participants were split into two experimental groups and exposed to the scenario with service failure. One group was exposed to a service failure when the clients were alone, and the other group was exposed to a service failure when the clients were in a group. Specifically, the participants were instructed to imagine the failure situation based on the hotel mentioned in the first part of the study. In the client-in-a-group condition, although the focus was on the individual in a group, the service failure happened for the entire group. In this condition, the participant had to imagine that he/she was traveling with university colleagues when the failure happened. On the other side, the participants exposed to the client-alone condition had to imagine that they were traveling alone when they made a reservation in the hotel. The scenarios are presented in the Appendix.

After the participants’ exposure to the service failure, the level of trust (T2) of each participant was surveyed, both for those who experienced the failure as individuals and those who experienced it as members of a group. The effect of an individual versus a group membership experience of trust violation was quantified by considering the difference between T1 and T2.

In the third part of the procedure, the participants were exposed to the scenario of trust recovery strategies. The trust recovery was simulated through an apology made by the service provider. Specifically, the apology was made by the manager at the front desk of the hotel. For the individuals in the client-alone condition, the apology was directed to a single consumer in isolation. For individuals-in-a-group condition, the apology was directed to all clients; however, the focus of the analysis remained on the individual in the group and not on the entire group.

After the trust recovery tactic, the level of trust (T3) of each participant was surveyed. This procedure allowed the quantification of the effect of the trust recovery tactic on those who experienced the individual recovery situation and those who experienced the trust recovery situation in a group. It is important to note that each participant exposed to the trust violation in the client-alone condition was exposed to the trust recovery under the same condition. The same is true for the client-in-a-group condition.

Measures

Trust was measured using the scale described by Sirdeshmukh et al. (2002) with four items measured through a seven-point scale (αT1 = .81; αT2 = .88; αT3 = .91). In the analysis, the effect of the trust violation and recovery was considered so that the variation in trust was derived after the violation (ΔTrust_Failure = T2 − T1), and the change in trust was derived after the recovery (ΔTrust_Recovery = T3 − T2).

A nominal variable was used to check the manipulation. In this variable, the participants were asked to answer whether they were alone or in a group when they arrived at the hotel and learned that the room was not available for occupancy.

To assess the realism of the scenario, we asked the participants to indicate on a seven- point scale whether the situation presented was realistic. The realism (M = 5.48) was found to be significantly above the midpoint of 4 overall (p < .001) and within each of the experimental conditions (p < .001), thus supporting the realism of the scenario.

The statistical analysis controlled for how long the participants had known the hotel and if they had previously complained, how they perceived the severity of the failure (through a scale from Mattila, 2001), and how satisfied they were with the hotel’s response (from a scale adapted from Sirdeshmukh et al., 2002). None of these variables had a significant controlling effect on the results of this experiment.

Results

Manipulation check. The manipulation was correctly perceived by the participants. Specifically, 78.9% of the individual clients said they were alone when they were exposed to a scenario with the failure and recovery, and 98.3% of the clients in a group said that they were in a group when the scenario exposed a failure and recovery to a client in a group.

Test of H1. ANOVA showed that the presence of a group had a significant effect on individual perceptions of the trust violation (F(1, 113) = 4.114; p < .05; ηp2 = .035). Specifically, the participants who were exposed to a failure condition in a group (ΔMgroup = 3.06 ± 1.61) showed a greater reduction in their trust (ΔTrust_Failure) in the service provider than those who were exposed to the failure condition alone (ΔMind = 2.52 ± 1.19). These results confirm H1 and are shown in Figure 1. The power of this test, calculated with the software G*Power test, was 0.52.

Test of H3: The presence of a group had a significant effect on individual perceptions of the trust recovery using an apology (F(1, 113) = 4.922; p < .05; η . = .042). The participants who were exposed to a trust recovery action who were in a group (Mgroup = 1.56 ± 1.17) reported more positive changes in trust (ΔTrust_Recovery) than those who were exposed to a trust recovery action when they were alone (Mind = 1.09 ± 1.09). These results confirm H3 and are shown in Figure 1. The power of this test was 0.60.

