1) The document examines how attributions of causality and length of the service recovery process impact customer perceptions, satisfaction, and behavioral intentions after a service failure. 2) It suggests that customer behavioral intentions are more favorable for stable service recoveries where the cause is not likely to recur, and for employee-based recoveries where the employee has control. 3) Customer evaluations and intentions are also predicted to be more positive for expedient and less complicated recovery processes that remedy failures quickly.

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Journal of Marketing Theory and Practice
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Attributions and Outcomes of the Service
Recovery Process
Scott R. Swanson & Scott W. Kelley
To cite this article: Scott R. Swanson & Scott W. Kelley (2001) Attributions and Outcomes
of the Service Recovery Process, Journal of Marketing Theory and Practice, 9:4, 50-65, DOI:
10.1080/10696679.2001.11501903
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2. ATTRIBUTIONS AND OUTCOMES OF THE
SERVICE RECOVERY PROCESS
Scott R. Swanson
University of Wisconsin-Whitewater
Scott W. Kelley
University of Kentucky
Drawing on attribution theory and the services marketing literature, the authors examine how the allocation ofcausality and length
ofthe service recovery process impact post-recovery consumer perceptions ofservice quality, customer satisfaction and behavioral
intentions for word-of-mouth and repurchase. Results of a scenario-based repeated measures design suggest that 1) customer
behavioral intentions are more favorable in stable service recoveries, 2) an employee based service recovery results in more
favorable evaluations and word-of-mouth intentions, and 3) customer evaluations and behavioral intentions will be more positive
for service failures remedied by expeditious and less complicated recovery processes. Managerial implications and future research
directions are presented.
INTRODUCTION
It is widely recognized that no service system is perfect.
Mistakes do occur. Fortunately, accepting the inevitability of
service failures does not imply the automatic loss of
customers. When a service failure occurs, "the customer's
confidence in the firm hangs in the balance. The company can
make things better with the customer - at least to some extent -
or make things worse" (Berry, Parasuraman, and Zeithaml
1994, p. 38). It has even been suggested that, through a
phenomenon known as the service recovery paradox, a
successful recovery can result in a more favorable encounter
than if the transaction had been performed correctly the first
time (e.g., Hart, Heskett, and Sasser 1990). In order to better
retain service customers, it is essential, therefore, that
marketers understand the manner by which customers come to
accept (or reject) service recovery attempts.
50 Journal ofMarketing THEORY AND PRACTICE
There are substantial economic benefits to those that can
master the art of service recovery. For example, it has been
noted that a 5% decrease in the customer defection rate can
boost profits from 25% to 95% (Jacob 1994). Other
researchers have suggested that long-term customers generate
increasingly more profits year after year (Reichheld and Sasser
1990). This increase in profits manifests itselfthrough several
sources. First, costs decline due to the reduced expense of
replacing defecting customers. Second, repeat customers often
make fewer demands on employee time due to realistic
expectations. At the same time, employees may become more
efficient due to familiarity with the customer's needs. Third,
the costs ofpromotion, credit verification, and new account set
up to attract a new customer can be as much as five times the
cost of retaining the original customer (Peters 1988). In
addition to the economic benefits of retaining customers
through effective recoveries, the less tangible losses associated

3. with dissatisfied customersgenerating negative word-of-mouth
about the service provider and the service firm should be
considered (Keaveney 1995). As a result, many companies are
now viewing customers as valuable assets with the realization
that their customer portfolio is the "ultimate source of their
companies' growth and profitability" (McDougall 1995).
The impact of the service recovery process on customer
evaluations and repurchase intentions is an important topic for
both services marketing researchers and managers. There have
been relatively few theoretically based empirical studies on
service recovery to date (e.g., Smith and Bolton 1998; Smith,
Bolton, and Wagner 1998; Tax, Brown, and Chandrashekaran
1998). A great deal of the service recovery research has
primarilyconsisted ofidentifying and classifying recovery types
(e.g., Bitner, Booms, and Tetreault 1990; Kelley, Hoffinan, and
Davis 1993). More systematic theoretically based empirical
research is necessary to advance our knowledge ofthe service
failure/recovery phenomenon. Itisproposed inthis studythatthe
perceived causes ofa recovery can have a significant impact on
post-service recovery evaluations and behaviors. Attribution
theory is proposed as a theoretical basis that can provide
additional insight into the factors that determine customer
perceptions ofan organization's recovery efforts in response to
a service failure.
Subsequently we provide a briefoverview ofattribution theory,
present hypotheses based on attribution theory and services
literature, and then test our hypotheses in three different service
industries using a scenario-based experimental design. Finally,
we report our results, present implications for managers, and
discuss limitations and future research directions.
ATTRIBUTION THEORY
Attribution theory is a collection of several theories that are
concerned with the assignment of causal inferences and how
these interpretations influence evaluations and behavior. The
attribution field has grown from a variety of research streams.
These works include: Heider's seminal writing on naive
psychology (1958), Bem's self-perception theory (1965,1967,
1972), Jones and associates' correspondence of inference work
(Jones and Davis 1965; Jones and McGillis 1976), Kelley's
theory ofexternalattribution(1967, 1971, 1972, 1973),and more
recently, the research ofWeiner(1980, 1985a, 1985b). The wide
array ofsocial interaction phenomenato which attributiontheory
can beappliedhas made thistheory oneofthe primary paradigms
insocial psychology. As aresult, attributiontheory has alsobeen
widely adoptedbymarketingscholars(e.g., Folkes 1984;Curren
and Folkes 1987; Wofford and Goodwin 1990; Gooding and
Kinicki 1995).
Heider(1958) recognized that some attributional characteristics
fluctuate (e.g., effort and luck), while others are relatively fixed
(e.g., ability). This stability of a cause determines shifts in
expectancies (Weiner 1980). Thus, if circumstances are
perceived to remain the same (i.e., stable), then outcomes
experienced will be presumed to continue. However, if the
causal conditions are perceived as being likely to change (i.e.,
unstable), there will be uncertainty about subsequent outcomes.
Heider (1958) also identified an internal-external causal
dimension as fundamental to the attribution process. He noted
that outcomes ofany action depend on two sets ofconditions,
"factorswithinthe person and factors withintheenvironment" (p.
82). Weiner(1980) classifiedthis internal-externaldistinctionas
the locus ofcausality dimension.
Attributiontheory alsorecognizesthat individualsarenotalways
constrained by environmental factors, but make choices. Heider
(1958) related controllabilityto creditand blame. Forexample,
ifan organization has control in preventing a service failure, but
fails to do so, consumers may blame the firm. Conversely, the
company may be more likely to be given credit for positive
actions. In sum, individuals experience and/or witness events
and make inferences about the causes of these occurrences in
order to exercise control of their world. These causes can be
classifiedwithin thethreeprincipaldimensions ofstability(Isthe
cause likely to recur?), locus (Who is responsible?), and
controllability (Did the responsible party have control over the
cause?) (Bitner 1990).
Attribution theory has previously provided significant insights
into product failure experiences. For example, failure
attributed to a seller is more likely to 1) elicit complaints to the
firm and warnings to others (Richins 1983; Curren and Folkes
1987); 2) lead to less satisfaction (Oliver and DeSarbo 1988);
and 3) impact beliefs that the customer is owed apologies
and/or refunds (Folks 1984; Kelley, Hoffman, and Davis
1993). Ifa customer determines that the responsible party for
a failure had control over the cause they will be angrier, have
lower repurchase intentions, and have a greater desire to
complain (Folkes, Koletsky, and Graham 1987). Stability has
been found to influence the type of redress preferred when a
product fails; "compared to unstable reasons, stable
attributions lead consumers to more strongly prefer refunds
rather than exchanges" (Folkes 1988, p. 557). When product
failure is perceived as due to stable factors the customer
believes failure will re-occur and they express a desire for a
monetary refund. Ifthe failure is perceived as due to unstable
causes, then subsequent product satisfaction is expected and
product exchange is preferred (Folkes 1984).
