Measuring service quality of banks: Scale development and validation

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Journal of Retailing and Consumer Services 12 (2005) 373–383 www.elsevier.com/locate/jretconser

Measuring service quality of banks: Scale development and validation Osman M. Karatepea,, Ugur Yavasb, Emin Babakusc a

School of Tourism and Hospitality Management, Eastern Mediterranean University, Gazimagusa, Turkish Republic of Northern Cyprus, Via Mersin 10, Turkey b Department of Management and Marketing, College of Business, East Tennessee State University, Johnson, TN 36714, USA c Department of Marketing and Supply Chain Management, Fogelman College of Business and Economics, The University of Memphis, Memphis, TN 38152, USA

Abstract By employing a multi-stage, multi-phase, and multi-sample approach, this paper reports on the construction of a service quality scale. Customer perceptions of service quality of retail banks in Northern Cyprus serve as the study setting. The parsimonious 20item four-dimensional scale consisting of service environment (four items), interaction quality (seven items), empathy (five items), and reliability (four items) exhibits sound psychometric properties. Scale development procedures and managerial applications of the derived scale are discussed. r 2005 Elsevier Ltd. All rights reserved. Keywords: Service quality; Banking; Scale development; Northern Cyprus

1. Introduction A deliberate attempt to study services marketing and service quality issues dates back to the mid-1960s (Rathmell, 1966). However, interest on the topic has gained considerable momentum within the past two decades or so. This is not surprising. On the one hand, delivery of high service quality to customers offers firms an opportunity to differentiate themselves in competitive markets. On the other hand, high service quality results in customer satisfaction and loyalty, greater willingness to recommend to someone else, reduction in customer complaints, and improved customer retention rates (see, for example, Bitner, 1990; Danaher, 1997; Headley and Miller, 1993; Levesque and McDougall, 1996; Magi and Julander, 1996; Zeithaml et al., 1996). Today, service quality is considered a critical measure of Corresponding author. Tel.: +90 392 630 1116; fax: +90 392 365 1584. E-mail addresses: [email protected] (O.M. Karatepe), [email protected] (U. Yavas), [email protected] (E. Babakus).

0969-6989/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.jretconser.2005.01.001

organizational performance and continues to compel the attention of practitioners and academics (Lassar et al., 2000; Yavas and Yasin, 2001). Unlike goods quality, which can be measured with some objectivity, service quality is abstract and elusive. The unique features of services such as inseparability of production and consumption, intangibility, and heterogeneity make measurement of quality a very complex issue. In the absence of objective measures, firms must rely on consumers’ perceptions of service quality to identify their strengths and/or weaknesses, and design appropriate strategies. This makes development of psychometrically sound and managerially useful instruments to measure service quality imperative. The purpose of this paper is to develop and test a service quality instrument by using the retail banking services in Northern Cyprus as a case in point. This objective is consistent with growing sentiments for developing context-specific (e.g., industry and/or culture-specific) service quality measures in light of the difficulties involved with universal/global measures (Aldlaigan and Buttle, 2002; Babakus and Boller, 1992; Robinson, 1999; Winsted, 1997). The stages

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outlined in Churchill’s (1979) now-classic paradigm for developing better measures of marketing constructs are employed in accomplishing this objective. In the past, Churchill’s (1979) paradigm has been used to develop not only measures of other marketing constructs (Webster, 1990, 1993) but service quality measures as well (Aldlaigan and Buttle, 2002; Parasuraman et al., 1988). In the next section, we provide a review of the relevant literature. This is followed by the method and results of an empirical study. We conclude the paper with a discussion of the implications of the results and suggestions for future research.

2. Relevant literature A canvassing of the growing body of literature on service quality suggests that two schools of thought dominate the extant thinking. One is the Nordic school of thought based on Gro¨nroos’s (1984) two-dimensional model. And the other is the North American school of thought based on Parasuraman et al.’s (1988) fivedimensional SERVQUAL model. Considering other significant conceptual and empirical works in the area, it appears that service quality encompasses (1) customers’ experiences with the tangibles, reliability, responsiveness, assurance, and empathy aspects of the services delivered by a firm (Parasuraman et al., 1988); (2) technical and functional quality (Gro¨nroos, 1984); (3) service product, service environment, and service delivery (Rust and Oliver, 1994); and (4) interaction quality, physical environment quality, and outcome quality (Brady and Cronin, 2001). Our review of this body of literature points out two major limitations. First, as noted by Babakus and Boller (1992), there is a need to develop industry-specific measures of service quality. This is particularly important from a managerial perspective (Shemwell and Yavas, 1999). Because many of the questions in existing instruments (notably SERVQUAL batteries) intended to be applied across situations/services just do not apply in a specific context and force researchers to drastically alter the items (Babakus and Boller, 1992; Babakus and Mangold, 1992; Carman, 1990; McAlexander et al., 1994). However, as Shemwell and Yavas (1999) cogently argue, the more specific the scale items are in a service quality instrument and the more applicable they are to a manager’s own contextual circumstance, the better s/he will be able to use the information. Thus, instead of taking an existing instrument and trying to fit it to the context, a better approach is to develop an instrument specifically for the focal service. While many studies in banking measure service quality by replicating or adopting Parasuraman et al.’s (1988) SERVQUAL model (see, for example, Angur et al., 1999; Athanasso-

