Parasuraman 1996 - The behavioral consequences of service quality

' ValarieA. ZeithamI, Leonard L Berry, & A. Parasuraman

The Behavioral Consequences ofService Quality

If service quality relates to retention of customers at the aggregate level, as other research has indicated, then ev-idence of its Impact on customers' behavioral responses should be detectable. The authors offer a conceptualmodei of the impact of service quality on particular behaviors that signai whether customers remain with or defectfrom a company. Results from a multicompany empirical study examining relationships from the model concerningcustomers' behavioral intentions show strong evidence of their being influenced by service quality. The findingsalso reveal differences in the nature of the quality-intentions link across different dimensions of behavioral inten-tions. The authors' discussion centers on ways the results and research approach of their study can be helpful toresearchers and managers. . . .

Delivering quality service is considered an essentialstrategy for success and survival in today's competi-tive environment (Dawkins and Reichheld 1990;

Parasuraman, ZeithamI, and Ben7 1985; Reichheld andSas.ser 1990; ZeithamI, Parasuraman, and Berry 1990). Dur-ing the 1980s, the primary emphasis of both academic andmanagerial effort focused on determining what service qual-ity meant to customers and developing strategies to meet cus-tomer expectations (e.g., Parasuraman, ZeithamI, and Berry1985. 1988). Since then, many organizations—includingthose whose primary offerings involve physical goods suchas automobiles or computers^—have instituted measurementand management approaches to improve their service. Theservice-quality agenda has now shifted and reconfigured toinclude other issues. The issue of highest priority today in-volves understanding the impact of service quality on profitand other financial outcomes of the organization (Greising1994; Rust, Zahorik, and Keiningham 1995).

Executives of many companies in the 1980s were will-ing to trust their intuitive sense that better service wouldlead to improved financial success and thus committed re-sources to improving service prior to having documentationof the financial payoff. Some of these companies, such asFederal Express and Xerox, have been richly rewarded fortheir efforts (Germano 1992; Reams and Nadler 1992). Butexecutives in other companies have been reluctant to investin service improvements without solid evidence of their fi-nancial soundness. And in the current era of downsizing andstreamlining, interest in tools to ascertain and monitor thepayoff from service investments is high.

Valarie A. Zeithami is Principal, Partners for Service Excellence, a consult-ing firm specializing in strategy, measurement, and implementation of ser-vice quality. Leonard L. Berry is JCPenney Chair of Retailing Studies andProfessor of Marketing, Texas A&M University. A. Parasuraman is Professorand Holder of the James W. McLamore Chair in Marketing, University ofMiami. The authors thank the editor and five anonymous JM reviewers tortheir constructive comments and suggestions on earlier drafts of this arti-cle. They also thank the Marketing Science Institute and four of its corpo-rate sponsors for supporting the research on which this article is based.

Research on the relationship between service quality andprofits has begun to accumulate, and one thing is clear: Thelink between service quality and profits is neither straight-forward nor simple (Greising 1994; Zahorik and Rust 1992).The intermediate links between service quality and profitshave not been well understood. To delineate the complex re-lationship between these two variables, researchers andmanagers must investigate and understand many other rela-tionships, each of which is an integral part of the composite.One such relationship—between service quality and behav-ioral intention.s—is the primary focus of our present re-search. In the remainder of this introductory section, we pro-vide a general overview of the extant knowledge about thelink between service quality and profits. We then outline ourspecific objectives and how our study attempts to extendcurrent knowledge.

Seminal studies using the PIMS (Profit Impact of Mar-ket Strategy) data set have uncovered significant associa-tions among service quality, marketing variables, and prof-itability. Findings from these studies show that companiesoffering superior service achieve higher-than-nonnal marketshare growth (Buzzell and Gale 1987), that the mechanismsby which service quality infiuences profits include increasedmarket share and premium prices (Phillips, Chang, andBuzzel! 1983), and that businesses in the top quintile of rel-ative service quality on average realize an 8% higher pricethan their competitors (Gale 1992). Evidence from compa-nies large enough to have multiple outlets also suggest apositive quality-profitability relationship: The Hospital Cor-poration of America found a strong link between perceivedquality of patient care and profitability across its many hos-pitals (Koska 1990); and the Ford Motor Company hasdemonstrated that dealers with high service-quality scoreshave higher-than-normal profit, retum on investment, andprofit per new vehicle sold (Ford Motor Company 1990).

Although the previous findings document the financialand strategic impact of service quality across firms or out-lets, the evidence is often too general to answer the ques-tions foremost in executives' minds: If I invest in service

Journal of MarketingVol. 60 (April 1996), 31-46 Service Quality/31

quality, will it pay off for my company? How will servicequality pay off"? How much should we invest in service qual-ity to receive the best retum? In addressing such questions,researchers (Fomell and Wemerfelt 1987. 1988; Rust andZahorik 1993; Zahorik and Rust 1992) distinguish betweenoffensive effects (capturing new customers) and defensiveeffects (retaining customers). Determining the offensive im-pact of service quality parallels the age-old search for theadvertising-sales connection. Service quality's effect.s—similar to advertising's effects—are cumulative, and there-fore evidence of the link may develop slowly. And, similarto advertising, service quality is one of many variables—in-cluding pricing, advertising, efficiency, and image—that si-multaneously influence profits. Furthermore, spending onservice per se does not guarantee results, because strategyand execution must both be considered.

On the other hand, evaluating the defensive impact ofservice quality through customer retention promises to helpcompanies gauge the financial impact of service quality. Therelationship between retention and profits recently has beenestimated by a variety of researchers (e.g., Anderson andSullivan 1990; Fomell and Wemerfelt 1987, 1988; Reich-held and Sasser 1990) and companies (e.g., IBM). If the re-lationship between service quality and retention can be sim-ilarly documented, the financial implications for a givencompany or even a given service initiative can be calibrat-ed. Zahorik and Rust (1992) distinguish among five tasksthat must be completed to model the impact of service onprofits: (I) identifying the key service attributes to includein the model, (2) selecting the most important attributes, (3)modeling the link between programs and attitudes, (4) mod-eling behavioral response to service programs, and (5) mod-eling the impact of service programs on profits.

The research we describe involves the first four tasksthat Zahorik and Rust (1992) propose and concentrates onthe fourth, namely, modeling behavioral response to qualityservice. All four of tbese tasks are firmly in the domain ofmarketing and the first three have been studied extensivelyin the last decade (for a review, see Zahorik and Rust 1992).In contrast, the fourth attribute, the impact of service quali-ty on behavioral response, has been the subject of only a fewmarketing studies to date (Boulding et al. 1993; Cronin andTaylor 1992).

The underlying premise of our article is that if servicequality relates to retention of customers at the aggregate(i.e., firm) level, as other research has suggested, then evi-dence of its impact on customers' behavioral responsesshould be detectable. The consequences of service-qualityperceptions on individual-level behavioral intentions can beviewed as signals of retention or defection and are desirableto monitor. With that in mind, our objectives are four-fold:

1. To -summarize existing evidence about the behavioral con-sequences of service quality at the individual customerlevel.

2. To otTer a conceptual model of the impact of service qualityon particular behaviors that signal whether customers re-main with or detect from the company.

3. To report the results of an empirical study examining rela-tionships between service quality and customers' behavioralintentions.

4. To suggest a research agenda whereby information about in-dividual-level behavioral consequences of service qualitycan t>e monitored and linked to sales and customer-retentiondata to provide ongoing evidence of the financial impact ofservice quality.