In sum, attributions have been found to influence how
consumers communicate (Richins 1983; Curren and Folkes
1987; Folkes, Koletsky, and Graham 1987), satisfaction or
dissatisfaction (Oliverand DeSarbo 1988), preferred recovery
(Folkes 1984; 1988), and future repurchase intentions (Folkes,
Koletsky, and Graham 1987). As with product failures,
attribution theory may also provide insights into consumer
perceptions and intentions relative to service recovery
experiences. Specific attribution based hypotheses are
presented next.
Fall 2001 51

4. HYPOTHESES
Service recovery involves the specific actions taken in
response to a service failure (Gronroos 1988a). The service
recovery actions resulting from a service failure might be
implemented consistently or inconsistently (stability).
Previous research also indicates the locus of these actions
might be the service organization, the service employee, or the
service customer (Kelley, Hoffman, and Davis 1993). In
addition, the length of time required to execute the recovery
may vary (Hart, Heskett, and Sasser 1990; Kelley, Hoffman,
and Davis 1993). In the following attribution-based
hypotheses we specifically address the causal dimensions of
stability and locus, with controllability held constant. We also
consider the time involved in the recovery across three
different services.
HI -- Stability and Service FaiIurelRecovery Outcomes
Researchers have demonstrated that consumers engage in
attribution search for various product failures (e.g., Folkes
1984; Curren and Folkes 1987; Wofford and Goodwin 1990;
Gooding and Kinicki 1995). Generally, if the causes of an
outcome are expected to remain unchanged then an increased
degree of certainty is associated with evaluations and future
behaviors. The higher degree of certainty should result in
more favorable evaluations and future behaviors, assuming the
encounter in question was acceptable. But, ifthe causes ofan
outcome are expected to change, then the lack ofcertainty has
an adverse impact on evaluations and future behaviors (Weiner
1980). In this fashion, the stability of a causal attribution
associated with a service recovery has an impact on customer
evaluations and future behaviors.
Further supporting evidence of this relationship is offered
through services research that demonstrates customers greatly
value consistency and reliability in service delivery (Berry,
Parasuraman, and ZeithamI1994). For example, a dry cleaner
damages a customer's shirt. After the dry cleaner apologizes,
the customer is told to purchase a replacement and the
company will reimburse him for the cost of the shirt. This is
always the procedure followed for any damage to a customer's
clothing (i.e., stable). However, if over a number ofyears the
customer finds that in similar situations he one time gets
reimbursed, another time an in-house repair is attempted, and
yet in another incident only an explanation is offered, he can
no longer anticipate with any degree ofcertainty what outcome
will be received if there is a service failure (i.e., unstable).
Based on research from the attribution theory and services
literature, recoveries perceived as stable should lead to more
favorable customer evaluations and behaviors. Thus, the
following hypothesis is proposed:
HI: Stable service recovery attributions will result in more
favorable recovery evaluations and behavioral intentions than
unstable service recovery attributions.
52 Journal ofMarketing THEORY AND PRACTICE
H2 -- Locus and Service FaiIurelRecovery Outcomes
Consumerand marketing researchers (e.g., Richins 1983; Folkes
1984; Curren and Folkes 1987; Oliver and DeSarbo 1988) have
investigated internal and external customer locus attributions.
Valle and Wallendorfs (1977) content analysis of customer
attributions for causes of satisfaction and dissatisfaction with
products suggests the importance of utilizing an expanded
external locus dimension in consumer research. Subsequent
services research conducted using the critical incidenttechnique
(CIT) indicates that service customers perceive that their
experiences can be primarily attributed to eitherthe service fum,
the service employee, or the service customer (Bitner, Booms,
and Tetreault 1990; Kelley, Hoffman, and Davis 1993). For
example, service failures may be attributed by the customer to
him- orherself("l should have made myselfclearer"), the contact
employee ("that was the rudest person I ever met"), or the
company ("ifthat is their policy I will go elsewhere"). Service
recovery can also be attributed to the customer ("I really know
how to get things straightened out"), the contact person ("that
young woman sure took care ofthat problem promptly"), or the
fum ("that company really stands behind its work"). Although
attribution theory does not specifically address the impact of
mUltiple external locus dimensions, it is expected that locus
attributions to the customer, service employee, and service fum
will be differentially related to recovery outcomes (c.f., Folkes,
Koletsky, and Graham 1987; Bitner 1990).
In one ofthe few studies that directly examined consequences of
locus attributions on customer evaluations, Oliver and DeSarbo
(1988) found that successful outcomes resulting from an external
locus lead to greater satisfaction than when success is attributed
to the self. In the context ofthe present study, one would expect
customers to evaluate service recoveries attributed to either the
service employee or firm more favorably than those attributed to
themselves (i.e., customers). Further, previous research suggests
that recoveries enacted by frontline personnel may be evaluated
more favorably than recoveries attributed to the organization or
its higher-level representatives (Hart, Heskett, and Sasser 1990;
Kelley, Hoffman, and Davis 1993). Specifically, the following
hypothesis is suggested:
H2: Service recoveries attributed to the service employee will
result in the most favorable recovery evaluations and behavioral
intentions followed by service recoveries attributedto the service
firm and customer, respectively.
H3 - Recovery Time Differences and Service FailurelRecovery
Outcomes
The three sets ofscenarios used in this research (Airline, Cable
TV, Credit Card) considervarying service recovery times. These
scenarios are presented in Appendix Table 1 and their
development is subsequently discussed in detail. Previous
research (e.g., Hart, Heskett, and Sasser 1990; Kelley, Hoffman,
and Davis 1993) has suggested that customer evaluations of
recovery in complex and lengthy service recovery processes may

5. be less positive than for service failures remedied by less
complex and shorter recovery processes. The varying levels of
complexity and length ofthe service recoveries investigated are
expected to lead to differences across the three industries
considered.
The Airline failurelrecovery scenario involves a relatively
complex and lengthy recovery process related to lost luggage.
For instance, the lost luggage recovery might be enacted in the
following way: I) the airline employee receives the customer
complaint and completes the forms necessary to begin the
recovery process, 2) the airline baggage personnel locate the
luggage and place it on the right plane, 3) baggage personnel at
the destination unloadthe luggage, and4)otherairline personnel
deliverthe luggage to you the next day. The airline scenario has
the longest recovery time ofthe scenarios utilized (i.e., the next
aftemoon).
In contrast, the Cable TV service recovery process is less
complex and much shorter. In this case the customer calls the
CableTV company and arepairperson is there to fix the problem
within twohours. The numberofpeople involved in the recovery
process is smaller and the time involved in the recovery process
is much shorter. In the CreditCardscenariothe service recovery
process is even less complex and shorter. The customercalls the
CreditCardcompany, speaks with one individualandthe account
is correctedimmediately. The simplicityand limitedeffortonthe
part of the customer in this case may increase the satisfaction
level derived from the recovery. Therefore, based on scenario
recovery complexity and time differences the following
hypothesis is proposed:
H3: Service recovery evaluations and the resulting behavioral
intentions will be more favorable in recoveries that are less
complex and more timely.
Service FailurelRecovery Outcomes
In this study we consider four principle outcomes of service
failure/recovery encounters. These outcomes includeevaluative
outcomes and behavioral outcomes. Thetwo principleevaluative
outcomes of the service failurelrecovery encounter that we
considerare perceived service quality and customersatisfaction.
