Abstract # 008-0161

WHAT HAVE YOU DONE FOR ME LATELY? THE IMPACT OF FAILURE SEVERITY, PRIOR FAILURE, AND COMPANY

CONTROL ON SERVICE RECOVERY OUTCOMES

Matthew S. WoodSuresh K. Tadisina

Southern Illinois University

POMS 19th Annual ConferenceLa Jolla, California, U.S.A.

May 9 to May 12, 2008

ABSTRACTThis study empirically investigates the role of service failure severity, prior service failures, and the level of company control on service recovery outcomes. A scenario based experimental design is used to manipulate the factors followed by the measurement of the levels of customer satisfaction, recovery disconfirmation, and word of mouth. The context of academic advisement services is adopted which captures the influence of high switching cost, a commonly overlooked factor in existing service research. The anticipated results of this study highlight the importance of individual contextual variables, but also points out the influence of the interaction of contextual variables on service recovery outcomes. The paper concludes with a discussion of the practical implications and limitations of the study.

INTRODUCTION

Service failure recovery is a critical issue effecting customer satisfaction and

retention in today’s highly competitive markets. The importance of managers

understanding the criticality of effective service recovery cannot be underestimated.

Service failure has been identified as a key driver in customer switching behavior and

successful recovery can make the difference between customer retention or defection

(Roos, 1999). This is important because the cost of finding new customers far outweighs

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the cost of retaining existing customers. Under some conditions, a five percent reduction

in customer defections can increase profits by as much as one hundred percent (Reicheld

and Sasser, 1990). As such, increasing our understanding of the service recovery process

and its relevant outcomes has become a salient research objective.

Research on the service recovery process and its impact on customer satisfaction

has steadily grown (e.g. McCollough, Berry and Yadav, 2000; Maxham and Netemeyer,

2002). Much of this research has focused on satisfaction and repurchase intentions as

main recovery outcomes. As such, Matos, et al., (2007) conducted a meta-analysis of the

service recovery literature and recently called for more attention to additional salient

outcome variables, such as word of mouth and corporate image. Additionally, these

authors highlight that many important factors such as switching cost, previous service

failure experiences, and the level of perceived company control over the failure have

received limited empirical attention. Hence, the purpose of this study is to explore the

impact of some of these neglected factors on important outcome variables that have also

been commonly overlooked within the extant service recovery literature. More

specifically, this study explores the research question: what is the effect of the perceived

level of prior failures, degree of company control, and failure severity on customer

satisfaction, word of mouth, and recovery disconfirmation?

Existing research on service recovery has also remained limited in terms of its

contextual range. By this we mean that most service recovery research is limited to a

subset of common service industries. Most common among these are banks, airlines, and

hotels (e.g. Levesque and McDougall, 2000). While greatly enhancing our understanding

of service recovery outcomes within these contexts, they are limited in their application

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to other service industries where switching costs may be extraordinarily high or where

other unique factors influence the service recovery process. In an effort to address this

current gap this study is conducted using higher education as the context for the service

recovery. More specifically, the service recovery variables are applied to an academic

advisement service scenario. This unique setting is important because the switching costs

are inordinately high within this environment.

In the academic advisement service context it can be argued that once a student

has actually attended a specific university they become involved in a path dependent

process. Obviously, the student has the ability to switch to another education provider;

however, there are credit transfer and program compatibility issues to be considered. As

such, the cost associated with credit loss and the extra time required for the completion of

idiosyncratic courses at an alternate university become very salient switching cost

considerations. Thus, it can be logically inferred that the longer a student is in a program

at a given university the higher the switching costs associated with a change in the

education service provider. The advantage of considering high switching costs in this

study is that we capture the effects on service recovery outcomes and the results of the

study are likely to inform similar high switching cost service contexts, which have been

largely neglected within the extant literature (Matos et al., 2007).

THEORETICAL FRAMEWORK AND HYPOTHESES

A service failure is defined as “a flawed outcome that reflects a breakdown in

reliability” (Berry & Parasuraman, 1991). A service failure implies a negative imbalance

in an exchange relationship, whereby the customer does not receive what is expected. At

the point of failure, the perceived economic or social losses of the customer form the

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basis for recovery expectations (Smith, Bolton and Wagner, 1999). Some studies have

explored customer evaluations of service recovery efforts using social exchange theory

and equity theory as a conceptual foundation (e.g. McCollough, Berry, and Yadav, 2000).