poulos, 1997; Blanchard and Galloway, 1994; Donthu and Yoo, 1998; Lloyd-Walker and Cheung, 1998; Marshall and Smith, 1999; McDougall and Levesque, 1994; Newman and Cowling, 1996; Yavas and Benkenstein, 2001), a few studies address this weakness and present new models or approaches to the measurement of service quality in general and in banking in particular. For instance, Mersha and Adlaka (1992) applied the Delphi technique to a sample of MBA students to generate attributes of poor and good service quality. They then converted the 12 attributes thus identified into scales and analyzed students’ perceptions of service quality in five services, one of which was retail banking. The authors concluded that the list of attributes they generated was similar to the five dimensions of SERVQUAL (i.e., tangibles, reliability, responsiveness, assurance, and empathy). In another study, Avkiran (1994) developed a multi-dimensional instrument for measuring customer-perceived quality in retail branch banking. Using SERVQUAL as a starting point and then adding items that he extracted from a qualitative study commissioned to establish quality service standards, Avkiran (1994) followed an iterative process and identified staff conduct, credibility, communication, and access to teller services as the final dimensions of service quality. The scale developed by Bahia and Nantel (2000) based on expert opinions revealed six dimensions of service quality. These were termed: effectiveness and assurance, access, price, tangibles, service portfolio, and reliability. More recently, Aldlaigan and Buttle (2002), based on the technical and functional service quality schema proposed by Gro¨nroos (1984), developed a scale to measure service quality perceptions of bank customers. Their study resulted in SYSTRA-SQ, which consists of service system quality, behavioral service quality, service transactional accuracy, and machine service quality. As can be inferred from the statement of its purpose, our study builds upon these works and, by applying an iterative procedure, develops a service quality instrument specifically for retail banking in Northern Cyprus. Such a comprehensive effort to develop a new scale to measure service quality in the particular setting was also encouraged by a number of bank managers in Northern Cyprus whom we contacted during the initial phases of the current study. After examining the copies of existing instruments we provided them, the managers clearly indicated the need for a tailor-made measure for the Northern Cyprus context. Second, there is a need to develop service quality measures that are country/culture specific. This is because, as is the case with other marketing and management constructs and measures (Benkhoff, 1997; Hofstede, 1990; Yavas, 1997), quality constructs/measures in general (Yavas and Konyar, 2002), and service quality constructs/measures in particular (Mattila,

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1999a) that are developed in one culture (notably a western culture) may not be applicable in a different cultural setting. Drawing attention to this limitation of extant research, Mattila (1999b) argues that the definition of service quality depends on consumers’ cultural heritage, particularly on variations along power distance and communication context. Malhotra et al. (1994) share this view and posit that the cultural differences (e.g., individualism/collectivism, power distance) between countries are likely to have varying effects on the definition of service quality. This is shown to be true in a research by Winsted (1997) who compared Japanese and US consumers. Focusing on provider behaviors as indicators of service encounter quality, Winsted (1997) not only identified new quality dimensions that had not been a part of service quality concept until then, but also demonstrated that the number and meanings of service quality dimensions varied between US and Japanese consumers. For instance, the ‘‘authenticity’’ dimension, which refers to genuineness of service providers’ behaviors, was an important component of service quality for Japanese consumers while this dimension did not surface in the case of the US consumers. Despite some cross-cultural commonalities (Espinoza, 1999), the weight of evidence suggests that culture plays a significant role on the definition of the service quality construct (Kettinger et al., 1995). In recognition of this, calls are made to develop culture-specific measures of service quality (Winsted, 1997). Indeed, Imrie et al. (2002) recently stated that managers should avoid employing the SERVQUAL scale globally and instead they should develop ‘‘a new, culturally bounded measure of service quality’’ (p. 17). Our study develops a service quality measure specifically for the Turkish (Northern Cyprus) setting. It should be noted that this is not the first study dealing with service quality measurement issues in Turkey and Northern Cyprus. Yet it differs from previous studies in one very important respect. While all previous studies took an existing instrument as is (or translated it into Turkish) (Akan, 1995; Johns et al., 2004; Karatepe and Avci, 2002; Kozak et al., 2003; Yavas and Bilgin, 1996; Yavas and Arsan, 1995), the present study develops a measure that represents the first comprehensive effort to understand how service quality assumes meaning in this cultural context. It should also be added that several previous studies dealing with service quality in banking (Yavas and Bilgin, 1996) and other service industries (Johns et al., 2004; Karatepe and Avci, 2002) in the Turkish/Northern Cyprus setting failed to replicate the five-dimensional structure purported in the SERVQUAL scale. Even those few studies which were able to replicate the five-dimensional structure reported other psychometric problems. For instance, while Kozak et al.’s (2003) study which examined service quality of airlines in Northern Cyprus was able to partially

