Managing Service Quality: An International JournalPath analysis of perceived service quality, satisfaction and loyalty in Greek insuranceEvangelos Tsoukatos Graham K. Rand

Article information:To cite this document:Evangelos Tsoukatos Graham K. Rand, (2006),"Path analysis of perceived service quality, satisfaction andloyalty in Greek insurance", Managing Service Quality: An International Journal, Vol. 16 Iss 5 pp. 501 - 519Permanent link to this document:http://dx.doi.org/10.1108/09604520610686746

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Users who downloaded this article also downloaded:Albert Caruana, (2002),"Service loyalty: The effects of service quality and the mediating roleof customer satisfaction", European Journal of Marketing, Vol. 36 Iss 7/8 pp. 811-828 http://dx.doi.org/10.1108/03090560210430818Evangelos Tsoukatos, Graham K. Rand, (2007),"Cultural influences on service quality and customersatisfaction: evidence from Greek insurance", Managing Service Quality: An International Journal, Vol. 17Iss 4 pp. 467-485 http://dx.doi.org/10.1108/09604520710760571Eugene Sivadas, Jamie L. Baker-Prewitt, (2000),"An examination of the relationship between servicequality, customer satisfaction, and store loyalty", International Journal of Retail & DistributionManagement, Vol. 28 Iss 2 pp. 73-82 http://dx.doi.org/10.1108/09590550010315223

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Path analysis of perceived servicequality, satisfaction and loyalty in

Greek insuranceEvangelos Tsoukatos and Graham K. Rand

Department of Management Science,Lancaster University Management School, Lancaster, UK

Abstract

Purpose – The purpose of this article is to investigate the path service quality ! customersatisfaction ! loyalty, at the level of constructs, drawing from the Greek insurance industry.

Design/methodology/approach – A SERVQUAL type service-quality instrument is developed forGreek insurance. Confirmatory and exploratory factor analyses are used to determine the scale’sdimensionality. Path analysis is utilized to examine a model linking service quality, customersatisfaction and loyalty at the level of constructs’ individual determinants.

Findings – SERVQUAL’s dimensionality is not confirmed. A non-tangibles, tangibles structureexists in Greek insurance. “Tangibles” does not affect customer satisfaction while WOM is anantecedent of repurchasing intentions. Satisfaction does not directly influence the latter.

Research limitations/implications – This study suffers the limitation that it tests the fit of themodel within the limits of a single service industry. Another limitation is availability sampling.However, the satisfactory fit of the estimated model allows for the study to be a reliable comparisonbasis for future research.

Practical implications – Insurance managers may use GIQUAL for measuring the quality ofinsurance services offered. They must improve the intangible rather than the tangible elements ofservice and direct their support mechanisms towards developing customers willing to engage inpositive WOM. The proposed model can be used to provide comparable findings across sectors,countries and time provided that, in each case, an appropriately customized SERVQUAL type scale isused.

Originality/value – This study explores the service quality, satisfaction, and loyalty path at thelevel of specific dimensions drawing from Greek insurance.

Keywords SERVQUAL, Customer satisfaction, Customer loyalty, Insurance, Greece

Paper type Research paper

IntroductionCompanies use aggressive marketing strategies, to attract new customers and increasemarket share at the expense of competitors, and/or defensive strategies, to protectproducts and markets from the competition by maximizing customer retention undercertain cost constraints (Fornell, 1992; Ennew and Binks, 1996; Abdel-Maguid Lotayif,2004; Roberts, 2005). Although, traditionally, more effort is dedicated to aggressivemarketing (Fornell, 1992), research has shown that defensive strategies can be moreprofitable. Increased customer retention can be more rewarding than market shareenlargement or cost decreases. Small increases in the customer retention rate can

The current issue and full text archive of this journal is available at

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The authors wish to thank Dr Marianna Sigala, two anonymous reviewers and the Editor,Professor Jay Kandampully for their valuable comments and suggestions.

Service quality inGreek insurance

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pp. 501-519q Emerald Group Publishing Limited

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generate considerable improvement in profitability through reduced cost of attractingnew customers and/or increased sales to old customers (Lenskold, 2003; Lombardi,2005). Long-lasting customer-provider relationships lead to increased cross selling,possibly at higher prices, and positive word-of-mouth (WOM) communication. Theinitial cost of attracting customers has already been paid back and, because of theexperience curve, customers can be served more efficiently (Ennew and Binks, 1996;Heskett et al., 1997). Rose (1990) reports that a credit card customer who stays with thesame company for ten years is three times more profitable than those who stay for fiveyears.

In insurance, high retention rates are closely related with the economic performanceof companies (Diacon and O’Brien, 2002). Insurers in the USA consider retention as themost important determinant of economic success (Moore and Santomero, 1999), as theselling cost of an insurance policy is not recovered unless the policy is renewed for atleast three or four years (Zeithaml et al., 1996). In property and casualty insurance, thelonger customers remain with a company, the less likely they are to submit claims(Peppers and Rogers, 2004). In the UK the industry is concerned about low customerretention (Personal Investment Authority, 2001) attributed, by some, to extensivecustomer dissatisfaction and scepticism about the quality of delivered services (Diaconand O’ Brien, 2002). The industry considers that understanding customers’ behaviourafter the initial purchase will help insurers to maintain longer customer-insurerrelations (Harrison, 2003).

