Explanations: if, when, and how they aid service recovery

Explanations: if, when, and how they aid service recovery Graham Bradley School of Applied Psychology, Griffith University, Gold Coast, Australia, and

Beverley Sparks Department of Tourism, Leisure, Hotel and Sport Management, Griffith University, Gold Coast, Australia Abstract Purpose – This study aims to investigate if, when, and how the use of four different types of explanations affect customer satisfaction after a service failure. Design/methodology/approach – The study used written scenarios of a hypothetical service failure to manipulate explanation type, failure magnitude and compensation offered. Participants were randomly assigned to read and respond to one version of the scenario, whilst imagining they were the customer experiencing the service failure. Findings – The paper finds that explanation type, explanation quality, failure magnitude and compensation each had significant effects on customer evaluations. Explanation type and explanation quality interactively affected the extent to which customers were satisfied with service recovery: Apologies and excuses yielded higher satisfaction levels than did justifications and referential accounts but only when the explanations were perceived to be of high (vs low) quality. Specific types of attributions and forms of justice were shown to mediate the effects of three of the explanation types. Practical implications – The study shows that customer evaluations following service failure vary with the type of explanation provided. Service firms need to provide an explanation in such circumstances, preferably a high quality excuse or apology, and need to understand the “process variables” that determine whether the explanation will satisfy aggrieved customers. Originality/value – This is one of very few studies that have compared the efficacy of different types of explanations in service situations. The research sheds light not only on what types of explanations work best, but also on how they have their effect. Keywords Explanations, Service recovery, Justice, Attributions, Customer satisfaction, Australia, Service failures Paper type Research paper

An executive summary for managers and executive readers can be found at the end of this article.

1

Who hasn’t experienced a service failure at some time? Whether it be a rude waiter, an overbooked flight, or a hotel room not ready on check-in, as consumers we experience dissatisfying service events with alarming frequency. Following such failures, customers report strong desires to receive an explanation of what went wrong (McColl-Kennedy and Sparks, 2003). But what kind of explanation should service employees and managers give? Should they apologize, claim innocence, downplay the problem, or resolutely defend their actions? Are some of these types of explanations more effective than others? If so, under what conditions, and through what processes, do these effects occur? The current study sought answers to these questions.

2

3

4

excuses, i.e., those that invoke mitigating circumstances in order to absolve the service organization of responsibility for the adverse outcome; justifications, i.e., those that involve admission of responsibility, but which legitimize the service organization’s actions on the basis of shared needs and/ or higher goals; referential (or reframing) accounts, i.e., those that seek to minimize the perceived unfavorability of the failure by invoking downward comparisons (e.g., with those who are worse off following the service failure); and apologies, i.e., those involving an admission of failure and an expression of remorse.

As Folger and Cropanzano (1998, p. 143) have noted, there is currently “no complete theory of when and why some explanations produce beneficial effects”. Research comparing different explanation types has yielded inconsistent results (Bobocel and Zdaniuk, 2005). For example, Shaw et al.’s (2003) meta-analysis of 36 studies – most of which were conducted in organizational contexts – concluded that excuses are more effective than justifications. Several studies investigating service failures have, however, cast doubt on the generalizability of this conclusion. In particular, surveys of aggrieved customers (e.g., Tax et al., 1998) typically find that excuses are viewed negatively. Similarly, Conlon and Murray

Using explanations to recover from service failure Drawing on the work of Bies (1987) and Folger and Cropanzano (1998), four types of explanations can be distinguished: The current issue and full text archive of this journal is available at www.emeraldinsight.com/0887-6045.htm

Journal of Services Marketing 26/1 (2012) 41– 50 q Emerald Group Publishing Limited [ISSN 0887-6045] [DOI 10.1108/08876041211199715]

This research was funded by the Service Industry Research Centre, Griffith University, Australia.

41

Explanations: if, when, and how they aid service recovery

Journal of Services Marketing

Graham Bradley and Beverley Sparks

Volume 26 · Number 1 · 2012 · 41 –50

(1996) found that explanations in which companies accepted, rather than avoided, responsibility (e.g., justifications, rather than excuses) resulted in the most favorable consumer evaluations. Our own past research (Bradley and Sparks, 2009) found that the use of apologies resulted in greater customer satisfaction than did the use of referential accounts. In general, however, apologies and referential accounts have not been widely studied, and the few available findings are also inconsistent (Bolkan and Daly, 2009; Conlon and Murray, 1996; Conlon and Ross, 1997; see also Bobocel and Zdaniuk, 2005). Given this limited and inconsistent past research comparing explanation types, we proposed and tested a non-directional hypothesis:

associated with explanation quality, that is, the influence of explanation type will increase as quality improves. The reasoning here is that, because aggrieved customers are in a dissatisfied state, they are unlikely to accept any explanation, regardless of type, unless it meets some minimum quality threshold. Past research (e.g., Bies et al., 1988; Shapiro, 1991) supports the proposition that explanations must be of moderately high quality to have an effect. We extend the logic behind these past findings in predicting that as quality increases beyond this threshold, aggrieved customers will become increasingly attuned to content differences between explanation types:

H1.

H2.

Customer responses vary with explanation type.

Moderators of the explanation-recovery relationship

H3.

