The current issue and full text archive of this journal is available at www.emeraldinsight.com/1450-2194.htm

Key determinants of service quality in retail banking

Service quality in retail banking

Evangelos Tsoukatos Department of Finance and Insurance, TEI of Crete, Agios Nikolaos, Greece, and

85

Evmorfia Mastrojianni National Bank of Greece, Athens, Greece Abstract Purpose – The purpose of this study is to build a retail-banking specific quality scale and, through its examination and comparison with the SERVQUAL and BSQ metrics that are currently used in banking, to deepen understanding of quality determinants in the industry. Furthermore, the study is set to provide additional input to the debate over generic against setting/industry/time-specific quality metrics. Design/methodology/approach – The study is implemented through a two-stage process of literature review and empirical survey. Evidence drawn from Greek retail banking, through a specially designed research tool, is analyzed through reliability, factorial and regression analysis to determine the 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 SERVQUAL provides the skeleton on which setting-specific scales should be built. Practical implications – Bank managers are provided with a reliable and valid metric of service quality in retail banking. Its dimensionality implies that under credit-crunch conditions service delivery 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 extensively researched, the continuously changing marketing environment calls for an ongoing assessment of quality factors. With respect to its academic value, the study accumulates knowledge that will eventually outgrow the boundaries of academia and pervade management. Keywords Customer service management, Retailing, Banking, Face-to-face communications, Greece Paper type Research paper

1. Introduction The twin objectives of this study are first, to build a retail-banking specific service quality scale, examine its item and factorial structure, asses its reliability and validity and 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 the ever persisting debate over generic against setting/industry/time specific service quality metrics (e.g. Ford et al., 1993; Asubonteng et al., 1996; Angur et al., 1999; Imrie et al., 2002; Sureshchander et al., 2002; Wang et al., 2004; Tsoukatos, 2009). It is expected that the study’s findings will prove significant to both academia and practice. Adding to a scholarly debate on managerial issues contributes towards piling-up knowledge that will eventually exceed the boundaries of academia and pervade management. With respect to practice, the better service firms (in this case banks) are

EuroMed Journal of Business Vol. 5 No. 1, 2010 pp. 85-100 q Emerald Group Publishing Limited 1450-2194 DOI 10.1108/14502191011043170

EMJB 5,1

86

equipped to appraise service delivery performance the more effective they become in monitoring quality strategies and assessing their impact. This study provides banks with a strong tool, nicknamed BANQUAL-R after its origins from retail banking, for assessing and monitoring their service delivery performance. The rationale of this study rests on existing literature on the decisive importance of service excellence/superiority that compels firms to improvise innovative means of monitoring their performance in meeting customers’ service preferences (Zemke, 2002; Tsoukatos, 2009). Although this issue is extensively researched, the continuously changing marketing environment necessitates a correspondingly continuous evaluation of quality factors. To protect/gain market shares, organizations need to outperform competitors in offering satisfaction to customers (Reichheld and Sasser, 1990; Reichheld, 1996; Gro¨nroos, 2000; Marwa, 2005; Tsoukatos, 2008). This is a great challenge to service organizations as failing to meet changing customer requirements may put a firm’s own survival to danger. As regards banks, customer longevity can only be achieved through delivering high quality services (Berry et al., 1985; Capon et al., 1990; Berry and Parasuraman, 1991; Anderson et al., 1994; Rust et al., 1995; Lassar et al., 2000) especially under unregulated and volatile financial market conditions (Colgate and Lang, 2001). Banks need to understand customers’ service requirements and comprehend the impact of service delivery performance on customers’ attitudes (Gerrard and Cunningham, 2001; Beckett, 2000). Effective monitoring guarantees fast service flaw detection, which, in turn, allows for fixing quality 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 and Buttle, 2002). Despite being generic and might lack the full potential to grasp the particularities of the industry, SERVQUAL is mostly used in banking services assessment (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 of busines is neglected by bankers with respect to face-to-face service. For a long number of years now banks invest heavily on automated means of retail service delivery (Hunt and 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 financial crises, (and we certainly are in the heart of a crisis of unprecedented extent) when customers’ trust to banking institutions is put to danger. Yet, customer retention in banking depends on trust and confidence (Hennig-Thurau et al., 2002). In such situations, the importance of relational marketing becomes even greater and excellence emerges as the determinant attribute of service (Kim et al., 2009). Customers need to be assured as they consider safety no more guaranteed by regulatory control (Myers and Alpert, 1968). The remainder of this paper is organized as follows: Section 2 reviews the literature on two major areas, service quality and its measurement and service quality assessment in the banking sector. Section 3 follows with illustrating the methodology of the empirical part of this study. It starts with portraying the service setting, goes on to explain the empirical research design, implementation and data analyzes and finally presents key statistical results. Sections 4 to 6 discuss the study’s findings, implications