Perceived service quality and customer satisfaction are dominating the marketingliterature. However, the debate on which of the two can better predict customer loyalty,whether the two constructs are related and, if they are, which are the causal directionsof their relations, and whether customers are in a position to distinguish between them,is still unresolved (Bitner and Hubert, 1994; Saurina and Coenders, 2002). Thetheoretical background and the empirical support for these issues come mostly fromthe USA, UK and Canada.

The purpose of our study is to investigate the path service quality ! customersatisfaction ! loyalty, at the level of constructs’ individual determinants rather thanon the aggregate, drawing from the Greek insurance industry. Our study might be ofvalue because of the unique cultural characteristics of the Greek society from which thestudy draws evidence (Hofstede, 1980), and the examination of the influences to andfrom the individual dimensions of constructs which differentiates our study fromstudies that examine only the aggregate relationships between constructs anddisregard the effect of individual determinants.

After a short literature review, a brief presentation of the Greek insurance industryis given, followed by the methodology and the main results of our study. Then, themanagement implications are summarized and finally, the main conclusions anddirections for further research are outlined.

Literature reviewService qualityAs most developed economies are now services rather than products oriented, servicequality takes a prominent position in the marketing-management literature. Servicequality is usually defined as the customer’s impression of the relativeinferiority/superiority of a service provider and its services (Bitner and Hubert,

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1994) and is often considered similar to the customer’s overall attitude towards thecompany (Parasuraman et al., 1988; Zeithaml, 1988; Bitner, 1990). Researchers havetried to conceptualize and measure service quality and explain its relation to the overallperformance of companies and organizations.

A common denominator of research on service quality is the conclusion that,because services are intangible, heterogeneous, and their “production” and“consumption” are usually inseparable, the process used by customers to evaluateservice quality is exceptionally composite and cannot be easily identified. The idea thatservices are evaluated both by the outcome and by the production and delivery processis commonly accepted. Gronroos (1982) considers services as products requiring, to alarge extent, the consumer’s involvement in the process of production andconsumption; during which consumers compare their expectations about the servicewith what they actually receive. The result of this comparison is perceived servicequality (Parasuraman et al., 1985, 1988). Gronroos (1982) suggests that the consumers’expectations are also influenced by marketing activities, external influences andword-of-mouth. He identifies two types of service quality; “technical”, related to whatthe customer gets from a service and “functional”, associated with how the service isdelivered.

The SERVQUAL scaleProminent in the measurement of service quality literature is the “gap analysis model”often referred to as the “gaps model” (Parasuraman et al., 1985) and the SERVQUALscale for the measurement of service quality (Parasuraman et al., 1988, 1991b), which isbased on the gap analysis model. Customers provide two scores, in identical Likertscales, for each of 22 service attributes; one score indicating their expectations of theservice delivered by excellent companies in a specific service sector and the otherreflecting their perceptions of the service delivered by a service provider within thatsector. Service quality for each attribute is then quantified as the difference betweenthese two scores.

Originally Parasuraman et al. (1985) identified ten general dimensions of servicequality but, as a result of subsequent research, these were collapsed into fivecategories: tangibles, reliability, responsiveness, assurance and empathy(Parasuraman et al., 1988, 1991a, b). SERVQUAL has been designed for a variety ofservice sectors. According to Parasuraman et al. (1988):

It provides a basic skeleton through its expectations and perceptions format, encompassingstatements for each of the five service-quality dimensions. The skeleton, when necessary, canbe adapted or supplemented to fit the characteristics of specific research needs of a particularorganization.

Despite a great deal of criticism addressed to it on conceptual and methodologicalgrounds (e.g. Buttle, 1996; Bebko, 2000; Yoon and Ekinci, 2003), SERVQUAL has beensuccessfully used in many different settings around the world (e.g. Tsoukatos et al.,2004; Ugboma et al., 2004; Tahir and Wan Ismail, 2005).

Service quality and customer satisfactionImplicit in the services marketing literature is the idea that the (dis)satisfaction ofcustomers is related to service quality and influences their behavioural intentions aswell as the organization’s performance (Woodside et al., 1989; Van der Wiele et al.,

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2002). The direction of the causal relationships between quality and satisfaction hasbeen the subject of extensive debate. The view of Parasuraman et al. (1988), thatsatisfaction over a time period leads to a general perception of service quality, hasgained support from other researchers (Bitner, 1990). Others argue that perceivedservice quality is an antecedent of customer satisfaction. Cronin and Taylor (1992)reported that all coefficients in the path service quality ! satisfaction ! purchasingintentions were significant while those in the path satisfaction ! service quality !purchasing intentions were not. A third line of argument is that the distinction betweenservice quality and customer satisfaction is unclear, especially in situations offrequently delivered services (Bolton and Drew, 1991). In Spain, Saurina and Coenders(2002) found that customers do not perceive satisfaction and overall quality as differentconstructs. However, the prevailing idea is that service quality is an antecedent ofcustomer satisfaction and that satisfaction influences the behaviour of customers morethan service quality.