Bobocel and Zdaniuk (2005) urged researchers to investigate factors that moderate the effects of explanations. One type of moderator relates to qualities of the service failure itself – its nature, timing, frequency, intensity, foreseeability, and so on. A potentially important variable is the magnitude of the problem. Bobocel and Zdaniuk (2005) reviewed the evidence, concluding that the influence of explanations generally increases as the event being explained becomes more serious. This is consistent with the so-called “fair process” effect: as outcomes worsen, process factors (e.g., the provision of an explanation) play an increasingly influential role in fairness (and other) judgments. To further examine this issue, we manipulated service failure magnitude, and tested the hypothesis that increases in magnitude enhance differences in the efficacy of the explanation types. The second set of factors that may moderate the impact of explanation type relates to the other strategies adopted to recover from service failure. One commonly used strategy is to offer compensation. Not surprisingly, past research (e.g., Baer and Hill, 1994; Goodwin and Ross, 1992; Levesque and McDougall, 2000; Webster and Sundaram, 1998; Wirtz and Mattila, 2004) reveals that post-failure customer reactions become more favorable with increasing amounts of compensation. Other research (e.g., Sparks and Callan, 1995) has found that the effects of explanations vary with levels of compensation. We predicted that explanation type would be a stronger determinant of customer evaluations when compensation is low than when customers are generously recompensed. Our rationale was that customers who receive little or no compensation have scant information, other than the explanation, upon which to base their evaluation. Customers’ assessments are thus greatly influenced by the specific features of the explanation they receive. In contrast, when compensation is generous, explanation content carries less weight. The third category of potential moderators is the quality of the explanation itself. Following Folger and Cropanzano (1998), Sitkin and Bies (1993) and others, we conceive explanation quality as involving two facets: adequacy (detailed, informative, clear) and sincerity (honest, truthful). The meta-analysis by Shaw et al. (2003) concluded that the quality of an explanation directly affects attitudinal and behavioral outcomes. We expected to replicate this main effect, and further predicted an enhancing effect to be

H4.

H5.

Customer evaluations are more favorable when (a) the service failure is of low (vs high) magnitude, (b) the offer of compensation is high (vs low), and (c) explanation quality is high (vs low). The effects of explanation type on customer evaluations are moderated by failure magnitude: larger differences between explanation types occur under conditions of high (vs low) magnitude. The effects of explanation type are moderated by compensation level: larger differences between explanation types occur when compensation is low (vs high). The effects of explanation type are moderated by perceptions of explanation quality: larger differences between explanation types occur when explanation quality is high (vs low).

Mediators of the effects of explanations Folger and Cropanzano (1998, p. 144) noted that the “process by which social accounts exert their beneficial influences is a complicated one. Much more research is needed”. Two main theoretical perspectives – justice theory and attribution theory – provide clues as to the processes through which explanations impact consumer evaluations. The first of these perspectives, justice theory (e.g., Adams, 1965; Greenberg, 1993; Thibaut and Walker, 1975), suggests that the effects of explanations are mediated by perceptions of justice or fairness. Several types of justice are distinguished: distributive (i.e., fairness of outcomes), procedural (i.e., fairness of formal procedures used to allocate outcomes), and interactional (i.e., fairness of interpersonal treatment during the process). Interactional justice can be further divided into interpersonal, which relates to the extent to which the parties are polite, courteous, and respectful of each other, and informational, which relates to the extent to which appropriate and relevant information is communicated between the parties. Under the justice framework, service failure and recovery affect customer evaluations by altering customers’ sense of whether they have been treated fairly. Thus, for example, following a service failure, the use of apologies has been shown to lead to increases in interactional (or interpersonal) justice (Goodwin and Ross, 1992; Wirtz and Mattila, 2004), which in turn helps sustain satisfaction. Referential accounts (of the kind “your outcome is better than that received by other customers”) may impact customers’ sense of distributive justice and thereby reduce dissatisfaction. Similarly, justifications (i.e., provision of reasoned arguments in support of the firm’s actions) may enhance 42




Volume 26 · Number 1 · 2012 · 41 –50

customers’ sense of informational justice. Finally, excuses, by asserting that the firm’s own systems and policies were not to blame for the problem, help safeguard customer faith in procedural justice. According to the second perspective, attribution theory (e.g., Weiner, 2000), all events – particularly, unexpected ones – trigger searches for meaning. Attributions may be of several kinds, including locus (what caused this event?), stability (will it persist?), and controllability (is it preventable?). Service failures, like other unexpected events, may be attributed to a variety of causes (e.g., management, service providers, customers, luck), and may be regarded as more or less stable, and more or less controllable. Failures are most likely to give rise to negative customer evaluations when they are attributed to the firm (they caused it!), and are thought to be both stable (they always do!) and controllable (they could have avoided it!). According to some researchers (e.g., Conlon and Ross, 1997; Sparks and Callan, 1995), explanations affect customer satisfaction by changing these attributions. Excuses, in particular, shift the locus of causation away from the service firm, and increase the likelihood that the customer will believe the failure was uncontrollable. Similarly, attributions of stability may be weakened by apologies, because an apology may suggest that such lapses are infrequent and unexpected. On the basis of justice and attribution theories, we thus propose that the effects of the different explanation types are mediated by distinctive cognitive processes, with seven different pathways (four paths through justice perceptions, and three through attributions) mediating these effects. Specifically, we propose that adverse customer reactions can be reduced by each of the explanation types by way of the cognitive pathways specified in our sixth hypothesis.

variables, and relationships between these variables, are depicted in Figure 1.

H6.

Method Participants Participants were 461 residents of Queensland, Australia, whose names had been randomly selected from the local telephone directory (response rate ¼ 21 percent). The sample included 302 females (66 percent) and 159 males (34 percent). Ages varied from 17 to 82 years (Mean ¼ 48:8, SD ¼ 16:5). Participants dined at restaurants an average 3.7 times per month (range ¼ 0-20 times, SD ¼ 2:9). Materials The scenarios were developed and refined through two pilot studies using convenience samples of community members (ns ¼ 64 and 40). Similar to several past studies (e.g., Baker et al., 2008), scenarios described two diners receiving a delayed start and slow service. Specifically, the diners are celebrating one of their birthdays; they arrive promptly at the time of their reservation, but are kept waiting at the bar for 30 minutes before they inquire of the head waiter as to the availability of their table. The versions of this scenario differed in respect of the three independent variables only. To manipulate explanation type, the waiter provided one of the following explanations for the service failure: Our electricity was turned off for a couple of hours this afternoon. It’s not our fault. The whole area was blacked out, making it really hard for restaurants like ours. We haven’t been able to make up for the lost time (excuse). We miscalculated how many people would turn up tonight. It’s our fault. We always over-book a bit, because we’ve learnt from experience that not everyone who reserves a table actually shows up. We wouldn’t be able to stay in business if we let those tables go empty each night (justification).

Unfavorable customer reactions to service failure are reduced by (a) apologies by way of changes in perceptions of interactional justice and changes in stability attributions, (b) justifications though changes in perceptions of informational justice, (c) referential accounts through changes in distributive justice perceptions, and (d) excuses through changed procedural justice perceptions, locus attributions and controllability attributions.

