crm 5

European Journal of Social Sciences – Volume 17, Number 2 (2010)

200

SERVQUAL, Customer Satisfaction and

Behavioural Intentions in Retailing

C.N. Krishna Naik Head and Chairman, Board of Studies, Sri Krishna Devaraya Institute of Management

Sri Krishna Devaraya University, Anantapur, Andhra Pradesh, India E-mail: [email protected]

Swapna Bhargavi Gantasala

Assistant Professor, School of Management, Aurora’s P.G. College Ramanthapur, Hyderabad, India

E-mail: [email protected]

Gantasala Venugopal Prabhakar Director of Experiential Education, School of Management, New York Institute of Technology

P.O.Box: 840878, Zahran Street, Amman, Jordan. 11184 E-mail: [email protected] or [email protected]

Abstract The researchers set forth to ascertain and probe through the development of a service quality questionnaire in the retail scenario whether the typology to which service belongs may explain the relationship between service quality (SQ) and Behavioural Intentions (BI) and Customer Satisfaction (SAT). An exploratory factor analysis has been employed for respondents who visit retail outlets in the South Indian City of Hyderabad. Then a more representative sample was used for respondents visiting Pantaloon outlets in a second-order confirmatory factor analysis. The researchers find that the dominant dimensions of service quality are Tangibles, Recovery, Responsiveness, and Knowledge. The results establish the direct influence of SQ on Behavioural intentions, and the mediating role of SAT on influencing Behavioural Intentions. SAT is found to be a strong driver of Behavioural Intentions in the context of retail sector in India. The study focuses on only the retail sector and considers respondents from one major retail outlet operating in all the formats. It may therefore not be possible to generalize the results to all the service sector industries. Future research could possibly examine the role of SQ and SAT on Behavioural Intentions of customers in other service sectors. Service Managers in these Retail operations may do well to design marketing and operating strategies that focus on these dominant SQ dimensions to improve Customer Satisfaction (SAT) and in the process positively impact Behavioural Intentions (BI). Keywords: Service Quality, Customer Satisfaction, Behavioural Intentions, Retailing.

Introduction Service sector plays an increasingly significant role in the Indian economy. Growth in the service sector in recent years has been manifold and has far exceeded the growth in manufacturing sector. Services also contribute substantially to the GDP and to exports from the country. It is obvious


201

therefore that the service sector has garnered the attention of service managers, academic researchers and they have in turn directed their efforts in comprehending the dynamics involving customer satisfaction and behavioural intentions. What is of interest is how customers perceive service quality and the impact of service quality on their satisfaction levels and intentions. What is of pivotal interest is the repeat business that satisfied customer brings along when he/she is satisfied with service quality on offer. Satisfied customers who stay with a particular retail outlet have long term impact on profitability in several ways. Huge expenditure incurred in attracting new customers could be brought down considerably if the outlet has a sizeable customer base that remains loyal. Another behavioural impact is the positive word of mouth that satisfied and loyal customers spread amongst prospective customers (Anderson and Sullivan, 1990; Reichheld and Sasser, 1990; Zeithaml et al., 1996). Service Quality, Customer Satisfaction and Behavioural Intentions There are many facets of the three important aspects considered for this study. Should research focus only on customer perceptions alone in the service quality construct (Cronin and Taylor, 1992) or should Expectations – Perceptions also be used (Parasuraman et al., 1988)? If expectations were to be considered the pertinent question is whether the disconfirmation (Expectations-Perceptions) be computed or measured (Dabholkar et al., 2000). Research has also pointed out that the service quality construct cannot be a global one when operationalized and need to be context specific (Babakus and Boller,1992; Lapierre,1996; Levitt, 1981) to have practical utility. Research has yielded conflicting results and this could be because of the structural typological differences that exist when studying different industries. This endeavour is to research the possibility that the typology of service and operationalization of the service quality construct. Two or more industries may exhibit similar relationship between SERQUAL, SAT and BI if they belong to the same industry. This argument is supported Lapierre’s (1996) observations. Literature Review Service typology components, marketing-oriented and operations-oriented views of service were chronicled by Cook et al. (1999). They opined that … although information gathered through the process analysis of services can be utilized for future service process design, this design should also take into account the marketing-oriented dimensions of the service product. Similarly, future service product design should take into account both the marketing-oriented and operations-oriented aspects of the service.