In addressing these objectives, we provide a concisesynthesis of the extant literature on the subject and extendthe literature in three significant ways. First, our study in-volves a comprehensive (multicompany/multi-industry) ex-amination of service quality's impact at the individual-con-sumer level rather than at the company/industry level, as isthe case in most previous studies. Second, in addition to ex-amining the general relafionship between service qualityand behavioral intentions, we explore changes in thestrength of this relationship that are due to potential moder-ating effects of different levels of service relative to cus-tomers' expectation levels. Third, we incorporate a more ex-tensive multiple-item behavioral-intentions measure thanhas been used in previous research and examine servicequality's impact on specific types of behavioral intentions.

Conceptual Framework andHypotheses

Background

Lowering customer defection rates can be profitable to com-panies. In fact, research has shown that it is a more prof-itable strategy than gaining market share or reducing costs.'For exatnple, in an empirical study linking customer satis-faction to profits, Fomell and Wemerfelt (1987, 1988) ex-amine the impact of complaint-handling programs on cus-tomer retention and conclude that marketing resources arebetter spent keeping existing customers than attracting newones. In support of this position. Reichheld and Sasser(1990, p. 105) assert that customer defections have astronger impact on a company's profits than "scale, marketshare, unit costs, and many other factors usually associatedwith competitive advantage." For this reason, they extol thebenefits of zero customer defections as an overall companyperformance standard:

Ultimately, defections should be a key performance mea-sure for senior management and a fundamental componentof incentive systems. Managers should know the compa-ny's defection rate, what happens to profits when the ratemoves up or down, and why defections occur (p. 111).

Research and company efforts to quantify the financial impactof defection and retention have intensified in recent years.

Financial impact of defection. When customers are lost,new ones must be attracted to replace them, and replacementcomes at a high cost. Capturing new customers is expensive

'This is not to say that companies should focus on customer re-tention to the exclusion of strategies to attract new customers. Forinstance, share-building strategies should be a high priority forcompanies that are new entrants or operate in emerging markets.However, for companies wilh an established customer base (espe-cially in mature markets with entrenched competitors) the net re-turn on investments could be much higher for retention strategiesthan for strategies lo attract new customers.

32 / Journal of Marketing, April 1996

FIGURE 1The Behavioral and Financial Consequences of Service Quality

SERVICEQUALITY

BEHAVIORALINTENTIONS

Focus of present study

Empirical links demonstrated in macrostudies

Remain

BEHAVIOR

Defect

— ^

— ^

±iOngoing Revenue

Increased SpendingPrice Premium

Referred Customers

FINANCIALCONSEQUENCES

=iDecreased Spending

Lost CustomersCosts to Attract New

Customers

for it involves advertising, promotion, and sales costs, aswell as start-up operating expenses. New customers areoften unprofitable for a period of time after acquisition: Inthe insurance industry, for example, the insurer typicallydoes not recover selling costs until the third or fourth year ofthe relationship. Capturing customers from other companiesis also an expensive proposition: Anderson and Sullivan{1990) find that a greater degree of service improvement isnecessary to make a customer switch from a competitor thanto retain a current customer.

Financial impact of retention. The longevity of a cus-tomer's relationship favorahly inHuences profitability. Cus-tomers who remain with a firm for a period of years hecausethey are pleased with the service are more likely than short-term customers to buy additional services and spread favor-able word-of-mouth communication. The firm also may heahle to charge a higher price than other companies charge,because these custotners value maititaining the relationship.The initial costs of attracting and establishing these cus-tomers have already been absorbed and. due to experience-curve effects, they often can be served more efficiently (Re-ichheld and Sas.ser 1990). Rose (1990) supports this view,contending that profit on credit card services purchased by aten-year customer is on average three times greater than fora five-year customer.

Although the financial impacts of defection and reten-tion have been studied at a macro level (i.e., company or in-dustry level), the micro-level (i.e., individual-level) process-es through which these impacts occur have not been wellunderstood. To attempt to fill this void, we develop and test

a conceptual model focusing on individual-level behavioralconsequences of service quality.

A Model of the Behavioral Consequences ofService Quality

Figure I is a conceptual model that depicts the behavioralconsequences of service quality as intervening variables he-tween service quality and the financial gains or losses fromretention or defection. The left portion of the model is at thelevel of the individual customer and proposes that setricequality and behavioral intentions are related and. thus, thatservice quality is a determinant of whether a customer ulti-mately remains with or defects from a company.

Starting on the left, the model begins with a customer'sassessment of service quality and posits that when servicequality assessments are high, the customer's behavioral inten-tions are favorable, whicb strengthetis his or her relationshipwith the company. When service quality assessments are low.the customer's behavioral intentions are unfavorable and therelationship is more likely to be weakened. Behavioral inten-tions can be viewed as indicators that signal whether cus-tomers will remain with or defect from tbe company.

Some of the links in Figure I (shown by dotted arrows)bave been demonstrated empirically in several aggregate-level studies using overall multicompany analysis (e.g.,Buzzeil and Gale 1987; Gale 1992; Reichheld and Sasser1990). However, the mediating roles of behavioral inten-tions and actual behavior on the relationship between ser-vice quality and financial pertormance are not well under-stood, especially at the individual-customer level. We at-tempt to add to our knowledge in this regard by undertaking

Service Quality / 33

an in-depth conceptual and empirical examination of thefirst link in the sequence of effects posited in Figure 1. Aswe discu.ss in subsequent sections, multiple measures of ser-vice quality and behavioral intentions were operationalizedand used in surveys of customers from four different com-panies. For ease of exposition in this section, the dependentconstruct is split broadly into favorable and unfavorable be-havioral intentions.

Favorable behavioral intentions. Certain behaviors sig-nal that customers are forging bonds with a company. Whencustomers praise the firm, express preference for the compa-ny over others, increase the volume of their purchases, oragreeably pay a price premium, they are indicating behav-iorally that they are bonding with the company. Recent re-search offers some evidence that customer satisfaction and/orservice-quality perceptions positively affect intentions to he-have in these ways. However, most of the research opera-tionalizes behavioral intentions in a unidimensional wayrather than delineate specific types of behavior. For example.Cronin and Taylor (1992), using a single-item purchase-in-tention scale, find a positive correlation with service qualityand custotner satisfaction. Anderson and Sullivan (1990). inanalyzing data from a study of customer satisfaction amongSwedish consumers, find that stated repurchase intention isstrongly related to stated satisfaction across product cate-gories. A study conducted hy Woodside. Frey, and Daly(1989) uncovers a significant association between overall pa-tient satisfaction and intent to choose the hospital again.

Several studies have examined the association betweenservice quality and more specific behavioral intentions. Inprevious studies (see Parasuratnan. Berry, and ZeilhamlI99la; Parasuraman. Zeithaml, and Berry 1988). we find apositive and significant relationship between customers'perceptions of service quality and their willingness to rec-ommend the company. Boulding and colleagues (1993). inone of two studies they conducted, find a positive correla-tion between service quality and a 2-item measure of repur-cba.se intentions and willingness to recommend. In a secondstudy involving university students, tbey find strong linksbetween service quality and behavioral intentions that are ofstrategic itiiportance to the school, including saying positivethings about tbe school, planning to contribute money to tbeclass pledge on graduation, and planning to recommend theschool to employers as a place from which to recruit.