Discussion

This study found that in a situation of trust violation because of a service failure, the presence of a group of clients increased a client’s perception of failure, thus intensifying the negative effects that the failure had on the client’s trust. In the same way, the findings for trust recovery suggested that the group’s presence intensified the effect of the recovery tactic (apology). Specifically, the level of trust measured after the recovery action was significantly higher for a client in a group than for a client alone.

Although the findings show that responses to the violation and trust recovery varied depending on whether the client was in a group or alone, they do not provide enough insights into the mechanism behind this effect. As proposed by H2 and H4, the group polarization effect may explain why, in situations in which a client is in a group, this client’s emotions are more extreme; therefore, the violation is negatively intensified, and the trust recovery is positively influenced. This mechanism was tested in study 2.

The findings of study 1 are possibly dependent on the trust recovery tactic (apology) used; therefore, an alternative tactic may give different results. An alternative trust recovery tactic was added in study 2 to test this.

Study 2

This study aimed to verify H2 and H4 and include a new test for H1 and H3. The scenario used both social (apology) and economic (financial compensation) recovery tactics. Financial compensation was used as a trust recovery tactic because it is one of the most described strategies in the literature (Desmet, Cremer, & Dijk, 2011).

Design and Participants

As in the first study, a mixed design was used. The experimental treatments used a factorial 2 (number of persons: group versus individual) × 2 (trust recovery tactics: apology versus financial compensation) between-subjects design with random assignment to the experimental conditions. Moreover, the treatments used a within-subjects design for the trust and group polarization measures, which were measured at three points in time.

The final sample included 780 bank employees who were invited to participate in an online study through the Qualtrics platform. The experimental conditions ranged from 179 to 211 participants per condition. The respondents were aged between 18 and 64 years (M = 40.33 ± 10.92); 54% were male.

Procedures

The scenario proposed a failure in passenger transportation in a taxicab. All participants had used a taxicab service in the last year, 77.7% in the last six months. The procedures were similar to those of study 1. The participants had to remember the last time they used a taxicab service and were asked to think of this service during the study. Then, they were instructed to imagine that they and their work colleagues called a taxicab (group condition) or that they were alone when they called the taxi (individual condition) to manipulate the number of persons. An apology or a financial compensation for the failure is offered by the taxi driver to manipulate the trust recovery tactic. The scenarios are presented in the Appendix.

In this study, in addition to the trust measures, negative emotions were measured to test the group polarization effect.

Measures

Trust was measured using the scale used in the first study (αT1 = .91; αT2 = .94; αT3 = .93). The group polarization effect was measured by changes in the negative emotions of the participants. Negative emotions were measured using four items that were adapted from the Positive and Negative Affect Schedule questionnaire (Watson, Clark, & Tellegen, 1988). The items used were distressed, upset, angry, and nervous (αNegEmotions1 = .87; αNegEmotions2 = .92; αNegEmotions3 = .91). The group polarization effect after the trust violation was measured using the difference between NegEmotions2 and NegEmotions1 (ΔGP_Failure). In the same way, the group polarization effect after the trust recovery was measured using the difference between NegEmotions3 and NegEmotions2 (ΔGP_Recovery).

A nominal variable was used to check the manipulation of the number of persons. In this variable, the participants were asked to answer whether they were alone or in a group when they called and used the taxi service. Additionally, the participants were asked if the taxi driver offered an apology or a financial compensation to check the manipulation of the trust recovery tactic.