The behavioral outcomes considered include word-of-mouth
intentions and repurchase intentions. Subsequently, all four of
these outcomes are briefly discussed.
PerceivedService Quality. Several authors distinguish between
two basic types of service quality (e.g., Gronroos 1983b;
Parasuraman, Zeithaml, and Berry 1985; Kelley, Hoffman, and
Davis 1993). The first quality dimension is technical quality,
which relates to what is delivered, and isjudged by the customer
after the service is performed. Technical or outcome quality is
the culmination of having a service need met. The second
dimension focuses on how the service is delivered (fUnctional or
processquality), and relatestothe experiencethatacustomerhas
while the need is being met. The focus of our research is on
servicerecoveryprocess quality. Causal attributions associated
with service recoveries are predicted to influence customer
perceptions ofservice recovery process quality, even when the
service recovery outcome is held constant.
Customer Satisfaction. Satisfied customers experience "a
pleasurable level of consumption-related fulfillment" (Oliver
1997, p. 13). Consumers rely on customerexpectationsto reach
a judgment regarding the fulfillment response associated with
customersatisfaction(e.g., DrogeandHalstead 1991;Oliverand
DeSarbo 1988). A customer is assumed to have expectations
regarding service performance, and these expectations are
compared with actual perceptions ofperformance as the service
is consumed. It is reasonable to assume that this same internal
process occurs regarding service failures and recoveries.
Word-ol-Mouth Intentions. Several services marketing
researchers have considered the word-of-mouth intentions
associatedwithserviceencounters(e.g., Parasuraman,Zeithaml,
and Berry 1988; Parasuraman, Berry, and Zeithaml 1991;
Boulding et al. 1993). Word-of-mouth communications are
recognized as a very common and important form of
communication for services marketers and customers. The
majority of dissatisfied customers will participate in private
word-of-mouth as opposed to either taking no action, or
registering a formal complaint of some form (Richins 1983).
This study focuses on consumers' intended word-of-mouth
communications following aservice failure/recovery encounter.
Repurchase Intentions. The benefits of maintaining a base of
long-term customersare widely recognizedbyservicesmarketers
(e.g., Reichheld and Sasser 1990). One means of assessing
customerloyaltyand hencethe likelihoodofcustomers returning
is through their repurchase intentions (Jones and Sasser 1995).
In this study we specifically consider the post-service
failurelrecovery repurchase intentions ofconsumers.
THE STUDY
Research Methods
Two levelsofservice recoverystability (stableand unstable) and
three levels of service recovery locus (customer, service
employee, and service firm) were manipulated in a scenario
format. The design was replicated within subjects across three
service industries in which the complexity and length of the
service recovery process were manipulated (Airline, Cable TV
and Credit Card). Subjects were randomly assigned to receive
one of the six experimental conditions in each of the three
services.
Scenario Development
The scenarios used in this study were developed in several
separate stages using independent samples. In order to obtain a
better understanding of common, naturally occurring service
failures and their outcomes, the Critical IncidentTechnique was
Fall 2001 53

6. utilized. This procedure adhered to the suggestions of other
authors (e.g., Elig and Frieze 1979; Lichtenstein and Bearden
1986) by using open-ended elicitation to determine realistic
service failures and their outcomes.
In the fIrst stage, seventy-six service failure/recovery incidents
were collected early in the semester from student subjects (43
male, 33 female) enrolled in two Marketing Principles courses
taught by one of the authors at a large southeastern university.
Twenty-two different types ofservices with a full range ofpoor
to excellent recoveries were cited in the collected critical
incidents. Failure incidents from eightdifferenttypes ofservices
were chosen for further developmentbased on their frequency of
occurrence in the pool(Airline-lost luggage; Auto Repair-work
delay; Cable Television-loss ofservice; Credit Card Company-
incorrect billing; Dry Cleaners-damaged shirt; Fast Food-cold
meal; Hotel-overbooked on rooms; Theater-movie projector
breakdown). Each ofthese failure incidents could be classified
as a core service failure, as they were due to mistakes or other
technical problems with the service itself(Keaveney 1995). Core
service failures were one of the major critical incident groups
identified by Bitner, Booms, and Tetreault(1990) and have been
subsequently considered by several other researchers (e.g.,
Kelley, Hoffman, and Davis 1993; Bitner, Booms, and Mohr
1994; Keaveney 1995).
In the secondstage, we identifIed satisfactory service recoveries.
A number of alternative service recovery resolutions were
generated for each of the eight retained failures. Four expert
judges (faculty members and doctoral students familiar with the
services literature) and a sample of forty-one undergraduate
business students (23 male, 18 female) enrolled in an
introductory marketing course were presented with the eight
incidents and asked to write down three possible resolutions to
each situation: one poor, one satisfactory, and one excellent
resolution. The subjects had not participated in the reporting of
the original critical incidents. The responses ofthe expertjudges
and forty-one participants were then utilized to identifY a range
ofpossible recoveries for each incident.
In the third stage, fifty-eight undergraduate businessstudents (30
male, 28 female) that had not participated in the previous
scenario development stages were asked to scale the service
recoveries for each incidentusinga9-pointLikert-type scale with
the labelsofPoorResolution(1),SatisfactoryResolution(5), and
Excellent Resolution (9). The subjects were from an advanced
marketing course and were ｰｲ･､ｯｭｩｮ｡ｴｾｹ＠ seniors (87.9%) that
ranged in age from 20 to 34 years ( x = 22.1 years). This
procedure provided a means for quantifYing the ratings of the
service recoveries being considered. Service recoveries with
wide variances in theirratings were discarded (standarddeviation
of 2.0 or more). Based on the computed mean values, one
satisfactoryrecovery for each servicefailure incidentwas chosen
for inclusion in the study. In this stage of the scenario
development process, subjects also rated the realism ofeach of
the eight service failure incidents on a 9-point Likert-type scale
with anchors ofVery Realistic (I) and Very Unrealistic (9). The
54 Journal ofMarketing THEORY AND PRACTICE
mean values for the realism scale ranged from 2.31 to 3.80.
Thus, all eight ofthe service failure incidents were perceived as
realistic.
Pre-Test I ofScenario Manipulations. Scenarios were further
developed and refined through the following procedures. First,
recoveries associated with eachoftheeightfailure incidentswere
developed by the authors to fIt each ofthe six cells included in
the design. Then fIve expert judges familiar with the services
and attribution theory literature (faculty members and doctoral
students) reviewed each of the failure/recovery scenarios and
classified causes of the recoveries into discrete attribution
categories (i.e., stable or unstable; customer-attributed, service
employee-attributed, orservice fIrm-attributed; andcontrolledor
uncontrolled). After two rounds ofrevisions, the scenarios were
reviewed by a second group of three faculty members also
familiar with both the services and attribution theory literature.
Their review resulted in additional wording changes.
The scenarios were pre-tested with 91 (51 male, 40 female)
undergraduate business students to determine realism and ifthe
attribution manipulations were being ｰ･ｲ｣･ｩｶ･ｾ＠ as anticipated.
Subjects ranged in age from 18 to 33 years ( x = 21.8 years),
were predominately juniors (n = 26) and seniors (n = 59), and
were drawn from classes that had not participated in the earlier
scenario development stages. After reading each scenario,
subjects completed a fifteen-item modifIed Causal Dimensions
Scale (Russell 1982) designed to assess causal perceptions ofa
particular situation in terms of the locus, stability, and
controllability dimensions. The original scale consisted ofnine
randomly presentedsemanticdifferentialstatementsthatrelate to
aparticularsituation,three for each causaldimension. Due to the
enlarged locus dimension utilized in this study, the locus scale
items were adapted to capture customer perceptions of
attributions forthe self(i.e., customer),the serviceemployee,and
the service firm. For each locus dimension respondents were
asked if I) taking action was something that was "Outside" or
"Inside" of"You", "The Employee", or "The Firm", 2) taking
action was something about "Other(s)" or "You", "The
Employee", or "The Firm", and 3) if the action taken reflected
"The Situation" or "You", "The Employee", or "The Firm".