Because of the delays, we are going to be turning away everyone who arrives from now on. I’m afraid we won’t be able to find a table for them at all tonight. But that won’t happen to you (referential). I am terribly sorry this has happened to you. I know you have been waiting a while and it is annoying when you have made a booking. Please accept the restaurant’s apology for this delay (apology).

To manipulate compensation, diners were offered either two complimentary meals (high compensation), a single complimentary entre´e (partial), a complimentary drink (token), or nothing (no compensation). Failure magnitude was manipulated by extending the duration of delay before the diners were seated by a further 15 minutes in the high (versus low) magnitude condition, and by varying the speed of service throughout the meal (“very” versus “a bit” slow, respectively). There were two dependent variables. One (satisfaction with the recovery attempt) was quite narrow, while the other (attitude to the restaurant) was operationalized more broadly to include global attitudes, return intent and word of mouth intent. The questionnaire contained multi-item scales measuring these dependent variables, plus items measuring explanation quality, the four types of justice, and the three types of attributions. Manipulation and credibility checks were also included. Items measuring all variables were selected from instruments established in the literature, and modified as appropriate on the basis of the pilot study data. Unless otherwise indicated, all items had response alternatives ranging from 1 (strongly disagree) to 7 (strongly agree).

The current study In their landmark review, Bobocel and Zdaniuk (2005) identified several imperatives that will set the agenda for future research on explanations. These include the need to explore the effects of apologies more systematically, to investigate the psychological mechanisms through which explanations have their effects, and to identify contextual factors that influence how people construe explanations. The current study contributes knowledge in each of these areas. We tested main, moderated and mediated effects of four explanations types on customer evaluations. Participants responded to one of 32 written scenarios depicting a service failure in a restaurant setting. Three independent variables (explanation type, failure magnitude and compensation) were experimentally manipulated in the scenarios, and a further variable, explanation quality, was measured and treated as a fourth between-groups factor in the main analyses. Key 43




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Figure 1 Conceptual model of the variables examined

respectively) or frequency of dining out (rs ¼ 20:03 and 2 0.00, respectively).

Table I gives the source of, number of items in, and a sample item from, each of these scales. A copy of the full response booklet is available from the first author.

Realism and manipulation checks Respondents generally found the scenarios realistic and believable. One sample t-tests showed that mean responses to the three realism questions were significantly greater than the scale mid-point in all 32 experimental conditions (ps , 0:001 in 94 of the 96 tests, with p , 0:02 in the remaining two tests). Analyses of variance (ANOVAs) were conducted to confirm the efficacy of the manipulations of the three independent variables. First, as expected, there was a significant main effect on perceptions of size of compensation associated with the compensation manipulation, F(3,452Þ ¼ 186:4, p , :0005, partial h2 ¼ 0:55. However, planned contrasts revealed that the largest difference in compensation ratings was between the lower two and the higher two compensation levels: no compensation condition, mean ¼ 1:93 (SD ¼ 1:33), token compensation ¼ 2:59 (1.50), partial compensation ¼ 5:27 (1.32), and full compensation ¼ 5:37 (1.43). Given this lack of clear differentiation between compensation conditions, and the desirability of retaining approximately equal cell sizes, the “no” and “token” compensation conditions were collapsed into one (n ¼ 232), as were the “partial” and “full” compensation conditions (n ¼ 224), and the ANOVA was rerun using just two levels of compensation. Results indicated a significant main effect for compensation, F(1, 440Þ ¼ 532:8, p , 0:0005, partial h2 ¼ 0:54. Perceptions of the compensation received differed as expected between these two groups, so this variable was treated as having only two levels (lower vs higher) in subsequent analyses. In the second manipulation check, perception of failure magnitude was the dependent variable. As expected, there was a significant main effect associated with the magnitude manipulation, F (1, 444Þ ¼ 12:44, p , :0005, partial h2 ¼ 0:03. Ratings of perceived magnitude were higher in the high (M ¼ 5:88, SD ¼ 1:09) than in the low (M ¼ 5:52, SD ¼ 1:10) magnitude conditions.

Procedure Participants were randomly assigned to conditions represented by different combinations of explanation type, compensation level and failure magnitude. A single wave of data collection was employed. Participants were mailed a copy of the research materials, a covering letter and a reply-paid envelope. They were instructed to read the scenario, imagine that they were the customer, and respond to the questions regarding how they were likely to think, feel and act when in such circumstances. All responses were anonymous.

Results Preliminary factor analyses and descriptive statistics Missing values were not imputed; hence, analyses are based on slightly different ns. A series of confirmatory factor analyses supported the divergent validity of the hypothesized four types of explanation, four types of justice, and two outcome variables. In the first of these sets of analyses, we used as input the covariance matrix of the eight items that checked the manipulations of the explanation types, we compared the fit of four-, three-, two- and single-factor solutions, and found the four-factor solution to provide the best fit. This procedure was repeated, and corresponding results obtained, for analyses involving the 12 justice items and the eight items measuring the two DVs. Details of these analyses are available on request. Following reversal of negatively-worded items, responses to all items measuring common constructs were averaged to form composite scales. Higher scores indicate higher levels of the relevant construct. Table I gives descriptive statistics for the study variables. All scales displayed good to very good reliability. Neither dependent variable differed by gender (satisfaction, tð457Þ ¼ 0:07, p . 0:05; attitude to the restaurant, tð456Þ ¼ 1:47, p . 0:05), age (rs ¼ 0:00 and 2 0.06, 44




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Table I Details of the response scales Scale

Dependent variables Satisfaction with recovery attempt Attitude to the restaurant

No. of items Source(s)

3 5

6

Distributive justice

3

Bobocel et al. (1998); Gilliland et al. (2001) Blodgett et al. (1997)

Procedural justice

3

Bobocel et al. (1998)

Interpersonal justice Informational justice Attribution of stability Attribution of controllability Attribution of locus

3

Smith et al. (1999)

3

Greenberg (1993)

3

Smith et al. (1999).

