Effects of Service Quality on Customer Retention and Word-Of-Mouth in a Retail Setting: Comparative Study of Different Scales



Citation:

Sang-Lin Han and Sung-Tai Hong (2005) ,"Effects of Service Quality on Customer Retention and Word-Of-Mouth in a Retail Setting: Comparative Study of Different Scales", in AP - Asia Pacific Advances in Consumer Research Volume 6, eds. Yong-Uon Ha and Youjae Yi, Duluth, MN : Association for Consumer Research, Pages: 316-321.

Asia Pacific Advances in Consumer Research Volume 6, 2005      Pages 316-321

EFFECTS OF SERVICE QUALITY ON CUSTOMER RETENTION AND WORD-OF-MOUTH IN A RETAIL SETTING: COMPARATIVE STUDY OF DIFFERENT SCALES

Sang-Lin Han, Hanyang University, Korea

Sung-Tai Hong, Hanyang University, Korea

INTRODUCTION

In the past two decades, many studies of the service marketing area have tried to define service quality and develop instruments to measure it. Since Parasuraman et al. (1988) introduced the service quality instrument, called SERVQUAL, many studies have used SERVQUAL to measure service quality in various domains, ranging from financial services (Lin, 1999), health services (Dean, 1999), travel agent services (Kaynama, 2000), and retailing services (Mehta, 2000), to restaurants (Lee and Hing 1995). However, since SERVQUAL was originally developed to measure the general service quality, it didn=t fully consider the underlying characteristics of a specific industry such as retailing. Recently, as the Korean retail industry is becoming more competitive, there is a general agreement that the most important retailing strategy for creating competitive advantage is the delivery of high service quality. Also, many global retailers, such as Tesco (British discount store and Wal-Mart (American discount store), are making the Korean retail market even more competitive and therefore, many retail managers are interested in the management of service quality in retail environment.

From the retail manager’s perspective, the level of service quality is highly correlated with the level of customer retention and customer’s favorable word-of-mouth behavior. In this sense, most retail managers would be very interested in the question of how to increase their retention rate and therefore, to elaborate this question in more detail, service quality study in a retail setting would be very important. Current measures of service quality including SERVQUAL do not adequately capture customers’ perceptions of service quality for retail stores such as department or specialty stores. Therefore, the main objective of this study is to investigate the usefulness and applicability of the different methods including SERVQUAL in measuring the service quality of retail environment and their relationships to customer retention and word-of-mouth behavior. By exploring the suitability of each different measurement method of retail service quality, this study enhances the understanding of the major dimensions of retail service quality and the analysis of the effect of service quality on customer retention and word-of-mouth behavior.

THEORETICAL BACKGROUNDS

Service quality is generally perceived to be a tool that can be used to create a competitive advantage and therefore, substantial research into service and service quality has been undertaken in the last 20 years. Bitner et al. (1990) define service quality as "the consumers’ overall impression of the relative inferiority/superiority of the organization and its services." The most common definition of service quality is the discrepancy between a consumer’s expectations and perceptions of the service received. Accordingly, service quality is defined as how well a delivered service level matches a customer’s expectation. Parasuraman et al. (1988, 1991) identified more detailed dimensions of service quality and developed a well-known instrument, called SERVQUAL, to measure a customer’s perceptions and expectations from service. The SERVQUAL instrument consists of five underlying dimensions, with two sets of 22 item statements for the 'expectation’ and 'perception’ sections of the questionnaire. Perceived service quality is measured by subtracting customer perception scores from customer expectation scores, both for each dimension and overall. The five dimensions of SERVQUAL are (Parasuraman et al., 1988, 1991):

(1) Tangibles, which pertain to the physical facilities, equipment, personnel, and communication materials.

(2) Reliability, which refers to the ability to perform the promised services dependably and accurately.

(3) Responsiveness, which refers to the willingness of service providers to help customers and provide prompt service.

(4) Assurance, which relates to the knowledge and courtesy of employees and their ability to convey trust and confidence.

(5) Empathy, which refers to the provision of caring and individualized attention to customers.

Since the SERVQUAL was developed in 1988, various researchers have recognized that both the instrument itself and the conceptualization of service quality may benefit from further refinement (for example, Finn and Lamb 1991, Lee and Hing 1995). They have argued that the SEVQUAL instrument needs to be customized to the specific service area. One study by Babakus and Mangold (1989) showed that SERVQUAL is not 5-dimensional in a health care setting.