Control was assessed by asking subjects if the outcome of a
scenario was I) "Intended" or "Unintended", 2) "Controllable"
or "Uncontrollable", and if3) "Someone was Responsible" or
"No One was Responsible". Stability items asked subjects ifthe
action taken in a particular scenario was perceived as I)
"Permanent" or"Temporary", 2) "Stable" or"Unstable", and 3)
"Unchanging" or"Changing". Asingle-item 9-pointLikert-type
scale was again utilized to assess scenario realism (l="Very
Realistic" and 9="Very Unrealistic").
The modifiedCausal Dimensions Scalewas factoranalyzedwith
varimax rotation. FindingsconfIrmedasingle-factorstructure for
each ofthe five causal dimensions. Coefficient alphas for each
causal dimension were as follows: stability (a = .64); control (a
= .38); customer (a = .73); employee (a = .70); and firm (a =
.74). The manipulations were tested further via ANOVA. All of

7. the treatment manipulations, with the exception ofthe stability
manipulation for one scenario were found to be statistically
significant(p< .05). Significantinteractionsofmanipulationson
manipulation check scales were not identified in any of the
scenarios and Duncan's Multiple Range post-hoc test revealed
significant differences between expected group means (Perdue
and Summers 1986).
In addition to demonstrating that the intended impact of the
manipulations was successful, the manipulation effects should
be of a sufficient magnitude (Perdue and Summers 1986). A
statistic developed to provide a conservative estimate of the
strength ofassociation or relation between the independentand
dependent variables (in this situation, the manipulation check
measure is being analyzed) is the omega-squared statistic (w).
Omega-squared is suggested as an appropriate indicator of
effect size in the context of ANOVA models (Green 1978;
Kerlinger 1986; Perdue and Summers 1986; Tabachnick and
Fidell 1989). The function of omega-squared in ANOVA is
similar to R2 in the context ofmultiple regression (Green 1978).
"Omega-squared represents the proportion of variance in the
dependent variable accounted for by agiven main or interaction
effect" (Perdue and Summers 1986, p. 323). Effect sizes in this
pretestrangedfrom .06to .21. Significantdifferences andmeans
falling at the appropriate scale ends suggested the service
recovery causes were being properly classified. Findings
generally indicated that the independent variable manipulations
were effective. However, due to modest effectsizes and the low
coefficientalphaassociatedwith the control dimension, asecond
pre-test was conducted.
Pre-Test I! of Scenario Manipulations. Failure/recovery
scenarios depicting each of the six treatment cells from five
service industries (i.e., Airline-lost luggage; Auto Repair-work
delay; Cable Television-loss ofservice; CreditCard Company-
incorrect billing; Hotel-overbooked on rooms) were retained
based on the results of the first pre-test. These five sets of
scenarios were pre-tested a second time after the following
changes were made: 1) the manipulations were integrated into
the cover page ofthe questionnaire, 2) the service failure was
more clearly delineated from the recovery in the scenarios, and
3) the wording of the stability manipulation statements was
strengthened by changing "rarely" and "always" to
"inconsistent" and "consistent", respectively. Business
students from classes not previously utilized in the scenario
development process (52 male, 33 female) were randomly
assigned to one ofthe six manipulation conditions, scenarioorder
was randomized for each subject. Subjectsranged in age from 20
to 39 years ( x= 24.0 years) and consisted ofseniors (n = 65)
and MBA students (n = 20). Factor analysis with varimax
rotation again confirmed a single-factor structure for each ofthe
five proposed causal dimension sub-scales. Coefficient alphas
for each ofthe causal dimensions were as follows: stability (a =
.79); control (a =.64); customer (a =.77); employee (a =.82);
and firm (a = .84). One significant interaction was found
(Stability with Locus) in one ofthe service industries (i.e., Auto
Repair-work delay). This service industry scenario was
eliminated from further consideration. All of the treatment
manipulations were statistically significant. Duncan's Multiple
Range post-hoc procedure revealed significant differences
betweengroupmeans in all cases. Thefmdings indicatedthatthe
causes were properly classified, and the independent variable
manipUlations were effective. Based on effect sizes and realism
scores, scenarios from the Airline, Cable TV, and Credit Card
industries were retained. All three scenarios were perceived as
both controlled (Airline: x= 1.84, SD = .80; Cable TV: x=
2.07, SD =.86; Credit Card: x=2.16, SD =1.07), and realistic
(Airline: x=1.96, SD =1.32; Cable TV: x=2.84, SD =1.80;
Credit Card: x= 2.20, SD = 1.29). The hotel scenarios were
eliminated from further study as locus effect sizes were lower in
every category relative to the other three services. To further
strengthen the stability and locusmanipulations, wordingchanges
were again made and a third pre-test was conducted.
Pre-Test II! ofScenario Manipulations. Similar to the earlier
pre-tests, subjects were randomly assigned to one of the six
experimentalconditionswith scenarioindustryorderrandomized.
Subjects included86 (49 male, 37 female) businessstudentsfrom
classes that had notparticipated in the earliere!"e-tests. Students
ranged in age from 19 to 50 years ( x = 23.9 years)
Dimensionality ofthe manipulation check items was examined
via factor analysis with varimax rotation. Coefficient alphas for
the causal dimensions were as follows: stability (a = .90);
customer (a = .83); employee (a = .82); and firm (a = .82).
Utilizing ANOVA, all of the treatments were found to be
statistically significant. ANOVA results and omega-squared
statistics are presented in Table 1. Duncan's MUltiple Range
post-hoc test revealed significant differences between group
means in all cases. The fmdings indicated that the scenario
manipulationswere beingproperly classifiedandwere effective.
The three failure scenarios and descriptions for each of the
treatment cells retained for the fmal study are presented in
Appendix Table 1.
Data Collection Method
A convenience sample of customers of four child-care centers
located in a large southeasterncity servedas the samplingframe.
Packets containing a cover letter and two identical surveys were
distributed to each potential respondent at all four centers. The
second survey in the packet was included with a request that
"anyone else in your home over the age of 18" fill out the
questionnaire as well. The packets were placed in a location
(either a file or box depending on the particular center) that was
checked daily by each child's parent or guardian. If siblings
attended a particular center, a single packet was placed in the
youngest child's file or box. A reminder letter was distributed
five days after the initial survey distribution. A survey drop box
was provided and placed prominently near the front entrance
desk of each child-care center. For each center's cooperation,
frozen flavored treats were provided for all ofthe children.
Fall 2001 55

8. TABLE 1
ANOVA RESULTS OF PRE-TEST III SCENARIO MANIPULATIONS
Dependent Variable F-Ratio' W' F-Ratio'
Airline
Stability
Locus
Customer
Employee
Firm
147.06 .63
34.93
30.15
45.64
.44
.40
.51
Cable TV
Stability
Locus
Customer
Employee
Firm
100.55
7.97
.54
.06 32.18
22.45
27.84
.42
.34
.39
Credit Card
Stability
Locus
Customer
Employee
Firm
'All effects significant at the p<.OOllevei
182.87
Subjects randomly received only one of the six experimental
conditions. The order of presentation of the industries was
randomized for each subject as well. A total 0[332 survey
packets (i.e., 664 surveys) were distributed. There were 212
surveys returned (response rate = 32%), ofwhich 29 were blank,
resulting in a useable response rate of28% (n = 183).