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support the five-dimensional structure of SERVQUAL, their study failed to provide evidence for discriminant validity. Likewise, Yavas and Arsan (1995) tested the dimensionality of the SERVQUAL scale in banking and found a five-factor solution. Yet, most of the items did not load on their underlying dimensions. At a time when service quality becomes a pressing issue in Turkish banking (Babakus et al., 2003), our study can provide managers with a much needed specific instrument. Also, our study addresses the voids in the literature and adds to the compendium of knowledge in the area.

3. Study 3.1. Step 1: qualitative study (item generation) To generate items that comprise the domain of service quality in retail banking services, a team of interviewers conducted one-on-one interviews with a judgmental sample of 86 bank customers. The interviews were audio tape-recorded. In these interviews, based on their experiences and prior dealings with banks, participants were asked to talk about their expectations from bank services. To code the qualitative data thus obtained, similar to prior studies (Brady and Cronin, 2001; Richins, 1997), a content analytic approach was employed. In the first stage, after listening to the tapes, three independent coders prepared paragraphs/field notes. All three coders agreed on the overall content of each paragraph/field note. In the second stage, the same coders generated a total of 56 items and agreed on 43 of these items yielding an inter-judge reliability coefficient of .91. After a closer scrutiny, three coders agreed that 12 of the 43 items highly overlapped. After elimination of highly redundant items, this reexamination resulted in a total of 31 items. In the final stage, three coders were asked to categorize the 31 items into groups based on content similarities of items. The three coders, working individually and then as a group, identified five distinct categories. Transcripts and items in each category were further examined by a team of researchers to assign a higher-level meaning to each category. This exercise led to the identification and labeling of the following dimensions of service quality: service environment (four items), interaction quality (eight items), reliability (five items), empathy (10 items), and technology (four items). Table 1 presents a listing of these items. Service environment refers to the appearance of the service providers and appearance of the interior and exterior of the bank facilities. Interaction quality encompasses attitudes and behaviors of the service providers and their interaction style with customers. Empathy is defined as individualized attention given to customers and willingness of the bank personnel to help

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Table 1 Item-to-total correlations and varimax-rotated factor loadings (First stage, n ¼ 115) Items

Item-to-total correlations

Factor loadings F1

F2

F3

F4

F5

— 0.32 — — — — — 0.57 — —

— — — — 0.61 0.65 0.61 0.42 — —

Q01. Q02. Q03. Q04. Q05. Q06. Q07. Q08. Q09. Q10.

The exterior of this bank is visually appealing The interior of this bank is visually attractive Employees of this bank have neat appearances The interior of this bank is spacious The ATMs of this bank are technologically well-equipped There is an adequate number of employees in this bank There is an adequate number of ATMs in this bank The computerized system in this bank functions properly Employees of this bank have the knowledge to respond to problems Employees of this bank are polite to customers

0.48 0.56 0.49 0.36 0.36 0.28 0.34 0.42 0.61 0.49

— — — — — — — 0.30 0.55 0.81

0.66 0.69 0.68 0.42 0.33 — — — 0.39 —

— — — — — — 0.40 — — —

Q11. Q12. Q13. Q14. Q15.

Employees of this bank are experienced Employees of this bank instill confidence in customers Employees of this bank are understanding of customers Employees of this bank serve customers in good manner There is a warm relationship between employees of this bank and customers

0.65 0.70 0.58 0.59 0.60

0.62 0.67 0.84 0.82 0.86

0.31 0.34 — — —

— — — — —

— — — — —

Q16. Q17. Q18. Q19. Q20.

