2010 Key Determinants of Service Quality in Retail Banking

Key determinants of servicequality in retail banking

Evangelos TsoukatosDepartment of Finance and Insurance, TEI of Crete, Agios Nikolaos, Greece,

and

Evmorfia MastrojianniNational Bank of Greece, Athens, Greece

Abstract

Purpose – The purpose of this study is to build a retail-banking specific quality scale and, throughits examination and comparison with the SERVQUAL and BSQ metrics that are currently used inbanking, to deepen understanding of quality determinants in the industry. Furthermore, the study isset to provide additional input to the debate over generic against setting/industry/time-specific qualitymetrics.

Design/methodology/approach – The study is implemented through a two-stage process ofliterature review and empirical survey. Evidence drawn from Greek retail banking, through a speciallydesigned research tool, is analyzed through reliability, factorial and regression analysis to determinethe scale’s item and factorial structure and assess its reliability and validity.

Findings – The BANQUAL-R metric is introduced, with key elements assurance/empathy,effectiveness, reliability and confidence, a combination of SERVQUAL and BSQ dimensions.Findings back the setting-specific approach of service quality and the notion that SERVQUALprovides the skeleton on which setting-specific scales should be built.

Practical implications – Bank managers are provided with a reliable and valid metric of servicequality in retail banking. Its dimensionality implies that under credit-crunch conditions servicedelivery should be directed towards reinstating customers’ trust and confidence that are put in danger.Banks should redirect resources from tangibles to the human contact-related service elements.

Originality/value – Although the subject of “service quality measurement” is extensivelyresearched, the continuously changing marketing environment calls for an ongoing assessment ofquality factors. With respect to its academic value, the study accumulates knowledge that willeventually outgrow the boundaries of academia and pervade management.

Keywords Customer service management, Retailing, Banking, Face-to-face communications, Greece

Paper type Research paper

1. IntroductionThe twin objectives of this study are first, to build a retail-banking specific servicequality scale, examine its item and factorial structure, asses its reliability and validityand compare the scale against metrics that are currently used in banking and second,to deepen our understanding of service quality determinants and provide input to theever persisting debate over generic against setting/industry/time specific servicequality metrics (e.g. Ford et al., 1993; Asubonteng et al., 1996; Angur et al., 1999; Imrieet al., 2002; Sureshchander et al., 2002; Wang et al., 2004; Tsoukatos, 2009). It isexpected that the study’s findings will prove significant to both academia and practice.Adding to a scholarly debate on managerial issues contributes towards piling-upknowledge that will eventually exceed the boundaries of academia and pervademanagement. With respect to practice, the better service firms (in this case banks) are

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

www.emeraldinsight.com/1450-2194.htm

Service quality inretail banking

85

EuroMed Journal of BusinessVol. 5 No. 1, 2010

pp. 85-100q Emerald Group Publishing Limited

1450-2194DOI 10.1108/14502191011043170

equipped to appraise service delivery performance the more effective they become inmonitoring quality strategies and assessing their impact. This study provides bankswith a strong tool, nicknamed BANQUAL-R after its origins from retail banking, forassessing and monitoring their service delivery performance.

The rationale of this study rests on existing literature on the decisive importance ofservice excellence/superiority that compels firms to improvise innovative means ofmonitoring their performance in meeting customers’ service preferences (Zemke, 2002;Tsoukatos, 2009). Although this issue is extensively researched, the continuouslychanging marketing environment necessitates a correspondingly continuousevaluation of quality factors. To protect/gain market shares, organizations need tooutperform competitors in offering satisfaction to customers (Reichheld and Sasser,1990; Reichheld, 1996; Gronroos, 2000; Marwa, 2005; Tsoukatos, 2008). This is a greatchallenge to service organizations as failing to meet changing customer requirementsmay put a firm’s own survival to danger. As regards banks, customer longevity canonly be achieved through delivering high quality services (Berry et al., 1985; Caponet al., 1990; Berry and Parasuraman, 1991; Anderson et al., 1994; Rust et al., 1995;Lassar et al., 2000) especially under unregulated and volatile financial marketconditions (Colgate and Lang, 2001). Banks need to understand customers’ servicerequirements and comprehend the impact of service delivery performance oncustomers’ attitudes (Gerrard and Cunningham, 2001; Beckett, 2000). Effectivemonitoring guarantees fast service flaw detection, which, in turn, allows for fixingquality leakages before real damage is done on institutional image (Tsoukatos, 2008).Yet, research up to date on banking-specific determinants of service quality is limited,with very few worth noticing exceptions (e.g. Bahia and Nantel, 2000; Aldlaigan andButtle, 2002). Despite being generic and might lack the full potential to grasp theparticularities of the industry, SERVQUAL is mostly used in banking servicesassessment (e.g. Angur et al., 1999; Newman, 2001; Dash, 2006).

The focus of this study on retail-banking rests upon the the fact that this line ofbusines is neglected by bankers with respect to face-to-face service. For a long numberof years now banks invest heavily on automated means of retail service delivery (Huntand Menon, 2006). However, to the eyes of customers, retail and corporate alike, the“unique selling proposition” (Kotler, 1997) of a bank remains face-to-face banking(Angur et al., 1999; Hunt and Menon, 2006). This is especially true in times of financialcrises, (and we certainly are in the heart of a crisis of unprecedented extent) whencustomers’ trust to banking institutions is put to danger. Yet, customer retention inbanking depends on trust and confidence (Hennig-Thurau et al., 2002). In suchsituations, the importance of relational marketing becomes even greater and excellenceemerges as the determinant attribute of service (Kim et al., 2009). Customers need to beassured as they consider safety no more guaranteed by regulatory control (Myers andAlpert, 1968).