Subjects. Respondents ranged in age from 18 to 71 years ( x=
32.7), with females making up 64.4% of the sample. Most
respondents attended some college (36.9%) or were college
graduates (27.9%). Respondents were familiar with services, as
139 (77.2%Lhad worked in a service business ranging from 1to
40 years ( x = 8.57 years). About one-half of the subjects
(50.2%) had experienced problems similarto those presented in
the scenarios (Airline = 51.4%, Cable TV = 43.9%, and Credit
Card = 55.2%).
Measurement
Service recovery process quality was defined as the customer's
subjective evaluation of 'how' the service recovery was
delivered, relative to the level ofperformance that this particular
type ofservice company can and should deliver (i.e., the desired
service level) (Zeithaml, Berry, and Parasuraman 1993). An
attribute based seven-item, 9-point Likert-type scale applicableto
service recovery process quality was utilized. The SERVQUAL
instrument (Parasuraman, Berry, and Zeithaml 1991, 1993;
Parasuraman, Zeithaml, and Berry 1994)provided the foundation
for the service recovery process quality measure used in this
study.
Service recovery satisfaction was defmed as the customer's
overall psychological state resulting from his/her comparison of
expectations ofwhata service provider 'will' offer, and the actual
recovery experience (Zeithaml, Berry, and Parasuraman 1993).
56 Journal ofMarketing THEORY AND PRACTICE
.68
40.58
41.57
35.07
.48
.49
.45
Similar to other studies (e.g., Kelley and Davis 1994; Oliva,
Oliver, and MacMillan 1992), satisfaction/dissatisfaction is
assumed to be unidimensional ranging from Lower Than
Expected (1) to Higher Than Expected (9). An attribute based
nine-item, 9-point Likert-type scale derived from appropriate
SERVQUAL items was used to measure service recovery
satisfaction (Parasuraman, Berry, and Zeithaml 1991, 1993;
Parasuraman, Zeithaml, and Berry 1994). Word-of-mouth
intentions were defmed as the customer's belief that he or she
would discuss the incident either favorably, unfavorably, or
neutrally with at least one person within the customer's social net
(i.e., family member, friend, acquaintance), who was not directly
involved in the service failure/recovery encounter. This
definition is consistent with Day and Landon's (1977) private
response classification, as well as the word-of-mouth
conceptualization ofRichins (1983). Word-of-mouth intentions
were measured through a four-item, 7-point scale. Subjects
indicated their likelihood of praising/criticizing and
recommending/warning others about the service firm. Finally,
repurchase intentions were defined as a customer's beliefthat he
or she would purchase from the same service firm at some future
date. Repurchase intentions were operationalizedthrough a four-
item, 7-point scale similar to that utilized by Halstead and Page
(1992). The complete perceptions and behavioral intentions
scales are provided in Appendix Table 2.
RESULTS
Factor analysis ofthe evaluative and behavioral intentions items
was conducted using oblique rotation due to correlations among
the constructs (Pedhazur and Schmelkin 1991; Tabachnick and
FideJl 1989). Items loaded well on the appropriate factors.
Word-of-mouth in the Credit Card industry was the only variable
with potential cross-loading concerns (see Table 2). Although
WOM ICredit Card and WOM2Credit Card cross-loaded with the

9. repurchase intentions factor (F4) they were kept with the word-
of-mouth factor (F3) to be consistent with the Airline and Cable
TV industries. None ofthe items were deleted based on an item-
to-total correlation criterion ofr < .25 (Nunnally 1978). All of
the scales demonstrated strong internal consistency with alphas
ranging from .87 to .97 (see Table 2). The total variance
extracted by the four factors was 80.5%, 79.l%, and 78.0% for
the Airline, Cable TV, and Credit Card scenarios, respectively.
Hypotheses were tested utilizing a repeated measures design of
the multivariate analysis of variance (MANOVA) procedure.
The Mauchly test of sphericity was used to determine if an
adjustment in the degrees of freedom was necessary, thus
affecting critical Fvalues. The Mauchly test findings indicated
rejection ofthe null hypothesis thatthe error covariance matrix
ofthe orthonormalized transformed dependent variables were
proportional to an identity matrix for both quality (W = .960,
P< .05) and satisfaction (W =.961, P< .05). The Greenhouse-
Geisser epsilon was used to adjust the degrees of freedom for
the averaged tests of significance for these variables.
The repeated measures MANOVA indicated significant
between-subjects effects for both stability (F4,456 = 3.65, P <
.01), and locus (Fg916= 2.34, P< .05), but not their interaction
(Fg916 = 1.21, P >..05). In addition, there was a significant
within-subjects effect (i.e., recovery time) (Fg694 = 19.88, P <
.001). Significant interactions also resulted for recovery time
by stability (Fg694 = 3.21, P < .01) and recovery time by locus
(F16 060= 1.71, P< .05). Univariate tests indicated 1) recovery
quaiity was statistically significant (p < .05) in the recovery
time by stability interaction, and 2) satisfaction (p < .05) and
word-of-mouth intentions (p < .05) were statistically
significant in the recovery time by locus interaction. In the
following discussion of the findings relative to the specific
hypotheses, significant findings were based on a .05 (or less)
probability level.
HI - Stability
Findings suggest (see Tables 3-6) that for service recovery
quality the anticipated effect of stability was present for the
Airline industry ( Xstable = 5.13; Xunstable = 4.56), but was not
present in either the Cable TV ( Xstable = 5.51; Xunstable = 5.69)
or Credit Card ( Xstable = 6.14; Xunstable = 6.13) industries.
Examination of the means across the three service settings
considered indicated that stable recoveries result in enhanced
positive word-of-mouth intentions ( Xstable = 4.58; Xunstable =
4}5), and more favorable repurchase intentions ( Xstable= 4.90;
Xunstable =4.53). These findings provide partial support for H1.
H2 - Locus
In the Airline industry, the Duncan's post-hoc test indicated
satisfaction with the recovery attributed primarily to the
customer was rated significantly lower ( x= 4.45) than
satisfaction with the recovery attributed either primarily to the
firm ( x=5.11) or employee ( x=4.99). In the Credit Card
industry recoveries primarily attributed to the !lrm were
associated with the lowest levels of satisfaction ( x = 5.93),
which significan.!.ly differed from those primarily attributed to
the employee ( x = 6.44). Recovery satisfaction evaluations
ｦｯｾ＠ the Cable TV Jndustry ｦｯｬｬｾｷ･､＠ those predicted in H2
( xcustomer = 5.46; xfirm = 5.61; Xemployee = 5.95), but were not
significantly different.
The mean distribution pattern for word-of-mouth intentions in
the recovery time by locus interaction was very similar to
those observed for recovery satisfaction. In the Airline
industry, recoveries primarily attributed to the customer
resulted in significantly lower word-of-mouth intentions ( x
= 3.66). The Duncan's Multiple Range post-hoc test further
revealed no significant group mean differenc:.s between
recoveries primarQy attributed to either the firm ( x =3.93) or
the employee ( x = 3.91). For the Cable TV industry,
recoveries primarily attributed to the employee were
significantly_more likely to elicit favorable ｾｯｲ､ＭｯｦＭｭｯｵｴｨ＠
intentions ( ｾ･ｭｰｬｯｹ･･＠ = 5.15) relative to firm ( Xfirm =4.48) or
customer ( xcustomer = 4.71) attributed recoveries. Further
examination of the means across the three service recovery
times indicated partial support for H2. Specifically, recoveries
primarily attributed to the employee lead to ｨｩｧｾ･ｲ＠ levels of
ｰｾｲ｣･ｩｶ･､＠ recovery quality ( Xcustomer = 5.29; xfirm = 5.40;
Xemployee = 5.73) across service industries.