Additional research has evolved that focuses on justice based theories for the explanation

of service recovery outcomes (Oliver, 1997). This literature suggests that consumers

form satisfaction judgments and behavioral intentions based on the level of perceived

justice (Andreessen, 2000). As such, if the customer experiences a service failure and

seeks remedy, satisfaction and repurchase intentions will be closely tied to whether or not

the recovery efforts were perceived as being fair and just in both the process and the

outcome. It has been shown that customers who experience just and fair recovery have

higher levels of customer satisfaction and repurchase intention (Goodwin and Ross,

1992).

As an outgrowth of equity theory, one of the most widely adopted models for

understanding customer responses to service failures is the disconfirmation paradigm

(Oliver and Bearden, 1985; Oliver and Burk, 1999). The disconfirmation paradigm has

provided insights into understanding customer reactions to service recovery, which is the

focus of this paper. The disconfirmation paradigm holds that customers compare

perceived service performance to expectations. There are two types of disconfirmation:

initial disconfirmation and recovery disconfirmation. Initial disconfirmation is the

discrepancy between the customer’s expectation that the service will be provided without

a failure and the actual initial service performance. Recovery disconfirmation is the

discrepancy between the consumers expectations regarding what the service provider will

do given a service failure and perceptions of the actual steps taken by the service provider

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in response to a failure (McCollough, Berry, and Yadav, 2000). The focus of this

research is on the recovery disconfirmation dimension of the paradigm.

Recovery expectations have become the standard for the evaluation of service

recovery performance within modern service firms (Kelly and Davis, 1994). This seems

appropriate given the large literature that asserts that many customers do not complain

when service is below expectations, but those that do are motivated by recovery

expectations (e.g. Richens, 1983; Singh, 1990). However, there are likely to be many

factors that impact the relationship between the service recovery efforts and satisfaction

based outcomes. In simple terms, factors such as, the customers previous experience and

criticalitly of the service provided, could easily impact the recovery expectations of the

customer which in turn effects their evaluation of the effectiveness of the service

recovery activities. This research seeks to explore three of these expectation altering

factors: failure severity, prior failure, and company control of the service failure.

Failure SeverityFailure severity is defined as the degree to which the service failure impacts the

customer. When failure severity is high the customer is much more likely to react

negatively to the recovery process and the recovery outcome (Levesque and McDougal,

2000). For example, if an auto repair shop customer comes to pick up their car at the

agreed upon time and the repairs are not completed this is obviously a service failure. If

the customer has other transportation it may not be considered severe. However, if it is

their only mode of transportation and the customer is leaving for a business trip, the

perceived level of severity would likely be high. In the later case we would expect to see

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the customer’s level of disconfirmation to rise rapidly and the level of satisfaction to be

much lower, despite recovery efforts.

Interestingly, a recent study by Craighead, Karwan, and Miller (2004) found that

customers were often the most upset with minor service failures. According to the

authors their findings indicated that severe failures are sometimes inevitable, but may be

understandable as long as adequate reparations are quickly made. The findings from the

Craighead et al. (2004) study point to the idea that severe failures may not have as

significant an impact on service recovery outcomes as originally thought. In contrast,

other researchers found that severe service failures were related to lower levels of

customer satisfaction and repurchase intentions (Ok, Back, and Shanklin, 2006). In light

of the contradictory findings on the role of severity in service recovery outcomes, the

conceptualizations in this research follow studies that have shown support for the

negative relationship between severity and satisfaction. As such, the exploration of

service recovery within the academic advisement service setting is conceptualized to

follow a similar pattern as reflected by the following hypothesis:

H1: Higher levels of failure severity will have a negative effect on post service recovery outcomes.

Prior FailureService consumers usually have a history of interactions with the firm. This history

can be thought of as cumulative satisfaction that is based on their evaluations of multiple

experiences with the firm over time (Bolton and Drew, 1991). Alternatively, customers

may visit the service firm only one time with the outcome being transaction-specific

satisfaction or dissatisfaction. While transaction-specific satisfaction is certainly

important, it is often the satisfaction of repeat customers that enable the success of a

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service business. Hence, the focus in this paper is on the cumulative aspect of the

customer-provider relationship.