Cronin and Taylor (1992) argued that using the difference score between expectation and performance in SERVQUAL may not be appropriate and they have developed a new instrument, which is called SERVPERF, to measure service quality based only on customer perception of performance. After many studies have examined the suitability of SERVQUAL in measuring service quality in different types of service, they tried to adapt the original 22 SERVQUAL items to various service contexts by slightly changing the original items. In the area of retail market, only few researches (Finn and Lamb 1991, Dabholkar et al. 1996, Mehta 2000) have tried to measure the quality of retail service. Dabholkar’s study (1996) concluded that there are 5 underlying dimensions of service quality in a retail environment such as physical aspects, reliability, personal interaction, problem solving, and policy. In their study, based on the partial disaggregation technique and cross validation, they developed a new measurement scale, called Retail Service Quality Scale (RSQC), for retail stores. The five dimensions of retail service quality of RSQC are:

(1) Physical aspects, which is similar to tangibles dimension of SERVQUAL. This dimension includes the appearance and convenience of the physical facilities of the retail store.

(2) Reliability, which is similar to the SERVQUAL reliability dimension, except that it has two subdimensions and a couple of other variables.

(3) Personal interaction, which has two subdimensionsBservice employees inspiring confidence and being courteous/helpful.

(4) Problem solving, which addresses the handling of product returns and exchanges as well as of complaints.

(5) Policy, which captures aspects of service quality that are directly influenced by store policy.

Because Dabholkar’s measurement scale was designed specifically for the retail environment, we assume that the Retail Service Quality Scale is a better method to correctly measure the quality of service in a retail environment than the other general scales such as SERVQUAL or SERVPERF.

RESEARCH HYPOTHESES

The growing importance of service quality in the retail environment leads us to examine the following questions concerning the relationships among service quality, customer retention rate, and word-of-mouth in the retail environment.

B Is Retail Service Quality Scale a more appropriate method to measure the quality of retail service than SERVQUAL or SERVPERF?

B Is service quality significantly associated with retailer’s customer retention?

B Does the level of service quality influence customer retention and word-of-mouth behavior?

Mummalaneni and Wilson (1989) argue that satisfaction leads to binding the customer and the seller together and strengthening their relationship. Once a customer has decided that he or she is no longer satisfied with the product or service, the process of the dissolution of the bonding between the customer and the provider becomes salient. Also, there is widespread consensus among scholars (e.g. Wilson, 1995) that greater satisfaction increases the level of a customer’s commitment to the seller. Recently, in the information system area, some research has begun to try to investigate the relationship between Internet service quality and customer retention rate (McKinney et al., 2002). In a study of electronic commerce channel preference, Devaraj et al. (2002) also showed that service quality is one of the major determinants of the proportion of long-term customers.

One of the key issues for service providers as a result of the increased competition is "churn," or customer movement to the competing company. Therefore, how to increase the level of customer retention has been one of the key questions to most marketing managers in the retail industry. Some marketing researchers have showed that quality of service is the key factor for determining the service switching intentions (Keaveney and Parthasarathy, 2001). Using data on the online industry, Chen and Hitt (2002) investigated how service characteristics affect the level of customer switching and retention. Similarly, Zeithaml, Berry, and Parasuraman (1996) emphasize the importance of measuring future behavioral intentions of customers to assess their potential to remain with or leave the service organization. On these grounds, we have the following research hypotheses regarding service quality, customer retention, and word-of-mouth behavior.

H1: Retail Service Quality Scale is more appropriate method to measure the retail service quality than the other general scales such as SERVQUAL or SERVPERF.

H2: Service quality of retailers is positively related to the level of customer retention.

H3: Service quality of retailers is positively related to the word-of-mouth behavior.

RESEARCH METHOD

Research Design and Sample

Prior studies (Finn and Lamb 1991, Mehta 2000) empirically tested whether SERVQUAL is an appropriate instrument to assess retail service quality. Even though some studies showed that SERVQUAL is an appropriate scale for measuring service quality, some other studies (Finn and Lamb 1991) showed that SERVQUAL scales do not capture the essence of the service quality construct in retailing. In our study, modified scale items of SERVQUAL were designed to deal with the unique features of retail services. The basic methodology was to apply the modified SERVQUAL, SERVPERF, and Retail Service Quality Scale instruments to the study sample, and then compare the goodness-of fit indices of each model to choose the most appropriate method for retail service quality measurement. The measurement model and structural equation model were validated. The effects of service quality on customer retention and word-of-mouth were also investigated and the research hypotheses were tested.