and limitations-recommendations for further research respectively. Finally, section 7 presents the study’s overall conclusions. Literature review Service quality As Lewis and Booms (1983) have put it, “service quality is a measure of how well the service level delivered matches customer expectations. Delivering quality service means conforming to customer expectations on a consistent basis”. The literature conceptualizes service quality as the result of the comparison between delivered and expected service performance (Parasuraman et al., 1985). Customers’ perceive the relative inferiority/superiority of services by comparing a firm’s actual performance with their expectations, shaped by experience and/or memories (Gro¨nroos, 1982, 1984; Lehtinen and Lehtinen, 1982; Lewis and Booms, 1983; Bitner and Hubert, 1994; George and Hazlett, 1997). The result of this comparison is perceived service quality (Gro¨nroos, 1982, 1984; Takeuchi and Quelch, 1983; Parasuraman et al., 1985, 1988), a customer’s judgement “related but not equivalent to satisfaction” of the overall excellence or superiority of a service (Parasuraman et al., 1988). Under the framework of the disconfirmation paradigm (Oliver, 1980), the Nordic (Gro¨nroos, 1982, 1984) and the American (Parasuraman et al., 1985, 1988) models of perceived service quality are dominating the literature. The former distinguishes between “technical” and “functional” quality, reflecting the service outcome and process respectively. Customers’ perceptions of these dimensions are filtered through corporate 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 for measuring 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 in fact 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 SERVPERF in 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 sector The main objective of the financial system is to “encourage individuals and institutions to save and to transfer those savings to individuals and institutions planning to invest

Service quality in retail banking

87

EMJB 5,1

88

in new projects, products and services” (Rose and Hudgins, 2005). For centuries, and up until a few decades past, banks had been the system’s dominant players. However, this is not the case anymore. Through the years, a number of radical regulatory, structural and technological changes transformed institutions and the system as a whole (Angur et al., 1999; Panopoulou, 2001) by enhancing service proliferation, increasing investors’ interest-sensitiveness and enabling institutional consolidation, geographic expansion and convergence (Rose and Hudgins, 2005). Through all these changes and transformations, an immensely competitive business environment emerged; a global financial market where banks must add to their traditional business adversaries (other banks) multinational conglomerates that literally invade local markets to get major chunks of market shares. At the same time, increased interest sensitiveness turned former depositors into investors while individuals are now more than before willing to borrow in order to maintain their living standards. Moreover, while technology brought customers “just a click” away from competition (Hunt and Menon, 2006) and increased market transparency (Granados, 2005), the differentiation of products and services between institutions is minimum. It is often a matter of just a few hours before competitors can present clones or improved versions of any new banking product that enters the market (Devlin, 1995; Johannesssen et al., 1999). Under such state of market complexity it is imperative for banks to achieve customer longevity, which can only be accomplished through service excellence (Lassar et al., 2000). Despite service superiority’s importance, the banking industry is short of a bank-specific widely recognized instrument for service quality assessment (Bahia and Nantel, 2000). Quality studies in traditional face-to-face banking have mainly adopted the five dimensional SERVQUAL (Parasuraman et al. 1985, 1988)/SERVPERF (Cronin and Taylor, 1992b) approach or some customized version of 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 occasional situations (Bahia and Nantel, 2000) are not usually identified in public. To build their BSQ retail banking-specific metric, Bahia and Nantel (2000) started from 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 not at all represented in the original list. As regards quality attributes, they analyzed an extensive list of items, some bank-specific and others generic, mainly from the banking literature. 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 they recognized their study’s main limitation, notably its implementation only in the French language. BSQ was subsequently used in numerous occasions including some settings