Individual companies are also monitoring the impact ofservice quality on selected behavioral intentions. For exam-ple. Northwest Airlines found that the preference index (i.e.,the preference for Northwest Airlines as the airline passengerslike to fly) increased substantially in 1992, compared to 1991,following a major company effort to improve service. Asmeasured in random surveys, preference rose in Minneapolis(from 70% to 75%). Detroit (from 49% to 59%). and Mem-phis (from 48% to 63%) (Executive Report on Customer Sat-isfaction 1992). Toyota found that intent to repurchase a Toy-ota automobile increased from a base of 37% to 45% with apositive sales experience, from 37% to 79% with a positiveservice experience, and from 37% to 91% with both positivesales and service experiences (McLaughlin 1993).

By integrating research findings and anecdotal evidence,a list of specific indicators of favorable behavioral inten-tions can be compiled. These include saying positive thingsabout the company to others (Boulding et al. 1993), recom-mending the company or service to others (Parasuraman.Berry, and Zeithaml 1991a; Parasuraman. Zeithaml. andBerry 1988; Reichheld and Sasser 1990), paying a price pre-mium to the company, and remaining loyal to the company(LaBarberaand Mazursky 1983; Newman and Werbel 1973;Rust and Zahorik 1993). Loyalty may be manifested tn mul-tiple ways; for example, by expressing a preference for acompany over others, by continuing to purcbase from it. orby increasing business with it in the future.

Unfavorable behavioral intentions. Customers perceiv-ing service performance to be inferior are likely to exhibitbebaviors signaling tbey are poised to leave tbe company orspend less with the company. Tbese behaviors include com-plaining, which is viewed by many researchers as a combi-nation of negative responses that stem from dissatisfactionand predict or accompany defection (Riehins 1983;Scaglione 1988).

Complaining behavior itself is conceptualized as multi-faceted. According to Singh (1988), dissatisfaction leads toconsumer-complaining behavior (CCB) that is tnanifested invoice responses (such as seeking redress from the seller),private responses (negative word-of-mouth communica-tion), or third-party responses (taking legal action). Histhree-dimensional typology of complaining bebavior.founded on the object of the complaints (seller, friend, thirdparty), is statistically superior to previous models of CCB.Maute and Forrester (1993) find strong supptm for a three-way classification of dissatisfaction responses based on Hir-sbman's (1970) exit, voice, and loyalty responses (loyaltybeing tbe decision to remain with the company despite dis-satisfaction). Solnick and Hemenway (1992) ob.serve thatthough voice and exit (in their view the two main hebavioralmanifestations of dissatisfaction) can be substitutes for eachother, they often occur together. In the context of a healthmaintenance organization, they find tbat complainitig cus-tomers were four and one-half times more likely to leave theplan voluntarily than noncomplaining customers.

Specific indicators of unfavorable behavioral intentionssuggested by the preceding discussion include differenttypes of complaining (e.g., complaining to friends or exter-nal agencies) and contemplation of switching to competi-tors. Another indicator of eventual defection is a decrease inthe amount of business a customer does with a company.

Differential impact of service-quality levels. Althoughsuperior service is likely to foster favorable behaviors andreduce the likelihood of unfavorable bebaviors, an impor-tant unresolved issue is the service-quality level tbat com-panies must target to have the desired impact on behaviors.How tiiuch service quality is enough to retain customers? Isthere a level of service beyond which there are diminishingretums in terms of strengthening behavioral intentions?Does the degree of association between service quality andbebavioral intentions change at different quality levels?

Little published evidence directly addresses tbese ques-tions. However, a study by Gale (1992), whicb quantitative-


ly as.sesses the relationship between level of service qualityand willingness to purchase at AT&T, offers some indirectinsight. Of AT&T's customers who rated the company'soverall quality as excellent, over 90% expressed willingnessto purchase from AT&T again. For customers rating the ser-vice as good. fair, or poor, the percentages decreased to60%, 17%. and 0%. respectively. According to these data,willingness to repurchase increased at a steeper rate (i.e., by43%) as the service-quality rating improved from fair togood than when it went from poor to fair (17%) or fromgood to excellent (30%). These results suggest that the im-pact of service quality on willingness to repurchase is mostpronounced in some intermediate level of service quality.

Coyne (1989, p. 73), however, makes the opposite predic-tion on the basis of research relating to the impact of customersatisfaction with service in a consumer-durable context;

There appear to be thresholds of service for affecting cus-tomer behavior.... When satisfaction rose above a certainthreshold, repurchase loyalty climbed rapidly. In contrast,when satisfaction fell below a different threshold, cus-tomer loyalty declined equally rapidly. However, betweenthese ihresholds, loyalty was relatively flat. I believe thisiwin threshold framework applies to a wide variety of ser-vice situations.

A similar categorization of service levels follows one de-finition of service quality in the literature—the extent towhich a service meets or exceeds customer expectations(Parasuraman, Zeithaml. and Berry 1985, 1988)— and fromrecent research explicating the expectations construct as twolevels of expectations (Zeithaml, Berry, and Parasuraman1993). The first level Is desired service, which Is the level ofservice the customer hopes to receive, consisting of a blend ofwhat the customer believes can and should be delivered. Tbesecond, lower level of expectations is adequate .service,which is the level of service the customer will accept. Ade-quate service is the minimum service a company can provideand still hope to meet customers' basic needs. A zone of tol-erance, bounded on the lower end by adequate service and onthe upper end by desired service, captures the range of servicewithin which a company is meeting customer expectations.

Although the zone-of-tolerance framework seems struc-turally similar to Coyne's (1989) twin-threshold framework,the managerial implications of the two frameworks are dif-ferent. Coyne, invoking the flat satisfaction-loyalty relation-ship he hypothesizes between the two thresholds, suggeststhat unless a company already has a strong reputation forservice, it may not benefit by improving service much be-yond the lower threshold: "If a company is already abovethe minimum acceptable threshold, but nearer the lower endof the service satisfaction band, investments to incremental-ly change position may not be warranted" (p. 75). In con-trast, we have argued previously (see Parasuraman, Berry,and Zeithaml 1991b, p. 47) that firms operating within thezone of tolerance, whiie possibly enjoying competitive ad-vantage, sbould continue to improve service, even to thepoint of exceeding the desired service level: "To develop atrue customer franchise—unwavering customer loyalty—firms must exceed not only the adequate service level butalso the desired service level." Although we do not refer lo

the slope of the service performance-loyalty relationship,our prior recommendation implies an upward sloping(rather than tlat) relationship within the zone of tolerance.

Available evidence suggests that the sensitivity of behav-ioral intentions to changes in service quality is likely to varyfrom below to within to above the zone of tolerance, thoughthere is no con.scnsus about the nature of this variation acrossthe three regions of quality. A key empirical question iswhether the relationship between behavioral intentions andservice quality is flat or upward sloping witbin the zone oftolerance and. if it is upward sloping, whether or not it issteeper than the relationship below and above the zone.

The discussion in the preceding sections implies that,though service quality is positively associated with favor-able behavioral intentions and negatively related to unfavor-able behavioral intentions, customers' perceptions of theservice relative to their adequate and desired service levelsmoderate these associations. More formally, we posit.

H|: The .service quality-tiehavioral intentions relationship (a)is positive (negative) tor favorable (unfavorable) behav-ioral intentions and (b) has a different slope below andabove the zone of tolerance relative to within it.