The remainder of this paper is organized as follows: Section 2 reviews the literatureon two major areas, service quality and its measurement and service qualityassessment in the banking sector. Section 3 follows with illustrating the methodologyof the empirical part of this study. It starts with portraying the service setting, goes onto explain the empirical research design, implementation and data analyzes and finallypresents key statistical results. Sections 4 to 6 discuss the study’s findings, implications

EMJB5,1

86

and limitations-recommendations for further research respectively. Finally, section 7presents the study’s overall conclusions.

Literature reviewService qualityAs Lewis and Booms (1983) have put it, “service quality is a measure of how well theservice level delivered matches customer expectations. Delivering quality servicemeans conforming to customer expectations on a consistent basis”. The literatureconceptualizes service quality as the result of the comparison between delivered andexpected service performance (Parasuraman et al., 1985). Customers’ perceive therelative inferiority/superiority of services by comparing a firm’s actual performancewith their expectations, shaped by experience and/or memories (Gronroos, 1982, 1984;Lehtinen and Lehtinen, 1982; Lewis and Booms, 1983; Bitner and Hubert, 1994; Georgeand Hazlett, 1997). The result of this comparison is perceived service quality (Gronroos,1982, 1984; Takeuchi and Quelch, 1983; Parasuraman et al., 1985, 1988), a customer’sjudgement “related but not equivalent to satisfaction” of the overall excellence orsuperiority of a service (Parasuraman et al., 1988).

Under the framework of the disconfirmation paradigm (Oliver, 1980), the Nordic(Gronroos, 1982, 1984) and the American (Parasuraman et al., 1985, 1988) models ofperceived service quality are dominating the literature. The former distinguishesbetween “technical” and “functional” quality, reflecting the service outcome andprocess respectively. Customers’ perceptions of these dimensions are filtered throughcorporate image. The American model, also known as the “gaps analysis model”defines service quality across five dimensions (Parasuraman et al., 1985):

(1) reliability;

(2) responsiveness;

(3) assurance;

(4) empathy; and

(5) tangibles.

A by-product of the “gaps analysis model” is SERVQUAL a 22-item generic scale formeasuring service quality (Parasuraman et al., 1988).

The disconfirmation paradigm is not without its critics. Cronin and Taylor (1992a)proposed that service quality is better operationalized in terms of “performance-only”rather than “performance-minus-expectations” and introduced SERVPERF, which infact is the performance only part of the SERVQUAL scale. Cronin and Taylor (1992b),proved that, statistically, SERVPERF performs better than SERVQUAL. However,SERVQUAL’s “performance-minus-expectations” approach outperforms SERVPERFin identifying the exact causes of defects in service delivery (e.g. Angur et al., 1999;Stafford et al., 1999; Johns et al., 2004). On these grounds, this study adopts the“performance-minus-expectations” approach of the disconfirmation paradigm (Oliver,1980) for its empirical part.

Service quality assessment in the banking sectorThe main objective of the financial system is to “encourage individuals and institutionsto save and to transfer those savings to individuals and institutions planning to invest


87

in new projects, products and services” (Rose and Hudgins, 2005). For centuries, and upuntil a few decades past, banks had been the system’s dominant players. However, thisis not the case anymore. Through the years, a number of radical regulatory, structuraland technological changes transformed institutions and the system as a whole (Anguret al., 1999; Panopoulou, 2001) by enhancing service proliferation, increasing investors’interest-sensitiveness and enabling institutional consolidation, geographic expansionand convergence (Rose and Hudgins, 2005). Through all these changes andtransformations, an immensely competitive business environment emerged; a globalfinancial market where banks must add to their traditional business adversaries (otherbanks) multinational conglomerates that literally invade local markets to get majorchunks of market shares. At the same time, increased interest sensitiveness turnedformer depositors into investors while individuals are now more than before willing toborrow in order to maintain their living standards. Moreover, while technologybrought customers “just a click” away from competition (Hunt and Menon, 2006) andincreased market transparency (Granados, 2005), the differentiation of products andservices between institutions is minimum. It is often a matter of just a few hours beforecompetitors can present clones or improved versions of any new banking product thatenters the market (Devlin, 1995; Johannesssen et al., 1999).

Under such state of market complexity it is imperative for banks to achievecustomer longevity, which can only be accomplished through service excellence(Lassar et al., 2000). Despite service superiority’s importance, the banking industry isshort of a bank-specific widely recognized instrument for service quality assessment(Bahia and Nantel, 2000). Quality studies in traditional face-to-face banking havemainly adopted the five dimensional SERVQUAL (Parasuraman et al. 1985,1988)/SERVPERF (Cronin and Taylor, 1992b) approach or some customized versionof it (e.g. Yavas et al., 1997; Cronin and Taylor, 1992b; Newman, 2001; Angur et al.,1999; Lassar et al., 2000; Chi-Cui et al., 2003; Balestrini and Huo, 2005; Dash, 2006 etc.).Company proprietary scales that are specifically developed to address occasionalsituations (Bahia and Nantel, 2000) are not usually identified in public.

To build their BSQ retail banking-specific metric, Bahia and Nantel (2000) startedfrom 15 dimensions after adding to the initially ten dimensions of Parasuraman et al.(1985), elements from the seven Ps of marketing that they considered as partially or notat all represented in the original list. As regards quality attributes, they analyzed anextensive list of items, some bank-specific and others generic, mainly from the bankingliterature. After appropriate analysis of evidence drawn from French speaking Canada,they came up with a 31-items/six-dimension scale:

(1) effectiveness and assurance;

(2) access;

(3) price;

(4) tangibles;

(5) service portfolio; and

(6) reliability.

Bahia and Nantel (2000) proposed BSQ as an alternative to SERVQUAL although theyrecognized their study’s main limitation, notably its implementation only in the Frenchlanguage. BSQ was subsequently used in numerous occasions including some settings

EMJB5,1

88

in Greece (Glaveli et al., 2006; Petridou et al., 2007), where from the present study drawsevidence too.