Consequences of service quality and customer satisfactionAlthough some early studies connected quality to customer retention (Steenkamp,1989), profitability (Reichheld and Sasser, 1990), market share (Buzzell and Gale, 1987)and profits (Phillips et al., 1983), companies became concerned with quality’s impact onprofits and general economic performance (Nelson et al., 1992; Aaker and Jacobson,1994; Greising, 1994; Rust et al., 1995) only in the early 1990s. Until then, the maineconomic variable connected with service quality was cost (Bohan and Horney, 1991;Carr, 1992). Rust et al. (1995) verified the links between product quality, service qualityand market share while Anderson and Sullivan (1993) investigated the impact ofservice quality on repurchasing intentions and Chumpitaz and Paparoidamis (2004) itsinfluence on marketing performance through customer satisfaction.

In some cases quality was found directly connected to economic performance. Forexample, Shlesinger and Graf von der Shulenburg (1993) proposed that corporateimage and service quality influence the customers’ decisions to choose insurers andaffect the price they are prepared to pay for a policy while Daskalopoulou and Petrou(2005) found that service quality influences the retail store performance. However, theimpact of quality investments on profits cannot always be directly assessed because itusually is long-term; many other variables (price, distribution, competition,effectiveness, image and publicity) influence profits; and simply spending on qualitydoes not automatically lead to profits because the strategy and functionality of theinvestment are also important (Zeithaml et al., 1996). The links between service qualityand profits are not always straightforward (Greising, 1994). Although there arepowerful conceptual arguments that such links exist (Anderson and Fornell, 1994;Zeithaml, 2000), there is considerable debate on their nature.

A moderating variable between service quality/customer satisfaction and economicperformance is loyalty. Loyalty is the extent to which customers wish to keep theirrelationship to a supplier, and usually results from how much they believe that thevalue they receive from this supplier is higher compared to others. Loyalty isbehaviourally expressed by retention (Bansal and Taylor, 1999) and emotionally(Ranaweera and Prabhu, 2003), by WOM; the extent to which customers are willing toinform others on service incidents that have given them satisfaction (Soderlund, 1998).

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Positive correlations between customer satisfaction and retention, loyalty andWOM have been reported in a number of studies (Parasuraman et al., 1988; Reichheldand Sasser, 1990; Fornell, 1992; Anderson and Sullivan, 1993; Mittal and Kamakura,2001; Chumpitaz and Paparoidamis, 2004). Woodside et al. (1989) identified significantlinks between the overall satisfaction of hospital patients and their intention to choosethe same hospital again. Nelson et al. (1992) extend the link to the profitability of thehospital. Rust et al. (1995) argue about the economic consequences of complaintshandling systems while Anderson and Sullivan (1993) report that the customers’repurchase intentions in Sweden are strongly connected to their satisfaction fromspecific product categories. Anderson et al. (1994) argue that higher levels of customersatisfaction increase loyalty, decrease price elasticity, protect current market shares,decrease the cost of failures and of attracting new customers and help companies tobuild a positive corporate image.

Financial service suppliers, worldwide, have recognized that a persistent customersatisfaction program is a most effective method of retaining customers and, hence,reducing the need of investments for attracting new ones. Services of high qualityresult in more repeat sales and market share improvement (Buzzell and Gale, 1987).Lewis (1993) supports these findings and considers service quality as one of the mosteffective, and yet most difficult, means of creating competitive advantage andimproving business performance. The financial service provider’s image is related tothe dimension of satisfaction often reported as “corporate quality” (Athanassopoulos,2000). Van der Wiele et al. (2002) provided some evidence for the links betweencustomer satisfaction and overall business performance while Lee and Hwan (2005)found that in banking, from the customer’s perspective, customer satisfaction directlyinfluences purchase intentions while, from the perspective of management, itsignificantly influences profitability. Zeithaml et al. (1996) propose that by improvingthe quality of services, the favourable behavioural intentions of customers areincreased while the unfavourable are decreased. They point out that when customerspraise a company, prefer it against others, increase the volume of their purchases orknowingly pay more for its products and/or services, they certainly have a bond withthe company.

The existence of the economic consequences of customer satisfaction wasdocumented in a number of industries in Sweden (Fornell, 1992) and elsewhere(Anderson et al., 1994). This led a number of major national economies to measuresatisfaction at the industry level using nationwide surveys to estimate retentionrates, WOM and economic performance (Fornell, 1992).