Customers form expectations, which are internal standards or benchmarks against

which they judge the quality of service they receive. Previous research has indicated that

prior experiences with the organization are key determinates of customers’ expectations

(Parasuraman, Zeithmal, and Berry, 1985). Thus, for customers with satisfactory past

experiences, expectations for recovery tend to be high, this makes the process of service

failure recovery especially important (Kelley and Davis, 1994). From a relationship

perspective, when a regular customer experiences a failure they will feel they deserve to

be granted voice and reward in return for their loyalty. This phenomenon is likely to be

even more salient in situations where there are high switching costs. Before a customer

engages the services of a provider in a high switching cost situation, they are likely to

closely evaluate the provider during the provider selection process. Once the customer

selects the service provider they are likely to view the selection as a large commitment on

their part and, in turn, expect a similar type of commitment from the provider. In this

case, the customer is likely to have very high expectations of what the provider will do in

the case of a service failure. In this way, the magnitude of the switching costs directly

impact service recovery expectations, a phenomenon explored in this study.

In a similar vein, Maxham and Netemeyer (2002) posit that satisfactory recoveries

will only yield short term satisfaction gains because multiple failures will lead to

customer inferences that the service problems are inherent to the firm. As such, when a

customer experiences a failure for the second time they are much more likely to attribute

the cause of the failure to the firm, rather than when the customer experienced failure for

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the first time (Maxham and Netemeyer, 2002). Applying this logic to the context of

academic advisement services we would expect to find that prior service failures will

have a significant negative impact on post service recovery outcomes. As such, we

hypothesize that the relationship between perceived prior failures and service recovery

outcomes is negative as stated in the following hypothesis:

H2: Higher levels of prior failure will have a negative effect on post service recovery outcomes.

In contrast to the one-way linear relationship discussed above, higher levels of

perceived prior failure may have the opposite effect on service recovery outcomes. It is

possible that perceived prior failures may have lowered the customer expectations to such

a degree that the service recovery will have a greater positive effect than if the prior

failures had not occurred. This logic is consistent with the literature on the service

recovery paradox (e.g., Magnini et al., 2007). The service recovery paradox occurs when

a customer experiences a high level of satisfaction, because of the failure-recovery

incident, than if the failure had not occurred at all. Empirical support for the existence of

the service recovery paradox has been limited (for a review see: Matos, 2007), but

provides the basis for a valid counter argument to the idea that higher levels of perceived

prior failure will always lead to lower levels of post service recovery outcomes.

However, our study focuses on the hypothesis as stated earlier.

Company Control of the Service Failure

Another key factor in service recovery outcomes is likely to be the level of

perceived company control over the service failure. If the service failure is perceived to

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be out of the control of the company customers may be more likely to excuse the failure.

For example, when an airline cancels a flight due to weather it is likely that customers

may be more understanding than if the weather is clear and there is no externally

attributable cause for the cancellation. This logic is borne out in research that indicates

when a customer perceives the service failure as outside of the control of the company

they are more likely to forgive the problem (Maxham and Netemeyer, 2002). It is also

consistent with product based research that indicates the perceived reason for a product’s

failure is an important predictor of how customers will react to the product failure

(Folkes, 1984). Consistent with this literature we would expect to find that the level of

perceived company control within the academic advisement service will greatly impact

service recovery outcomes as highlighted by the following hypothesis:

H3: Higher levels of perceived company control will have a negative effect on post service recovery outcomes.

Each of the key service recovery factors identified above can be conceptualized as being

entirely independent. However, in reality, there is a high probability that the factors will

interact with each other to impact service recovery outcomes. Previous research has

shown that the interaction between situational factors impacts the relative effectiveness of

service recovery strategies. For example, Levesque and McDougall (2000) conducted an

empirical study that showed compensation strategies were only effective when in low

criticality scenarios. This means that the interaction between severity and the service

recovery compensation strategy was a significant predictor of service recovery

satisfaction. In the context of this study, one could argue, for example, that a prior

service failure coupled with a severe service failure would result in higher levels of

negative service recovery outcomes than when either of these factors is considered alone.