Fitzsimmons (2004) elaborates on marketing-oriented views and classifies service dimensions that include intangibility, differentiation, object of transformation, type of customer, and commitment. Under the operations-oriented view, dimensions include customer contact, customer involvement, labour intensity, degree of customization, and degree of employee direction. It is clear from their work that service quality has both marketing and operations orientations. The researchers therefore set out to explore both for this empirical study. For this purpose, the classification suggested by Schmenner (1986) is the genesis. On one front, Schmenner took into consideration the labour contribution or staff responsiveness and on the other the customer contact to ensure service quality. He proposes that for a high labour-intensive service business would involve little plant and equipment but a considerable labour time. In the retail sector, staff need not handle sophisticated equipment but would be needed to expend considerable time serving customers. Also, customer interaction with the service process is high in the retail sector. Other researchers who have considered customer contact, customer involvement and the amount of discretion that the service provider has are Kellogg and Chase, 1995; Mills and Marguiles, 1980; Lovelock, 1983. As elaborated by Schmenner, retail outlets need to make their services “cordial” or responsive (one of the service quality dimension), develop innovative marketing practices to attract and retain customers (customer satisfaction), utilize the latest


202

technologies (billing, stock maintenance, ordering, processing transactions), physical settings (assortment, aisles, walkways, shelves, displays, which are the tangible dimension of service quality) and having staff with knowledge of operations and of the latest arrivals. Service Quality The SERVQUAL instrument proposed by Parasuraman et al. (1988) uses the disconfirmation approach wherein the gap between customer’s expectations and the actual performance of the service promised is measured. An alternative approach to this could be the SERVPERF which is the measurement of the customer’s perception of the performance of the service offered by a provider. This is the assessment of the adequacy of the performance of the service and its quality (Gro¨ nroos, 1988, 1990; Cronin and Taylor, 1992; Peter et al., 1993; Brown et al., 1993; Bebko, 2000). When it comes to service quality dimensions, research indicates the futility of a universal construct for assessing service quality (Levitt, 1981; Lovelock, 1983) and also that service quality is industry or context specific (Babakus and Boller, 1992). It connotes to a construct being operational (non-global) and at the same time be context specific. Lapierre’s (1996) also used an alternative set of operational measures to suit a specific context and suggest that a universal construct may be inappropriate.

The researchers then study the dimensions that suitably and appropriately could predict BI and impact SAT. Babakus and Mangold (1989), Cronin and Taylor (1992), and Brown et al. (1993) do suggest uni-dimensionality of SERVQUAL. Other studies in SERVQUAL show replications of dimensions that vary from three to five (Llosa et al., 1998; McDougal and Levesque, 1992), and ten (Carman, 1990). Another issue that needs to be addressed is which of the service quality dimensions are dominant in a given industry given that they are operationalized. Schmenner (1986) indicates that the likely dominant dimensions are “Tangibles” (that include physical setting, layout, assortments, and appearance of sales staff), “Responsiveness” (the promptness or readiness of sales staff or employees to serve customers and provide requisite information), “Recovery” (the extent to which remedial measures are initiated when there is a complaint or suggestion), and “Knowledge” (competencies and skills that staff possess in order to serve customers better).

For the retail sector, there is low interaction of staff with customers (brief and infrequent) and therefore the service providers often design services that are standard offering little scope for flexibility for the staff. It must be however emphasized here that the dimension “Accessibility and Flexibility” dimension (the ability of the retail outlet to design and deliver the service that matches customer expectations by managing its operating hours, layout, systems, recreational and other value added facilities) and the “Reliability” dimension (the extent to which customers can depend on the quality of products on display in the outlet, delivery of promised offers and promotions) do have a bearing in the retail industry.

The authors propose Proposition 1

In the retail industry, the dominant dimensions of service quality are “Tangibles”, “Responsiveness”, “Recovery”, and “Knowledge”. The dimension “Accessibility and Flexibility” as well as the “Reliability” dimension are expected to play a less dominant role in influencing Customer Satisfaction (SAT) and subsequently on Behavioural Intentions (BI).