An interesting approach was that of Aldlaigan and Buttle (2002) who developedSYSTRA-SQ, a retail-banking specific service quality scale based on the Nordic model(Gronroos, 1982, 1984). They started with an impressive number of 963 itemsdescribing customers’ service quality perceptions and concluded with a21-items/four-dimensions service quality scale with key elements:

(1) service system quality;

(2) behavioural service quality;

(3) service transactional accuracy; and

(4) machine service quality.

Aldlaigan and Buttle (2002) proposed that customers evaluate SQ at two levels:organizational and transactional and reported that the parsimony, reliability andvalidity of SYSTRA-SQ suggest that the measure is of high utility to the bankingindustry. However, the authors did not fully explain exactly how they went down from963 to 21 items. Moreover, no further attempts to build banking-specific scales on thebasis of Gronroos (1982, 1984) model are reported in the literature.

The above mentioned literature findings provide the theoretical background for theempirical part of this study the methodology of which is described in the followingsection.

MethodologySettingThis study draws evidence from Greek retail banking. The industry consists of 65banks operating through 3,894 “bricks and mortar” outlets and 67,113 employees(HBA, 2009). The system is highly deregulated and competitive. The entry of Greeceinto the European Monetary Union (EMU) compelled banks to improve their efficiencyin order to defend their market shares from foreign competition and their profits frompressures on interest rates spreads (Lymperopoulos and Chaniotakis, 2005). A gradualtransformation of outlets from large, inflexible facilities to smaller sales oriented unitshas been evident through the past years. The 20.05 employees per outlet of 2001 havebeen reduced, by almost 15%, to less than 17 in 2007 (HBA, 2009).

Banks made considerable efforts to modify their organizational structures and changetheir traditional ways of conducting business. Technology has been widely adopted inservice delivery. From 2000 to 2007, ATMs have been doubled from 3,605 to 7,270 withoffsite ATMs almost quadrupled from 758 to 2,800 (HBA, 2009). Besides ATMs otheravailable automated means of retail banking are not commonly used due to technologicalilliteracy, low internet penetration (Observatory for the Greek IS, 2009) and securityconcerns, all leading to increased customers’ preference to traditional face-to-facebanking. However, most major banks invest heavily on internet banking in order toprotect or improve their innovator image (Valakas and Chaniotakis, 2000) as customersare more likely to trust proven innovators when the internet banking market really opens(Jayawardhena and Foley, 2000). For similar reasons, banks also offer phone services,even though customers’ security concerns associated with phone banking are evenstronger than those related to internet (Marinakis and Karanikolas, 2007).


89

Research instrument and data collectionA five sections research instrument (demographics, service expectations, serviceperformance, overall satisfaction and word-of-mouth communication) was especiallydesigned and used for data collection. Service expectations and performance scoreswere measured in identical seven-point Likert scales as was overall satisfaction andintentions to recommend the bank to friends and relatives.

The battery of service attributes was built through reviewing the literature andconducting two focus groups of banking customers and employees. The first stage,literature review, produced an extensive list of 70 items from SERVQUAL, customizedSERVQUAL and bank-specific service quality measures such as BSQ (e.g.Parasuraman et al., 1988; Cronin and Taylor, 1992b; Bahia and Nantel, 2000;Lociacono et al., 2000; Zeithaml et al., 2000, 2002; Wolfinbarger and Gilly, 2001; Ibrahimet al., 2006; Tsoukatos and Rand, 2006). Focus groups discussions reduced the listdown to 31 items that were incorporated in the research instrument after being severaltimes translated back and forth from Greek to English for ensuring functionalequivalence (Tsoukatos and Rand, 2006).

Data were collected in early spring 2008, from a convenience sample of n ¼ 91retail-banking customers, in three downtown Athens branches of the two leadingGreek banks. Respondents were adults, residing in Greece but not necessarily Greeknationals, consumers of both traditional and automated retail banking services whohad at least one face-to-face banking transaction during the last month. Conveniencesampling is very common in service-quality/customer-satisfaction research mainly dueto random sampling requirements for population homogeneity, hardly met in practice,and high costs associated with locating chosen population items (see Brady et al., 2002;Wang et al., 2004; Semeijn et al., 2005). The major weakness of convenience sampling isthat it does not provide any built-in means of eliminating or assessing sampling bias(Tsoukatos, 2009). There has been no evidence, however, that the aforementionedsample deviates in any respect from the general population regarding customers’attitudes towards retail banking. After taking also into consideration this study’s timeand cost constraints the sample was considered sufficient. Its adequacy for conductingthe appropriate statistical analyzes is discussed later on in this paper.

The research instrument was administered through personal interviews conductedon the spot inside branch premises. To minimize bias, caused by poor service,prospective respondents were approached and interviewed prior to conducting theirintended transactions. Despite the questionnaire’s complexity the response ratereached 50.27%. The method of personal interviews is superior to self-administeredquestionnaires in perceptual or attitudinal surveys (Groves, 1989), while face-to-faceadministration maximizes response rates and field researchers’ availability to answerrespondents’ questions (Ibrahim et al., 2006).

Data analysisPerformance-minus-expectations scores (Qi ¼ Pi – Ei) of service attributes wereappropriately analyzed for:

. initial scale purification;

. unveiling the metric’s underlying structure; and

. assessing its reliability and validity.

EMJB5,1

90

Statistical treatment involved Cronbach’s alpha Reliability Analysis, ExploratoryFactor Analysis and Linear Regression Analysis. All analyzes were executed by usingthe SPSS for Windows statistical package.