This bank does not make its customers stand in a queue for a long time Employees of this bank enact transactions on a timely manner Employees of this bank always help customers Employees of this bank provide individualized attention to customers Employees of this bank are willing to solve customer problems

0.48 0.68 0.68 0.69 0.64

— 0.33 0.64 0.58 0.59

— — — 0.40 —

0.75 0.76 0.45 0.41 0.43

— — — — —

— — — — —

Q21. Q22. Q23. Q24. Q25.

Employees of this bank provide error-free service This bank is financially dependable Employees of this bank carry out customer transactions confidentially Employees of this bank provide customers with precise information This bank informs customers about its financial operation accurately

0.56 0.19 0.48 0.64 0.48

0.53 — — 0.46 —

0.41 — 0.36 0.47 —

— — — — —

— 0.59 0.44 0.51 0.56

— — — — —

Q26. Q27. Q28. Q29. Q30. Q31.

This bank has convenient working hours Employees of this bank provide equal treatment to all customers Employees of this bank know customers’ needs. Employees of this bank are sensitive to customers’ needs Employees of this bank meet customer requests quickly The internet banking services of this bank are widespread

0.15 0.45 0.56 0.74 0.69 0.06 Eigenvalue % of variance explained Coefficient alpha

— — 0.32 0.57 0.59 — 10.50 35.01 0.91

0.43 0.63 0.58 0.43 0.50 — 2.48 8.28 0.73

0.35 — — 0.33 — — 1.78 5.95 0.82

0.51 — — — — — 1.70 5.66 0.73

— 0.31 — — —

— — — — — — 1.57 5.22 0.44

Note: Items 1–4 represent ‘service environment’. Items 5, 7, 8, and 31 refer to ‘technology’. Items 9–15 and 30 represent ‘interaction quality’. Items 6, 16–20, and 26–29 represent ‘empathy’. Items 21–25 refer to ‘reliability’. The factor loadings less than .30 are not shown. Reliability coefficients (coefficient alpha) are based on the a priori designation (as designated by coders) of the items to their respective dimensions.

customers and resolve their problems in a timely manner. Reliability refers to dependability of service and accuracy of records and information. Finally, technology dimension was defined as the quality of ATMs and the proper functioning of computerized systems. 3.2. Step 2: quantitative study: first stage Data for the initial refinement of the 31-item instrument were obtained from a sample of 115 customers of a large bank as they exited the bank after completing a transaction there. Every third customer leaving the premises was approached to collect the data. This sample size, relative to the number of initial scale

items, compares favorably with sample sizes used by other studies in the early stages of scale development (Parasuraman et al., 1988; Webster, 1990). The majority of the respondents (75 percent) were between the ages of 17–46 and male (65 percent). Little over one-half had college and 35 percent high school degrees. The sample profile, in terms of age and education composition, was representative of the bank’s customer population. The questionnaire administered to the respondents consisted of two parts. The first part was designed to measure customers’ assessments of their bank’s service quality with respect to the items identified during the qualitative phase of the study. There is a debate in literature on whether the expectations, or the perceptions, or the gap between the two constitute a better

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measure of service quality. In this study, service quality was measured using ‘‘perceptions-only’’ approach. Specifically, service quality items were transformed into Likert-scales and the respondents were asked to indicate their perceptions of their bank on each item using a fivepoint scale ranging from ‘‘5 ¼ strongly agree’’ to ‘‘1 ¼ strongly disagree’’. The choice of performance-only scores was based on the widely discussed methodological and theoretical concerns associated with the use of expectations scores (Babakus and Boller, 1992; Cronin and Taylor, 1992, 1994; Parasuraman et al., 1994; Robinson, 1999) as well as the difference (gap) scores (Brown et al., 1993; Teas, 1993, 1994). Indeed, after a thorough review of the prior literature and evidence from three new studies they conducted, Brady et al. (2002) authoritatively declared that the ‘‘performance-only’’ measures of service quality are superior to other approaches. The second part of the survey included three singleitem measures relating to overall service quality, overall customer satisfaction, and purchase intention. Responses to overall service quality item were elicited on a five-point scale ranging from ‘‘5 ¼ very good’’ to ‘‘1 ¼ very poor’’. Responses to customer satisfaction item were elicited on a five-point scale ranging from ‘‘5 ¼ extremely satisfied’’ to ‘‘1 ¼ extremely dissatisfied’’. Finally, responses to purchase intention item were elicited on a five-point scale ranging from ‘‘5 ¼ very high’’ to ‘‘1 ¼ very low’’. Churchill (1979) suggests that purification of an instrument should start with the computation of coefficient alphas. This was done for the five dimensions identified by the coders. The coefficient alphas ranged from .44 to .91 across the five dimensions (Table 1). Following reliability analysis, exploratory factor analysis (principal components with varimax rotation) was applied to the data. As shown in Table 1, this analysis resulted in a five-factor solution. However, items representing technology dimension did not emerge as a viable factor as indicated by low factor loadings and/or high cross-loadings. In light of factor analysis results and poor reliability (.44), the technology dimension was discarded altogether. In addition, one item each from interaction quality and reliability dimensions and five items from empathy dimension were deleted due to high cross-loadings or factor loadings below .50. The four factors thus retained were: service environment (four items), interaction quality (seven items), empathy (five items), and reliability (four items). Confirmatory factor analysis using LISREL (Jo¨reskog and So¨rbom, 1993) was then applied to the fourfactor measurement model to further test dimensionality as well as convergent and discriminant validity. As shown in Table 2, the results of the confirmatory factor analysis demonstrated a moderate fit of the four-factor measurement model to the data on the basis of a number