Of particular interest is the positive WOM of satisfied customers, which may attractnew customers. Satisfied customers are known to provide positive WOM to individualswho have no relation to a specific transaction (Ranaweera and Prabhu, 2003)influencing, thus, their purchasing intentions (Silverman, 2001; Mitchel, 2005). PositiveWOM decreases the need for marketing expenses and can increase revenues if newcustomers are attracted (Reichheld and Sasser, 1990). Because its positive resultscannot be always directly measured, positive WOM is sometimes considered as a sidebenefit of satisfaction, or as a low-cost alternative promotional solution. In the USA,marketers often consider WOM as the promotional vehicle to be used when there is nobudget available for alternative actions (File and Prince, 1992).

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Rust et al. (1995) presented a conceptual framework which they called return onquality (ROQ). They used ROQ to prove that the impact of service quality on thebehaviour of customers leads to improved profitability. In a retail banking setting,Hallowell (1996) confirms the service-profit chain (Heskett et al., 1994) thathypothesises the path customer satisfaction ! customer loyalty ! profitability.Zeithaml (2000) explained the links between service quality, behavioural intentions,behaviour and financial consequences to the company. However, more recentstudies challenge the belief that customer satisfaction uniformly leads to customerprofitability (Keiningham et al., 2005).

Loyalty is behaviourally expressed, in insurance, through the customer’s intentionto keep/renew an existing and/or purchase a new policy from the same supplier.Switching barriers do exist in insurance, usually in the life sector where contracts arelong-term and purchasing a new, or reinstating a lapsed, policy presupposesinsurability (Vaughan and Vaughan, 2003, p. 255). In the non-life sector, switchingbarriers are less restrictive as contracts are usually short-term and the customer’sprivileges regarding a policy can be transferred to another insurer.

The Greek insurance industryThe annual worth of the Greek insurance industry is $4.54 billion in premiums(2004), of which 47.4 and 52.6 per cent in life and non-life insurance respectively,and $3.21 billion in settled claims. Although its magnitude is less than themagnitude of a medium-sized European insurer (Eureko alone wrote $8.31 billionin gross premiums in 2005), 99 insurance companies are currently competing inthe market (2005). Whilst it is almost impossible to track the nationality ofinvestments in an open European economy such as the Greek economy, one canreasonably speculate that most insurance companies in Greece, even thoseestablished under the Greek law, belong to non-Greek interests. However, the stateis a major market player as, in 2004, the aggregate market shares of the threestate owned insurance companies were 26.25 percent in non-life and 22.40 percentin life insurance. The capital base of the Greek insurance companies is poor. Whilein EU insurance investments reached 54.5 percent of the GDP in 2001, in Greecethe corresponding figure was a mere 4.82 percent in 2004, despite an almostcontinuous upward trend since 1993.

Research has shown that the quality of services and the achievement of customersatisfaction and loyalty are fundamental for the survival of insurers. The quality ofafter sales services, in particular, can lead to very positive results through customerloyalty, positive WOM, repetitive sales and cross-selling (Taylor, 2001). However,many insurers appear unwilling to take the necessary actions to improve their image.This creates problems for them as the market is extremely competitive andcontinuously becomes more so (Taylor, 2001). The Greek insurance market is noexception. Widespread customer mistrust towards the industry is evident as 48 percentof Greek consumers accept as true that the industry lacks expertise while 34 percent ofthem are convinced that insurers often try to find various pretexts in order to avoidfulfilling their promises (ICAP, 2003). Service quality, in Greek insurance, has becomethe most important factor for both maintaining existing portfolios and acquiring newbusiness (Tsoukatos, 2003).

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MethodologyThis study followed a series of stages. First, a questionnaire was designed to measureservice quality in Greek insurance, evaluate the customers’ overall satisfaction andassess the sentimental and behavioural dimensions of their loyalty to their insurers.The second stage involved the administration of the questionnaire for data collection,the appraisal of the measurement method and the confirmation or rejection of thedimensionality of service quality suggested by Parasuraman et al.(1988). In the thirdstage, path analysis was used to establish the causal relations between the constructs.

First stage – questionnaire designA four-section questionnaire was used for this study. After a series of demographicquestions in the first section, the second section consisted of GIQUAL, a SERVQUALtype metric customized for Greek insurance. In the third section customers wererequested to indicate, on a 1 to 10 scale, their overall satisfaction with their insurer andits services. Finally, the fourth section was aiming at the appraisal of customers’emotional and behavioural loyalty levels by inviting respondents to provide answers tothe questions “would you recommend your insurer and its services to friends andrelatives?”, by which their intention to engage in positive WOM was examined, and“how would you cover an additional insurance need of yours in the future?”, by whichtheir behavioural loyalty level was assessed. The alternatives here were “will buy fromthe same insurer”, “will search the market for the best deal” and “will buy from adifferent insurer”. It might be argued that these options are not exclusive, becausewhen a customer searches the market for the best deal, the customer will eventuallybuy either from the same or from a different insurer. However, the alternatives arereflecting different levels of behavioural loyalty in terms of intentions rather thanoutcomes and, hence, they are exclusive.

The questionnaire was translated several times back and forth from Greek toEnglish to ensure functional equivalence of its items in the two languages.