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Thus, the interaction effects of failure severity, prior failure, and company control on

service recovery outcomes will be explored by the following hypotheses:

H4: Failure severity will have a greater negative impact on service recovery outcomes when the level of prior service failure is higher (Fig. 4a-c).

H5: Failure severity will have a greater negative impact on service recovery outcomes when the degree of company control is higher (Fig. 5a-c).

H6: The level of perceived prior failure will have a greater negative impact on service recovery outcomes when the degree of company control is higher (Fig. 6a-c).

METHODS

Experimental Design and ScenarioThis study will use a 2 x 2 x 2 between subject’s factorial experimental design

with 2 levels of severity, 2 levels of prior failure, and 2 levels of company control as the

three factors being manipulated. The research hypotheses will be tested through the use

of role playing (scenario-based) experiments wherein participants read scenarios and

respond accordingly. The scenarios for each condition portray a service failure followed

by a recovery while the levels of the other factors are varied. The instructions on the

questionnaire will ask participants to carefully read the scenario and assume that the

scenario had just happened to them.

The scenario based experiments will be conducted to investigate the impact of

varying the levels of failure severity, prior failure, and company control on satisfaction

based outcome variables. The scenario approach has several advantages. Bitner et al.

(1990) informs us that scenario based experiments allow difficult manipulations to be

more readily operationalized and provide the researchers with control over otherwise

unmanageable variables. A scenario approach also avoids the ethical considerations

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associated with observing or enacting actual service failures (Smith and Bolton, 1998).

Finally, it eliminates the undesirability of subjecting customers to failure situations.

Due to its many advantages service scenarios are frequently utilized in service

failure and recovery research (e.g. Matilla and Patterson, 2004). In this case, the use of

scenarios allows controlled manipulation of the service failure variables while avoiding

the response bias due to memory limitations and rationalizations likely to be present with

recollections of actual service failure experiences (McCullough, et al., 2000).

Additionally, the selection of academic advisement services as the context for this study

ensures that participants are familiar with the service and thus are able to readily adopt

the role of the consumer in each of the scenarios.

Treatment ConditionsThe scenarios developed for the manipulation of severity, prior service failure,

and company control were cast in the setting of academic advisement services. All

participants will be given common background information as follows:

You are an undergraduate student at a major state university. It is the summer before your senior year and you have scheduled an appointment with your academic advisor. During your summer advisement meeting you are informed that one of the classes you need to graduate is only offered during the summer and you have just missed it. As it currently stands this means that your graduation date will be pushed back until after the completion of the next summer semester.

As you discuss the situation with your advisor you are informed that because of the advisement office’s failure to inform you of the course scheduling problem the tuition and fees for the missed course will be waived. You will be able to take the course the following summer at no charge.

For the manipulation of failure severity the following language will be added to the

background information:

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Low condition: Pushing back your graduation date is a minor concern because you have already accepted a job offer, but you are not scheduled to start until late August – after the summer graduation date. High condition: Pushing back your graduation date is a major concern because you have already accepted a job offer and you are scheduled to start in early June – well before the summer graduation date. It is unlikely that your employer will hold the job until after the completion of the summer term.

For the manipulation of prior failure the following language will be added to the

background information:

Low condition: You have had no previous problems with your academic advisement and course scheduling. All of your advisement sessions up to this point have gone smoothly and indicated that you were on schedule to graduate.

High condition: A similar incident has happened before. During your sophomore year you had to take an overload in one semester in order to stay on pace for graduation.

For the manipulation of company control the following language will be added to the

background information:

Low condition: The advisor informs you that the class was changed to a summer course at the last minute because the instructor abruptly resigned. High condition: The advisor informs you that the class has been offered during the summer semester for several years and it was simply overlooked.

The set of scenario conditions are reflected in Table 1 below which identifies all of the

eight cells associated with a 2 x 2 x 2 factorial design.