Data for model testing were obtained through the questionnaire survey to the customers of the major department stores of Korea. A total of six stores in Seoul, the largest metropolitan city in Korea, were involved in the study. Three different questionnaires including SERVQUAL, SERVPERF, and Retail Service Quality Scale were prepared and a sample of 300 respondents answered o the each different questionnaire making 900 total respondents. Graduate students majoring in marketing, who were assigned to a specific store, administered the questionnaire. The questionnaire was self-administered at the store locations and customers who had not previously shopped at the stores were excluded from the sample.

Measurement Model and Second-order Confirmatory Factor Analysis

In this research, we followed the updated measure development paradigm proposed by Gerbing and Anderson (1988) as well as the traditional procedure suggested by Churchill (1979) to develop better measures of marketing constructs. Exploratory factor analysis (EFA) was conducted for data screening and dimensionality check and then, confirmatory factor analysis was conducted. In an effort to achieve reliability and validity of the measurement model, first-order and second-order confirmatory factor analysis (CFA) were conducted for SERVQUAL, SERVPERF, and Retail Service Quality Scale models.

Figure 1, Figure 2, and Figure 3 illustrate the estimated parameters of the five-dimensional second-order factor model based on the Confirmatory Factor Analysis (CFA). As seen in the figures, each model showed the differences in factor loadings and 5-dimensional structure of service quality.

We compared the goodness-of-fit indices of each different second-order CFA model and Table 1 shows the comparison of the three different models of service quality. Even though there was not a big difference among those three models, we concluded that, Retail Service Quality Scale is a more appropriate method than SERVQUAL or SERVPERF in terms of the goodness-of-fit indices.

Because the Retail Service Quality Scale was considered as the better model to measure the service quality than the other models, this model was diagnosed in more detail. The paths from the second-order factor of service quality to the first-order dimensions were strong and significant and, the indicator loadings of items to their respective constructs were also strong. The t-scores ranged from 5.37 to 19.46, indicating that all factor loadings are significant and providing evidence to support the convergent validity of the items measured (Anderson and Gerbing 1988). Composite reliability, a measure of internal consistency comparable to coefficient alpha (Fornell and Larcker 1981), was in excess of 0.70, implying acceptable level of reliability for each of the constructs. To assess the degree of associations among the 5 subdimensions, a formal test of discriminant validity was conducted by using the chi-square difference test. This suggests that the better model will be the one in which the two constructs are viewed as distinct, yet correlated factors (Anderson and Gerbing 1988, Bagozzi el al. 1991). In all ten paired comparisons of the different models, the chi-square difference test was significant, suggesting that the constructs are distinct. In sum, all these diagnostics suggest that the measurement model of Retail Service Quality Scales should be accepted as a good representation of the data and we accepted research hypothesis 1.

FIGURE 1

SECOND-ORDER CFA MODEL OF RETAILER=S SERVICE QUALITY: SERVQUAL MODEL

FIGURE 2

SECOND-ORDER CFA MODEL OF RETAILER=S SERVICE QUALITY: SERVPERF MODEL

FIGURE 3

SECOND-ORDER CFA MODEL OF THE RETAILER=S SERVICE QUALITY: RETAIL SERVICE QUALITY SCALE

TABLE 1

GOODNESS-OF-FIT INDICES OF THE DIFFERENT MODELS OF SERVICE QUALITY MEASUREMENT

FIGURE 4

STRUCTURAL MODEL OF SERVICE QUALITY, CUSTOMER RETENTION, AND WORD-OF-MOUTH

Structural Model and Hypotheses Testing

To test the research hypotheses and investigate the effects of service quality on customer retention and word-of-mouth, we conducted covariance structure analysis by using LISREL 8. The final structural model of retail service quality was tested and, as seen in Figure 4, the results showed that service quality has positive impact on customer retention (b=0.84) and word-of-mouth (b=0.83) accordingly. Therefore, research hypotheses 2 and 3 were supported with strong statistical significance. This confirms the recent results of Keaveney and Parthasarathy (2001) that service continuers show a higher satisfaction level than service switchers. Cronin and Taylor (1992) also showed that service quality influences customer satisfaction, even though they measured service quality with performance perceptioN only.