H3 - Recovery Time Differences
In H3 it was expected that service recovery evaluations and
intentions would be more favorable in recoveries that were
less complex and more timely. In the context ofour study it
was expected that evaluations and intentions would be more
favorable for the Credit Card scenario followed by the Cable
TV and Airline scenarios, respectively. The Duncan'sMUltiple
Range post-hoc testrevealed ｳｩｧｮｩｦｩ｣ｾｴ＠ differences ｢･ｴｷｾ･ｮ＠ all
group means for recovery quality ( XCreditCard = 6.14; xCableTV
= 5.59; ｾａｩｲｬｩｮ･＠ =4.86), satisfaction ( XCreditCard = 6.2,!! XCableTv
= 5.68; xAirline =4.85), and repurchase intentions ( xCreditCard =
5.37; Xcable TV = 5.01; XAirline = 4.06). Word-of-mouth
ｩｮｾｮｴｩｯｮｳ＠ were also significantly ｾｩｧｨ･ｲ＠ for the Credit Card
( Xwom = 4.94) and Cable TV ( xwo..!!) = 4.80) scenarios in
comparison to the Airline recovery ( xwom = 3.83) (see Tables
3-6). In summary, H3 was supported.
DISCUSSION AND MANAGERIAL IMPLICATIONS
By definition, stability attributions represent customer
perceptions of the likelihood of having the same recovery
experience if the circumstance occurred again in the future.
The findings associated with HI suggest customer behavioral
intentions (i.e., word-of-mouth and repurchase intentions) are
more favorable when customers believe that the recovery they
received is consistently implemented (i.e., is stable) when
failures do occur. Managers might take this into consideration
by making organizational policies regarding service recovery
Fall 2001 57

10. TABLE 2
ED FACTOR PATTERN AND RELlABn..rry COEFFICIENTS (ALPHAS) FOR MAIN STUDY BEHAVIORAL PERCEPTIONS AND
INTENTIONS SCALES'
Airline
FI F2 F3 F4
(.97)
.86
.82
.81
.82
.81
.78
.74
.94
.93
(.93)
.86
.88
.82
.74
(.95)
.55
.78
.86
.93
.89
.82
.93
(.89)
.73
.74
.81
.92
Cable TV
Fl . F2 F3 F4
(.97)
.89
.94
.92
.83
.78
.78
.81
.93
.85
(.90)
.76
.79
.79
.65
(.95)
.62
.69
.80
.91
.87
.86
.83
(.88)
.72
•.72
.82
.80
Fl
(.97)
.88
.90
.85
.87
.96
.88
.79
.89
.83
Credit Card
F2 F3
(.91)
.87
.91
.86
.61
(.95)
.84
.84
.87
.90
.84
.79
.89
.55
.65
hmlJarentheses are reliability coefficients. The other numbers are factor loadings -obtained after oblique rotation of the initial solutions. Loadin
e been omitted. The total variance extracted by the four factors was 80.5%, 78.0%, and 79.1% for the Airline, Credit Card, and Cable TV sa
Eigenvalues for each of the factors were as follows: Fl == 14.42 airline, 14.00 Cable TV, 12.73 OeditCard; F2 = 3.43 airline, 3.24 Cable TV. 4.44 Credit Card; F3
e TV, 1.21 OeditCard; F4 = .766 airline•.939 Cable TV, .819 Cr,ditCard·
nd behavioral intentions labels correspond to those of the items listed in Appendix Table 2.
Marketing THEORY AND PRACTICE

11. TABLE 3
DESCRITPTIVE STATISTICS FOR SERVICE RECOVERY PROCESS QUALITY
Airline Cable TV Credit Card Total
Standard Standard Standard Stan
Stability Mean Deviation Mean Deviation Mean Deviation Mean Devi
Stable 4.94 1.43 5.11 .86 5.87 .98 5.29 1.
Unstable 4.27 1.12 5.53 1.51 6.73 1.37 5.30 1.
Total 4.62 ＱＮｾ＠ 5.30 1.21 6.27 1.24 5.29 1.
Stable 5.18 1.92 5.75 1.34 6.40 1.35 5.80 1.
Unstable 4.89 1.60 6.05 1.03 6.03 1.13 5.65 1.
Total 5.04 1.76 5:90 1.21 6.23 1.25 5.73 1.
Stable 5.30 1.46 5.67 1.52 6.13 1.10 5.72 1.
Unstable 4.48 1.34 5.43 1.32 5.64 1.13 5.06 1.
Total 4.90 1.45 5.55 1.42 5.89 1.13 5.40 1.
Stable 5.13 1.62 5.51 1.28 6.14 1.17 5.59 1.
Unstable 4.56 1.39 5.69 1.31 6.13 1.28 5.35 1.
Total 4.86 1.54 5.59 1.29 6.14 1.22 5.48 1.
TABLE 4
DESCRIPTIVE STATISTICS FOR CUSTOMER SATISFACTION WITH TIlE RECOVERY
Airline Cable TV Credit Card Total
Standard Standard Standard Stan
Stability Mean Deviation Mean Deviation Mean Deviation Mean- Devi
Stable 4.52 1.26 5.16 1.28 5.84 1.06 5.13 1.
Unstable 4.38 .99 5.81 1.63 6.83 1.36 5.42 '1:6
Total 4.45 1.14 5.46 1.48 6.30 1.30 5.26 1.4
Stable 5.00 1.90 5.99 1.28 6.70 1.40 5.92 1.
Unstable 4.99 1.63 5.90 1.07 6.15 1.03 5.74 1.
Total 4.99 1.76 5.95 1.18 6.44 1.26 5.83 1.
Stable 5.24 1.57 5.66 1.61 6.11 1.16 5.73 1.
Unstable 4.97 1.40 5.57 1.18 5.75 1.05 5.35 1.
Total 5.11 1.48 5.61 1.41 5.93 1.11 5.55 1.4
Stable 4.91 1.61 5.61 1.42 6.23 1.26 5.58 1.
Unstable 4.78 1.39 5.77 1.30 6.24 1.22 5.51 1.
Total 4.85 1.51 5.68 1.36 6.24 1.24 5.55 1.5
Fall 2001 59

12. clear to both employees and customers, and ensuring that
policies are implemented in a consistent manner. In addition,
when service recoveries are implemented it might be good
practice to clearly convey to the customer that the recovery
received is consistently implemented by the representatives of
the service organization in the rare instances when a service
failure occurs.
Within the stability manipulation, recovery qualityperceptions
varied, likely due to subtle differences in the failures
associated with each scenario. In the Cable TV scenarios the
service provider simply performed the service incorrectly.
Similarly, in the Credit Card scenarios the service provider
was wrongfully "demanding" possession of something
belonging to the customer (money). In the Airline scenarios
the airline was in possession ofsomething rightfully belonging
to the customer (luggage). When a service organization
wrongfully has possession of something belonging to the
customer (e.g., lost luggage, lost dry cleaning, misplaced
jewelry), the customer's primary focus when evaluating the
recovery is on having this property returned. In situations
where tangible objects are involved, customers place added
importance on knowing that the problem will be resolved
consistently (Le., stable recovery).
A second generalization drawn from our results concerns who
should execute the service recovery. Based on the findings it
seems safe to recommend that service failures should be
resolved by front-line service personnel whenever possible.
The constant across the three service settings investigated was
the key role of the service employee in perceived recovery
process quality. Our findings provide empirical support of
earlier contentions that proper training and empowerment of
front-line service employees is extremely important to
successfully carry out a service recovery program (Hart,
Heskett, and Sasser 1990).