The GIQUAL scaleGIQUAL is a SERVQUAL (Parasuraman et al., 1988, 1991b) type instrument,customized for the Greek insurance industry (Tsoukatos et al., 2004). As a result ofextensive consultation with a group of senior insurance executives four items wereadded to the original SERVQUAL battery. “Insurance products’ price”, was added toSERVQUAL’s Tangibles items and “offer quality products and services”, “contractswith clear terms” and “settling claims with no unnecessary delays” were added in thelist of Reliability items. Thus, the GIQUAL battery initially included 26 items.Although many believe that SERVQUAL type instruments should contain exactly 22items, in numerous cases scales with different numbers of items were used (Saurinaand Coenders, 2002). After all, SERVQUAL was in the first place meant to serve as abasic skeleton, which could and should be customized to better capture the qualitycharacteristics of individual industries under examination (Parasuraman et al., 1988).The inclusion of item “offer quality products and services” in a scale designed tomeasure service quality might seem odd. However, the expression “offer qualityproducts and services” when translated into Greek is interpreted as “offer suitable (welldesigned) products and services” that is, products and services that adequately coverthe insurance needs of consumers. Hence, this item is consistent with the scale.

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Second stage – data collection and appraisal of GIQUALAfter being piloted, the questionnaire was used for data collection in three major Greekcities using the availability sampling technique. Respondents were over the age of 25and had some service experience with their insurer or insurance agent within the lastthree months. The method of personal interview was chosen for the survey as beingsuperior to self-administered questionnaires in perceptual or attitudinal surveys(Groves, 1989).

The n ¼ 519 final sample included 321 customers of three major insurers, whichparticipated in the study, and 197 customers of insurers not participating in the study,interviewed at random in the market places of Heraklion and Chania. The threeparticipating insurers produced lists of 150, 150 and 300 of their customers respectivelyin Heraklion, Chania and Athens. Each customer in the three lists was contacted byphone by an interviewer who, after explaining about the study, asked for a personalinterview to be taken in the customer’s work-place or home. The overall response ratewas 53.7 per cent (62.7, 49.3 and 51.4 per cent) with 94, 74 and 154 valid questionnairesrespectively. The respondents’ demographics were considered by the management ofthe participating insurers as representing their respective customer bases.

The coefficient alpha was used for the examination of the scale’s reliability(Tabachnick and Fidell, 2001). The removal of items or their redeployment betweendimensions was based on the “increase of alpha if item deleted” criterion (Pallant,2001). After removing one item (“insurance products’ price”), as it did not fit to anydimension, GIQUAL remained with 25 items (Table I). The alphas of the individualdimensions were 0.87 for tangibles, 0.94 for reliability, 0.93 for responsiveness, 0.93 forassurance, and 0.90 for empathy.

To investigate whether SERVQUAL’s dimensionality is also applicable in theGIQUAL scale, confirmatory factor analysis was employed. The robust maximumlikelihood estimation method was used to allow for the absence of MultivariateNormality. For 256 degrees of freedom, both the Satorra and Bentler (1988) statistic(573.21; p ¼ 0:0) and the goodness of fit index (0.83) indicated a bad fit. Thus, thehypothesis that GIQUAL shares the dimensionality of SERVQUAL could not beaccepted.

Instead, exploratory factor analysis produced a two-dimensional solutionexplaining 65.7 per cent of the variance. Specifically, the items that formedSERVQUAL’s reliability, responsiveness, assurance and empathy dimensionscollapsed into a single 21-item factor that explained 50.1 per cent of the varianceand was named non-tangibles. The four remaining items formed a second factor, calledtangibles, which explained 15.6 per cent of the variance. For the analysis, themaximum likelihood factor extraction method was used and the rotated solution wasproduced by Varimax rotation. The criterion of meaningful factor loading was set to0.4.

Third stage – path analysisPath analysis was then employed for studying the relationships between quality,overall satisfaction and loyalty. The advantage of path analysis over regression is thatit concurrently performs multiple regression analyses while it produces an overallassessment of the model’s fit, usually based on a chi square statistic (Singh and Wilkes,

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1996). In addition, several goodness-of-fit indexes are available to better judge themodel’s fit. The LISREL 8.7 statistical package was used at this stage.

The literature on the aggregate relationships between service quality, customersatisfaction and loyalty is quite rich but this is not the case when the constructs’individual dimensions are taken into account. We contribute to this by testing a model(Figure 1) that includes the dimensions of service quality, as identified in the secondstage of the analysis, and the dimensions of loyalty, as described in the literature.