Regarding the realism of the scenario, 76.2% of the participants thought that the situation was real (nominal variable). No difference in perceived realism existed between the experimental groups (p > .05). The statistical analysis controlled for whether the clients previously used the same taxicab company, if they previously complained, and how satisfied they were with the company’s response. None of these variables had a significant controlling effect on the results of the experiment (p > .05). The perceived severity of the failure was also measured through a measure from Mattila (2001) with two items (α = .86). A significant control effect of the perceived severity of the failure was found in the test of H1 (F(1, 777) = 91.879, p < .001) and H3 (F(1, 775) = 5.302, p < .01). This variable was therefore included in the finalmodel for the analysis of H1 and H3. However, it is important to note that the number of persons(F(1, 776) = .000, p = .990), the type of trust recovery tactic (F(1, 776) = 1.077, p = .300), andinteraction (F(1, 776) = .534, p = .465) had no effect on the perception of failure severity.

Results

Manipulation check. As in study 1, a nominal variable was used. For the four conditions, at least 87% of the participants correctly indicated the condition to which they were exposed.

Test of H1. The presence of a group had a significant effect on individual perceptions of the trust violation (F(1, 777) = 5.575; p < .05; ηp2 = .007). The participants exposed to a service failure in a group (Mgroup= 2.55 ± 1.67) reported a greater reduction of trust in the service provider than those exposed to individual failure (Mind= 2.28 ± 1.60). This finding supports H1. The power of this test was 0.65.

Test of H2. The variation in negative emotions was observed to be greater for individuals who were exposed in a group context (M = 2.87) than for those who were exposed to the client- alone context (M = 2.40, F(1, 778) = 11.387, p < .001; η 2 = .014). This result confirms that the presence of a group polarizes individual responses more extremely. The power of this test was 0.92.

H2 was tested using a script proposed by Preacher and Hayes (2004) and Zhao, Lynch, and Chen (2010). In the test, the number of individuals in the violation situation was the independent variable, group polarization (ΔGP_Failure) was the mediator variable, and the change in trust after failure (ΔTrust_Failure) was the dependent variable.

The effect of the independent variable on the mediating variable was positive and significant (a = .47; t = 3.37; p < .001), and the effect of ΔGP_Failure on the variation of trust was also similarly significant and positive (b = .20; t = 6.63; p < .001). The overall effect of the number of individuals on the variation of trust (c = .26; t = 2.22; p < .05) also showed significant results, whereas the direct effect (c’ = .17; t = 1.46; p = .14) did not show significant values. The indirect effect was also positive and significant (a x b = .09) because the confidence intervals did not include zero (.04 to .16), which would indicate a null effect. The analyses that support H2 are shown in Figure 2.

Test of H3. The presence of a group had a significant effect on individual perceptions of the trust recovery (F(1, 775) = 3.960; p < .05, ηp2 = .005). If the participants were in a group when they were exposed to a trust recovery action (Mgroup= 1.05 ± 1.27), they had a more positive variation in trust than those who were exposed to individual recovery (Mind= 0.85 ± 1.41). This finding supports H3. The power of the test was 0.51.

Furthermore, the results show that no main effect related to the type of recovery tactic on trust was observed (F(1, 775) = 2.853, p = .092, ηp2 = .004), and no effect from the interaction between the individual/group and the type of recovery tactic on trust was observed (F(1, 775) = .060; p = .807, ηp2 = .000).

Test of H4. The presence of a group had an effect on the variation of individual negative emotions (F(1, 778) = 13.120, p < .001, ηp2 = .017). The participants in a group had a higher variation (M = 1.10) than the isolated participants had (M = 0.73). The power of this test was 0.95.

In testing H4, the number of individuals present in the situation (context of recovery) was the independent variable, the change in negative emotions (ΔGP_Recovery) was the mediator variable, and the change in trust after recovery (ΔTrust_Recovery) was the dependent variable.

The effect of the independent variable on the mediator was positive and significant (a = .37; t = 3.62; p < .001). In the same way, the effect of ΔGP_Recovery on ΔTrust_Recovery was significant and positive (b = .39; t = 12.91; p < .001). The total effect (c = .19; t = 1.93; p = .0536) was significant, and the indirect effect was significant and positive (a x b = .15) because the confidence intervals did not include zero (.07 to .24), which would render the effect null. The direct effect (c’ = .04; t = .45; p = .65) was not significant, which indicates full mediation. These results, which support H4, are presented in Figure 3.