Impact of problem e.xperience and resolution. Anotheraspect of service provision that can influence behavioral in-tentions involves the problem experience of customers.When customers encounter service problems, these experi-ences are likely to affect bebavioral intentions adversely.However, the impact of problem resolution on customers'intentions is less clear One view, based primarily on anec-dotal evidence, is that superior problem resolution forgesstronger bonds between customers and the company tbanwould exist had no service problem occurred. For example,J. W. Marriott, chief executive officer of the Marriott hotelchain, states: "Sometimes those [disgruntledl customerswhom you make that extra effort to gain back become themost loyal customers that you have" (Lovelock 1994, p.214). The reasoning underlying this view seems to be tbat aservice problem gives a company the opportunity to demon-strate its commitment to customer service through excellentrecovery efforts. On the other hand, empirical evidence sug-gests that service failures may weaken the customer-compa-ny bond even when the problem is resolved satisfactorily(Boiton and Drew 1992). We report (see Zeithaml. Parasur-aman. and Berry 1990) that customers who experienced norecent service problem with a company have significantlybetter service-quality perceptions than customers who expe-rienced a recent service problem that was satisfactorily re-solved. A plausible explanation for this finding is that satis-factory problem-resolution service, though perhaps pleasingto customers, does not cause them to forget the service fail-ure. And the memory of the failed service negatively affectscustomers' overall perception of the company's service. Theexisting empirical evidence on this question leads to oursecond hypothesis:

Hi: Favorable (unfavorable) behavioral intentions are (a) high-est (lowest) for customers experiencing no service prob-lem; (b) next highest (lowest) for customers experiencingservice problems that are resolved, and (c) lowest (highest)

Service Quality/35

for customers experiencing service problems that are notresolved.

In summary, the first hypothesis suggests a positive(negative) relationsbip between service quality and favor-able (unfavorable) behavioral intentions, the strength ofwhich is different below and above the zone of tolerance rel-ative to that within it. Hi, along with H2, is depicted in Fig-ure 2. which details the portion of the behavioral conse-quences model on which we focus in the present study.

MethodologySample Design and Mail Survey

Four companies that provide services to end or business cus-tomers were sponsors of the research study. Questionnaireswere mailed to business customers of a computer manufac-turer, as well as to end customers of a retail chain, automo-bile insurer, and life insurer. The sponsoring companies gen-erated mailing lists from their cunent customer bases. Theretail chain, automobile insurer, and lite insurer each pro-vided random samples of 24(X) customers. The computermanufacturer provided a larger random sample of 5270 cus-tomers, because it wanted to conduct its own detailed, seg-ment-by-segment analysis following the completion of tbemain study. A total of 12,470 questionnaires were mailed.

Surveys were mailed with a cover letter and postage-paid retum envelope to all customers in the sample. Thecover letter appeared on company letterhead and was signedby a senior company official. Respondents were requestedto retum completed questionnaires to a marketing researchcompany hired to assist with data collection and coding. Areminder postcard was sent two weeks after mailing thequestionnaires.

Overall response rate was 25% (3069 questionnaires).Company-specific response rates were 30% (1566 question-naires) for the computer manufacturer; 22% (522 question-naires) for the retail chain; 24% (568 questionnaires) for theautomobile insurer; and 17% (413 questionnaires) for thelife insurer. Demographic profiles of the respondent sampleswere reviewed by managers in the respective companies andconsidered to be representative of their customer bases.

Survey Instrument

Operationalization of service quality. Several measuresof service quality were included in the questionnaire: (1) anoverall, single-item rating scale with anchors at I (extreme-ly poor) and 9 (extremely good); (2) a multiple-item scale ofperceived service from an expanded version of theSERVQUAL scale we originally developed (see Parasura-man, Zeithaml, and Berry 1988) and later refined (see Para-suraman, Berry, and Zeithaml 1991a); and (3) two categori-

FIGURE 2Hypothesized Effects of Service Quality on Behavioral Intentions

PerformanceRelative to Adequateand Desired Service

Service Quality

Perceived ServicePerformance

Problem Experienceand Resolution

ExperiencedProblem?

1Yes

ProblemResolved?

No

Yes

NoH2.

J

Behavioral Intentions

High

Medium

Low

Favorable* Say positive things• Recommend company- Remain loyal to company- Spend more with company- Pay price premium

High

Medium

Low

Unfavorable* Say negative things- Switch to another company- Complain to external agencies- Do less business with company

~^


cal questions to measure whether respondents had experi-enced a recent service problem with tbe company and, if so,whether the problem was resolved to their satisfaction.

Tbe second measure (i.e., the revised SERVQUAL bat-tery) represented tbe service dimensions of reliability (fiveitems), responsiveness (tbree items), assurance (four items),empatby (four items), and tangibles (five items). Consistentwitb the expanded conceptualization of customers' serviceexpectations (ZeithamI, Berry, and Parasuraman 1993). re-spondents were asked to indicate tbeir adequate- and de-sired-service levels in addition to their perceptions of eachSERVQUAL item. Thus, separate ratings of adequate, de-sired, and perceived service were obtained on tbree 9-pointscales (1 = low, 9 = high) arranged as three adjacentcolumns next to the SERVQUAL battery on theque.stionnaire.-

Tbe questionnaire containing tbe SERVQUAL batterywith the three columns of ratings used in this study was oneof three different questionnaire formats evaluated in a larg-er methodological study (Parasuraman, ZeithamI, and Berry1994a). As such, the adequate-, desired-, and perceived-ser-vice scores used in tbe present study were based on a partialsample from each company (the otber two questionnaire for-mats did not produce separate scores for tbese variables). Atotal of 1009 questionnaires contained scores for tbe ade-quate-, desired-, and perceived-service variables: 498 fromthe computer manufacturer, 188 from the retail chain, 191from the automobile insurer, and 132 from the life insurer.All three questionnaire formats contained measures for theremaining study variables (overall service quality, behav-ioral intentions, and incidence of service-problem experi-ence and satisfactory problem resolution). Therefore, scoresfor these variables were based on the full sample.

Operationalization of behavioral intentions. Previous re-search bas not captured the full range of potential behaviorslikely to be triggered by service quality, Cronin and Taylor(1992) focus solely on purcbase intentions and measure tbeconstruct witb a single-item scale. In tbe tlrst of two studiesby Boulding and colleagues (1993), repurcbase intentionsand willingness to recommend were the only two behavioralintentions measured. In tbe second study, involving servicequality of an educational institution, tbey used a 6-item scalecomprised largely of education-specitlc items, such as intentto contribute money to the class pledge and intent to recom-mend tbe school to employers as a place to recruit.

A 13-item battery was developed to gauge a wider rangeof behavioral intentions tban have been suggested in the lit-erature or by anecdotal evidence from companies.^ This bat-tery included items to capture several facets of behavioralintentions not incorporated in previous service-quality stud-ies: likelihood of paying a price premium and remainingloyal to a company even when its prices go up, intent to domore business with the firm in the future, and complaint in-

e adequate- and desired-level service ratings were used indefining the lower and upper boundaries of the ône of tolerance toverify the tiiflerential slopes predicted by H||, tor the quality-in-tentions relationship.

Â copy of the instrument containing the behavioral-intentionsand service-quality question.s can be obtained from the third author.

tentions when service problems occur. The 13 items weregrouped into four a priori categories: word-of-moutb com-munications, purchase intentions, price sensitivity, and com-plaining bebavior. (These groupings were not made knownto respondents.) The last two categories contained items notincluded in prior service-quality research. Eacb item was ac-companied by a 7-point likelihood scale (I = not at all like-ly, and 7 = extremely likely).