Sample size adequacy assessment. Before performing the statistical analyzes, then ¼ 91 sample was examined for size adequacy and found sufficient. Regardingreliability analysis, Yurdugul (2008) proved that the minimum sample size required forcoefficient alpha depends on the largest eigenvalue of Principal Components Analysis(PCA). For a value exceeding 6.00, the sample alpha coefficient is an especially robustestimator of the population alpha even with samples as low as n ¼ 30. As regardsfactorial analysis, Fabrigar et al. (1999) proved that the minimum sample size shoulddepend on the extent to which factors are overdetermined and the level ofcommunalities. A sample in the area of n < 100 would produce accurate results if allfactors are overdetermined and communalities exceed 0.70 on average (Fabrigar et al.,1999). In this study, the first PCA eigenvalue is 14.62, all four factors areoverdetermined (each represented by at least five variables) and communalities are0.74 on average. These, in combination with the KMO statistic (0.899) and the Bartlett’stest of sphericity (significant at p , 0.001), clearly indicate that the n ¼ 91 sample issufficient for both Reliability and Factorial analysis. For linear regression theminimum sample size is determined as a function of four parameters: a) the probabilityof Type I error (alpha level), b) the number of predictors (excluding the intercept), c) theexpected effect size (f-square) and d) the desired statistical power level (Cohen et al.,2003). For alpha level ¼ 0.05, expected effect size ¼ 0.35 (large) and desired statisticalpower level ¼ 0.95 the minimum sample size estimate, for one predictor, is 39. Hence,the n ¼ 91 sample is also sufficient for regression analysis.

Scale purification. As already mentioned, Cronbach’s (1951) alpha reliabilityanalysis was employed for initial scale purification. On the basis of the “alpha increaseif item deleted” criterion (Tabachnick and Fidell, 2001) four of the initially 31 serviceattributes were removed leaving the scale with 27 (Table I) items and a very high alphavalue of 0.966 indicating excellent overall internal consistency (Tabachnick and Fidell,2001).

Factorial analysis. The R-type approach is employed for Exploratory FactorAnalysis, as the study deals with relations between variables (Stewart, 1981). Acombination of PCA, for initial extraction, and varimax rotation revealed an orthogonal(Tabachnick and Fidell, 2001) four-factor structure, explaining 72.78% of totalvariance. The number of factors was determined by a combination of the roots andscree-test criteria (Stewart, 1981), while the threshold for meaningful factor loadingwas set to 0.55, the minimum “good” factor loading score (Comrey and Lee, 1992;Tabachnick and Fidell, 2001).

The four orthogonal factors (Table I): Assurance and Empathy, Effectiveness,Reliability and Confidence explained 21.52%, 20.82%, 16.44% and 13.99% of variancerespectively. Moreover, out of the 27 factor loadings, nine were in excess of 0.71(excellent), 11 in excess of 0.63 (very good) and seven in excess of 0.55 (good) (Comreyand Lee, 1992) indicating adequate factor measurement by the scale’s attributes(Tabachnick and Fidell, 2001).

Reliability and validity assessment. The Cronbach’s (1951) alpha scores of 0.931,0.942, 0.894 and 0.896 for the dimensions Assurance/Empathy, Effectiveness, Reliabilityand Confidence respectively indicate high internal consistency of the BANQUAL-R


91

Overalla¼

0.966

Com

ponent

Assurance

andem

pathy

21.52%

ofvariance

a¼

0.931

Effectiveness20.82%

ofvariance

a¼

0.942

Reliability16.44%

ofvariance

a¼

0.894

Confidence

13.99%

ofvariance

a¼

0.896

Understandingcustom

ers’individual

needs

0.823

Understandingcustom

ers’individual

goals

0.788

Havingcustom

ers’interest

atheart

0.712

Easyaccess

toservicepersonnel

0.705

Employeesinstillingconfidence

incustom

ers

0.665

Courteousness

0.654

0.581

Understandingcustom

ers’problems

0.645

Avoidtechnical

jargon

when

talkingto

custom

ers

0.574

Innovativeproductsandservices

0.741

Error-freestatem

ents,billsetc.

0.738

Promptservice

0.670

Employeesknow

ingexactlywhat

they’redoing

0.666

Employees’professionalism

0.659

Inform

custom

ersexactlywhen

they

willbeserved

0.655

Employeeswelltrained

inusingtechnology

0.648

Highqualityof

services

0.611

Clear

term

sin

contracts

0.796

Fullrangeof

productsandservices

0.761

Com

petitivepricing

0.707

Keepingtimepromises

0.668

Respondhonestlyto

custom

ers’requirem

ents

0.647

Doingtheservicerightthefirsttime

0.559

Custom

ers’confidence

indocuments,statem

ents

etc.

0.869

Secure

filingsystem

s0.836

Safeuse

ofalternativeservicechannels

0.674

Custom

ersfeelingsafe

intheirtransactions

0.593

Custom

ers’confidence

intheservice

0.572

Note:Extraction

method:Principal

componentanalysis;Rotationmethod:Varim

axwithKaiserNormalization;Rotationconverged

innineiterations

Table I.Rotated componentmatrix

EMJB5,1

92

measure, reflecting the scale’s high reliability (Tabachnick and Fidell, 2001). Constructvalidity is secured on the grounds that the battery of service attributes comes fromtheoretically well-supported literature sources including SERVQUAL (Parasuramanet al., 1988), BSQ (Bahia and Nantel, 2000) and others that have been used in a multitudeof settings around the world. The nomological/predictive validity of the scale is assessedby examining the association of service quality in retail banking, that the BANQUAL-Rscale is meant to measure, with other constructs to which SQ is theoretically related(Peter, 1981). In this case Customer Satisfaction (CS) and Word of Mouth communication(WOM) (Tsoukatos and Rand, 2006) scores were separately regressed against overall SQscores. Both regression models produced statistically significant adjusted R-squarescores of 0.529 and 0.362 respectively, indicating that SQ scores, produced byBANQUAL-R can indeed predict CS and WOM as theory suggests (Bahia and Nantel,2000), an indication of the scale’s nomological/predictive validity (Peter, 1981).