Table 1: Experimental ConditionsPrior Failure - Low Prior Failure - High

Company Control - Low

Company Control - High

Company Control - Low

Company Control - High

Severity - Low LLL LHL LLH LHH

Severity - High HLL HHL HLH HHH

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Manipulation ChecksThree variable specific manipulation check questions will be used. Each of the

questions is designed to asses whether or not the participant’s accurately recognized the

manipulations associated with the given conditions. The manipulation check questions

are as follows:

Failure Severity: The academic advisement problem that I experienced was a:____ Minor problem____ Major problem

Prior Failure: The academic advisement problem that I experienced has happened to me:

____ At least one time prior to this incident____ Never before this incident

Company Control: The academic advisement problem that I experienced was:____ Out of the advisement office’s control____ Within the advisement office’s control

Measures of Outcome Variables

Post-recovery Satisfaction: The antecedent to satisfaction is the service recovery

remedy. The remedy is defined as the method the firm uses to rectify the customer’s

unsatisfactory experiences (offering of the missed class at no charge). Satisfaction with

the remedy is then defined as the subject’s evaluation of the service failure recovery

efforts (Harris, et al., 2006). As such, post recovery satisfaction will be measured using

four items on a nine point Likert-type scale, anchored by strongly disagree (1) and

strongly agree (9). The four item scale was adopted directly from Maxham and

Netemeyer (2002) and has proven to be a reliable scale with a Cronbach’s alpha of .87.

The response scores for the four items will be averaged to form an overall measure of

post-recovery satisfaction. The individual scale items are as follows:

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1. On this particular occasion, the academic advisement office provided a satisfactory resolution to my problem.

2. The academic advisement experience met my needs3. I am not satisfied with the academic advisements office’s handling of this

particular problem.4. Overall, I am satisfied with my academic advisement experience

Word of Mouth: Word of mouth is defined as the customer’s propensity to speak

negatively or positively to others about the service provider. Word of mouth will be

measured using five items on a nine point Likert-type scale, anchored by strongly

disagree (1) and strongly agree (9). The four item scale was adopted directly from Wood

and Karau (2007) and has proven to be a reliable scale with a Cronbach’s alpha of .83.

The response scores for the five items will be averaged to form an overall measure of

word of mouth. The individual scale items are as follows:

1. I would complain to friends about this university.2. I would say negative things to others in the community about this

university.3. I would speak highly of this university.4. If asked by a media representative I would likely speak negatively about

this university.5. I would speak positively about this university to family and friends.

Recovery Disconfirmation: Recovery disconfirmation is defined as the difference

between the customer’s expectations regarding what the service provider will do given a

service failure and perceptions of the actual steps taken by the service provider in

response to a failure (McCollough, Berry, and Yadav, 2000). Recovery disconfirmation

will be measured using three items on a nine point Likert-type scale, anchored by

strongly disagree (1) and strongly agree (9). The three item scale was adopted directly

from McCollough, Berry, and Yadav, (2000) and has proven to be a reliable scale with a

Cronbach’s alpha of .81. The response scores for the three items will be averaged to

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form an overall measure of recovery disconfirmation. The individual scale items are as

follows:

1. The compensation for my problem was much better than I expected.2. The university should have done more for me in response to the

scheduling problem.3. The university responded very well to the scheduling problem.

Validity and Reliability of MeasuresThe suggested measures and the corresponding instrument will be analyzed for

reliability and validity. All the scales will be evaluated for reliability using Cronbach’s

Alpha. Once reliability has been established the next step is to evaluate validity of the

scales examining content, criterion, and construct validity.

Participants

Upper level undergraduate students will be recruited for participation in this

study. The students will be offered an incentive of extra course credit in exchange for

their participation in the study. When considering the number of participants required for

a successful experimental study it is important to consider the level of statistical power

desired. Power is defined as the probability of correctly rejecting a false hypothesis when

a particular alternative hypothesis is true (Howell, 2007). Thus, a more powerful

experiment is one that has a better chance of rejecting the null hypothesis. For this study

a statistical program (G*Power) was used to calculate the required sample size for the

recommended power level of .80 or greater (Howell, 2007). This analysis revealed that a

sample size of 50 per cell is required to reach sufficient power. Thus a total sample size

of 400 would be required for this study.