The results of model testing showed satisfactory goodness-of-fit indices. In general, the goodness-of-fit was high (GFI=0.90) indicating that a major proportion of the variances and covariances in the data was accounted for by the model. More specifically, the root mean square error of approximation (RMSEA=0.056) is below the .08 cutoff recommended in the literature (e.g. Browne & Cudeck 1993). The adjusted GFI and other fit indices (AGFI=0.88, NFI=0.97, NNFI=0.98, CFI=0.98) clearly meet the requirements recommended in the literature (Bagozzi and Yi, 1988, Baumgartner and Homburg, 1996) and these magnitudes indicate that the model fits the data adequately.

CONCLUSIONS AND IMPLICATIONS

Many studies have emphasized the need to develop valid and reliable measures of the service quality in a specific industry. Most researches have made much efforts to apply SERVQUAL, a commonly used measure of service quality, to the various industries. In this research, we tried to find the most appropriate method of measuring service quality of retailers. Among the three different methods including SERVQUAL, SERVPERF, and Dabholkar’s scale, we concluded that Dabholakar’s Retail Service Quality Scale (Dabholkar et al. 1996) is a better methods than SERVQUAL or SERVPERF. Furthermore, we explored the effect of service quality on customer retention and word-of-mouth of customers. This study reveals that service quality does influence the level of customer retention and word-of-mouth behavior.

The results from the present study suggest several implications for the use of service quality scales in the retail environment. This study has the potential to make managerial and methodological contributions to the analysis of retail service quality. This research provides retail managers with a scale to assess the quality of their service from the perspective of the five underlying dimensions. This study also provides marketing managers, especially in the retail environment, with an insight to understand how to increase customer retention level. Identifying customer perceptions of service quality for a particular retail store allows retail managers to better tailor their marketing efforts and customer management to increase the retention rate. In this sense, the results of this study will be used for an efficient management of CRM strategy. Methodologically, this research attempted to examine the suitability of SERVQUAL, SERVPERF, and Retail Service Quality Scale to measure the service quality in a retail setting. The assessments of reliabilities and validities of measurement scale through LISREL analysis confirm the correspondence rules between the empirical and theoretical concepts (Bagozzi 1984). These methodological attempts and the purified measurement items of the study will provide a valuable guidance to the future empirical research into retail service quality.

REFERENCES

Anderson, J. and S. Gerbing (1988), "Structural Equation Modeling in Practice: A Review and Recommended Two-step Approach," Psychological Bulletin, vol. 103(3), 411-423.

Babakus, E. and W.G. Mangold (1989), "Adapting the 'SERVQUAL’ Scale to Health Care Environment: An Empirical Assessment," American Marketing Association Educators’ Conference Proceedings, Chicago: IL, pp.195.

Bagozzi, R. (1984), "A Prospectus for Theory Construction in Marketing," Journal of Marketing, 11-29.

Bagozzi, R. and Yi, Y. (188), "On the Evaluation of Structural Equation Models," Journal of the Academy of Marketing Science, 16 (Spring), pp. 74-94.

Baumgartner, H. and C. Homburg (1996), "Applications of Structural Equation Modeling in Marketing and Consumer Research: A Review," International Journal of Research in Marketing, 13, pp. 139-161.

Bitner, M. (1990), "Evaluating Service Encounters: The Effects of Physical Surroundings and Employee Responses," Journal of Marketing, Vol.54(April), 69-82.

Bitner, M., B. Booms, and M. Tetreault (1990), "The Service Encounter: Diagnosing Favorable and Unfavorable Incidents," Journal of Marketing, Vol.54(Jan.), pp. 71-84.

Bolton, R. and J. Drew (1991), "A Longitudinal Analysis of the Impacts of Service Changes on Customer Attitudes," Journal of Marketing, 55 (1), 1-9.

Brown, T., G. Churchill, and J.P. Peter (1993), "Improving the Measurement of service Quality," Journal of Retailing, 69(Spring), 127-139.