Discussion of resultsThe 27-item BANQUAL-R scale consists of 12 SERVQUAL, seven BSQ, two commonin SERVQUAl and BSQ and six setting-specific items. In this respect, the metric is ahybrid of the SERVQUAL and BSQ scales. The factorial structure of BANQUAL-Rconsists of SERVQUAL’s Empathy, and Assurance (Parasuraman et al., 1988), BSQ’sEffectiveness (Bahia and Nantel, 2000), Reliability which is common in SERVQUALand BSQ and finally Confidence. The latter is mainly associated with serviceinnovation leading to increased intangibility of previously tangible servicecomponents such as physical records, archives etc. Assurance/Empathy andEffectiveness are the primary and secondary factors respectively (explaining verysimilar variance percentages) while Reliability and Confidence are in the third and lastpositions, explaining 16.44 per cent and 13.99 per cent of variance.

In the majority of SERVQUAL applications Reliability is found to be the mostimportant of dimensions, interchangeably followed by Responsiveness and Assurancewhile Empathy is usually more important only from Tangibles (Zeithaml et al., 1990).Yet, in this study Assurance/Empathy is the most important service element closelyfollowed by Effectiveness (also including certain Responsiveness items), whileReliability is in the third position. In the BSQ study (Bahia and Nantel, 2000)Effectiveness was found to be the most important dimensions, followed by Assurance,Access, Price, Tangibles, Service Portfolio and Reliability. It is clear that thedimensionality of BANQUAL-R indicates a different set of priorities than thosementioned by the SERVQUAL and BSQ studies.

In line with a recent similar finding of Tsoukatos and Rand (2006) in Greek retailinsurance, a notable key feature of the BANQUAL-R scale is the complete absence oftangible elements, such as equipment, facilities etc, from its battery of attributes.Although no solid evidence exists for this repeated finding, it may be attributed to thatcustomers take tangible elements of service for granted as banks and other financialinstitutions have been and still are investing heavily on equipment and facilities.

Last but not least, the validity assessment of the scale reconfirms that servicequality is an antecedent of customer satisfaction and customer loyalty. The lower fit ofthe direct regression model SQ (WOM refers to the argument that the moderatingvariable between Service Quality and Loyalty is Customer Satisfaction (Cronin andTaylor, 1992a; Tsoukatos and Rand, 2006).


93

ImplicationsThere are several important implications from this study’s findings. The studycontributes to the ever persisting debate over the appropriateness of generic (e.g.Parasuraman et al., 1988), industry-specific (e.g. Glaveli et al., 2006; Petridou et al.,2007) or setting-specific (e.g. Asubonteng et al., 1996; Angur et al., 1999) measures, withthe term setting-specific being more restrictive that the term industry-specific as theformer includes additional elements such as culture, language, time etc. Findings areclearly in favour of the setting-specific perspective. Neither SERVQUAL nor BSQper-se can fully grasp the particularities of the study’s setting and therefore any resultsfrom the use of either one of these metrics must be treated with caution.

At the same time, the study backs previous research findings indicating that servicequality in the banking sector is not so much different from service quality in general(Bahia and Nantel, 2000). The battery of attributes and factorial structure of theBANQUAL-R includes four of the five SERVQUAL dimensions (Assurance, Empathy,Reliability and Responsiveness as part of Effectiveness) plus retail-banking specificelements such as Effectiveness and Confidence. This dimensionality provides supportto the argument that although SERVQUAL cannot be the exclusive base for servicequality assessment (Zeithaml and Parasuraman, 2004) it should not be disregarded asit provides the skeleton on which any industry or setting specific service measure isbuilt (Parasuraman et al., 1988, 1991; Beckman and Velfkamp, 1995).

In managerial terms, the study provides the industry with the BANQUAL-R metric,a valid and reliable retail-banking specific scale of service quality. However, followingour previous argument about the setting-specific perspective of service quality scales,BANQUAL-R should be treated with caution as service settings elements change withtime. The absence of Tangible elements from the BANQUAL-R battery of items,consistent with the similar finding of Tsoukatos and Rand (2006) in retail insurance,implies that financial institutions in general and banks in particular should redirectresources from Tangibles to other, more important to customers, elements of service.

The importance ranking of BANQUAL-R dimensions indicates that what retailcustomers’ need more is personal attention and human contact both instilling trust andconfidence in customers. This is inconsistent with both the majority of SERVQUALstudies (Tsoukatos, 2009) and the BSQ study in which Effectiveness was found to bethe most important of dimensions (Bahia and Nantel, 2000). The first position ofAssurance/Empathy in this study is consistent with previous research findings that infinancial crises situations, such as the one at the core of which we currently are,customers’ trust to the service is put to danger (Hennig-Thurau et al., 2002) and needsto be reinstated as safety is no more taken for granted (Myers and Alpert, 1968). Thepositioning of Effectiveness in the second position, with similar importance to that ofAssurance/Empathy, provides support to Bahia and Nantel’s (2001) similar argumentand can be interpreted as a banking-specific feature of service quality. RegardingReliability, there is no solid evidence on why it is so low in importance in both the BSQand the BANQUAL-R studies. A plausible explanation might be that banks areby-definition considered reliable and so, Reliability is taken for granted. Similarly, thelast position of Confidence implies that customers have finally overcome their concernsregarding the use of ICT in banking transactions.

EMJB5,1

94

Limitations of the study and avenues for further researchDespite its important findings, the study suffers a number of limitations namelyconvenience sampling and drawing from a single banking industry. Although,convenience sampling is common practice in management studies (e.g. Brady et al.,2002; Wang et al., 2004; Semeijn et al., 2005) variables such as location and sampleunrepresentativeness may have affected findings. Nevertheless, given the time andcost constraints the sampling procedure is considered adequate. An additionallimitation that the study suffers is that it failed to evaluate the relative performance ofthe BANQUAL-R, SERVQUAl and BSQ scales in assessing service quality in the samesetting. Such an examination would strengthen the value of this study.