Discussion

As in study 1, in a situation of trust violation, the perception of a client in the presence of a group of clients was found to be more extreme, intensifying the negative effects of the failure on trust. The perception of the clients in the trust recovery situation also confirmed the findings of study 1, showing that in the presence of a group, the change in trust for a client is greater than when a client is alone. This finding indicates that the group’s presence increases the effects of the recovery action.

In addition to study 1, the second study tested two trust recovery tactics, a social tactic (apology) and an economic tactic (not being charged for the transportation), with the intent of validating the results of the previous study by using another type of recovery tactic. It is important to note that there is no main effect and interaction effect from the trust recovery tactic after a service failure, unlike Basso and Pizzutti (2016) who found this difference after a double deviation. The results of study 2 showed that regardless of the recovery tactic used, the group’s presence increased the effects of the recovery action as perceived by the participant.

The mediation analysis showed that the effects of a failure and a recovery effort for a group or individual on trust changes are mediated by the group polarization effect. After a failure, the group polarization effect makes a client’s behavior in a group more extreme than that of a client alone; therefore, trust violation is perceived as higher in the collective context than in the individual context. On the other hand, in a trust recovery situation using an apology or financial compensation, the client in a group experiences a higher trust recovery than the client alone. This finding shows that the group polarization effect occurs in both directions, intensifying both negative feelings (violation) and positive ones (recovery).

General Discussion

To increase knowledge of client trust, this research analyzed trust violation and recovery in situations of individual and collective consumption. Overall, recent research has shown concern with the effects of groups on the behavior of individuals in situations of service failures (Du et al., 2014) and in service recovery situations (Zhou et al., 2013). However, to date, the literature has not advanced to analyzing how situations of individual and collective consumption influence trust violations and recovery after service failures.

The results of this study indicate that the effect of the group will amplify the erosion of trust in cases of failure and increase the effectiveness of trust recovery attempts. Therefore, when a client is in a group during a consumption situation in which failures occur, the group’s presence produces more expressive variations in a client’s trust (greater trust decrease) than when the client is in isolation. The results show that the effect of the trust violation is higher when clients are in a group than when they are in isolation. These results are consistent with Du et al.’s (2014) study of gaps in service, which showed that anger and intention to complain were higher for consumers who suffered a group failure than for an individual failure. However, it is noteworthy that these authors did not attempt to analyze clients’ trust after a failure nor offer any service or trust recovery strategies.

This research advances previous studies on trust violation and recovery in the services marketing literature (Xie & Peng, 2009; Laer & de Ruyter, 2010; Basso & Pizzutti, 2016). Its contribution goes beyond the main effect of groups on trust by explaining the mechanism behind this effect. The results demonstrate that manipulating the number of individuals involved in the event has a significant effect on the variation of trust and that this relationship is mediated by the group polarization effect. The presence of others causes individuals in a group to have more extreme reactions (more extreme negative emotions in this study) than when they are alone, thus affecting the level of trust after the failure and recovery. When a client assimilates the feelings of others through the group polarization effect, it enhances both their negative (trust violation) and positive reactions (trust recovery). This study also demonstrated that these reactions do not depend on the type of service (tourism/hotel or transportation/cab) or trust recovery tactic used (social or economic). Furthermore, on the basis of social influence theory (Abrams & Hogg, 1990; Turner, 1991; Cialdini & Goldstein, 2004), the clients in a group tend to adjust their behaviors and attitudes when seeking the acceptance of a group, so presenting a negative attitude in the violation step and a positive one in the recovery step could be the attempt of an individual to adjust his/her behaviors to the supposed behaviors of the other members of the group.

On the basis of these findings, this study contributes to the literature in three ways. First, it confirms that the trust perceived after a violation and recovery is not the same for an isolated client as for a client in a group. That is, there is a group effect on individual trust that makes the trust variation more negative (violation) or positive (recovery). This study contributes to the services marketing literature by demonstrating that the presence of a group has an effect on relational variables (not only transactional variables, as in Zhou et al., 2013 and Du et al., 2014), and to the trust recovery literature by demonstrating that the effect of a group also affects a condition of trust that is based on a previous relationship, not only on initial trust, as demonstrated by Kim et al. (2013).