3 4

Wirtz and MattilaWirtz and Matilla (2004) Cranage and Mattila (2005)

Manipulation checks Apology Justification

2 2

Original items Original items

Referential account

2

Original items

Excuse

2

Original items

Compensation

2

Original items

2 3a

Mean

Collie et al. (2002); Webster Overall I was satisfied with the way the service delay and Sundaram (1998) problem was handled My overall impression of this restaurant is a positive one Blodgett et al. (1997); Davidow (2000); Webster and Sundaram (1998)

Moderating and mediating variables Explanation quality

Failure magnitude Realism/credibility

Sample item

Original items Willson and McNamara (1982)

In explaining why the problem occurred, I felt the waiter really meant what he said Taking everything into account, I think the outcome I received was fair Given the circumstances, the procedure used to solve this problem was fair The waiter did not give me the courtesy I was due (reverse scored) The waiter gave me what I was entitled to in terms of information and an explanation It would seem that service problems are a rare event at this restaurant (reverse scored) The service problem was beyond the control of the restaurant (reversed scored) % of responsibility for the delay assigned to the waiter þ the restaurant management vs other factors

In this scenario, the waiter said he was sorry The waiter made an effort to justify the actions of the restaurant that led to the service delay The waiter explained that what happened to me was not as bad as what happened to some other diners The waiter explained that the restaurant was not to blame for the delay The waiter provided a very generous offer to compensate me for this breakdown in service The delay in service was a serious inconvenience There are service problems like this in real life

Standard Cronbach deviation alpha

3.27

1.47

0.85

3.11

1.42

0.92

3.74

1.39

0.89

3.47

1.69

0.92

3.55

1.47

0.86

4.03

1.41

0.84

3.38

1.39

0.80

5.28

1.19

0.81

5.19

1.39

0.84

78.1

23.4

–

3.87 3.81

1.88 1.81

0.74 0.77

2.57

1.50

0.74

3.01

1.81

0.82

3.77

2.08

0.92

5.71 –

1.11 –

0.64 –

Note: aNot summated

The third manipulation check revealed a significant multivariate main effect for the explanation manipulation, F (12, 1,323Þ ¼ 102:6, p , 0:0005, partial h2 ¼ 0:48. Univariate tests revealed that this effect was significant on all four dependent variables: apology, F (3,442Þ ¼ 48:0, h2 ¼ 0:25, justification, p , 0:0005, partial h2 ¼ 0:45, F (3,442Þ ¼ 122:4, p , 0:0005, partial referential, F (3,442Þ ¼ 75:5, p , :0005, partial h2 ¼ 0:34, and excuse, F (3,442Þ ¼ 175:7, p , 0:0005, partial h2 ¼ 0:54. Follow-up ANOVAs with planned contrasts indicated that respondents who had received each of the explanation types were more likely (p , 0:0005) to agree that the particular explanation they had received was present in the scenario they read, than were respondents who had received any of the other types of explanation.

Main and interactive effects H1-H5 predicted main and interactive effects of explanation type, explanation quality, compensation and failure magnitude on customer evaluations. Explanation quality was a measured, rather than a manipulated, variable. Because perceptions of explanation quality were correlated with explanation type, the creation of groups based on a median split on explanation quality would have resulted in unequal cells sizes. To overcome this problem, median splits were performed separately for each explanation type. Using this newly-created explanation quality variable, a 4 (explanation typeÞ £ 2 ðcompensationÞ £ 2 ðmagnitudeÞ £ 2 (explanation quality) between-subjects multivariate ANOVA was computed, with recovery satisfaction and attitude to the restaurant as the dependent variables. 45




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Significant multivariate main effects were obtained for all four independent variables: explanation type, F (6, 838Þ ¼ 7:11, p , 0:0005, partial h2 ¼ 0:05, compensation, F (2 419Þ ¼ 30:6, p , 0:0005, partial h2 ¼ 0:13, magnitude, F (2,419Þ ¼ 4:05, p ¼ 0:018, partial h2 ¼ 0:02, and explanation quality, F (2,419Þ ¼ 75:2, p , 0:0005, partial h2 ¼ 26. Contrary to expectations, neither the explanation type £ magnitude, F (6, 838Þ ¼ 0:54, p ¼ 0:779, partial h2 , 0:01, nor the explanation type £ compensation, F (6,838Þ ¼ 0:41, p ¼ 0:871, partial h2 , 0:01, interactions approached significance. The only significant interaction was between explanation type and explanation quality, F (6,838Þ ¼ 3:20, p ¼ 0:004, partial h2 ¼ 0:02. Table II presents the cell means pertaining to this interaction. As can be seen, excuses and apologies were associated with more favorable customer evaluations than were justifications and referential accounts. Univariate tests revealed the interaction was significant on satisfaction, F (3,420Þ ¼ 5:09, p ¼ 0:002, partial h2 ¼ 0:04, but not on attitude to the restaurant, F (3,420Þ ¼ 2:42, p ¼ 0:066, partial h2 ¼ 0:02. Simple effects analyses were performed to follow up the explanation type £ quality interactive effect on satisfaction. The sample was split by explanation quality, and one-way ANOVAs with post hoc (Tukey) tests conducted in each subsample. When explanation quality was low, there was no difference in satisfaction between the explanation types, F (3,225Þ ¼ 0:51 p ¼ 0:675, partial h2 ¼ 0:01. When quality was high, however, the effect of explanation type was significant, F (3,221Þ ¼ 5:71, p ¼ 0:001, partial h2 ¼ 0:07. Post hoc tests revealed that, in this high-quality explanation sub-sample only, apology was associated with higher satisfaction than were justification (p ¼ 0:003) and referential (p ¼ 0:025), whilst excuse produced greater satisfaction that did justification (p ¼ 0:026). Since the explanation type £ explanation quality interaction was not significant on attitude to the restaurant, main effects were interpreted. Significant effects on attitudes were found for both type, F (3,420Þ ¼ 10:53, p , 0:0005, partial h2 ¼ 0:07, and quality, F (1,420Þ ¼ 91:14, p , 0:0005, partial h2 ¼ 0:18. Post hoc tests revealed that excuses resulted in more favorable attitudes to the restaurant than did either justifications or referential accounts (both ps , 0:0005). Attitudes were more favorable following high, rather than low, quality explanations. Univariate tests revealed that the main effect of compensation was significant on both satisfaction, F (1,420Þ ¼ 60:0 p , 0:0005, partial h2 ¼ 0:13, and attitude to the restaurant, F (1,420Þ ¼ 32:8, p , 0:0005, partial