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of fit statistics (w2 ¼ 391.65, df ¼ 164, GFI ¼ .74, AGFI ¼ .67, NFI ¼ .73, NNFI ¼ .78, CFI ¼ .81, IFI ¼ .81, SRMR ¼ .092, RMSEA ¼ .11). Furthermore, the magnitudes of the factor loading estimates ranged from .37 to .92 where a majority of the factor loadings were higher than .70. And all t-values were greater than 2.00. Pairwise confirmatory factor analyses and w2 difference tests revealed that the dimensions are distinct. Hence, confirmatory factor analyses results provide evidence regarding convergent and discriminant validity of the measure (Anderson and Gerbing, 1988). Also, as reported in Table 2, internal consistency reliability estimates exceeded the .70 cut-off value recommended by Nunnally (1978). Additional assessment of the scale was undertaken using composite scores for each dimension, which were calculated by averaging scores across items representing that dimension. The correlations among the four dimensions of the scale ranged from .37 (service environment and interaction quality) to .62 (empathy and interaction quality). The correlations between service quality composite dimension scores and the overall service quality (range between .50 and .60), customer satisfaction (range between .49 and .60), and purchase intention (range between .40 and .58) provided further evidence for the viability of the scale. Collectively, the results from the initial sample are highly encouraging regarding the reliability, convergent, and discriminant validity of the scale. Hence, the entire scale was used without further alteration during the second stage of the quantitative study. 3.3. Step 3: quantitative study: second stage To further evaluate the 20-item scale and its psychometric properties, a large-scale study was undertaken. Prior to data collection, managements of 10 banks were contacted and permission was sought to interview their customers on the premises right after completing a transaction. Eight banks granted permission to the research team. The number of respondents to be drawn from each bank was determined proportional to the number of customers of each of the banks. And, again every third customer leaving the premises was approached to collect the data. To get a representative sample of customers, data collection took place during all operating hours. After a 3-month period, usable responses were obtained from a total of 1220 customers. This sample size is much larger than the sample sizes used in similar scale development studies (Parasuraman et al., 1988, 1991; Webster, 1990) and well exceeds the 1000 observations sample size guideline recommended for factor analysis (Tabachnick and Fidell, 1996) as well as the 10 to 1 ratio of sample size to number of scale items guidelines suggested by Nunnally (1978). Eightyfour percent of the respondents were between the ages of

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Table 2 Scale items, reliabilities and confirmatory factor analysis results (first stage, n ¼ 115) Scale items

Standardized loadings

T-values

Service environment (SERENV) The exterior of this bank is visually appealing The interior of this bank is visually attractive Employees of this bank have neat appearances The interior of this bank is spacious

0.80 0.92 0.44 0.37

9.31 11.18 4.67 3.91

0.59 0.78 0.67 0.73 0.84 0.84 0.87

6.78 9.69 7.84 8.86 10.89 10.83 11.42

Interaction quality (INTQUAL) Employees of this bank have the knowledge to respond to problems Employees of this bank are polite to customers Employees of this bank are experienced Employees of this bank instill confidence in customers Employees of this bank are understanding of customers Employees of this bank serve customers in good manner There is a warm relationship between employees of this bank and customers

0.73

0.91

Empathy (EMP) This bank does not make its customers stand in a queue for a long time Employees of this bank enact transactions on a timely manner Employees of this bank always help customers Employees of this bank provide individualized attention to customers Employees of this bank are willing to solve customer problems

0.52

5.76

0.76 0.88 0.78 0.83

9.22 11.55 9.67 10.59

Reliability (REL) Employees of this bank provide error-free service Employees of this bank carry out customer transactions confidentially Employees of this bank provide customers with precise information This bank informs customers about its financial operation accurately

0.67 0.57 0.89 0.64

7.70 6.24 11.23 7.17

Model fit statistics

Coefficient alpha

0.85

0.76

w2 ¼ 391.65, df ¼ 164, GFI ¼ 0.74, AGFI ¼ 0.67, NFI ¼ 0.73, NNFI ¼ 0.78, CFI ¼ 0.81, IFI ¼ 0.81, SRMR ¼ 0.092, RMSEA ¼ 0.11

Note: Each item is measured on a five-point scale ranging from ‘‘5 ¼ strongly agree’’ to ‘‘1 ¼ strongly disagree’’. All loadings are significant at the .01 level.