in Greece (Glaveli et al., 2006; Petridou et al., 2007), where from the present study draws evidence too. An interesting approach was that of Aldlaigan and Buttle (2002) who developed SYSTRA-SQ, a retail-banking specific service quality scale based on the Nordic model (Gro¨nroos, 1982, 1984). They started with an impressive number of 963 items describing customers’ service quality perceptions and concluded with a 21-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 and validity of SYSTRA-SQ suggest that the measure is of high utility to the banking industry. However, the authors did not fully explain exactly how they went down from 963 to 21 items. Moreover, no further attempts to build banking-specific scales on the basis of Gro¨nroos (1982, 1984) model are reported in the literature. The above mentioned literature findings provide the theoretical background for the empirical part of this study the methodology of which is described in the following section. Methodology Setting This study draws evidence from Greek retail banking. The industry consists of 65 banks operating through 3,894 “bricks and mortar” outlets and 67,113 employees (HBA, 2009). The system is highly deregulated and competitive. The entry of Greece into the European Monetary Union (EMU) compelled banks to improve their efficiency in order to defend their market shares from foreign competition and their profits from pressures on interest rates spreads (Lymperopoulos and Chaniotakis, 2005). A gradual transformation of outlets from large, inflexible facilities to smaller sales oriented units has been evident through the past years. The 20.05 employees per outlet of 2001 have been reduced, by almost 15%, to less than 17 in 2007 (HBA, 2009). Banks made considerable efforts to modify their organizational structures and change their traditional ways of conducting business. Technology has been widely adopted in service delivery. From 2000 to 2007, ATMs have been doubled from 3,605 to 7,270 with offsite ATMs almost quadrupled from 758 to 2,800 (HBA, 2009). Besides ATMs other available automated means of retail banking are not commonly used due to technological illiteracy, low internet penetration (Observatory for the Greek IS, 2009) and security concerns, all leading to increased customers’ preference to traditional face-to-face banking. However, most major banks invest heavily on internet banking in order to protect or improve their innovator image (Valakas and Chaniotakis, 2000) as customers are 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 even stronger than those related to internet (Marinakis and Karanikolas, 2007).

Service quality in retail banking

89

EMJB 5,1

90

Research instrument and data collection A five sections research instrument (demographics, service expectations, service performance, overall satisfaction and word-of-mouth communication) was especially designed and used for data collection. Service expectations and performance scores were measured in identical seven-point Likert scales as was overall satisfaction and intentions to recommend the bank to friends and relatives. The battery of service attributes was built through reviewing the literature and conducting two focus groups of banking customers and employees. The first stage, literature review, produced an extensive list of 70 items from SERVQUAL, customized SERVQUAL 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; Ibrahim et al., 2006; Tsoukatos and Rand, 2006). Focus groups discussions reduced the list down to 31 items that were incorporated in the research instrument after being several times translated back and forth from Greek to English for ensuring functional equivalence (Tsoukatos and Rand, 2006). Data were collected in early spring 2008, from a convenience sample of n ¼ 91 retail-banking customers, in three downtown Athens branches of the two leading Greek banks. Respondents were adults, residing in Greece but not necessarily Greek nationals, consumers of both traditional and automated retail banking services who had at least one face-to-face banking transaction during the last month. Convenience sampling is very common in service-quality/customer-satisfaction research mainly due to 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 is that it does not provide any built-in means of eliminating or assessing sampling bias (Tsoukatos, 2009). There has been no evidence, however, that the aforementioned sample deviates in any respect from the general population regarding customers’ attitudes towards retail banking. After taking also into consideration this study’s time and cost constraints the sample was considered sufficient. Its adequacy for conducting the appropriate statistical analyzes is discussed later on in this paper. The research instrument was administered through personal interviews conducted on the spot inside branch premises. To minimize bias, caused by poor service, prospective respondents were approached and interviewed prior to conducting their intended transactions. Despite the questionnaire’s complexity the response rate reached 50.27%. The method of personal interviews is superior to self-administered questionnaires in perceptual or attitudinal surveys (Groves, 1989), while face-to-face administration maximizes response rates and field researchers’ availability to answer respondents’ questions (Ibrahim et al., 2006). Data analysis Performance-minus-expectations scores (Qi ¼ Pi – Ei) of service attributes were appropriately analyzed for: . initial scale purification; . unveiling the metric’s underlying structure; and . assessing its reliability and validity.