h2 ¼ 0:07, with evaluations more positive following higher (vs lower) compensation. Similarly, the main effect of failure magnitude was significant on both satisfaction, F (1,420Þ ¼ 6:06, p ¼ 0:014, partial h2 ¼ 0:01, and attitude, F (1,420Þ ¼ 7:05, p ¼ 0:008, partial h2 ¼ 0:02, with evaluations more favorable after low rather than high failure magnitude. In sum, these findings provide support for H1 (main effect of explanation type), H2(a) (failure magnitude), H2(b) (compensation), H2(c) (explanation quality), and H5 (interaction of explanation type £ quality). The lack of significant explanation type £ compensation, and explanation type £ magnitude, interaction effects means that H3 and H4 must be rejected. Mediation analyses H6 predicted that the relationships between each of the explanation types and customer evaluations are mediated by particular justice and attribution variables. A total of seven mediation paths were hypothesized in relation to each dependent variable (hence, 14 mediated relationships in total). These hypotheses were tested using Baron and Kenny’s (1986) regression procedure, with the measures of perceived use of the explanation types as the predictors. Where evidence of mediation was obtained, Sobel’s (1982) z test assessed whether the indirect effect of the explanation type upon the criterion via the mediator differed significantly from zero. Perceived use of excuses did not predict satisfaction, b ¼ 0:07, p ¼ 0:118, and perceived use of referential accounts did not predict attitude to the restaurant, b ¼ 0:08, p ¼ 0:092. In these cases, there was no (significant) relationship between the explanation and customer evaluation to be mediated. Thus, of the 14 hypothesized mediated effects, the three that pertained to the excusesatisfaction relationship, and the one that pertained to the referential – restaurant attitude relationship, were not tested further. In addition, apologies did not predict attributions of stability, b ¼ 20:03, p ¼ 0:365, and thus two of the remaining ten hypothesized effects were not significant. Table III summarizes the results for the eight significant relationships. As can be seen, consistent with H6(a), the effect of apologies on both criteria was mediated by perceptions of interpersonal justice; consistent with H6(b), the effect of justifications on both criteria was mediated by perceptions of informational justice; in part support of H6(c), the effect of referential accounts on satisfaction (but not on attitude to the restaurant) was mediated by perceptions of distributive justice; and, in part support of H6(d), the effect of

Table II Means and standard deviations on recovery satisfaction and attitude to the restaurant for groups defined by explanation type and explanation quality

Explanation type Apology Justification Referential Excuse Total

Satisfaction with recovery attempt Low quality High quality Total Standard Standard Standard Mean deviation Mean deviation Mean deviation 2.57 2.72 2.44 2.51 2.56

1.06 1.27 1.22 1.01 1.14

4.49 1.39 3.53 3.72 4.28 4.00

3.55 1.35 1.32 1.44 1.42

1.57 3.09 3.11 3.37 3.27

2.49 1.41 1.37 1.52 1.47

46

Low quality Standard Mean deviation 1.16 2.32 2.47 2.81 2.53

3.85 1.00 1.67 1.02 1.10

Attitude to restaurant High quality Total Standard Standard Mean deviation Mean deviation 1.44 3.13 3.40 4.45 3.71

3.18 1.31 1.37 1.44 1.47

1.47 2.72 2.94 3.61 3.11

1.22 1.35 1.49 1.42




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Table III Summary of significant mediation paths using Sobel’s (1982) test

Predictor (perceived use of explanation type) Apology Justification Referential Excuse

Mediatora Interpersonal justice Informational justice Distributive justice Procedural justice Controllability attribution Locus attribution

Path from predictor to mediator B SE Bb 0.347 0.324 0.158 0.108 20.360 25.07

0.031 0.033 0.052 0.038 0.032 0.556

Criterion Satisfaction with recovery Attitude to the restaurant Path from Path from mediator to mediator to criterion criterion B SE Bc Sobel’s zd B SE Bc Sobel’s zd 0.690 0.780 0.663

0.040 0.040 0.027

9.39 * * 8.78 * 3.01 *

0.550 0.681

0.046 0.042

8.16 * * 8.39 * *

0.675 20.660 20.022

0.032 0.043 0.003

2.81 * 9.06 * * 5.65 * *

Notes: a All mediators that were not significantly predicted (p , 0:05) by the explanation type (in equation 2) have been excluded; bunstandardized regression coefficient (and standard error) for the explanation type as the sole predictor of the mediator (equation 2); cunstandardized regression coefficient (and standard error) for the mediator when jointly predicting the criterion (equation 3); d Sobel’s (1982) z test of whether the indirect effect of the explanation type on the criterion via each mediator differs significantly from zero; *p , :01; * *p , 0:001

excuses on attitude to the restaurant (but not on satisfaction) was mediated by perceptions of procedural justice, by controllability attributions, and by locus attributions.

et al. (1998). Just as in that study, our findings suggest that, relative to justifications, customers who are provided with low quality excuses do not view very favorably the individual who voices the explanation, but tend to maintain a relatively positive view of the organization as a whole. The use of an apology led to satisfaction ratings that were as high or higher than those obtained for any other explanation type, and also led to attitudes to the restaurant that were moderately high. These findings are consistent with evidence from Davidow (2000, 2003) that, whilst apologies have favorable effects on immediate judgments such as fairness perceptions, they have less favorable effects on more distal outcomes such as repurchase. Thus, in combination with past research, there are indications that excuses and apologies have contrasting effects: specifically, apologies are more likely to engender positive attitudes towards the service recovery process, and excuses are more likely to be associated with positive attitudes towards the service firm. Contrary to H3 and H4, respectively, explanation type did not interact with either failure magnitude or compensation. An argument based on one presented by Folger and Cropanzano (1998) may help explain the non-significant finding in relation to failure magnitude. According to this argument, explanation type has weak effects at both extremes of failure severity. When the failure is trivial, no explanation is required and any type will suffice; when it is severe, no type of explanation is sufficient to undo the damage. Only at intermediate levels is explanation type likely to make a difference. Perhaps the levels of severity used in the current scenarios fell beneath this intermediate zone. Further research is required to test this proposition. Regardless of the merits of this account, the two non-significant interactions effects are important because they suggest that contextual factors such as problem magnitude (at least when at the current levels,) and the offering of compensation, have little effect on explanation efficacy.