Analyses, Results, and DiscussionDimensions of Behavioral intentions

Factor analysis of the behavioral-intentions battery was con-ducted to examine the dimensionality of the items. Becausethe battery was designed to represent four categories of be-havioral intentions, a four-factor solution was obtained sep-arately for eacb company and subjected to oblique rotationto allow for potential cotrelation among the categories. Theitem clusters implied by the factor loadings differed fromthe a priori clusters and varied somewbat across the fourcompanies, Tbe general pattems of loadings suggested tbata five-factor solution may belp reconcile these differences.A five-factor solution produced an unambiguous factor pat-tem that was consistent across ail companies. This consis-tent pattem suggested a reconfiguration ofthe 13 items intofive dimensions: loyalty to company (loyalty), propensity toswitch (switch), willingness to pay more (pay more), exter-nal response to problem (external response), and intemal re-sponse to problem (internal response). In Table I. we presenttbe reconfigured bebavioral-intentions battery, and in Table2, we present the factor-load ing matrices supporting it,along with reliability coefficients for its multiple-itemcomponents.

Of the five factors, loyalty (with five items) and paymore (witb two items) exbibit consistent pattems of load-ings across the four companies. Switch (with two items) andextemal response (witb tbree items) also display a moderateto high degree of uniformity in factor loadings. The final di-mension, intemal response, contains just one item that loadson tbe fifth factor.

Tbe 5-item loyalty scale has excellent intemal consis-tency, which is evidenced by alpbas ranging from .93 to .94across tbe four companies. The 3-item extemal-responsescale bas alphas of at least .6, with tbe values in two of tbefour companies exceeding the threshold of ,7 thai Nunnally(1978) suggested. The 2-item scales measuring switch andpay more have somewhat weaker alphas, with several valuesfalling below .6, perhaps because of too tew items in tbe re-configured factors. In general, the alpha score for loyalty ishigh, particularly for an early study, Tbe alpba scores tor tbeother three factors with multiple items range from adequateto weak, indicating the need to add items to tbe scale in fur-ther research.

Although the factor structure of the behavioral-inten-tions battery differs somewbat from the a priori specifica-tion, the loadings suppoti the dichotomy in behavioral in-tentions of favorable and unfavorable categories. The largestfactor, loyalty, contains five favorable behavioral-intentionsitems: saying positive things about the company, recom-


TABLE 1Behavioral-Intentions Battery^

Behavioral-IntentionsDimension

ItemLabel Item Wording

Loyalty

Switch

Pay More

External Response

Internal Response

111213141516171819

no111112

113

Say positive things about XYZ to other people.Recommend XYZ to someone who seeks your advice.Encourage friends and relatives to do business with XYZ.Consider XYZ your first choice to buy services.Do more business with XYZ in the next few years.Do less business with XYZ in the next few years (-).Take some of your business to a competitor that offers better prices (-).Continue to do business with XYZ if its prices increase somewhat.Pay a higher price than competitors charge for the benefits you currently

receive from XYZ.Switch to a competitor if you experience a problem with XYZ's service.Complain to other customers if you experience a problem with XYZ's service.Complain to external agencies, such as the Better Business Bureau, if you

experience a problem with XYZ's service.Complain to XYZ's employees if you experience a problem with XYZ's service.

^The items were grouped as follows in the a priori categorization of the battery: Word-of-Mouth Communications—II, 12, 13; Purchase Inten-tions — 14,15,16; Price Sensitivity — 17,18, 19; Complaining Behavior — 110, 111, 112,113, Each item was accompanied by a 7-point likelihoodscale (1 = not at ait likely and 7 = extremely likely), items identified with a "-" were reverse scored.

mending the company to someone who seeks advice, en-couraging friends and relatives to do business with the com-pany, considering the company the first choice from whichto buy services, and doing more business with the companyin the next few years. Pay more contains two favorableitems: continuing to do business with the company even ifits prices increa.se somewhat and paying a higher price thancompetitors charge for the benefits currently received fromthe company.

The second and fourth factors comprise all unfavorablebehavioral-intentions items. Switch contains two of these:doing less business with the company in the next few yearsand taking some business to a competitor that offers betterprices. Extemal response includes items that relate to expe-riencing a service problem: switching to a competitor, com-plaining to other customers, and complaining to extemalagencies such as the Better Business Bureau.

The interpretation of intemal response, the fifth factorwith one item (complaining to the company's employees ifa service problem Is experienced), is unclear. Customersmore favorably disposed toward a company may be morelikely to complain intemally to give the company a "secondchance." Conversely, disgruntled customers with an unfa-vorable image of the company may be more likely to com-plain intemally to vent their frustrations. The equivocal in-terpretation of this factor and its being represented by justone item undennine its meaningfulness on conceptual andpsychometric grounds. As such, we deleted this single-itemmeasure from all subsequent analyses.

Relationship Between Service Quality andBehavioral intentionsThe first hypothesis predicted a positive (negative) quality-intentions relationship for favorable (unfavorable) behav-ioral intentions, with different slopes below and above the

zone of tolerance relative to within it. This hypothesis wastested by using multiple regression analysis to examine si-multaneously (I) whether the slope of the relationship with-in the zone of tolerance was significantly different from zeroand (2) whether this slope differed significantly from theslopes below and above the zone of tolerance. In accordancewith procedures discussed by Cohen and Cohen (1983,Chapter 8) for conducting this type of analysis, the follow-ing regression equation was estimated:

(I) Y= B,X

whereY = behavioral-intentions score;

X = service-quality score;

dl = dummy variable with a value of 1 if the perceived service isbelow the zone of tolerance. 0 otherwise;

d2 = dummy variable with a value of 1 if the perceived service isabove the zone of tolerance, 0 otherwise;

Bs = unstandardized regression coefficients; and

€ = error term.

The coefficients in Equation 1 that are relevant for examin-ing the first hypothesis are B,, B,, and B,. Specifically, B,represents the slope of the quality-Intentions relationshipwithin the zone of tolerance, whereas Bi and B, representchanges in B, below and above the zone of tolerance, re-spectively. Thus, B, + BT represents the slope below thezone, and B, + B3 represents the slope above the zone.

Service quality (the key independent variable X) wasoperationalized in two ways: as the rating on the 9-pointoverall-quality (OQ) scale and as a weighted-average per-ceived perfonnance (WP) score across the SERVQUAL di-mensions. Of late there has been debate in the literatureabout the most appropriate way to operationalize servicequality (cf Brown, Churchill, and Peter 1993; Cronin and


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Taylor 1992; Parasuraman. Berry, and ZeitbamI 1993; Para-suraman, ZeitbamI, and Berry 1994b; Teas 1993). Tbe cen-tral issue in this debate is whether service quality should bemeasured as the difference between customers" perceptionsand expectations ratings or simply as the perceptions rat-ings. Altbougb tbis issue continues to be debated, there issome agreement that a study's purpose may influence thecboice of which measure to use: The perceptions-only oper-ationalization is appropriate if the primary purpose of mea-suring service quality is to attempt to explain the variance insome dependent construct; the perceptions-minus-expecta-tions difference-score measure is appropriate if the primarypurpo.se is to diagnose accurately service shortfalls (Para-suraman, Zeiiliaml. and Berry 1994a). The purpose of ourpresent study is tbe former. Moreover, as we discuss subse-quently, the two expectations measures (i.e., the adequate-and desired-service levels) were independently incorporatedinto the analysis to operationalize the two dummy variablesdi and di. Therefore, the ratings from tbe SERVQUAL por-tion ofthe survey were used to operationalize service quali-ty as weighted-average performance scores, ratber than dif-ference scores.