Statistical treatment involved Cronbach’s alpha Reliability Analysis, Exploratory Factor Analysis and Linear Regression Analysis. All analyzes were executed by using the SPSS for Windows statistical package. Sample size adequacy assessment. Before performing the statistical analyzes, the n ¼ 91 sample was examined for size adequacy and found sufficient. Regarding reliability analysis, Yurdugu¨l (2008) proved that the minimum sample size required for coefficient alpha depends on the largest eigenvalue of Principal Components Analysis (PCA). For a value exceeding 6.00, the sample alpha coefficient is an especially robust estimator of the population alpha even with samples as low as n ¼ 30. As regards factorial analysis, Fabrigar et al. (1999) proved that the minimum sample size should depend on the extent to which factors are overdetermined and the level of communalities. A sample in the area of n < 100 would produce accurate results if all factors 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 are overdetermined (each represented by at least five variables) and communalities are 0.74 on average. These, in combination with the KMO statistic (0.899) and the Bartlett’s test of sphericity (significant at p , 0.001), clearly indicate that the n ¼ 91 sample is sufficient for both Reliability and Factorial analysis. For linear regression the minimum sample size is determined as a function of four parameters: a) the probability of Type I error (alpha level), b) the number of predictors (excluding the intercept), c) the expected 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 statistical power 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 reliability analysis was employed for initial scale purification. On the basis of the “alpha increase if item deleted” criterion (Tabachnick and Fidell, 2001) four of the initially 31 service attributes were removed leaving the scale with 27 (Table I) items and a very high alpha value of 0.966 indicating excellent overall internal consistency (Tabachnick and Fidell, 2001). Factorial analysis. The R-type approach is employed for Exploratory Factor Analysis, as the study deals with relations between variables (Stewart, 1981). A combination of PCA, for initial extraction, and varimax rotation revealed an orthogonal (Tabachnick and Fidell, 2001) four-factor structure, explaining 72.78% of total variance. The number of factors was determined by a combination of the roots and scree-test criteria (Stewart, 1981), while the threshold for meaningful factor loading was 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 variance respectively. 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) (Comrey and 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, Reliability and Confidence respectively indicate high internal consistency of the BANQUAL-R

Service quality in retail banking

91

Table I. Rotated component matrix 0.741 0.738 0.670 0.666 0.659 0.655 0.648 0.611

0.581

0.796 0.761 0.707 0.668 0.647 0.559 0.869 0.836 0.674 0.593 0.572

Note: Extraction method: Principal component analysis; Rotation method: Varimax with Kaiser Normalization; Rotation converged in nine iterations

0.823 0.788 0.712 0.705 0.665 0.654 0.645 0.574

Confidence 13.99% of variance a ¼ 0.896

92

Understanding customers’ individual needs Understanding customers’ individual goals Having customers’ interest at heart Easy access to service personnel Employees instilling confidence in customers Courteousness Understanding customers’ problems Avoid technical jargon when talking to customers Innovative products and services Error-free statements, bills etc. Prompt service Employees knowing exactly what they’re doing Employees’ professionalism Inform customers exactly when they will be served Employees well trained in using technology High quality of services Clear terms in contracts Full range of products and services Competitive pricing Keeping time promises Respond honestly to customers’ requirements Doing the service right the first time Customers’ confidence in documents, statements etc. Secure filing systems Safe use of alternative service channels Customers feeling safe in their transactions Customers’ confidence in the service

Overall a ¼ 0.966 Component Assurance and empathy Effectiveness 20.82% Reliability 16.44% 21.52% of variance of variance of variance a ¼ 0.931 a ¼ 0.942 a ¼ 0.894