Discussion Explanations are often used as a strategy to recover from service failures, yet research into consumer responses to alternative types of explanations is limited and has yielded inconsistent results. The current study examined if, when, and how four widely-used explanation types impact customer evaluations. The issues of “if” (main effects), and “when” (moderated effects), explanations impact these outcomes are discussed prior to an examination of “how” (mediated effects) they occur. Main and moderated effects of explanation type Consistent with past research findings (e.g., Conlon and Ross, 1997; Shapiro, 1991), explanation type had a significant effect on both dependent variables. Attitudes to the restaurant were more favorable following an excuse than following either a justification or referential account. As predicted in H5, the effects of explanation type on recovery satisfaction were moderated by the perceived quality of the account, with satisfaction differing by explanation type when quality was high, but not when it was low. Specifically, under conditions of high explanation quality only, apologies and excuses yielded greater levels of satisfaction than did justifications, whilst apologies also resulted in higher satisfaction than did referential accounts. The finding that excuses are more efficacious than justifications is consistent with conclusions from a metaanalysis by Shaw et al. (2003), but runs counter to findings from some past surveys of aggrieved customers (e.g., Conlon and Murray, 1996; Tax et al., 1998). One factor that may assist in making sense of the discrepant findings relates to the different outcome variables used. In our study the superiority of excuses over justifications was evident in the full sample when the criterion was global attitudes, but evident only in the sub-sample that regarded the explanations to be of high quality when satisfaction with the recovery attempt was the criterion. The finding bears a resemblance to that of Bobocel

Mediated effects of explanation type Consistent with H6, the effects of explanation type were mediated by justice and attribution variables. These effects were quite specific, with a different mediational pathway prominent for apologies (effects mediated by interpersonal 47




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justice), justifications (informational justice), and excuses (procedural justice and attributions). Similar links between these three explanation types and the justice variables were demonstrated in our previous research (Bradley and Sparks, 2009), and has been replicated here. The findings regarding attributions of locus and controllability as mediators of the effects of excuses extend our understanding of the process through which this type of explanation impacts customer evaluations. The finding that interpersonal and informational justice mediates the effects of different types of explanations adds to the accumulating evidence supporting the theoretical and empirical separation of these two forms of justice (Colquitt et al., 2001; Greenberg, 1993).

these explanation types. Our findings suggest that one factor to consider is the relative importance of ensuring satisfaction with the service recovery versus ensuring positive attitudes to the firm as a whole. In the former case, our data suggest that apologies may be more efficacious, whereas in the latter situation, excuses may work better. Either way, managers need to encourage service staff to explain service mishaps. While this advice may appear like “common sense”, explanations are not universally given in reality. Managers need to train staff to present their explanations in ways that are credible and that connote sincerity (McColl-Kennedy and Sparks, 2003). Our study also shows that the effects of the different explanation types are mediated by distinctive cognitive variables. Knowledge of the mechanisms by which explanations affect customer evaluations has several potential applications for effective service recovery practices. For example, this knowledge is useful for understanding the partial success of a recovery strategy and for identifying the point within a causal chain at which a strategy fails. These findings may be applied in training staff for complaint handling. After learning about the mechanisms by which recovery strategies have their impact, front-line staff should be able to develop deeper insights into why particular strategies work (or fail to do so), and may then be able to select and deliver more effective recovery responses.

Study limitations Findings from our research may not generalize beyond the sample, service context, and problem type studied. Replications using other methods, samples, and service contexts are required. The study required participants to read and imagine they were experiencing a hypothetical service scenario. The scenario method has considerable advantages in terms of feasibility, economy, control, and the ethics of research. Its limitations, particularly in relation to ecological and external validity, are also well known (see, e.g., Smith et al., 1999). While believability scores indicated that participants found the current scenarios realistic, it cannot be said with certainty that our findings would be replicated using other, field-based methods. The study could have been conducted by having customers report their responses to real-life explanations for service failures, but this method introduces other sources of bias (including lack of control over extraneous variables, comparison of nonstandardized explanations, and imperfect recall). More innovative methods, possibly including roleplay simulations and event-contingent diary studies, are recommended for future use.

References Adams, J.S. (1965), “Inequity in social exchange”, in Berkowitz, L. (Ed.), Advances in Experimental Social Psychology, Vol. 2, Academic Press, New York, NY, pp. 267-99. Baer, R. and Hill, D.J. (1994), “Excuse making: a prevalent company response to complaints?”, Journal of Consumer Satisfaction, Dissatisfaction, and Complaining Behavior, Vol. 7, pp. 143-51. Baker, T.L., Meyer, T. and Johnson, J.D. (2008), “Individual differences in perceptions of service failure and recovery: the role of race and discriminatory bias”, Journal of the Academy of Marketing Science, Vol. 36 No. 4, pp. 552-64. Baron, R.M. and Kenny, D.A. (1986), “The moderatormediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations”, Journal of Personality and Social Psychology, Vol. 51 No. 6, pp. 1173-82. Bies, R.J. (1987), “The predicament of injustice: the management of moral outrage”, in Staw, B.M. and Cummings, L.L. (Eds), Research in Organizational Behavior, Vol. 9, JAI Press, New York, NY, pp. 289-319. Bies, R.J., Shapiro, D.L. and Cummings, L.L. (1988), “Causal accounts and managing organizational conflicts: is it enough to say it’s not my fault?”, Communications Research, Vol. 15 No. 4, pp. 381-99. Blodgett, J.G., Hill, D.J. and Tax, S.S. (1997), “The effects of distributive, procedural, and interactional justice on postcomplaint behavior”, Journal of Retailing, Vol. 73 No. 2, pp. 185-210. Bobocel, D.R. and Zdaniuk, A. (2005), “How can explanations be used to foster organizational justice?”, in Greenberg, J. and Colquitt, J.A. (Eds), Handbook of Organizational Justice, Lawrence Erlbaum, Mahwah, NJ, pp. 469-98.