17 and 46. The majority of the respondents (66 percent) were male. Forty-two percent of the respondents had college and 45 percent high school degrees. The demographic breakdown of the sample is representative of retail bank customers in Northern Cyprus. Similar to the process employed in the first stage, we first computed coefficient alphas. As shown in Table 3, these coefficients ranged from .81 to .92. In addition, all corrected item-to-total correlations ranged from .46 to .75. In light of these results, there was no compelling reason to delete any items. Confirmatory factor analysis was employed to examine dimensionality, convergent, and discriminant validity. The results of the confirmatory factor analysis demonstrated a reasonable fit of the four-factor measurement model to the data on the basis of a number of fit statistics (w2 ¼ 1354.60, df ¼ 164, GFI ¼ .90, AGFI ¼ .87, NFI ¼ .92, NNFI ¼ .92, CFI ¼ .93, IFI ¼ .93, SRMR ¼ .047, RMSEA ¼ .077). The magnitudes of the standardized loadings ranged from .50 to .83 and all t-values were higher than 2.00, indicating convergence of items with their respective underlying dimensions. As can be seen from Table 3, the

overwhelming majority of the standardized loadings were above .70. Pairwise confirmatory factor analyses of the dimensions provided support for discriminant validity of the scale (Anderson and Gerbing, 1988). The scale was subjected to further validity assessment using composite scores for each dimension, which were calculated by averaging scores across items representing that dimension. As can be seen from Table 4, the correlations among the underlying dimensions ranged from .52 (service environment and reliability) to .76 (interaction quality and empathy). The correlations among the dimensions (intra-construct correlations) of the scale are consistently higher than their correlations with customer satisfaction and purchase intention (interconstruct correlations). Hence, the scale meets a fundamental requirement for convergence and discrimination in measurement (Bagozzi, 1981). Fig. 1 provides a partial nomological network and the results of additional analysis to address nomological validity issues. The literature suggests that perceived quality has a direct influence on purchase intention (Zeithaml et al., 1996) as well as an indirect effect via the

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Table 3 Scale items, reliabilities and confirmatory factor analysis results (second stage, n ¼ 1220) Scale items

Standardized loadings

T-values

Service environment (SERENV) The exterior of this bank is visually appealing The interior of this bank is visually attractive Employees of this bank have neat appearances The interior of this bank is spacious

0.72 0.80 0.67 0.71

26.78 31.08 24.30 26.25

0.70 0.81 0.72 0.79 0.82 0.83 0.80

27.60 33.92 28.58 32.28 34.68 34.78 33.32

Empathy (EMP) This bank does not make its customers stand in a queue for a long time Employees of this bank enact transactions on a timely manner Employees of this bank always help customers Employees of this bank provide individualized attention to customers Employees of this bank are willing to solve customer problems

0.50 0.69 0.83 0.81 0.77

17.80 26.44 34.69 33.62 30.65

Reliability (REL) Employees of this bank provide error-free service Employees of this bank carry out customer transactions confidentially Employees of this bank provide customers with precise information This bank informs customers about its financial operation accurately

0.73 0.69 0.82 0.68

27.93 26.25 33.00 25.36

Coefficient alpha 0.81

Interaction quality (INTQUAL) Employees of this bank have the knowledge to respond to problems Employees of this bank are polite to customers Employees of this bank are experienced Employees of this bank instill confidence in customers Employees of this bank are understanding of customers Employees of this bank serve customers in good manner There is a warm relationship between employees of this bank and customers

0.92

0.83

0.81

w2 ¼ 1354.60, df ¼ 164, GFI ¼ 0.90, AGFI ¼ 0.87, NFI ¼ 0.92, NNFI ¼ 0.92, CFI ¼ 0.93, IFI ¼ 0.93, SRMR ¼ 0.047, RMSEA ¼ 0.077

Model fit statistcis

Note: Each item is measured on a five-point scale ranging from ‘‘5 ¼ strongly agree’’ to ‘‘1 ¼ strongly disagree’’. All loadings are significant at the .01 level.