The variables NONTAN and TAN, expressing the customers’ average perceptionsalong the service quality dimensions non-tangibles and tangibles in the Greekinsurance industry, served as the model’s exogenous variables. The variables overall,

Tangibles EQUIPTE – Modern looking Equipment and TechnologyPHYSFA – Visually appealing physical facilitiesNEMPL – Neat appearing employees and agentsSERVMA – Visually appealing service materials

Reliability DOSOM – Keeping promisesPRODTS – Offer quality products and servicesNOAMB – Contracts with clear termsINDEMN – Settling claims with no unnecessary delaysINTESOL – Being interested on solving customers’ problemsFIRSTM – Perform the service right the first timeTIMELY – Provide services at the promised timeERRORFR – Issuing error free documents

Responsiveness TELCUS – Informing customers exactly when services will be performedPROMPT – Offering prompt service to customersWILLNG – Always willing to help customersTOBUSY – Never being too busy to respond to customer requests

Assurance SAFE – Customers feeling safe in their transactionsBEHAV – Employees and agents instilling confidence in customersCOUTES – Employees and agents being consistently courteous with

customersKNWLG – Employees and agents having the knowledge to respond to

customers’ requestsEmpathy INDVAT – Giving to customers individual attention

CONVHRS – Having convenient operating hoursPERSNL – Employees and agents giving customers personal attentionCUSINT – Having the customers’ best interests at heartUNSPEC – Employees and agents understanding the specific needs of

customersTable I.

GIQUAL items

Figure 1.From service quality to

loyalty

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measuring the customers’ overall satisfaction, recome, indicating the intention ofcustomers to recommend the company and its services, and intent, representing theirintentions to purchase again from the same insurer, were the model’s endogenousvariables.

The variables TAN, NONTAN and overall were defined as continuous. The firsttwo as having more than 15 distinct values and the third because it is common practiceto consider Likert variables with seven or more points as continuous in SEM. Thevariables recome (dichotomous) and intent (trichotomous), were treated as ordinal.Such variables are often regarded as “categorized” versions of Likert variables. In thecase of recome the answers “No” and “Yes” were considered as representing the lowestand highest point of a Likert scale. In the case of intent, “buy from some other insurer”was considered as corresponding to the lowest point of the Likert scale, “buy from thesame insurer” to the highest and “search the market for the best deal” as representingthe scale’s median point.

The link from recome to intent was included in the model for testing on the groundsof the long experience of one of the writers as an insurance agent in Greece. The logicbehind this hypothesis is that repurchasing insurance from a supplier is furtherstrengthening an existing relationship, making it more difficult for it to be terminatedand involves the payment of money. On the other hand, the act of simplyrecommending the supplier is just an emotional reaction that does not involve anypayment of money or impose restrictions. Recome and intent represent the emotionaland behavioural extent of the customer’s loyalty and as such there must be arelationship between them. On the grounds of the above-mentioned argument wehypothesized that the direction of this relation is from the former to the latter.

Model estimationMaximum likelihood (ML) is the most commonly used estimation method in SEM. Itmaximizes the probability that the observed covariances are drawn from a populationthat has its variance-covariance matrix generated by the process implied by the model,assuming multivariate normality. Multivariate normality is not generally met inpractice and several estimation methods for overcoming the fit problems arising fromits absence have been developed. ML, itself, is fairly robust against violations frommultivariate normality. However, to extend its applicability, corrections have beendeveloped to adjust ML estimators to account for non-normality including the Satorraand Bentler (1988) statistic incorporated in most SEM packages.

In terms of sample size, the minimum n for ML estimation should be at least 200,according to some researchers. Others suggest at least fifteen times the number ofobserved variables or five times the number of free parameters including error terms orten times the number of free parameters for strongly kurtotic data (Golob, 2003). Oursample size of n ¼ 519 meets all these requirements given that our model contains onlyfive variables. Before analysis, our data set was screened and found to deviate frommultivariate normality. Hence, the robust maximum likelihood estimation method,based on the correlation matrix and the asymptotic covariance matrix, was used andthe solution shown in Figure 1 was obtained.

The estimated path coefficients support the argument that quality is an antecedentand that it positively affects customer satisfaction (Cronin and Taylor, 1992). TAN andNONTAN as independent variables (IVs) explain 55.4 per cent (R 2 ¼ 0:554) of the

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variance of overall as dependent variable (DV). However, only the coefficient ofNONTAN is significant (standardized value ¼ 0:73, t-value ¼ 3:94) while thecoefficient of TAN is not (standardized value ¼ 0:02, t-value ¼ 0:15). This is aspecific characteristic of the Greek insurance industry and can be explained by itsstructure (Tsoukatos et al., 2004). This by no means weakens the case that servicequality is an antecedent of satisfaction.

The argument that satisfaction is an antecedent and positively influences loyalty isalso supported by the findings. The path coefficient from Overall to recome issignificant (standardized coefficient ¼ 0:84, t-value ¼ 9:79). Overall explains 71 percent (R 2 ¼ 0:71) of recome’s variance. Overall and recome, as IVs, explain 58.3 per centof the variance of intent as DV. However only the coefficient of recome is significant(standardized coefficient ¼ 0:79, t-value ¼ 2:12) while the coefficient of overall is not(standardized coefficient ¼ 20:04, t-value ¼ 20:09). This confirms our hypothesisthat emotional loyalty is an antecedent of the customer’s behavioural loyalty.Satisfaction does influence positively the customers’ behavioural intentions but onlythrough their intentions to get engaged in positive WOM for the supplier.