Managerial implications and conclusions Explanations are a widely-used, low-cost, yet underresearched, strategy for recovering from service failures. The current research has contributed to knowledge about explanations in several ways, each of which has implications for managing service quality. Most fundamentally, the study demonstrated that explanation type makes a difference to customer evaluations. The effects of explanations were shown to be stable across two levels of problem magnitude and two levels of compensation. In other ways, however, the effects were less straightforward than expected. For example, we found that apologies and excuses resulted in greater satisfaction with a recovery attempt than did justifications or referential accounts, but only when the explanations were perceived to be of high quality. When of low quality, explanation type did not differentially affect customer satisfaction. If replicated, this finding has important practical implications. It suggests that there may be a significant benefit in adding clarity, detail and credibility to apologies and excuses but this same effort may have limited impact on the efficacy of justifications and referential accounts. Excuses and apologies are logically incompatible – either service staff deny causing a problem or they apologize for doing so; they can hardly do both. Thus, following service failures, staff and their managers need to choose between 48




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Bobocel, D.R., Agar, S.E., Meyer, J.P. and Irving, P.G. (1998), “Managerial accounts and fairness perceptions in conflict resolution: differentiating the effects of minimizing responsibility and providing justification”, Basic and Applied Social Psychology, Vol. 20 No. 2, pp. 133-43. Bolkan, S. and Daly, J.A. (2009), “Organizational responses to consumer complaints: an examination of effective remediation tactics”, Journal of Applied Communication Research, Vol. 37 No. 1, pp. 21-39. Bradley, G.L. and Sparks, B.A. (2009), “Dealing with service failures: the use of explanations”, Journal of Travel and Tourism Research, Vol. 26 No. 2, pp. 129-43. Collie, T.A., Bradley, G.L. and Sparks, B.A. (2002), “Fair process revisited: differential effects of interactional and procedural justice in the presence of social comparison equity information”, Journal of Experimental Social Psychology, Vol. 38 No. 6, pp. 545-55. Colquitt, J.A., Conlon, D.E., Wesson, M.J., Porter, C.O.L.H. and Ng, K.Y. (2001), “Justice at the millennium: a metaanalytic review of 25 years of organizational justice research”, Journal of Applied Psychology, Vol. 86 No. 3, pp. 425-45. Conlon, D.E. and Murray, N.M. (1996), “Customer perceptions of corporate responses to product complaints: the role of explanations”, Academy of Management Journal, Vol. 39 No. 4, pp. 1040-56. Conlon, D.E. and Ross, W.H. (1997), “Appearances do count: the effects of outcomes and explanations on disputant fairness judgments and supervisory evaluations”, The International Journal of Conflict Management, Vol. 8 No. 1, pp. 5-31. Cranage, D. and Mattila, A. (2005), “Service recovery and pre-emptive strategies for service failure: both lead to customer satisfaction and loyalty, but for different reasons”, Journal of Hospitality and Leisure Marketing, Vol. 13 No. 4, pp. 161-81. Davidow, M. (2000), “The bottom line impact of organizational responses to customer complaints”, Journal of Hospitality and Tourism Research, Vol. 24 No. 4, pp. 473-90. Davidow, M. (2003), “Organisational responses to customer complaints: what works and what doesn’t”, Journal of Service Research, Vol. 5 No. 3, pp. 225-50. Folger, R. and Cropanzano, R. (1998), Organizational Justice and Human Resource Management, Sage, Thousand Oaks, CA. Gilliland, S.W., Groth, M., Baker, R.C., Dew, A., Polly, L.M. and Langdon, J.C. (2001), “Improving applicants’ reactions to rejection letters: an application of fairness theory”, Personnel Psychology, Vol. 54 No. 3, pp. 669-703. Goodwin, C. and Ross, I. (1992), “Consumer responses to service failures: influence of procedural and interactional fairness perceptions”, Journal of Business Research, Vol. 25 No. 2, pp. 149-63. Greenberg, J. (1993), “The social side of fairness: interpersonal and informational classes of organisational justice”, in Cropanzano, R. (Ed.), Justice in the Workplace: Approaching Fairness in Human Resource Management, Erlbaum, Hillsdale, NJ, pp. 79-103. Levesque, T.J. and McDougall, G.H. (2000), “Service problems and recovery strategies: an experiment”, Canadian Journal of Administrative Sciences, Vol. 17 No. 1, pp. 20-37.

McColl-Kennedy, J.R. and Sparks, B.A. (2003), “Application of fairness theory to service failures and service recovery”, Journal of Service Research, Vol. 5 No. 3, pp. 251-66. Shapiro, D.L. (1991), “The effects of explanations on negative reactions to deceit”, Administrative Science Quarterly, Vol. 36 No. 4, pp. 614-30. Shaw, J.C., Wild, E. and Colquitt, J.A. (2003), “To justify or excuse? A meta-analytic review of the effects of explanations”, Journal of Applied Psychology, Vol. 88 No. 3, pp. 444-58. Sitkin, S.B. and Bies, R.J. (1993), “Social accounts in conflict situations: using explanations to manage conflict”, Human Relations, Vol. 46 No. 3, pp. 349-70. Smith, A.K., Bolton, R.N. and Wagner, J. (1999), “A model of customer satisfaction with service encounters involving failure and recovery”, Journal of Marketing Research, Vol. 36 No. 3, pp. 356-72. Sobel, M.E. (1982), “Asymptotic intervals for indirect effects in structural equations models”, in Leinhart, S. (Ed.), Sociological Methodology, Jossey-Bass, San Francisco, CA, pp. 290-312. Sparks, B.A. and Callan, V.J. (1995), “Dealing with service breakdowns: the influence of explanations, offers and communication style on consumer complaint behaviour”, Proceedings of the World Marketing Congress, Academy of Marketing Science 7th Bi Annual Conference, pp. 106-15. Tax, S.S., Brown, S.W. and Chandrashekaran, M. (1998), “Customer evaluations of service complaint experiences: implications for relationship marketing”, Journal of Marketing, Vol. 62 No. 1, pp. 60-76. Thibaut, J. and Walker, L. (1975), Procedural Justice: A Psychological Analysis, Lawrence Erlbaum, Hillsdale, NJ. Webster, C. and Sundaram, D.S. (1998), “Service consumption criticality in failure recovery”, Journal of Business Research, Vol. 41 No. 2, pp. 153-9. Weiner, B. (2000), “Attributional thoughts about consumer behavior”, Journal of Consumer Research, Vol. 27 No. 3, pp. 382-7. Willson, P. and McNamara, J.R. (1982), “How perceptions of a simulated physician-patient interaction influence intended satisfaction and compliance”, Social Sciences and Medicine, Vol. 16 No. 19, pp. 1699-704. Wirtz, J. and Mattila, A.S. (2004), “Consumer responses to compensation, speed of recovery and apology after a service failure”, International Journal of Service Industry Management, Vol. 15 No. 2, pp. 150-66.