Nevertheless, any study’s limitations present opportunities for further research. It isobvious that a replication of this study on evidence from a number of retail bankingindustries would give the opportunity to examine more combinations of situationalvariables among which culture. Further research should encompass the crossexamination of the BANQUAL-R with other measures employed in retail banking, andin particular SERVQUAL and BSQ, in order to get solid evidence on the relativesuperiority/inferiority of the measures. Finally, the evolution of service expectations isrelated to the financial market conditions (Hennig-Thurau et al., 2002). This leads to thenecessity of re-examining the scale after the current financial crisis will come to an end.It is the authors’ belief that both the service attributes and the dimensionality of thescale will be different should the study is replicated under non credit-crunch conditions

ConclusionsAlthough the issue of “service quality determinants and measurement” is extensivelyresearched, this study provides original findings that contribute to both academia andpractice. It builds on previous research regarding service quality measurement in retailbanking, especially in view of current financial crisis conditions creating increasedneeds for constant service monitoring. With regard to practice the study provides areliable and valid scale for measuring service quality in retail banking that can beexploited in managerial decision making in banking.

However, further research is needed to improve our knowledge on service qualitymeasurement in retail banking. Despite the study’s limitations the reliability andvalidity of the proposed BANQUAL-R metric provides a sound reliable comparisonbasis for future research. The study provides the methodological framework for itsreplication in a multitude of combinations of situational variables in domestic orinternational settings.

References

Aldlaigan, A.H. and Buttle, F.A. (2002), “SYSTRA-SQ: a new measure of bank service quality”,International Journal of Service Industry Management, Vol. 13 No. 4, pp. 362-81.

Anderson, E.W., Fornell, C. and Lehman, D.R. (1994), “Customer satisfaction, market share, andprofitability: findings from Sweden”, Journal of Marketing, Vol. 58, pp. 53-66.

Angur, M.G., Nataraajan, R. and Jahera, J.S. Jr (1999), “Service quality in the banking industry:an assessment in a developing economy”, International Journal of Bank Marketing, Vol. 17No. 3, pp. 116-22.

Asubonteng, P., McCleary, K.J. and Swan, J.E. (1996), “SERVQUAL revisited: a critical review ofservice quality”, Journal of Services Marketing, Vol. 6 No. 6, pp. 62-81.


95

Backman, S.J. and Veldkamp, C. (1995), “Examination of the relationship between service qualityand user loyalty”, Journal of Park and Recreation Administration, Vol. 13 No. 2, pp. 29-41.

Bahia, K. and Nantel, J. (2000), “A reliable and valid measurement scale for perceived servicequality of banks”, International Journal of Bank Marketing, Vol. 18 No. 2, pp. 84-91.

Balestrini, P.P. and Huo, F. (2005), “Cross-cultural service quality expectations in the retailbanking sector: a study of Chinese and British customers”,Marketing Issues in Asia, Vol. 1No. 9.

Beckett, A. (2000), “Strategic and marketing implications of consumer behaviour in financialservices”, Service Industries Journal, Vol. 20 No. 3, pp. 191-208.

Berry, L.L. and Parasuraman, A. (1991), Marketing Services. Competing through Quality, The FreePress, New York, NY.

Berry, L.L., Zeithaml, V.A. and Parasuraman, A. (1985), “Quality counts in services too”,Business Horizons, Vol. 28 No. 3, pp. 44-50.

Bitner, M.J. and Hubbert, A.R. (1994), “Encounter satisfaction versus overall satisfaction versusservice quality: the consumer’s voice”, in Rust, R.T. (Ed.), Service Quality: New Directionsin Theory and Practice, Sage Publications, Thousand Oaks, CA, pp. 72-94.

Brady, M.K., Cronin, J.J. and Brand, R.R. (2002), “Performance-only measurement of servicequality: a replication and extension”, Journal of Business Research, Vol. 55, pp. 17-31.

Capon, N., Farley, J.U. and Hoenig, S. (1990), “Determinants of financial performance:a meta-analysis”, Management Science, Vol. 36 No. 10, pp. 1143-59.

Chi Cui, C., Lewis, B.R. and Park, W. (2003), “Service quality measurement in the banking sectorin South Korea”, International Journal of Bank Marketing, Vol. 21 No. 4, pp. 191-201.

Cohen, J., Cohen, P., West, S.G. and Aiken, L.S. (2003), Applied Multiple Regression/CorrelationAnalysis for the Behavioral Sciences, 3rd ed., Lawrence Erlbaum Associates, Mahwah, NJ.

Colgate, M. and Lang, B. (2001), “Switching barriers in consumer markets: an investigation of thefinancial services industry”, Journal of Consumer Marketing, Vol. 18 No. 4, pp. 332-47.

Comrey, A.L. and Lee, H.B. (1992), A First Course in Factor Analysis, 2nd ed., Lawrence ErlbaumAssociates, Hillsdale, NJ.

Cronbach, L.J. (1951), “Coefficient alpha and the internal structure of tests”, Psychometrika,Vol. 16, pp. 297-334.

Cronin, J.J. Jr and Taylor, S.A. (1992a), “Measuring service quality: a reexamination andextension”, Journal of Marketing, Vol. 56, pp. 55-68.

Cronin, J.J. Jr and Taylor, S.A. (1992b), “SERVPERF versus SERVQUAL: reconcilingperformance-based and perceptions-minus-expectations measurement of servicequality”, Journal of Marketing, Vol. 58 No. 1, pp. 125-31.

Dash, S. (2006), “Does culture influence service quality expectations? A test of cultural influencein banking service expectation”, The ICFAI Journal of Consumer Behaviour, Vol. 1 No. 2,pp. 16-30.

Devlin, J.F. (1995), “Technology and innovation in retail banking distribution”, InternationalJournal of Bank Marketing, Vol. 13 No. 4, pp. 19-25.

Fabrigar, L.R., Wegener, D.T., MacCallum, R.C. and Strahan, E.J. (1999), “Evaluating the use ofexploratory factor analysis in psychological research”, Psychological Methods, Vol. 4 No. 3,pp. 272-99.

Ford, J.W., Joseph, M. and Joseph, B. (1993), “Service quality in higher education: a comparison ofuniversities in the United States and New Zealand using SERVQUAL”, working paper,Old Dominion University, Norfolk, VA.