Second, this study contributes to the literature by showing that the effect of a group on a client’s trust does not depend on the recovery tactic used. The results indicate that manipulation of the trust recovery tactic does not interact with the number of clients in the trust recovery situation.

Finally, this research advances knowledge by showing that the group polarization effect offers a possible explanation for the mechanism by which the presence of a group influences the trust of the client. Neither the services marketing literature (Zhou et al., 2013; Du et al., 2014) nor the trust recovery literature (Kim et al., 2013) has previously demonstrated that the group polarization effect is responsible for changes in the trust levels of individuals in a group.

Managerial Implications

The findings provide clear evidence that consumer behavior changes when a consumer is a part of a group. Therefore, when organizations seek better results in trust recovery, managers should consider the number of individuals who are present during both the failure and the recovery. In situations in which clients are in groups, a service failure is expected to generate a greater impact on client trust and can create more pronounced negative behaviors, such as negative word-of-mouth and retaliation. Thus, failure scenarios that involve groups of clients should be given more attention than those involving isolated individuals to prevent future, repetitive failures.

Regardless of the chosen recovery tactic, when clients are in a group, they have a higher positive variation of trust than when they are alone. This group effect influences the efficacy of recovery tactics. Managers should therefore use situations in which individuals are in groups to promote their trust recovery actions by taking advantage of the group polarization effect. For example, by choosing to apologize after a mistake, as presented in study 1, a hotel manager can perform the recovery tactic (e.g., an apology) in the lobby, where other guests and staff are present. In the case of a service failure made by a bank or a delay in a medical consultation, these professionals can present their trust recovery tactics in a location where other customers are present. Thus, it is possible to take advantage of the group polarization effect of those who were not affected by the failure. Importantly, a trust recovery attempt does not need to be directed to all who are present. The presence of a small group at the time of recovery may be enough to ensure that the effects of the group are perceived without the need to extend the recovery action beyond those clients whose trust has been violated.

Limitations and Suggestions for Future Studies

The research is limited by the context of the scenarios of hotel services and passenger transportation that were used. The operationalization of the study does not consider whether the two scenarios occur in the context of leisure or a work experience. Leisure or tourism situations may arise when people have free time and are more flexible, which could reduce their perceptions of inconvenience caused by, for example, a delay. The use of two scenarios, a trip for tourism and one for a professional commitment, would have allowed us to validate the results in both situations. Thus, future studies are recommended to consider making the distinction between the two types of experiences in order to add greater external validity to the findings.

Further research could test the results of this study in real situations, in which the participants can effectively experience the conditions of consumption. This suggestion may be accomplished by a longitudinal monitoring of client behaviour without the interference characteristics of a laboratory experiment. This research could be achieved, for example, in the post-sales department of a travel agency to monitor in real time the experiences of its clients, identify different types of violations, and test different types of trust recovery tactics.

In the theoretical background, we used social influence theory, but we did not measure this influence. Therefore, future studies need to verify how social influence theory serves as the underlying mechanism that explains the effects presented in this study. Moreover, using social influence theory, future researchers can possibly explore the specific mechanisms under the social influence theory perspective that influence the behaviours of an individual in a group after trust violation and recovery.

In the same way, as the severity of a failure influences trust in the service recovery process (Weun et al., 2004), future studies could test the moderating role of the severity of failure in the relationship between the number of individuals present and changes in trust, confirming that a more serious failure will cause a greater violation of trust. Moreover, future studies could also examine other boundary conditions, such as tie strength (e.g., family versus strangers), culture (individualistic versus collectivistic), and power distance.

Acknowledgments

This research was supported by the Brazilian Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - Grant No. 448553/2014-7) and by the Support Program for Graduate Education of Private Institutions – PROSUP/CAPES (Scholarship for the first author).

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