About the authors Dr Graham Bradley is an Applied Social Psychologist with research interests in the areas of the service encounter, stress, work and wellbeing. He is employed in the School of Psychology, Griffith University, Queensland, Australia. He is a registered psychologist and a member of the Australian Psychological Society. He is the author of over 40 journal articles and 20 consultancy and technical reports. His work has been published in international journals in the fields of psychology (e.g., Journal of Applied Social Psychology, Journal of Experimental Social Psychology, Work and Stress) and services/ marketing (e.g., Journal of Service Research, Journal of Hospitality and Tourism Research, Psychology and Marketing). He has received research grants in excess of $500,000. Graham Bradley is the corresponding author and can be contacted at: [email protected] 49




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Dr Beverley Sparks is a Professor of Tourism and Hospitality Management at Griffith University, Queensland, Australia. She has a substantial track record of research especially into service failure/recovery, using experimental, survey and qualitative methods. She has led a number of research teams on funded projects, with grant projects totalling more than $1 million. She has published in prestigious journals such as the Journal of Service Research, Journal of Business Research, Cornell HRA Quarterly, Journal of Hospitality and Tourism Research, Annals of Tourism Research, Psychology and Marketing, and Journal of Experimental Social Psychology. She has edited one book and authored six book chapters.

Their own past research found that the use of apologies resulted in greater customer satisfaction than did the use of referential accounts. In general, however, apologies and referential accounts have not previously been widely studied. To test if, when and how the use of these four different types of explanations affect customer satisfaction after a service failure, they exposed diners to several different explanations from a waiter: Our electricity was turned off for a couple of hours this afternoon. It’s not our fault. The whole area was blacked out, making it really hard for restaurants like ours. We haven’t been able to make up for the lost time (excuse). We miscalculated how many people would turn up tonight. It’s our fault. We always overbook a bit, because we’ve learnt from experience that not everyone who reserves a table actually shows up. We wouldn’t be able to stay in business if we let those tables go empty each night (justification).

Executive summary and implications for managers and executives

Because of the delays, we are going to be turning away everyone who arrives from now on. I’m afraid we won’t be able to find a table for them at all tonight. But that won’t happen to you (referential).

This summary has been provided to allow managers and executives a rapid appreciation of the content of the article. Those with a particular interest in the topic covered may then read the article in toto to take advantage of the more comprehensive description of the research undertaken and its results to get the full benefit of the material present.

I am terribly sorry this has happened to you. I know you have been waiting a while and it is annoying when you have made a booking. Please accept the restaurant’s apology for this delay (apology).

The effects of explanations were shown to be stable across two levels of problem magnitude and two levels of compensation. In other ways, however, the effects were less straightforward than expected. For example, apologies and excuses resulted in greater satisfaction with a recovery attempt than did justifications or referential accounts, but only when the explanations were perceived to be of high quality. When of low quality, explanation type did not differentially affect customer satisfaction. If replicated, this finding has important practical implications. It suggests that there may be a significant benefit in adding clarity, detail and credibility to apologies and excuses but this same effort may have limited impact on the efficacy of justifications and referential accounts. Excuses and apologies are logically incompatible – either service staff deny causing a problem or they apologize for doing so; they can hardly do both. Thus, following service failures, staff and their managers need to choose between these explanation types. One factor to consider is the relative importance of ensuring satisfaction with the service recovery versus ensuring positive attitudes to the firm as a whole. In the former case, apologies may be more efficacious, whereas in the latter situation, excuses may work better. Either way, managers need to encourage service staff to explain service mishaps. While this advice may appear like common sense, explanations are not universally given in reality. Managers need to train staff to present their explanations in ways that are credible and that connote sincerity. After learning about the mechanisms by which recovery strategies have their impact, front-line staff should be able to develop deeper insights into why particular strategies work (or fail to do so), and may then be able to select and deliver more effective recovery responses.

Do not make excuses, make good. Several excuses are always less convincing than one. Do not do what you will have to find an excuse for. Excuses change nothing, but make everyone feel better. These sayings – and there are plenty of them – which express a basic truth about how and why we make excuses, or should not make them, are all very well. But there are times when we have to make an excuse, or give an explanation or apology, for something that has gone wrong. The problem for people in the service industries – where customers understandably get aggrieved when things go wrong – is how to put them right. Customers want to know what went wrong. But do they want an explanation or an excuse? Or do they want convincing that, although they are dissatisfied, their experience is nothing compared with the poor experience of others? Or do they want compensating? Or do they want a mix of all of that? And what explanations are more effective than others? In “Explanations: if, when, and how they aid service recovery” Graham L. Bradley and Beverley A. Sparks begin a study of reactions to service failures in a restaurant setting by referring to previous research which distinguishes four types of explanations: 1 excuses, i.e., those that invoke mitigating circumstances in order to absolve the service organization of responsibility for the adverse outcome; 2 justifications, i.e., those that involve admission of responsibility, but which legitimize the service organization’s actions on the basis of shared needs and/ or higher goals; 3 referential (or reframing) accounts, i.e., those that seek to minimize the perceived unfavorability of the failure by invoking downward comparisons (e.g., with those who are worse off following the service failure); and 4 apologies, i.e., those involving an admission of failure and an expression of remorse.

(A pre´cis of the article “Explanations: if, when, and how they aid service recovery”. Supplied by Marketing Consultants for Emerald.)

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