To determine WP, a perceived performance rating wasfirst computed lor each SERVQUAL dimension by averag-ing the ratings on the items forming the dimension. (The co-efficient alpha values for reliability [five items], responsive-ness [three items), assurance [four items), empathy [fouritems] and tangibles [five itcnis[ ranged from .80 to .96across tbe four samples.) To obtain the WP score, the aver-age performance ratings for the dimensions were thenweighted by tbe relative importance of the dimensions. Tomeasure tbe relative importance of tbe five dimensions, re-spondents were asked to allocate 100 points among tbe di-mensions according to how important each dimension wasto them in evaluating a company's service. The relativepoints allocated to the dimensions were used as weights incomputing the WP score.

The dummy variables d| and dj were operationalized bycomparing each respondent's WP score with his or herweighted-average adequate- and desired-service scores(computed using a procedure similar to tbat used in deter-mining WP). The d| value was 1 if WP was less tban theweigbted-average adequate-service score, 0 otherwise. Thedi value was I if WP was greater than the weighted-averagedesired-service score, 0 otherwise.

The regression analysis was performed separately forthe four companies, a.s well as for the combined sample. Ineacb instance, two equations were estimated for eacb be-havioral-intentions dimension: one using WP scores and thesecond using OQ scores as values for tbe independent vari-able X. The average score across items comprising the be-havioral-intentions dimension represented tbe dependentvariable Y. In Table 3, we summarize the regression-analy-sis results pertaining to the first hypothesis.

Tbe regression coefficients in the first two columns ofTable 3 (B| values) offer strong support for tbe hypothesizedqualiiy-intcntions links within the zone of tolerance. The co-efficients for WP and OQ are all in tbe hypothesized direc-tions—positive for loyalty and pay more and negative for

switch and extemal response—and, with few exceptions, arestatistically significant at/) < .01. The pattem of B| valuesacross the four companies suggests tbat the effects are gen-erally stronger for loyalty and switch tban for pay more andextemal response. Tbe results for the combined sample pro-vide additional insight into the relative infiuences of servicequality on the four behavioral-intentions dimensions: thestrongest effects of both WP and OQ are on loyalty (.70 and.55), followed by switcb (-.67 and -.47), pay more (.43 and.37), and extemal response (-.28 and -.21) in that order.

The regression coefficients in Table 3 in the columns forB2 and B, values pertain to the differential effects predictedby H| (the statistically significant BT and B^ coefficients areboldfaced). For eacb bebavioral-intentions dimension wiib-in a given company, the B2 coefficients for WP and OQ havethe same sign except in a few instances. Similarly, the signsof the B3 coefficients are identical for WP and OQ. The sta-bility in the signs of tbe slope-change coefficients acrosstwo different service-quality measures is encouraging interms of drawing inferences about the direction of changesin the quality-intentions link below and above the zone oftolerance. However, support for the strength of thesechanges is mixed, as is evidenced by tbe pattern of statisti-cal significance of these coefficients. Tberefore, based onthe presence of significant coefficients for at least one of thetwo service-quality measures (WP and OQ), only the fol-lowing inferences seem warranted.

In the compu ter-manufacturer sample, tbe quality-inten-tions relationship for loyalty and switch is flatter above tbezone of tolerance (implying diminished sensitivity to quali-ty improvements beyond the desired-service level), but isunchanged below the zone of tolerance. The relationship forpay more is flatter both below and above the zone. In the re-tail-chain sample, the relationship for loyalty, switch, andextemai response is flatter below tbe zone of tolerance butremains unchanged above the zone of tolerance (implyingundiminisbed retums for quality beyond the desired servicelevel). In the automobile-insurer sample, the relationship forloyalty is steeper below the zone of tolerance but remainsunchanged above tbe zone. However, the relationship forswitch is fiatter below tbe zone and considerably steeperabove tbe zone, whicb implies that there are increasing pay-offs as service improves from below to within to above thezone. Tbe relationship for extemal response in the automo-bile-insurer sample is similar to that in the retail-chain sam-ple (i.e., fiatter below the zone but unchanged above it). Allof the slope-change coefficients in the life-insurer sampleare nonsignificant. This lack of significance may be due toinsufficient data points below and above the zone—only 15respondents in the life-insurer sample bad WP scores below,and only 8 had WP scores above the zone. A similar defi-ciency may account for the lack of significance of any of tbeB3 coefficients in tbe retail-cbain sample; only 16 respon-dent.s had WP scores above the zone.

In tbe combined sample, tbe quality-intentions relation-ship for loyalty and switch is fiatter below but remains un-changed above the zone. Thus, exceeding tbe adequate-ser-vice threshold can sharply increase tbe payoffs (in terms offostering customer loyalty and curtailing propensity to

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TABLE 4Mean Scores for Service Quality and Behavioral Intentions

Company

Computer ManufacturerRetail ChainAutomobile InsurerLife Insurer

aMean scores on a 9-point scale.I'Mean scores on a 7-point scale.

Service

WP

7.36.57.87.6

Qualitya

OQ

6.86.27.37.0

Loyalty

5.34.95.65.0

Behavioral

Switch

3.83.93.13.1

Intentions'*

Pay More

3.93.23.53.4

ExternalResponse

3.64.34.23.8

switch). However, the combined-sample results for the paymore dimension reveal considerable fiattening of the quali-ty-intentions relationship above the zone of tolerance. Infact, the slope for the OQ-pay more relationship changesfrom .37 below the desired-service level to just .03 (.37 -.34) above. Thus, companies wishing to improve service be-yond Ihe desired-service level should do so cautiously andcost-effectively, because recouping the added expense bycharging price premiums may not be a viable option. Thequality-intentions relationship for external response—which, as indicated by its B| coefficients, is flatter withinthe zone than for the other three dimensions—remains un-changed below and above the zone as well. Thus, relative tothe other dimensions, extemal response appears much lessaffected by changes in quality over a wide range.

Tbe pattem of adjusted R-squared values in the last twocolumns of Table 3 offer two noteworthy insights based onthe overall ability of service-quality-related variables (d|,d2, and X) to explain the variation in scores on each behav-ioral-intentions dimension. First, the relationship of quality(both WP and OQ) with loyalty and switch is consistentlystronger in the two pure-service companies (automobile andlife insurers) than in the two product companies (computermanufacturer and retail chain); however, the reverse is truefor the quality-pay more relationship: The relationship isconsistently stronger in the two product companies than inthe two pure-service companies (for additional analyses, seethe Appendix). Second, the quality-pay more relationship isconsistently weaker than the quality-loyalty relationship inall four companies and the combined sample. We examinethe implications of these insights subsequently.

In Table 4, we summarize the mean scores for servicequality and behavioral intentions by company. An across-company comparison of the mean-score pattems providesadditional support for inferring that service quality is asso-ciated positively with favorable behavioral intentions andnegatively with unfavorable behavioral intentions. With fewexceptions, the better a company's service-quality scores,the higher are its loyalty and pay more means and the lowerare its switch and extemal response means. To illu.strate, theretail chain's WP and OQ scores are considerably lower thanthe corresponding scores for the automobile insurer. Match-ing behavioral-intentions data show that the retail chain'scustomers are less loyal and less willing to pay more—and

more prone to switch and complain externally—than the au-tomobile insurer's customers.