Antithetically, a service recovery locus - repurchase intentions
relationship was not supported in H2. Although our findings
indicated an employee-based recovery was important for
quality and satisfaction evaluations and word-of-mouth
intentions, who the recovery was attributed to was not
significantly related to customer repurchase intentions.
Overall, the results pertaining to H2 can be interpreted as
follows. First, it seems prudent to have contact employees
resolve service failure situations whenever possible due to the
favorable impact this has on quality, satisfaction and word-of-
mouth intentions. However, depending on their goals for the
recovery process, service managers should not be overly
discouraged when it is not possible for a failure to be resolved
by contact personnel. Future repurchase intentions are not
significantly different whetherthe employee, firm, or customer
is deemed primarily responsible for the recovery. For
managers primarily interested in getting customers who have
experienced a service failure to repurchase in the future it
seems to be less important as to who is primarily responsible
60 Journal ofMarketing THEORY AND PRACTICE
for the recovery process and more important that an
appropriate recovery outcome is provided.
H3 considered the impact of service recovery time and
complexity on customer evaluations and intentions. Service
recovery time and complexity was found to result in
significant differences for the four recovery outcomes as
predicted. The greater complexity and length of the Airline
recovery process resulted in the least favorable recovery
outcomes. The mid-level amount of time involved in the
Cable TV service recovery process resulted in that recovery
having outcomes at a mid-range level. Customers evaluated
the simple and quick Credit Card recovery the most favorably.
It is interesting to note that these differences emerged despite
the fact that our pre-testing results indicated that the recovery
outcomes were perceived as equivalently satisfactory. These
findings lend support to previous suggestions that customer
evaluations will be more positive for service failures remedied
by less complicated and expeditious recovery processes (Hart,
Heskett, and Sasser 1990; Kelley, Hoffman, and Davis 1993).
In the scenarios both time and complexity move together. In
order to better understand these relationships, future
researches may wish to further test these findings by varying
the time and complexity within rather than between services.
For example, by manipulating recovery time within services
it would be possible to account for the expectations that
customers have for the performance in the industry, as well as
account for how the type of failure (i.e., lost luggage, versus
cable problems, versus an incorrect charge) impacts
assessment ofthe recovery.
Based on the findings here, managers will want to take into
account the complexity and length of the serviCe recovery
process in their particular industry. Customer evaluations of
recovery in complex and lengthy service recovery processes
are less positive than for service failures remedied by less
complex and shorter service recovery processes. As a result,
failures encumbered by lengthy orcomplex recovery processes
may likely require more elaborate forms of compensation
during the course ofthe recovery. In addition, providing quick
and simple recoveries in the event of a service failure can
provide a strategic advantage in positioning the firm relative
to competitors.
Limitations and Future Research Directions
Our study provides an experimental investigation ofcustomer
attributions associated with service recovery. Our research
also extends existing service recovery literature and
knowledge by utilizing an expanded locus dimension.
Expanding the locus dimension beyond the traditional view
that dichotomizes locus into internal and external dimensions
enhances our understanding of service recovery. In addition,
our findings provide empirical support for previous conceptual
propositions related to the importance of service recovery
complexity and time. Systematic replication of this study in

13. other service settings is recommended in order to develop a
deeper understanding of the differences identified in this
research.
In order to establish the soundness of the methodology we
utilized mUltiple pre-tests in developing the scenarios. We
started with a qualitative approach (i.e., Critical Incident
Technique) and rigorously applied the methodology to ensure
that the independentvariable manipUlations were both properly
classified and effective. In the main study a repeated measures
design with multivariate analysis ofvariance was utilized with
established and well-recognized dependent measures. In
addition, independent samples were utilized for each of the
research phases in an effort to improve internal validity.
Convenience samples of college students were utilized in the
development of the scenarios, while the main study used a
convenience sample of day care customers. An important
issue is the appropriateness ofthe subjects given the nature of
the task at hand. In the development and early testing of the
proposed model, it seems apparent that students do experience
service failures and recoveries. In addition, there was not an
a priori reason to conclude that students do not attribute causes
to explain what happens to them, do not have perceptions, or
fail to develop intentions. However, our use of convenience
samples throughout the study introduces the possibility of
selection bias and must be noted as a limitation. Future
researchers may wish to utilize non-student based and
probability samples to address this potential threat to validity.
In addition, this study only included core service failures and
a limited number ofloci in the scenarios tested. In the future,
similar research methods could be used to assess the effects of
attributions on post-recovery evaluations and behaviors
associated with different service failure types (c.f., Bitner,
Booms, and Tetreault 1990; Kelley, Hoffman, and Davis 1993)
across additional locus categories. Case studies could also be
utilized to provide a richer understanding ofthese relationships
in service recovery. Finally, the use of Likert scales for this
type of study also has limitations. Other measurement tools
could be utilized in the future to address this issue.
An alternative interpretation of our findings also merits the
consideration of additional research. Specifically, the effects
we found may be influenced by the competitive nature ofthe
industries included in this study. The three industries
considered in the scenarios vary on several dimensions but in
particular their level of competition. The Airline industry is
characterized by varying levels of competition on routes.
Flight routes to some destinations are virtual monopolies,
while other flight routes are highly competitive and allow the
consumer to pick from several carriers. Local Cable TV
markets are generally characterized by very limited
competition. In fact, this industry might be termed a virtual
monopoly. While there are some competing options available,
such as satellite dishes and digital dish networks, often the
consumer perceives them as either impractical or cost
prohibitive. The Credit Card industry is highly competitive.
Consumers are bombarded with Credit Card offers through
direct mail and telephone. As a result, it is relatively easy for
customers to switch from one service provider to another.
Overall, the three industries considered cover three different
levels of competitiveness. The Airline industry might be
characterized as an industry with moderate levels of
competition and consumer choice. The Cable TV industry is
characterized by very limited levels of competition and
consumer choice, and the Credit Card industry provides an
example of an industry characterized by high levels of
competition and consumer choice. Thus, the three sets of
scenarios used in this research consider a cross-section of
service industries with regard to competition and the extent to
which customers have choices within the industry.
It is difficult to draw any well-grounded industry-specific
conclusions regarding service recovery based on this research.
Therefore, the following speculative interpretation of our
results is presented. Perhaps Airline customers recognize that
while they do have some choices, the choices they have are
limited. As a result, customers may take their limited choice
set into account and evaluate a stable recovery more favorably.
In the essentially noncompetitive Cable TV industry the locus
of the service recovery was a significant factor in customer
evaluations and intentions. In this case, customers may take
into account that they have essentially no viable alternatives.
This lack ofviable alternatives may lead to a customer thought
process something like this: "Even though I have limited
alternatives in this situation, it is nice to know that someone at
my local Cable TV company cares enough to quickly respond
to my request."