EMJB 5,1

measure, reflecting the scale’s high reliability (Tabachnick and Fidell, 2001). Construct validity is secured on the grounds that the battery of service attributes comes from theoretically well-supported literature sources including SERVQUAL (Parasuraman et al., 1988), BSQ (Bahia and Nantel, 2000) and others that have been used in a multitude of settings around the world. The nomological/predictive validity of the scale is assessed by examining the association of service quality in retail banking, that the BANQUAL-R scale 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 SQ scores. Both regression models produced statistically significant adjusted R-square scores of 0.529 and 0.362 respectively, indicating that SQ scores, produced by BANQUAL-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 results The 27-item BANQUAL-R scale consists of 12 SERVQUAL, seven BSQ, two common in SERVQUAl and BSQ and six setting-specific items. In this respect, the metric is a hybrid of the SERVQUAL and BSQ scales. The factorial structure of BANQUAL-R consists of SERVQUAL’s Empathy, and Assurance (Parasuraman et al., 1988), BSQ’s Effectiveness (Bahia and Nantel, 2000), Reliability which is common in SERVQUAL and BSQ and finally Confidence. The latter is mainly associated with service innovation leading to increased intangibility of previously tangible service components such as physical records, archives etc. Assurance/Empathy and Effectiveness are the primary and secondary factors respectively (explaining very similar variance percentages) while Reliability and Confidence are in the third and last positions, explaining 16.44 per cent and 13.99 per cent of variance. In the majority of SERVQUAL applications Reliability is found to be the most important of dimensions, interchangeably followed by Responsiveness and Assurance while Empathy is usually more important only from Tangibles (Zeithaml et al., 1990). Yet, in this study Assurance/Empathy is the most important service element closely followed by Effectiveness (also including certain Responsiveness items), while Reliability 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 the dimensionality of BANQUAL-R indicates a different set of priorities than those mentioned by the SERVQUAL and BSQ studies. In line with a recent similar finding of Tsoukatos and Rand (2006) in Greek retail insurance, a notable key feature of the BANQUAL-R scale is the complete absence of tangible 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 that customers take tangible elements of service for granted as banks and other financial institutions have been and still are investing heavily on equipment and facilities. Last but not least, the validity assessment of the scale reconfirms that service quality is an antecedent of customer satisfaction and customer loyalty. The lower fit of the direct regression model SQ (WOM refers to the argument that the moderating variable between Service Quality and Loyalty is Customer Satisfaction (Cronin and Taylor, 1992a; Tsoukatos and Rand, 2006).

Service quality in retail banking

93

EMJB 5,1

94

Implications There are several important implications from this study’s findings. The study contributes 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, with the term setting-specific being more restrictive that the term industry-specific as the former includes additional elements such as culture, language, time etc. Findings are clearly in favour of the setting-specific perspective. Neither SERVQUAL nor BSQ per-se can fully grasp the particularities of the study’s setting and therefore any results from 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 service quality 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 the BANQUAL-R includes four of the five SERVQUAL dimensions (Assurance, Empathy, Reliability and Responsiveness as part of Effectiveness) plus retail-banking specific elements such as Effectiveness and Confidence. This dimensionality provides support to the argument that although SERVQUAL cannot be the exclusive base for service quality assessment (Zeithaml and Parasuraman, 2004) it should not be disregarded as it provides the skeleton on which any industry or setting specific service measure is built (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, following our previous argument about the setting-specific perspective of service quality scales, BANQUAL-R should be treated with caution as service settings elements change with time. 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 redirect resources from Tangibles to other, more important to customers, elements of service. The importance ranking of BANQUAL-R dimensions indicates that what retail customers’ need more is personal attention and human contact both instilling trust and confidence in customers. This is inconsistent with both the majority of SERVQUAL studies (Tsoukatos, 2009) and the BSQ study in which Effectiveness was found to be the most important of dimensions (Bahia and Nantel, 2000). The first position of Assurance/Empathy in this study is consistent with previous research findings that in financial 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 needs to be reinstated as safety is no more taken for granted (Myers and Alpert, 1968). The positioning of Effectiveness in the second position, with similar importance to that of Assurance/Empathy, provides support to Bahia and Nantel’s (2001) similar argument and can be interpreted as a banking-specific feature of service quality. Regarding Reliability, there is no solid evidence on why it is so low in importance in both the BSQ and the BANQUAL-R studies. A plausible explanation might be that banks are by-definition considered reliable and so, Reliability is taken for granted. Similarly, the last position of Confidence implies that customers have finally overcome their concerns regarding the use of ICT in banking transactions.