Table 4 Means, standard deviations and correlations of study variables (Second stage, n ¼ 1220) Variables:

1

2

3

4

5

6

Service environment (SERENV) Interaction quality (INTQUAL) Empathy (EMP) Reliability (REL) Customer satisfaction (CSAT) Purchase intention (PINTENT)

1.00 0.60 0.54 0.52 0.51 0.50

1.00 0.76 0.71 0.65 0.65

1.00 0.70 1.00 0.62 0.61 1.00 0.65 0.62 0.71 1.00

Mean Standard deviation

3.54 3.94 3.70 3.91 3.95 3.84 0.87 0.77 0.80 0.71 0.69 0.79

Note: Composite scores for each measure were obtained by averaging scores across items representing that measure, except for customer satisfaction and purchase intention. The scores range from 1 to 5. A higher score indicates a more favorable response. All correlations are significant at the .01 level.

mediating role of customer satisfaction (Brady and Robertson, 2001). Using this simple nomological network, we tested a structural model with the four

composite dimension scores as indicators of service quality. The results in Fig. 1 show that the model fits the data rather well (w2 ¼ 18.66, df ¼ 8, p ¼ :017; SRMR ¼ .011, GFI ¼ .99, AGFI ¼ .99, NFI ¼ 1.00, NNFI ¼ 1.00, CFI ¼ 1.00, RMSEA ¼ .033), and both the direct and indirect effects of service quality on purchase intentions are significant. That is, the standardized regression coefficients from: (1) service quality to customer satisfaction (g1 ¼ :74; t ¼ 22:5); (2) service quality to purchase intention (g2 ¼ :45; t ¼ 13:5); and (3) customer satisfaction to purchase intention (b ¼ :41; t ¼ 14:6) are all statistically significant. Furthermore, it makes theoretical sense that service quality has its strongest effect on customer satisfaction since satisfaction is a mediator between service quality and purchase intention. In addition, 55 percent of the variance in customer satisfaction is accounted for by service quality, and 64 percent of the variance in purchase intention is explained by service quality and customer satisfaction jointly. Finally, an examination of standardized loadings in Fig. 1 suggests that interaction quality is the most important indicator of service quality (l2 ¼ :88),

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CSAT

SERENV

PINTENT

λ 1=.66*** β = 0.41 (t=14.6)

γ1= 0.74 (t=22.5)

INTQUAL

λ 2=0.88 (t=25.9)

SQUAL

CUSTSAT

PURCINT

λ 3=0.85 (t=25.2) γ2 = 0.45 (t=13.5)

EMP λ 4=0.81 (t=24.2)

REL

Proportion of variance explained (R2) in: Customer satisfaction (CUSTSAT): 0.55 Purchase intention (PURCINT): 0.64 Model fit statistics: 2 χ =18.66 (df=8, p=0.017) SRMR=0.011 GFI=0.99 AGFI=0.99 NFI=1.00 NNFI=1.00 CFI=1.00 RMSEA=0.033

Fig. 1. Assessing nomological validity of the service quality measure: the relationships among service quality (SQUAL), customer satisfaction (CUSTSAT), and purchase intention (PURCINT) constructs. Note: Since customer satisfaction and purchase intention were measured using single items, their error variances were set to zero in the structural model. Hence, their standardized loadings on their respective latent constructs were 1.00 by definition. T-values are shown in parentheses except for the loading of service environment (SERENV), which was initially fixed to 1.00 to set the metric for the underlying service quality construct.

followed by empathy (l3 ¼ :85), reliability (l4 ¼ :81), and service environment (l1 ¼ :66).

4. Discussion This study developed a 20-item survey instrument to measure bank customer perceptions of service quality in Northern Cyprus. The results showed that service quality could be conceptualized and measured as a four-dimensional construct consisting of service environment, interaction quality, empathy, and reliability. The scale exhibited high internal consistency reliability and met rigorous conceptual and empirical criteria for construct validity including content, convergent, discriminant, and nomological validity. Our results showed that interaction quality is the most important dimension of service quality followed by empathy, reliability, and service environment. The number of distinct dimensions, their meaning, and their order of importance show some similarities and differences with prior conceptualizations including Gro¨nroos (1984), Parasuraman et al. (1991), Brady and Cronin (2001), Rust and Oliver (1994), and Aldlaigan and Buttle (2002). There is a clear convergence in terms of conceptual meaning between our service environment dimension and the ‘tangibles’ dimension of SERVQUAL. This dimension is the least important indicator of service quality in this study as