Impact of Service-Problem Experience andResolution on Behavioral Intentions

Hi predicts that customers experiencing no service problemshave the best behavioral-intentions scores (highest for fa-vorable intentions and lowest for unfavorable intentions—Hia), customers experiencing problems that were resolvedwould have intermediate scores (H^ )- antl customers withunresolved service problems would have the worst scores(H, .). To test this hypothesis, the combined sample was clas-sified into three groups of respondents; tho.se experiencingno recent service problems; those experiencing problemsthat were resolved; and those experiencing problems thatwere not resolved. Analysis of variance was conducted todetermine whether scores on each behavioral-intentions di-mension differed across the groups. The F-values for all fourANOVAs were significant at p < .001. Eight prcspecifedcontrasts (first-group mean versus second group mean and.second-group mean versus third group mean for each of thefour behavioral-intentions dimensions) were also evaluated.In Table 5, we present the group means and the significancelevels for the planned contrasts.

The alpha level for testing the significance of individualcontrasts was reduced by applying the Bonferroni correctionto ensure that the overall probability of Type I error acrossall eight contrasts did not exceed .05 (for details, see foot-note b in Table 5). The evidence in Table 5 fully supports thesecond hypothesis for the loyalty, switch, and extemal re-sponse dimensions, and partially supports it for the paymore dimension. The findings clearly show that customersexperiencing no service problems have the strongest levelsof loyalty intentions and the weakest switch and extemal re-sponse intentions. However, their pay more intentions arenot significantly higher than those of customers experienc-ing service problems that were re.solved satisfactorily.Among customers experiencing recent service problems,those receiving satisfactory resolution have significantlyhigher loyalty and pay more intentions, and significantlylower switch and extemal response intentions, than thosewith unresolved problems. Thus, effective service recoverysignificantly improves all facets of behavioral intentions.However, with the possible exception of tbe pay more di-mension, the improvements do not restore intentions to the


levels expressed by customers not experiencing serviceproblems. These results are consistent with those from astudy in which Bolton and Drew (1992) examine the impactof problem experience and resolution on telephone cus-tomers' evaluation of billing service: Customers rated theservice substantially lower if (hey had experienced a billingproblem, and the effect of satisfactorily resolving the prob-lem did not completely offset its negative impact.

Discussion and ImplicationsWe developed a conceptual model of the behavioral and fi-nancial consequences of service quality (Figure I. A portionof the model—the quality-intentions link—^was empiricallyexamined at the individual-customer level in a multicompa-ny context. Two distinctive features of the study's empiricalcomponent were the development of a more extensive be-havioral-intentions battery than has been used in previousresearch and the investigation of changes in the quality-in-tentions link at different service levels relative to customers'expectations. The study's findings have important implica-tions for researchers and managers.

Directions for Further Research ; ': ,

The distinctive aspects of the empirical study contributeseveral new insights whose implications we subsequentlyexplore. However, our findings also reveal certain weak-nesses with methodological implications. First, the behav-ioral-intentions battery developed here, though more com-prehensive than intentions scales used in previous studies,needs further development. In particular, more items areneeded to strengthen the reliability of three of its compo-nents, namely, switch, pay more, and extemal response.With additional items, the scales should be reevaluated fortheir psychometric properties. Consideration should also begiven to augmenting and including in the battery intemal re-sponse—the component that was eliminated because it hadonly one item subject to equivocal interpretation. As we pre-

viously mentioned, customers favorably disposed toward acompany may complain to give it a .second chance, whilecustomers unfavorably disposed may also complain merelyto vent their frustrations. Therefore, in expanding this com-ponent, it would be useful (from a diagnostic standpoint) toadd items that capture why customers are likely or unlikelyto complain. For example, respondents could be asked torate the likelihood of the following {on the same 7-pointscale used in the behavioral-intentions battery):

• Complain to XYZ"s employees aboul a service problem be-cause [ am confident they will resolve the problem.

• Complain to XYZ's employees about a .service problem tohelp relieve my frustration (reverse scored).

Second, additional research is needed to examine furtherour tentative insights pertaining to intercompany differencesin the quality-intentions relationship and the changes in itsslope below and above the zone of tolerance relative towithin it. Although the total sample size was large for eachcompany, the subsamples of respondents below and abovethe zone were relatively small, and this possibly contributedto the lack of significance of some of the slope-change co-efficients. Obtaining larger samples of respondents belowand above the zone in further studies would facilitate a morerobust examination of changes in the quality-intentions rela-tionship. One option for doing so is to select samples fromcompanies that are well known for their excellent (or poor)service. Another option is to devise a suitable quota-sam-pling procedure to ensure large enough subsamples below,within, and above the zone. Mullicompany research usingsuch sampling procedures is needed for more definitive con-clusions about the intriguing differences uncovered in thisstudy, which concem changes in the quality-intentions linkwithin and across dimensions and companies.

In addition to addressing the previous issues, further re-search should also focus on aspects of the conceptual modelnot examined here. For example, the association betweenbehavioral intentions atid remaining with or defecting from

TABLE 5Mean Behavioral-Intentions Scores for Respondents Classified According to Service Problem Experience

Behavioral-IntentionsDimension

LoyaltySwitchPay MoreExternal

Response

Mean Scores

No ServiceProblems

(Group 1; n = 2153)

5.473.353.76

3.70

for Customers Experiencing^

Service ProblemsThat WereResolved

(Group 2; n = 455)

5.014 ^3;63

3.95

Service ProblemsThat Were

Not Resolved(Group 3; n = 346)

4.114.493.11

4.43

SignificancePlanned Ct

Group 1Mean versus

Group 2 Mean

-000.000.036

.000

Levels for)ntrastsb

Group 2Mean versus

Group 3 Mean

.000" .000

.000

.000

«The behavioral-intentions scores are on a 7-point likelihood scale.I'The reported significance levels are for one-tailed tests, because Hj implies directional comparisons of group means. Because multiple con-trasts were evaluated to test this hypothesis, the Bonferroni correction was applied to the customary alpha level of .05 to control the Type Ierror rate. Specifically, the alpha level was lowered by a factor of eight (the total number of planned contrasts) to yield a critical alpha level of.006 for testing the significance of each contrast (Myers 1979, pp, 298-300). At this reduced alpha level, seven of the eight planned contrastsare significant; the sole exception is the Group 1 versus Group 2 contrast for the pay more dimension.


(he company merits study. Rust and Zahorik (1993) suggestways to investigate this link, including panel data, longitu-dinal analysis with customers, and cross-sectional surveysasking customers about previous and current providers. Ad-ditional cross-sectional research might ask customers to in-dicate not only their behavioral intentions but also their ac-tual behaviors. For example, customers could be askedwhether they have said positive things about the company(actual behaviors) instead of how likely they would be tosay positive things (behavioral intentions). Such researchalso needs to be supplemented with longitudinal research toverify the causal direction of the quality-intentions link.Data from studies tracking service quality and behavioral in-tentions over time can be analyzed to determine the impactof service quality in a given period on behavioral intentionsin subsequent periods.

If the longitudinal data set also contains information forindividual customers on variables such as purchase frequen-cy and volume and new-customer referrals, the impact ofservice quality on actual behavior can be traced. Companiesthat have information systems linking customer data andpurchase data could also examine increases or decreases inspending that result from different levels of service quality.This type of research would provide direct evidence of thefinancial impact of service quality at the individual level.