Overall model fitDiamantopoulos and Siguaw (2000, p. 88) suggest that the results of the chi-squaretest used in conjunction with the RMSEA, ECVI, standardized RMR, GFI and CFIindices are sufficient to assess a model’s overall fit.

A non-significant chi-square statistic is an indication that the model can reproducethe population covariance matrix. In our case, the Satorra and Bentler (1988) scaledchi-square statistic was used and found to be non-significant. The root mean squareerror of approximation (RMSEA) indicates “how well would the model, with unknownbut optimally chosen parameter values, fit the population covariance matrix if it wereavailable”. The RMSEA’s value of less than 0.05 indicates a good fit for our model. Theexpected cross validation index (ECVI) assesses whether a model is likely to repeatitself across samples of the same size in the same population. In practice, the model’sECVI is used in conjunction with the ECVIs of the independence and saturated model.A hypothesized model can be considered as falling between these two extremes and itsECVI is expected to be lower than the ECVI of the independence model but higher thanthe one of the saturated model, as is the case here. The standardized root mean squareresidual (RMR) is a summary index of the standardized residuals and a value below0.05 is considered as indicating acceptable fit. The goodness of fit index (GFI) is anindicator of the amount of variance and covariance accounted for by the model and avalue exceeding 0.90 is considered as reflecting acceptable fit. Last, but not least, thecomparative fit index (CFI) is based on the non-centrality parameter and a valueexceeding 0.90 is an indication of good fit (Diamantopoulos and Siguaw, 2000, pp. 82-88;Kelloway, 1998, pp. 23-39).

Table II contains a summary of the model’s fit statistics, as produced by LISREL8.7. The combination of their values shows that the hypothesized modelunquestionably fits the available data.

Management implicationsThe importance of our findings for managerial decision-making processes is evident.Insurance managers seeking to improve their customers’ loyalty levels, in their effort to

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increase retention rates and attract new customers through WOM, may benefit byinformation about the effect of individual dimensions of service quality on customersatisfaction and of the latter on sentimental and behavioural loyalty. They may alsobenefit from the use of GIQUAL for measuring the quality of services offered by theircompanies for discovering possible quality flaws and/or benchmarking. Thelongitudinal use of GIQUAL, or some more advanced version of it, to monitor theprogression in time of their customers’ quality perceptions will help managers to takethe necessary measures for maintaining their company’s quality image.

Greek insurers must take into account that customers do not allow for the tangibleelements in assessing their level of satisfaction and, hence, primarily direct theirresources towards improving the human rather than the tangible element of theirservices.

Of particular interest is the finding that emotional loyalty (WOM) is an antecedentof behavioural loyalty (retention intentions) and that customer satisfaction does nothave a direct effect on the latter. Consequently, insurers should direct their before andafter sales service mechanism towards developing customers willing to get engaged inpositive WOM in favour of the company and its services rather than pressing theirsales systems for more direct sales.

Though this study is based on data drawn from the Greek insurance industry, theapproach followed can be generalized to apply to other financial services sectors orindeed to any service sector. Service managers can benefit from our approach byincluding in their analyses the effect of individual dimensions rather than examiningthe links in the path service quality ! customer satisfaction ! customer loyalty onthe aggregate. Thus, they will have the opportunity to accordingly employ theirresources. The proposed model can be used for empirical investigation to providefindings that may be compared across sectors, countries and time provided that anappropriately customized SERVQUAL type scale is used, in each case, to measureservice quality.

Conclusions and directions for further researchConclusions on the cross-cultural applicability of established models are of particularresearch interest. These conclusions concern the generality and universality of theproposed models. Because Greece is culturally different from the Anglo-Saxon

Value

Degrees of freedom 4Satorra-Bentler scaled chi-square 4:387 (p ¼ 0:356)Chi-square corrected for non-normality 3.593 (p ¼ 0:464)RMSEA 0:0137ECVI 0:215ECVI for saturated model 0:0581ECVI for independence model 3:834Standardized RMR 0:0382GFI 0:936CFI 1:00

Table II.Estimated model’s teststatistics

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countries (Hofstede, 1980) from which most marketing literature originates, our resultsare interesting in this respect.

The debate on the applicability of SERVQUAL or SERVQUAL type scales tomeasure service quality in the Greek cultural setting is open (Kosta et al., 2003;Kouthouris and Alexandris, 2005; Athanassopoulos et al., 2001; Theodorakis et al.,2001; Sigala, 2004; Sigala and Sakellaridis, 2004). We propose that SERVQUAL typescales can be used to measure service quality in a cultural setting such as that ofGreece.