EMJB5,1

96

George, P. and Hazlett, S.A. (1997), “The measurement of service quality: a new P-C-P attributemodel”, International Journal of Quality & Reliability Management, Vol. 14 No. 3,pp. 260-86.

Gerrard, P. and Cunningham, B. (2001), “Bank service quality: a comparison between a publiclyquoted bank and a government bank in Singapore”, Journal of Financial ServicesMarketing, Vol. 6 No. 1, pp. 50-66.

Glaveli, N., Petridou, E., Liassides, C. and Spathis, C. (2006), “Bank service quality: evidence fromfive Balkan countries”, Managing Service Quality, Vol. 16 No. 4, pp. 380-94.

Granados, N. (2005), “The impact of IT-driven market transparency on demand, prices, andmarket structure”, AMCIS 2005 Proceedings, available at: http://aisel.aisnet.org/amcis2005/68

Gronroos, C. (1982), Strategic Management and Marketing in the Service Sector, Swedish Schoolof Economics and Business Administration, Helsinki.

Gronroos, C. (1984), “A service quality model and its marketing implications”, European Journalof Marketing, Vol. 18 No. 4, pp. 36-44.

Gronroos, C. (2000), Service Management and Marketing: A Customer Relationship ManagementApproach, John Wiley & Sons, New York, NY.

Groves, R.M. (1989), Survey Errors and Survey Costs, John Wiley & Sons, New York, NY.

Hellenic Bank Association (2009), available at: www.hba.gr (accessed 10 April 2009).

Hennig-Thurau, T., Gwinner, K.P. and Gremler, D.D. (2002), “Understanding relationshipmarketing outcomes: an integration of relational benefits and relationship quality”,Journal of Service Research, Vol. 4 No. 3, pp. 230-47.

Hunt, R. and Menon, R. (2006), “Automate and engage to fulfil the true potential of the internet”,White Paper sponsored by: Adobe, Financial Insights, available at: www.adobe.com/financial/pdfs/idc_wp.pdf

Ibrahim, E.E., Joseph, M. and Ibeh, K.I.N. (2006), “Customers’ perception of electronic servicedelivery in the UK retail banking sector”, International Journal of Bank Marketing, Vol. 24No. 7, pp. 475-93.

Imrie, B.C., Cadogan, J.W. and McNaughton, R. (2002), “The service quality construct on a globalstage”, Managing Service Quality, Vol. 12 No. 1, pp. 10-18.

Jayawardhena, C. and Foley, P. (2000), “Changes in the banking sector: the case of internetbanking in the UK”, Internet Research: Electronic Networking Applications and Policy,Vol. 10 No. 1, pp. 19-30.

Johannessen, J-A., Olalsen, J. and Olsen, B. (1999), “Strategic use of information technology forincreased innovation and performance”, Information Management & Computer Security,Vol. 7 No. 1, pp. 5-22.

Johns, N., Avci, T. and Karatepe, O.M. (2004), “Measuring service quality of travel agents:evidence from Northern Cyprus”, The Service Industries Journal, Vol. 24 No. 3, pp. 82-100.

Kim, M., Lado, N. and Torres, A. (2009), “Evolutionary changes in service attribute importance ina crisis scenario: the Uruguayan financial crisis”, Journal of Service Research, Vol. 11 No. 4,pp. 429-40.

Kotler, P. (1997), Marketing Management: Analysis, Planning, Implementation, and Control,Prentice-Hall, Upper Saddle River, NJ.

Lassar, W.M., Manolis, C. and Winsor, R.D. (2000), “Service quality perspectives and satisfactionin private banking”, International Journal of Bank Marketing, Vol. 18 Nos 4/5, pp. 181-99.


97

Lehtinen, U. and Lehtinen, J.R. (1982), Service Quality: A Study of Quality Dimensions, ServiceManagement Institute, Helsinki.

Lewis, R.C. and Booms, B.H. (1983), “The marketing aspects of service quality”, in Berry, L.L.,Shostack, G. and Upah, G. (Eds), Emerging Perspectives on Services Marketing, AmericanMarketing Association, Chicago, IL, pp. 90-107.

Lociacono, E., Watson, R.T. and Goodhue, D. (2000), “WebQualTM: a web site qualityinstrument”, working paper, Worcester Polytechnic Institute, Worcester, MA.

Lymperopoulos, C. and Chaniotakis, I.E. (2005), “Factors affecting acceptance of the internet as amarketing-intelligence tool among employees of Greek bank branches”, InternationalJournal of Bank Marketing, Vol. 23 No. 6, pp. 484-505.

Marinakis, C.J. and Karanikolas, N.N. (2007), “Strengthening the security of E-bankingtransactions: the case of NBG”, Proceedings of the Annual Conference on Current Trendsin Informatics, Patras, 18-20 May, pp. 559-70.

Marwa, S.M. (2005), “Exploration of SERVQUAL’s efficacy via the diagnosis and improvementof service quality in Kenya’s insurance industry”, PhD thesis, Lancaster University,Lancaster.

Myers, J.H. and Alpert, M.I. (1968), “Determinant buying attitudes: meaning and measurement”,Journal of Marketing, Vol. 32 No. 4, pp. 13-20.

Newman, K. (2001), “Interrogating SERVQUAL: a critical assessment of service qualitymeasurement in a high street retail bank”, International Journal of BankMarketing, Vol. 19No. 3, pp. 126-39.

Observatory for the Greek Information Society (2009), available at: www.observatory.gr/page/default.asp?la¼2&id¼4 (accessed 10 April 2009).

Oliver, R.L. (1980), “A cognitive model of the antecedents and consequences of satisfactiondecisions”, Journal of Marketing Research, Vol. 17, November, pp. 460-9.

Panopoulou, M. (2001), “Technological and structural change in the European banking industry”,Working Paper 02-13, EIFC Consortium, Institute for New Technologies, United NationsUniversity.

Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985), “A conceptual model of service qualityand its implications for future research”, Journal of Marketing, Vol. 49, Fall, pp. 41-50.

Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988), “SERVQUAL: a multiple-item scale formeasuring consumer perceptions of service quality”, Journal of Retailing, Vol. 64, Spring,pp. 12-40.

Peter, J.P. (1981), “Construct validity: a review of basic issues and marketing practices”, Journalof Marketing Research, Vol. 18, May, pp. 133-45.

Petridou, E., Spathis, C., Glaveli, N. and Liassides, C. (2007), “Bank service quality: empiricalevidence from Greek and Bulgarian retail customers”, International Journal of Quality &Reliability Management, Vol. 24 No. 6, pp. 568-85.

Reichheld, F.F. (1996), “Learning from customer defections”, Harvard Business Review, Vol. 74No. 2, March-April, pp. 56-69.

Reichheld, F.F. and Sasser, W.E. (1990), “Zero defections: quality comes to services”, HarvardBusiness Review, Vol. 68 No. 5, September-October, pp. 105-11.

Rose, P.S. and Hudgins, S.C. (2005), Bank Management and Financial Services, InternationalEdition, McGraw-Hill, New York, NY.

Rust, R.T., Zahorik, A.J. and Keiningham, T.L. (1995), “Return on quality (ROQ): making servicequality financially accountable”, Journal of Marketing, Vol. 59 No. 2, pp. 58-70.

EMJB5,1

98

Semeijn, J., van Riel, A.C.R., van Birgelen, M.J.H. and Streukens, S. (2005), “E-services and offlinefulfilment: how e-loyalty is created”, Managing Service Quality, Vol. 15 No. 2, pp. 182-94.

Stafford, M.R., Prybutok, V., Wells, B.P. and Kappelman, L. (1999), “Assessing the fit andstability of alternative measures of service quality”, Journal of Applied Business Research,Vol. 15 No. 2, pp. 13-18.

Stewart, D.W. (1981), “The application and misapplication of factor analysis in marketingresearch”, Journal of Marketing Research, Vol. 18 No. 1, pp. 51-62.

Sureshchander, G.S., Chandrasekharan, R. and Anantharaman, R.N. (2002), “The relationshipbetween service quality and customer satisfaction – a factor specific approach”, Journal ofService Marketing, Vol. 16 No. 4, pp. 363-79.

Tabachnick, B.G. and Fidell, L.S. (2001), Using Multivariate Statistics, Allyn & Bacon, NeedhamHeights, MA.

Takeuchi, H. and Quelch, J.A. (1983), “Quality is more than making a good product”, HarvardBusiness Review, Vol. 61, July-August, pp. 139-45.

Tsoukatos, E. (2008), “Applying importance-performance analysis to assess service deliveryperformance: evidence from Greek insurance”, EuroMed Journal of Business, Vol. 3 No. 2,pp. 144-62.

Tsoukatos, E. (2009), Impact of Culture on Services Marketing: A HomeMarket Perspective, VDMVerlag Publishers, Saarbrucken.

Tsoukatos, E. and Rand, G.K. (2006), “Path analysis of perceived service quality, satisfaction andloyalty in Greek insurance”, Managing Service Quality, Vol. 16 No. 5, pp. 501-19.

Valakas, I. and Chaniotakis, I.E. (2000), “Integrated marketing communications in the age ofelectronic banking”, Proceedings of the 3rd Bank Marketing Conference, Hellenic Instituteof Marketing, Athens.

Wang, Y., Lo, H-P. and Yang, Y. (2004), “An integrated framework for service quality, customervalue, satisfaction: evidence from China’s telecommunication industry”, InformationSystems Frontiers, Vol. 6 No. 4, pp. 325-40.

Wolfinbarger, M.F. and Gilly, M.C. (2001), “Shopping online for freedom, control and fun”,California Management Review, Vol. 43 No. 2, pp. 34-55.

Yavas, U., Bilgin, Z. and Shemwell, D.J. (1997), “Service quality in the banking sector in anemerging economy: a consumer survey”, International Journal of Bank Marketing, Vol. 15Nos 6/7, pp. 217-23.

Yurdugul, H. (2008), “Minimum sample size for Cronbach’s coefficient alpha”, HacettepeUniversitesi Egitim Fakultesi Dergisi, Vol. 35, pp. 397-405.

Zeithaml, V.A. and Parasuraman, A. (2004), Service Quality, Relevant Knowledge Series,Marketing Science Institute, Cambridge, MA.

Zeithaml, V.A., Parasuraman, A. and Berry, L.L. (1990), Delivering Quality Service. BalancingCustomer Perceptions and Expectations, The Free Press, New York, NY.

Zeithaml, V.A., Parasuraman, A. and Malhotra, A. (2000), “e-Service quality: definition,dimensions and conceptual model”, working paper, Marketing Science Institute,Cambridge, MA.

Zeithaml, V.A., Parasuraman, A. and Malhotra, A. (2002), “Service quality delivery through websites: a critical review of extant knowledge”,Academy of Marketing Science Journal, Vol. 30No. 4, pp. 362-75.

Zemke, R. (2002), “A service quality refresher”, Training, Vol. 39 No. 7, pp. 46-8.


99

Further reading

Fuchs, M. (2005), “The internationalization of Austria’s financial sector since accession to theEuropean Union”, Monetary Policy and the Economy, Vol. Q2 No. 5, pp. 130-43.

Nunnally, J.C. (1988), Psychometric Theory, McGraw-Hill, Englewood Cliffs, NJ.

Parasuraman, A., Berry, L.L. and Zeithaml, V.A. (1991), “Refinement and reassessment of theSERVQUAL scale”, Journal of Retailing, Vol. 67 No. 4, pp. 420-50.

Corresponding authorEvangelos Tsoukatos can be contacted at: [email protected]

EMJB5,1

100

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

Documents

2010 Key Determinants of Service Quality in Retail Banking