Limitations of the study and avenues for further research Despite its important findings, the study suffers a number of limitations namely convenience 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 sample unrepresentativeness may have affected findings. Nevertheless, given the time and cost constraints the sampling procedure is considered adequate. An additional limitation that the study suffers is that it failed to evaluate the relative performance of the BANQUAL-R, SERVQUAl and BSQ scales in assessing service quality in the same setting. Such an examination would strengthen the value of this study. Nevertheless, any study’s limitations present opportunities for further research. It is obvious that a replication of this study on evidence from a number of retail banking industries would give the opportunity to examine more combinations of situational variables among which culture. Further research should encompass the cross examination of the BANQUAL-R with other measures employed in retail banking, and in particular SERVQUAL and BSQ, in order to get solid evidence on the relative superiority/inferiority of the measures. Finally, the evolution of service expectations is related to the financial market conditions (Hennig-Thurau et al., 2002). This leads to the necessity 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 the scale will be different should the study is replicated under non credit-crunch conditions Conclusions Although the issue of “service quality determinants and measurement” is extensively researched, this study provides original findings that contribute to both academia and practice. It builds on previous research regarding service quality measurement in retail banking, especially in view of current financial crisis conditions creating increased needs for constant service monitoring. With regard to practice the study provides a reliable and valid scale for measuring service quality in retail banking that can be exploited in managerial decision making in banking. However, further research is needed to improve our knowledge on service quality measurement in retail banking. Despite the study’s limitations the reliability and validity of the proposed BANQUAL-R metric provides a sound reliable comparison basis for future research. The study provides the methodological framework for its replication in a multitude of combinations of situational variables in domestic or international 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, and profitability: 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. 17 No. 3, pp. 116-22. Asubonteng, P., McCleary, K.J. and Swan, J.E. (1996), “SERVQUAL revisited: a critical review of service quality”, Journal of Services Marketing, Vol. 6 No. 6, pp. 62-81.

Service quality in retail banking

95

EMJB 5,1

96

Backman, S.J. and Veldkamp, C. (1995), “Examination of the relationship between service quality and 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 service quality 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 retail banking sector: a study of Chinese and British customers”, Marketing Issues in Asia, Vol. 1 No. 9. Beckett, A. (2000), “Strategic and marketing implications of consumer behaviour in financial services”, Service Industries Journal, Vol. 20 No. 3, pp. 191-208. Berry, L.L. and Parasuraman, A. (1991), Marketing Services. Competing through Quality, The Free Press, 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 versus service quality: the consumer’s voice”, in Rust, R.T. (Ed.), Service Quality: New Directions in 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 service quality: 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 sector in 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/Correlation Analysis 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 the financial 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 Erlbaum Associates, 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 and extension”, Journal of Marketing, Vol. 56, pp. 55-68. Cronin, J.J. Jr and Taylor, S.A. (1992b), “SERVPERF versus SERVQUAL: reconciling performance-based and perceptions-minus-expectations measurement of service quality”, Journal of Marketing, Vol. 58 No. 1, pp. 125-31. Dash, S. (2006), “Does culture influence service quality expectations? A test of cultural influence in 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”, International Journal 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 of exploratory 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 of universities in the United States and New Zealand using SERVQUAL”, working paper, Old Dominion University, Norfolk, VA.

George, P. and Hazlett, S.A. (1997), “The measurement of service quality: a new P-C-P attribute model”, 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 publicly quoted bank and a government bank in Singapore”, Journal of Financial Services Marketing, Vol. 6 No. 1, pp. 50-66. Glaveli, N., Petridou, E., Liassides, C. and Spathis, C. (2006), “Bank service quality: evidence from five 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, and market structure”, AMCIS 2005 Proceedings, available at: http://aisel.aisnet.org/amcis 2005/68 Gro¨nroos, C. (1982), Strategic Management and Marketing in the Service Sector, Swedish School of Economics and Business Administration, Helsinki. Gro¨nroos, C. (1984), “A service quality model and its marketing implications”, European Journal of Marketing, Vol. 18 No. 4, pp. 36-44. Gro¨nroos, C. (2000), Service Management and Marketing: A Customer Relationship Management Approach, 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 relationship marketing 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/fi nancial/pdfs/idc_wp.pdf Ibrahim, E.E., Joseph, M. and Ibeh, K.I.N. (2006), “Customers’ perception of electronic service delivery in the UK retail banking sector”, International Journal of Bank Marketing, Vol. 24 No. 7, pp. 475-93. Imrie, B.C., Cadogan, J.W. and McNaughton, R. (2002), “The service quality construct on a global stage”, Managing Service Quality, Vol. 12 No. 1, pp. 10-18. Jayawardhena, C. and Foley, P. (2000), “Changes in the banking sector: the case of internet banking 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 for increased 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 in a 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 satisfaction in private banking”, International Journal of Bank Marketing, Vol. 18 Nos 4/5, pp. 181-99.