well as previous studies using SERVQUAL (e.g. Parasuraman et al., 1991). Empathy and reliability dimensions in the current and SERVQUAL scales are also conceptually similar. In the present study, empathy was found slightly more important than the reliability dimension whereas SERVQUAL studies consistently identified reliability as the most important indicator of service quality. Interaction quality appears to overlap with the combined SERVQUAL dimensions of ‘responsiveness’ and ‘assurance’. Additionally, interaction quality identified in the current study is similar to that of Gro¨nroos’s (1984) functional quality. Several dimensions reported in our study are similar to those of Aldlaigan and Buttle (2002). For example, their behavioral service quality is similar to our interaction quality, and our reliability is similar to their service transactional accuracy. Finally, service environment dimension in Rust and Oliver (1994) and physical environment quality in Brady and Cronin (2001) are conceptually similar to our service environment dimension. A common theme emerging from these comparisons is that the meaning of service quality may have some universal aspects as demonstrated by the similarities in the underlying dimensions. However, significant variations may exist regarding the complexity (i.e., the number of underlying dimensions) of service quality concept and the importance attached to each dimension from one context to another. A comparison of the

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current study with those that were conducted in the banking sector in Turkey (a very similar culture and similar customer demographics) using SERVQUAL (Yavas and Arsan, 1995; Yavas and Bilgin, 1996) reveals that the service quality measure developed in this study provides a more specific view since it was guided by items suggested by bank customers in the first place. The items in Yavas and Bilgin (1996) produced a three-dimensional depiction of service quality with no apparent identification with the presumed SERVQUAL dimensions. While Yavas and Arsan (1995) found a fivedimensional structure, the items did not load on the appropriate factors designated in SERVQUAL. Our study produced a viable measure of retail banking service quality within the cultural context of Northern Cyprus. The technology dimension of service quality was initially considered based on the qualitative stage of the study. However, it did not emerge as a viable dimension in the later stages when subjected to empirical criteria. This result is potentially due to the fact that technologybased services (e.g., video-banking, internet-banking, telephone-banking) are not widely available and, wherever available, internet sites are not easy to navigate. Furthermore, the use of technology in this sector is hampered by poor electrical infrastructure. Frequent power outages render ATMs useless and cause frustration among customers. Also down-times and other ‘‘glitches’’ in computer systems are common occurrences in Northern Cyprus. However, bank consumers are aware of the benefits that technology can provide and this dimension of service quality will assume a more distinct meaning in the future as technology infrastructure improves in the country. Hence, future studies should pay attention to technology as a potentially critical dimension of service quality. The current study provides some useful insights for managerial action. First, bank managers can rely on this industry-specific scale in order to measure service quality delivered to their customers. By examining performance scores on each attribute within and across dimensions, improvement needs can be identified. Second, from a strategic standpoint, bank managers can determine the relative importance of the four service quality dimensions in predicting customer satisfaction and customer loyalty. By doing so, bank managers can determine which service quality dimension(s) they should pay most attention to. Third, multi-branch bank organizations can use the current scale to evaluate service quality delivered to customers in different branches and track the relative performance of various branches over time. Fourth, bank managers can employ the service quality scale to identify distinct customer clusters or segments with varying perceptions about service quality. Cluster profiles can provide valuable information on how to approach each segment for

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quality improvement initiatives. Focusing marketing efforts on the most unhappy cluster(s), for instance, may provide immediate relief for reducing defection rates (Brady and Cronin, 2001). Fifth, the service quality scale can also be administered to frontline employees and their customers simultaneously to compare customer perceptions of service quality with frontline employee perceptions. Finally, from a competitive standpoint, bank managers can use the existing scale to assess their strengths/weaknesses relative to competitors across service quality dimensions.

5. Concluding comments It should be underscored that given the premise that replication research is the mainstay of the scientific method and that empirical generalizations are central to knowledge development, our results can hardly be considered conclusive. Certainly more studies are needed to further validate the four-factor service quality measure derived in this study. In addition, while we followed well-established procedures throughout our study, at the qualitative stage, employment of the approach advocated by Zimmer and Golden (1988) might have been better. It is conceivable that using the same coders also as judges during the coding process in selecting and developing the items may have partially confounded our item pool. This study provides full support for neither the North American nor the Nordic school of thought regarding the dimensionality of service quality or the meanings of quality dimensions. However, the dimensions identified in this study show similarities to other service quality measures such as SERVQUAL and SYSTRA-SQ. This suggests that there may be some potentially universal facets of service quality and that perhaps we may not need to develop specific measures from scratch for each context. Instead, existing knowledge base may provide a useful starting point for adaptations to new contexts. Future research can shed further light on these issues.

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