However, the dimensionality of service quality proposed by Parasuraman et al.(1988) was not confirmed in Greek insurance. Customers clearly separate tangible fromnon-tangible quality attributes but they consider all non-tangible attributes as forminga single dimension. The five-dimension structure of service quality was based onresearch mostly in the USA. Customers in other cultures may perceive service qualitydifferently. Winsted (1997), Mattila (1999) and Furrer et al. (2000), among others, havefound that culture is an important determinant of customers’ perceptions of servicequality and its impact on their behaviour. Sigala and Sakellaridis (2004) extended thework of Furrer et al. (2000) to study the effect of web users’ cultural profiles on theire-service quality perceptions. Further research to examine culture’s impact on servicequality perceptions would be of particular interest, especially in Greece, which is top ofthe list regarding the uncertainty avoidance score (Hofstede, 1980).

The findings of Cronin and Taylor (1992) and other researchers (Parasuraman et al.,1988; Reichheld and Sasser, 1990; Fornell, 1992; Anderson and Sullivan, 1993) aboutthe causal relations between service quality perceptions, satisfaction and loyalty areconfirmed in the Greek insurance industry. The path service quality ! customersatisfaction ! loyalty is valid.

Most published studies on service quality, customer satisfaction and loyalty refer tothe relationships between the constructs in the aggregate. We have extended our modelto include the dimensions of service quality and loyalty. Indeed the path servicequality ! customer satisfaction exists, but the two service dimensions do not equallyinfluence customer satisfaction. In the case of the Greek insurance industry theinfluence of tangibles is not significant. This may be a result of the intangibility ofinsurance service, which is considered as among the most intangible services. Researchshows that the more intangible the service the higher the customers’ expectations forthe service’s non-tangible elements (Bebko, 2000) and that there is a positive correlationbetween the level of the service’s tangibility with the importance of its tangibledimension to the customers (Santos, 2002). However further research on the subject isnecessary.

The relation customer satisfaction ! loyalty is also confirmed. Our finding thatemotional loyalty is an antecedent of behavioural loyalty and that customersatisfaction does not have a direct effect on the latter, partially confirms the finding ofAthanassopoulos et al. (2001) that bank consumers in Greece react to customersatisfaction perceptions by engaging in WOM and that WOM encapsulates positiveloyalty in the Greek retail-banking environment. Is this a characteristic of otherindustries in the same or other cultural settings? Further research on the subject shouldbe undertaken.

The insurance industry is introverted regarding findings on service quality and itsconsequences. The Greek insurance industry is no exception. As far as we know no

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such study, based on data from the Greek insurance industry, has ever been published,even if such study exists. The management of insurance companies can exploit ourfindings towards understanding how their customers think and react.

Of research and managerial interest is the extension of the model to include thecultural characteristics of customers (Furrer et al., 2000). This will contribute towardsthe understanding of how culturally different customers perceive service quality andhow they link this to satisfaction and loyalty. It will also be interesting to expand themodel to include the economic consequences for companies and organizations of therelationships described in this paper. The Greek insurance industry, among others, willhave much to benefit from studying such an extended model.

This study suffers the limitation that it tests the fit of the model within the limits ofa single service industry. Further research should attempt to replicate the findings inother contexts. Availability sampling is another limitation of the study, especially sincea large proportion of the sample was drawn from customer lists of the participatingcompanies, the construction of which was out of the researchers’ control. However,availability sampling is quite common in the service-quality – customer-satisfactionliterature (see for instance Brady et al., 2002; Chang et al., 2002; Wang et al., 2004;Semeijn et al., 2005). Despite these limitations, the satisfactory fit of the estimatedmodel allows for the study to be a reliable comparison basis for future research.

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About the authorsEvangelos Tsoukatos has a BSc in Mathematics from Aristotelion University of Thessalonica in1976, Postgraduate Diploma and MA in OR from Lancaster University Management School in1977 and 1978 respectively. Evangelos Tsoukatos is a Lecturer in Insurance at the Department ofFinance and Insurance, Technological Educational Institute of Crete. His research interestsinclude Insurance Marketing and Service Quality. He is currently a Doctoral Candidate in theDepartment of Management Science, Lancaster University Management School, UK. EvangelosTsoukatos is the corresponding author and can be contacted at: tsoukat@sdo.teiher.gr

Graham K. Rand is a Senior Lecturer in Operational Research at Lancaster University, UK,interested in TQM and modern manufacturing systems such as MRP, JIT and particularly TOC.Former Council Member and Conference Chairman of the British Operational Research Society,and editor of the Journal of the Operational Research Society from 1991 to 1996. In 2006 theOperational Research Society appointed him as Companion of Operational Research. For theInternational Federation of Operational Research Societies (IFORS) he was Vice-President(1998-2000) and editor of International Abstracts in Operations Research (from 1980-1991) andInternational Transactions in Operational Research (2000-2005). Editor of the proceedings of the1987 IFORS Conference in Buenos Aires, and Chairman of the Program Committee for the 1990IFORS Conference in Athens. For the European Association of Operational Research Societies hewas chairman of the Gold Medal jury in 1995. Founder member of the International Society forInventory Research (1983) and the Operations Management Association (1985). EuropeanDirector of Omega Rho.

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