Service quality in retail banking

97

EMJB 5,1

98

Lehtinen, U. and Lehtinen, J.R. (1982), Service Quality: A Study of Quality Dimensions, Service Management 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, American Marketing Association, Chicago, IL, pp. 90-107. Lociacono, E., Watson, R.T. and Goodhue, D. (2000), “WebQualTM: a web site quality instrument”, working paper, Worcester Polytechnic Institute, Worcester, MA. Lymperopoulos, C. and Chaniotakis, I.E. (2005), “Factors affecting acceptance of the internet as a marketing-intelligence tool among employees of Greek bank branches”, International Journal of Bank Marketing, Vol. 23 No. 6, pp. 484-505. Marinakis, C.J. and Karanikolas, N.N. (2007), “Strengthening the security of E-banking transactions: the case of NBG”, Proceedings of the Annual Conference on Current Trends in Informatics, Patras, 18-20 May, pp. 559-70. Marwa, S.M. (2005), “Exploration of SERVQUAL’s efficacy via the diagnosis and improvement of 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 quality measurement in a high street retail bank”, International Journal of Bank Marketing, Vol. 19 No. 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 satisfaction decisions”, 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 Nations University. Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985), “A conceptual model of service quality and 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 for measuring 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”, Journal of Marketing Research, Vol. 18, May, pp. 133-45. Petridou, E., Spathis, C., Glaveli, N. and Liassides, C. (2007), “Bank service quality: empirical evidence 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. 74 No. 2, March-April, pp. 56-69. Reichheld, F.F. and Sasser, W.E. (1990), “Zero defections: quality comes to services”, Harvard Business Review, Vol. 68 No. 5, September-October, pp. 105-11. Rose, P.S. and Hudgins, S.C. (2005), Bank Management and Financial Services, International Edition, McGraw-Hill, New York, NY. Rust, R.T., Zahorik, A.J. and Keiningham, T.L. (1995), “Return on quality (ROQ): making service quality financially accountable”, Journal of Marketing, Vol. 59 No. 2, pp. 58-70.

Semeijn, J., van Riel, A.C.R., van Birgelen, M.J.H. and Streukens, S. (2005), “E-services and offline fulfilment: 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 and stability 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 marketing research”, Journal of Marketing Research, Vol. 18 No. 1, pp. 51-62. Sureshchander, G.S., Chandrasekharan, R. and Anantharaman, R.N. (2002), “The relationship between service quality and customer satisfaction – a factor specific approach”, Journal of Service Marketing, Vol. 16 No. 4, pp. 363-79. Tabachnick, B.G. and Fidell, L.S. (2001), Using Multivariate Statistics, Allyn & Bacon, Needham Heights, MA. Takeuchi, H. and Quelch, J.A. (1983), “Quality is more than making a good product”, Harvard Business Review, Vol. 61, July-August, pp. 139-45. Tsoukatos, E. (2008), “Applying importance-performance analysis to assess service delivery performance: 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 Home Market Perspective, VDM Verlag Publishers, Saarbru¨cken. Tsoukatos, E. and Rand, G.K. (2006), “Path analysis of perceived service quality, satisfaction and loyalty 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 of electronic banking”, Proceedings of the 3rd Bank Marketing Conference, Hellenic Institute of Marketing, Athens. Wang, Y., Lo, H-P. and Yang, Y. (2004), “An integrated framework for service quality, customer value, satisfaction: evidence from China’s telecommunication industry”, Information Systems 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 an emerging economy: a consumer survey”, International Journal of Bank Marketing, Vol. 15 Nos 6/7, pp. 217-23. Yurdugu¨l, H. (2008), “Minimum sample size for Cronbach’s coefficient alpha”, Hacettepe ¨ niversitesi Eg˘itim Faku¨ltesi Dergisi, Vol. 35, pp. 397-405. U 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. Balancing Customer 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 web sites: a critical review of extant knowledge”, Academy of Marketing Science Journal, Vol. 30 No. 4, pp. 362-75. Zemke, R. (2002), “A service quality refresher”, Training, Vol. 39 No. 7, pp. 46-8.

Service quality in retail banking

99

EMJB 5,1

100

Further reading Fuchs, M. (2005), “The internationalization of Austria’s financial sector since accession to the European 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 the SERVQUAL scale”, Journal of Retailing, Vol. 67 No. 4, pp. 420-50. Corresponding author Evangelos Tsoukatos can be contacted at: [email protected]

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