Browne, M.W. and R. Cudeck (1993), "Alternative Ways of Assessing Model Fit," in Bollen, Kenneth and J. Scott Long (eds.), Testing Structural Equation Models, Newbury Park, pp. 136-162.

Chen P. and L. Hitt (2002), "Measuring Switching Costs and the Determinants of Customer Retention in Internet-Enabled Businesses," Information Systems Research, 13(3), 255-274.

Churchill, G. (1979), "A Paradigm for Developing Better Measures of Marketing Constructs," Journal of Marketing Research, 16 (February), 64-73.

Cronin, J. and S. Taylor (1992), "Measuring Service Quality: A Reexamination and Extension," Journal of Marketing, 56 (3), pp. 55-68.

Dabholkar, Pratibha A., Dayle I. Thorpe, and, Joseph O. Rentz, " A Measure of Service Quality for Stores: Scale Development and Validation," Journal of the Academy of Marketing Science, Vol. 24, No. 1, 1996, pp.3-16.

Dean, A.M. (1999), "The Applicability of SERVQUAL in Different Health Care Environments," Health Marketing Quarterly, 16(3), p.1.

Devaraj, S., M. Fan, R. Kohli (2002), "Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics," Information Systems Research, 13(3), 316-333.

Finn, David W., and Charles W. Lamb. (1991), "An Evaluation of the SERVQUAL Scales in a Retailing Setting," Advances in Consumer Research, edited by R. Holman and M. R. Soloman, 483-490. Provo, UT: Association for Consumer Research.

Fornell, C. and D. Larcker (1981), "Evaluating Structural Equation Models and Unobservable Variables and Measurement Error," Journal of Marketing Research, 18 (February), 39-50.

Gefen, D. and P. Devine (2001), "Customer Loyalty to an Online Store: The Meaning of Online Service Quality," Proceedings of International Conference on Information Systems (ICIS), pp. 613-617.

Gerbing, D. and J. Anderson (1988), "An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment," Journal of Marketing Research, 25 (May), 186-192.

Kaynama, S.A. (2000), "A Proposal to Assess the Service Quality of Online Travel Agencies: An Exploratory Study," Journal of Professional Services Marketing, 21(1), p.63.

Keaveney, S. and M. Parthasarathy (2001), "Customer Switching Behavior in Online Services: An Exploratory Study of the Role of Selected Attitudinal, Behavioral, and Demographic Factors," Journal of the Academy of Marketing Science, vol. 29 (4), 374-390.

Kettinger, W.J. and Lee, C.C. 1994), "Perceived Service Quality and User Satisfaction with the Information Services Function," Decision Sciences, 25, pp. 737-766.

Lee, Y. and N. Hing (1995), " Measuring Quality in Restaurant Operations: An Application of the SERVQUAL Instrument," International Journal of Hospitality Management, Vol.14, pp.293-310.

Lin, X. (1999), "Service Quality Dimensions of Securities Brokerage Firms: What Customers Consider as Important," Journal of Professional Services Marketing, 20(1), p. 135.

McKinney, V., K. Yoon, F. Zahedi (2002), "The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach," Information System Research, 13(3), 296-315.

Mehta, S.C. (2000), "Measuring the Service Quality of Retailers Distributing Technical Products with High Service Component: An Evaluation and Extension," Journal of Professional Services Marketing, 20(2), p.33.

Mummalaneni, V. and D. Wilson (1989), "The Influence of a Close Personal Relationship Between a Buyer and a Seller on the Long-term Future of Their Role Relationship," Working Paper, ISBM, The Pennsylvania State University, University Park: PA.

Parasuraman, A., V. Zeithaml, and L. Berry (1988), "SERVQUAL: A Multiple Item Scale for Measuring Consumer Perceptions of Service Quality," Journal of Retailing, 64(1), pp. 12-40.

Parasuraman, A., V. Zeithaml, and L. Berry (1991), "Refinement and Reassess of the SERVQUAL Scale," Journal of Retailing, 67 (4), 420-450.

Wilson, D. (1995), "An Integrated Model of Buyer-Seller Relationships," Journal of the Academy of Marketing Science, Vol.23, No.4, 335-345.

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Authors

Sang-Lin Han, Hanyang University, Korea
Sung-Tai Hong, Hanyang University, Korea



Volume

AP - Asia Pacific Advances in Consumer Research Volume 6 | 2005



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