Finally, in the highly competitive Credit Card industry
customers valued stability in the recoveries attributed to the
service employee and firm. However, customers rated
unstable recoveries attributed to themselves most favorably of
all the treatments considered in this industry. Credit Card
customers have many choices and in many cases they are
bombarded with these choices on an almost daily basis. First,
as suggested by attribution theory, stable recoveries attributed
to employees and the firm were evaluated more favorably than
unstable recoveries attributed to the same loci. One possible
explanation for the finding concerning the unstable customer
attributed recovery might focus on the level ofrisk and control
associated with the customer's recovery effort. As noted in
the scenarios, customers in the unstable-customer locus cell
treatment rarely take charge in service failure/recovery
situations. Perhaps one reason these customers don't take
control ofthese situations is that there is some risk involved in
that their demands may not be net. However, in the highly
competitive Credit Card industry it might be argued that it is
relatively low risk to take charge in these situations. Credit
Card customers have many choices available. The result is
that it is a "no risk" setting in which to take charge as a
customer - you have many other options ifthings do not work
out. As a result, the customer that is unaccustomed to taking
charge in such situations may do so and feel good about the
Fall 2001 61

14. fact that he/she obtained some results when taking charge of
the failure situation.
time constraints, among others, may all affect the outcome
variables investigated here. Finally, this research restricted its
focus to recovery attributions. How service failure attributions
and recovery attributions interact to impact post-recovery
evaluations and behaviors needs investigation. Future
examination ofthese types of questions may provide a better
understanding of how marketers can use the service recovery
process as a strategic customer retention tool
Further research that extends our knowledge of the
relationships considered in this study will be beneficial. For
example, mood states and personality traits may impact
perceived quality and satisfaction with a particular service
recovery. In addition, lack ofalternatives, switching costs, and
APPENDIX TABLE I
RETAINED SERVICE FAILURE AND CORRESPONDING RECOVERY SCENARIOS
Airline Cable TV Credit Card
The Service Problem
While traveling on your usual airline, you arrive at
your final destination. You wait at the baggage
claim area, but your luggage does not appear with
the other passengers' items. After checking at the
customer service desk, you are told your luggage
has been mistakenly put on adifferent flight and is
expected to arrive at the airport tomorrow
afternoon.
The Service Outcomes
Stable - Customer:
You demand action. You receive an apology and
the luggage is delivered to you the next afternoon.
You consistently take the initiative to get your
complaints addressed.
Unstable - Customer:
You demand action. You receive an apology and
the luggage is delivered to you the next afternoon.
You inconsistently take the initiative to get your
complaints addressed.
Stable - Employee:
The service employee takes action. You receive
an apology and the luggage is delivered to you the
next afternoon. You have heard that this airlines'
employees consistently take the initiative to
address customer complaints.
Unstable - Employee:
The service employee takes action. You receive
an apology and the luggage is delivered to you the
next afternoon. You have heard that this airlines'
employees inconSistently take the initiative to
address customer complaints.
Stable - Firm:
The airline takes action. You receive an apology
and the luggage is delivered to you the next
afternoon. You have heard that this airline
consistentlytakes the initiative toaddress customer
complaints.
Unstable - Firm:
The airline takes action. You receive an apology
and the luggage is delivered to you the next
afternoon. You have heard that this airline
inconsistently takes the initiative to address
customer complaints.
In order to watch more ofyour favorite television
shows, you decide to have cable installed. Soon
afterthe cable representative hooksyourtelevision
up, your screen goes blank. You call the cable
company about the problem.
You demand action. A repair person shows up
two hours later and corrects the problem. You
consistently take the initiative to get your
complaints addressed.
You demand action. A repair person shows up
two hours later and corrects the problem. You
inconsistently take the initiative to get your
complaints addressed.
The employeetakes action. The employee returns
two hours later and corrects the problem. You
have heard that this cable companys' employees
consis-tentlytake the initiativeto addresscustomer
complaints.
The employee takes action. The employee returns
two hours later and corrects the problem. You
have heard that this cable companys' employees
inconsistently take the initiative to address
customer complaints.
The cable company takes action. A repair person
shows up two hours laterand corrects the problem.
You have heard that this cable company
consistentlytakes the initiativeto addresscustomer
complaints.
The cable company takes action. A repair person
shows up two hours laterand corrects the problem.
You have heard that this cable company
inconsistently takes the initiative to address
customer complaints.
62 Journal ofMarketing THEORY AND PRACTICE
You receive your credit card bill and it includes a
charge that you did not make.
You contact the creditcard company and demand
action. Your account is corrected immediately
andyoureceive an apology for any inconvenience.
You consistently take the initiative to get your
complaints addressed.
You contact the creditcard company and demand
action. Your account is corrected immediately
andyou receive an apologyfor any inconvenience.
You inconsistently take the initiative to get your
complaints addressed.
Aftercontactingthe creditcard company, aservice
employee takes action by apologizing for any
inconvenience, and immediately correcting your
account. You have heard that this credit card
companys' employees consistently take the
initiative to address customer complaints.
Aftercontactingthe creditcard company, aservice
employee takes action by apologizing for any
inconvenience, and immediately correcting your
account. You have heard that this credit card
companys' employees inconsistently take the
initiative to address customer complaints.
After contacting the credit card company, the
company takes action by apologizing for any
inconvenience, and immediately correcting your
account. You have heard that this credit card
companyconSistentlytakesthe initiativetoaddress
customer complaints.
After contacting the credit card company, the
company takes action by apologizing for any
inconvenience, and immediately correcting your
account. You have heard that this credit card
company inconsistently takes the initiative to
address customer complaints.

15. APPENDIX TABLE 2
PERCEPTIONS AND BEHAVIORAL INTENTIONS BATTERY
Item
Label Item Wording
QUAL I
QUAL2
QUAL3
QUAL4
QUAL5
QUAL6
QUAL7
Dependability in handling customer service problems.
Willingness to handle customer problems.
Ability ofemployees to handle customer complaints.
Courteousness ofemployees.
Employees who have the knowledge to answer customer questions.
Company has the customer's best interest at heart.
Employees treat customers in a caring manner.
Satisfactionb
SAT!
SAT2
SAD
SAT4
SAT5
SAT6
SAT7
SAT8
SAT9
Dependability in handling customer service problems.
Willingness to handle customer problems.
Ability ofemployees to handle customer complaints.
Courteousness ofemployees.
Employees who have the knowledge to answer customer questions.
Company has the customer's best interest at heart.
Employees treat customers in a caring manner.
How the service problem was corrected.
My feelings towards this service outcome can be described as.
WOMI
WOM2
WOM3
WOM4
Word-of-Mouth Intentions'
I would try to convince my friends and relatives to use thtL. .
I would be likely to recommend thiL to others.
I would be likely to convince my friends and relatives not to use thiL . (-)
I would warn others about using thiL . (-)
Repurchase Intentions'
BUYI Would you use this_ again ifyou had a choice?
BUY2 What is the likelihood that you will go back to thtL. next time you need this service?
BUY3 How likely would you be to repurchase from thiL in the future?
BUY4 What is the likelihood that you will switch to ｡ｮｯｴｨｾ＠ for this service? (-)
Items identified with a "-" were reverse scored.
'Each item was accompanied by a 9-point Likert-type scale with the labels: I-"Lower Than", 5-"The Same As", and 9 -"Higher Than" My Desired Service
Level.
bEach item was accompanied by a 9-point Likert-type scale with the labels: 1="Lower Than", 5="The Same As", and 9="Higher Than" I Would Have Expected.
'Each item was accompanied by a 7-point Likert-type scale with the anchors: 1="Definitey", 7="Definitely Not".
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AUTHOR BIOGRAPHY
Scott R. Swanson (Ph.D., University of Kentucky) is an assistant professor of marketing. His research interests include
issues related to services marketing, sports sponsorship, and pedagogy. His research has been published in the Journal
ofthe Academy ofMarketing Science, the Journal ofBusiness to Business Marketing, the European Journal of
Marketing, and Psychological Reports, among others.
AUTHOR BIOGRAPHY
Scott W. Kelley (D.B.A., University of Kentucky) is an associate professor ofmarketing and the Director ofthe UK
Center for Sports Marketing. His research has been published in the Journal ofthe Academy ofMarketing Science, the
Journal ofRetailing, the Journal ofBusiness Research, the Journal ofAdvertising, and the Journal ofPersonal Selling
and Sales Management, among others. His research interests include issues concerning services marketing and sports
marketing.
Fall 2001 65