Achieving sufficient statistical power has been identified as an important goal for

service recovery research. In their recent meta-analysis of service recovery literature

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Matos et al. (2007) found that majority of service recovery studies have relatively low

statistical power. They go on to assert that low power can lead to conflicting results

between similar studies, which is what they found in their meta-analysis. As such, this

study takes heed of these authors’s suggestion that future studies should provide

sufficient statistical power in order to strengthen the study’s results and the body of

service recovery literature as a whole.

ANTICIPATED RESULTS

The results of this study are anticipated to reveal a statistically significant main

effect for severity on satisfaction, word of mouth, and disconfirmation, such that the

greater the severity of the service failure the lower the levels of satisfaction and word of

mouth, and the higher the levels of disconfirmation. This finding would support the first

hypothesis. This main effect is graphically represented by Figure 1 in the Appendix. For

prior failure the expectation is that prior service failure is significantly related to the

outcome variables in that a previous service failure will negatively impact the recovery

related outcomes of the current service failure. As such, prior service failures would lead

to higher levels of disconfirmation, but lower levels of satisfaction and word of mouth,

which follows the predictions of hypothesis 2. For company control the study is

anticipated to reveal that the more control the organization is perceived to have over the

service failure, the more negative the outcome. Thus, higher levels of company control

will be significantly related to lower levels of satisfaction, and word of mouth, but higher

levels of disconfirmation. These findings would support the third hypothesis.

The results of this study are also expected to reveal several interaction effects.

Three two-way have been hypothesized. It is highly likely that each of the manipulated

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variables will interact to impact the relationships with the outcome variables. More

specifically, we expect to find that higher levels of prior service failure combined with

higher levels of company control will lead to a more negative service recovery outcome

than when the level of prior service failures is lower. Likewise, when higher levels of

failure severity are combined with higher levels of prior failure or higher levels of

company control we expect to see a more negative outcome than when the level of failure

severity is lower. In short, we anticipate the interaction effects to reveal that the

relationship between each variable and service recovery outcomes is strengthened in the

presence of high levels of another variable. All of the anticipated two-way interaction

effects are graphically represented in the appendix by Figures 4, 5, and 6. Finally,

several other examples of possible interactions could be conjectured; however, these

relationships would require actual data to demonstrate the interaction relationships

beyond the general relationships predicted by the hypotheses.

DISCUSSION AND CONCLUSION

The anticipated results of this study indicate that the contextual factors of prior

service failure, failure severity, and company control significantly impact service

recovery outcomes. In general, the anticipated results of this study indicate that higher

levels of each of these variables directly impact the probability that the customer will be

satisfied with the service recovery process. In addition, these variables interact with each

other in a way that makes effective service recovery even more difficult. As such, this

study is designed to provide an increased recognition of these salient factors, which may

lead to increased effectiveness in actual service recovery processes.

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At a more detailed level, the anticipated finding that prior service failures have an

impact on the outcome of current service recovery efforts indicates the importance of

cumulative satisfaction and the history of the relationship between the provider and the

customer. Additionally, the anticipated finding that recovery disconfirmation plays an

important role is also of significance. This means that the remedy for the service failure

must match the expectations of the customer. This study attempts to show that this

element is even more important when switching costs are high. When the customer is

unable to easily change service providers, as in the academic advisement setting, the

remedy becomes a crucial determinate of overall satisfaction and other recovery

outcomes. Finally, the anticipated interactions between the contextual factors also

provide us with important insights into the service recovery process. The anticipated

interaction effects demonstrate the ‘perfect storm’ of service failure. By this we mean,

that the cumulative affects of high levels of each of these variables leads to an

overwhelming negative service recovery outcome, which may be an insurmountable

obstacle for service providers. As such, service providers need to pay close attention to

the cumulative effects of multiple contextual factors in evaluating appropriate service

recovery actions. In short, this means that service recovery processes and remedies

should be evaluated within the context of multiple factors.

This study makes important contributions to the service recovery literature. The

exploration of contextual variables in a new setting, higher education, provides us with

new insights. This context is especially valuable because it introduces the latent factor of

high switching cost, which has been neglected in prior service recovery research (Matos,

et al., 2007). Additionally, this study explores the contextual factors of failure severity,

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prior failure, and company control simultaneously. While each of these factors has

received some empirical attention, this study adds to our understanding by considering

these factors individually, but more importantly it considers the interaction between these

factors. The interaction effects provide us with new insights that increase our

understanding of the service recovery process.