An intriguing finding worthy of further research is thepattem of across-company differences implied by the differ-ences in the adjusted R-squared values for the various qual-ity-intentions regression equations (Table 3). As previouslyhighlighted, the quality-intentions link for the loyalty andswitch dimensions is consistently stronger for the two pure-service companies than for the two product companies,whereas the reverse is true for pay more. Is it possible thatthe role of service within a firm's total offering (i.e.. coreversus supplemental component) is a plausible explanationfor this pattem of differences? Because service is all that apure-service provider, such as a life insurance company, de-livers in exchange for customers" money, customers' com-mitment to the company might be extremely responsive toservice-quality improvements; however, these customers'willingness to pay more may not be as responsive, becausethey may feel they have, in effect, already paid for high-quality service. Alternatively, the pay more findings maysimply reflect customers' general reluctance to pay for in-surance services and may not apply to pure services overall.

In contrast, because service is not the core of what aproduct company sells to customers, their commitment tothe company may be less sensitive to changes in servicequality (especially if product quality is mediocre); however,customers may be somewhat more willing to pay more forbetter service, because they may consider service to be anextra feature. To what extent and under what circumstancesare these speculations likely to be true? Furthemiore. whatis the nature and extent of the impact of factors other thanthe service component (e.g.. price, product characteristics)on customers' behavioral intentions? Additional conceptualand empirical research addressing these issues can improveour understanding of the behavioral consequences of servicequality.

Managerial Implications

The overall findings offer strong empirical support for theintuitive notion that improving service quality can increasefavorable behavioral intentions and decrease unfavorable in-tentions. The findings demonstrate the importance of strate-gies that can steer behavioral intentions in the right direc-tions, including striving to meet customers' desired-servicelevels (rather than merely performing at their adequate-ser-vice levels), emphasizing the prevention of service prob-lems, and effectively resolving problems that do occur.However, multiple findings suggest that companies wantingto improve service, especially beyond the desired-servicelevel, should do so in a cost-effective manner: the quali-ty-pay more relationship in the combined sample, while up-wardly sloping below the desired service level, becomes vir-tually flat above that level (Table 3); the adjusted R-squaredvalues for the quality-pay more regressions are weaker thanfor the quality-loyalty regressions across all companies andfor both measures of service quality (Table 3); and, in eachof the four companies, the mean score for pay more inten-tions is considerably lower than for loyalty intentions (Table4).

In addition to these general implications, the conceptualmodel and the empirical findings have specific implicationsfor firms' research and resource-allocation decisions per-taining to improving service quality. A salient issue on theservice quality research agenda of many companies is un-derstanding the impact of service quality on profits. One ofthe reasons it has been difficult for individual firms to cali-brate this impact is that the relationship is complex and con-sists of multiple intervening relationships. As was suggestedin our model of individual customer response, a chain of re-lationships is integral to the overall impact.

Companies first must examine the impact of their ser-vice-quality provision on customers' responses, includingintentions signaling behaviors that are potentially favorableor unfavorable to the company. For most companies, a bat-tery of behavioral-intentions questions could be incorporat-ed easily into the measurement systems currently used tocapture service-quality assessments. Doing so provides acontinuous source of infomiation relating to such questionsas.

•What levels of service quality must we deliver to retaincustomers?

•What service initiatives should we undertake to encouragecustomers to recommend the company, spend more with thecompany, or pay a price premium?

•What auributes should we focus on to reduce the likcHhoodof customers .spreading negative word-of-mouth communica-tions when service problems occur?

• To retain customers, should we spend our money on proac-tive service improvements or on handling complaints?

In essence, behavioral intentions become dependent vari-ables with potentially higher validity (because they are moreclosely related to actual behaviors) and richer diagnosticvalue than the "overall service quality" or "customer satis-faction" variables currently being used in most measure-ment programs.


Companies also can use surveys eliciting behavioral in-tentions as an early warning system to identify customers indanger of defection and lo take timely corrective action. Ithas long been speculated that many service customers ex-hibit "spurious loyalty"^—they remain witb companies theyare dissatisfied with because tbey see no alternatives. Localtelephone customers, with no choice in service providers,are often cited as customers exhibiting spurious loyalty. Be-eause of the deregulation of that industry—and the immi-nent competition among cable companies, long-distancecompanies, and other regional Bell companies—firms sup-plying local telephone service mighl be able to strengthencustomer retention by identifying customers most in dangerof defection.

Another measurement implication for companies to un-derstand better and leverage the relationships in the concep-tual model (Figure I) is to query customers about wbetberthey have manifested, rather than merely would manifest,favorable or unfavorable behaviors. Companies sbould alsoconsider connecting their marketing database to their ac-counting information systems to explore the links betweencustomers' assessments of service quality and tbeir purchasebehavior L. L. Bean has accounting systems that track vita!purchasing pattems of individual customers: what they pur-chase, when they buy. and how much they spend over time.Companies witb such systems could benefit from integrat-ing their customer-research database witb actual purcbasedata lo enhance their knowledge of how and to wbat extentbehavioral intentions act as precursors of purchasingbehavior.

Yet another facet of ihe conceptual model that meritsthorough analysis at the individual company level involvesactual increases or decreases in revenue from retention ordefection of customers. Reichheld and Sasser (1990) call foraccounting systems that capture the expected cash flowsfrom and life-time value of a loyal customer. This call couldbe expanded to include an examination of the revenue im-plications of individual bebavioraUintentions componentsas well. For example, what is the potential revenue differen-tial between customers indicating a low versus a highpropensity to switch? Is there a difference in the revenuestreams from customers expressing a strong versus a weakinclination to pay a price premium for a company's service?Examining such questions will facilitate a more detailed cal-ibration and a clearer understanding of tbe financial conse-quences of service quality.

AppendixTo formally test the significance of these and other inter-company differences implied in our discussion, an extendedversion of regression Equation I was estimated for the com-bined sample:

B4X +B7X +B|,,X B|,d,X

where

gi = dummy variable with a value of I whenthe company is the retail chain. 0otherwise;

g2 = dummy variable with a value of I whenthe company is the aulomobile insurer, 0otherwise;

g, = dummy variable with a value of I whenthe company is ihe life insurer, 0 other-wise; and

Y. X, di, di. B's. and € = same as those defined for Equation I.

Wben g|, g2. and g3 are 0. Equation Al pertains to thecomputer manufacturer, witb B, representing tbe slope of theqitality-intentions relationship within the tolerance zone andB2 and B3 representing changes in thai slope below andabove the zone, respectively. Using the computer manufac-turer as the reference company. B4 through B ,. B7 throughBy, and Bm through B|2 are the corresponding slope-changecoefficients for the retail chain, aulomobile insurer, and lifeinsurer, respectively. As in the case of Equation I. EquationAI was estimated for each of the four behavioral-intentionsdimensions. The results show consistent statistical signifi-cance (atp < .05) only for B,. With a few exceptions (that re-vealed no meaningful pattem). the slope-change coefficientsB2 through B|2 are not significant. An inadequate number ofsample units with nonzero values for the variables corre-sponding to these eleven coefficients (and the consequentlow variance for these variables) is a bighly plausible expla-nation for this lack of significance: Tbe percentage of re-spondents (relative to the total sample) witb nonzero valueswas less tban 5% for five of tbe variables, less tban 10% foranother five variables, and less than 20% for the remainingone variable. A set of tables summarizing these regression re-sults can be obtained from tbe third author.

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Parasuraman 1996 - The behavioral consequences of service quality