Practical ImplicationsThis study informs the practice of service recovery in several ways. First, service

providers should recognize the importance of contextual factors when developing their

service recovery processes and procedures. For example, a severe service failure will

likely require a remedy that is substantial in order to achieve a successful resolution to

the problem. When there is an existing history of service failures the service provider

must be especially diligent in their handling of the service recovery process.

Second, service providers should recognize that the service environment matters.

By this I mean that the switching cost associated with the service has a significant impact

on how service recovery processes and procedures should be designed. When a

consumer engages the services of an organization that is inherently difficult to change if

they experience poor service, then that customer feels that they have made a heavy

investment in the provider. As such, the consumer is likely to have high expectations in

the event of a service failure. These idiosyncrasies have been represented in this study

and should be recognized by practitioners in these high switching cost environments.

Finally, service providers should recognize that several factors can interact to

impact the outcome of the service recovery process. Thus, the design of service recovery

processes should recognize the salience of things like the prior service history when

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addressing service failures. Taking into account these factors would allow for remedy

adjustments to be made and thereby increase the likelihood that the service recovery

process is successful. By accounting for these important interactions the overall

effectiveness of the organizations service recovery efforts may be improved, which is the

goal of both academicians and practitioners.

LimitationsScenario based experimental research designs always raises questions of

generalizability, because scenarios can only attempt to simulate the complexity of real

world service recovery processes. To avoid potential problems, scenarios were carefully

and deliberately developed. A second threat to external validity is the reliance on students

for data collection. Of course students are not necessarily representative of the

population as a whole and their experiences with service recoveries may be limited.

However, given that the context of this study was academic advisement services it seems

logical to suggest that undergraduate students are the appropriate target for this study.

Students should easily be able to envision themselves in a service failure recovery

situation in this setting. As such, the generalizability of the findings from this study is

actually enhanced through the use of students as participants. Additionally, the use of

students allows for the collection of data in a controlled environment that reduces the

outside factors that would normally be associated with employees in actual organizations.

Future studies should consult the reactions of actual customers and managers in real

organizations in order to increase the generalizability of these and future service recovery

research findings. Despite its limitations, this study provides an important initial step

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towards increasing our understanding of the effects of specific contextual factors on

service recovery outcomes.

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APPENDIX

Failure Severity

Out

com

e

Satisfaction

Word of Mouth

Disconfirmation

Prior Failure

Out

com

e

Satisfaction

Word of Mouth

Fig 2: Prior Failure Main Effect

Disconfirmation

25

Fig 1: Failure Severity Main Effect

Company Control

Out

com

e

Satisfaction

Word of Mouth

Fig 3: Company Control Main Effect

Disconfirmation

Severity

Satis

fact

ion

Fig 4a: Severity x Prior Failure (PF) Interaction Effect

High PF

Low PF

26

Severity

Wor

d of

Mou

thFig 4b: Severity x Prior Failure (PF) Interaction Effect

High PF

Low PF

Severity

Dis

conf

irmat

ion

Fig 4c: Severity x Prior Failure (PF) Interaction Effect

High PF

Low PF

27

Severity

Satis

fact

ion

Fig 5a: Severity x Company Control (CC) Interaction Effect

High CC

Low CC

Severity

Wor

d of

Mou

th

Fig 5b: Severity x Company Control (CC) Interaction Effect

High CC

Low CC

28

Severity

Dis

conf

irmat

ion

Fig 5c: Severity x Company Control (CC) Interaction Effect

High CC

Low CC

Prior Failure

Satis

fact

ion

Fig 6a: Prior Failure x Company Control (CC) Interaction Effect

High CC

Low CC

29

Prior Failure

Wor

d of

Mou

th

Fig 6b: Prior Failure x Company Control (CC) Interaction Effect

High CC

Low CC

Prior Failure

Dis

conf

irmat

ion

Fig 6c: Prior Failure x Company Control (CC) Interaction Effect

High CC

Low CC

30