Dimensions considered for this empirical study are shown in Table 1.


203

Table 1: Questionnaire and Dimensions

Tangibles 1. The retail outlet is maintained clean 2. Appearance of the outlet from the outside is appealing 3. Interior decor in the outlet is attractive 4. The outlet maintains facilities that are state of the art 5. Staff down here are appropriately dressed 6. The lounge down is cosy 7. Parking space is adequate Responsiveness 8. Employees here are courteous 9. I do get special attention every time I visit 10. Staff are prompt in handling queries 11. Staff at the outlet are sensitive to our needs 12. Maintenance within the outlet is good 13. Staff are competent in handling peak customer traffic Knowledge 14. Sales staff are knowledgeable of products, prices, offers etc 15. I get all the pertinent information from staff in the outlet 16. Staff are adept at using technologies for processing transactions 17. Staff here are aware of special promos and offer prices Reliability and Trust 18. The sales staff process transactions sans errors 19. Bill verification and accuracy is very good here 20. The billing process is time-efficient 21. The automatic price teller is easy to use at the outlet Accessibility and Flexibility 22. The outlet is conveniently situated 23. Sales staff are available when needed most 24. Quick queue is available for customers with few merchandise 25. The physically challenged have ramps and other support facilities Recovery 26. Staff are empowered to compensate for damaged products 27. Staff are prompt to apologize in the event of a mistake 28. Convenient rooms for the elderly and children make it comfortable 29. Information on substitutes is forthcoming when a product is out of stock Customer Satisfaction 30. I am satisfied with this retail outlet 31. My decision to visit this outlet has been a wise one 32. I did the right thing when I decided to shop here 33. My shopping experience here has been an enjoyable one Behavioural Intentions 34. I would strongly recommend this outlet to my friends, colleagues and family members 35. I intend to visit this outlet at the first opportunity 36. I would visit other outlets of the same retail chain

Customer Satisfaction Researchers in customer satisfaction have included it as an affective construct and not as a cognitive construct (Oliver, 1997; Olsen, 2002). Customer Satisfaction has been defined by Rust and Oliver (1994) as the “customer’s fulfilment response,” which is an assessment and an emotion based reaction to a service provided. Cronin et al. (2000) studied service satisfaction using the dimensions viz. interest, enjoyment, surprise, anger, wise choice, and doing the right thing. However, the authors employ a modified version of the four emotion-based model presented by Westbrook and Oliver (1991). Questions regarding customer satisfaction appear in the questionnaire under Table 1.


204

Behavioural Intentions Repurchase intentions, word of mouth publicity; loyalty, price sensitivity and complaining behaviour are major components of Behavioural Intentions (BI) (Zeithaml, Berry, Parasuraman, (1996)). Zeithaml, Berry and Parasuraman also reiterate that high service quality leads to positive behavioural intentions and vice-versa. They also point out to the intention to stay with a brand or to defect as one the barometric indicators of Behavioural Intentions. Burton et al. (2003) support the view that customers’ experience dictates Behavioural Intentions and that a positive experience would prompt a satisfied customer to reuse the brand. Relating Service Quality, Customer Satisfaction and Behavioural Intentions Even if service quality and customer satisfaction were distinct constructs, literature does not clearly point to the causal ordering of SQ and SAT. One school of researchers believe that Customer satisfaction is antecedent to Service quality. Whereas the other group of researchers argued that Service quality is antecedent to Customer satisfaction and that a positive Service quality perception can lead to customer satisfaction which then results in positive Behavioural Intentions (Brady and Robertson, 2001). Interestingly there is a third perspective forwarded by Taylor and Cronin, 1994, who opine that neither of the above two constructs is an antecedent of the other. However, Dabholkar (1995) reiterates that the antecedent role of each construct is consumer specific. His argument is that for a consumer who is cognitive oriented, he or she will perceive the relationship as service quality being antecedent to satisfaction and if the consumer was affective oriented then he or she will perceive that satisfaction causes positive perceived service quality. Extensive research carried out by Brady and Robertson (2001) across cultures indicates that Service Quality (SQ) is an antecedent to Customer Satisfaction (SAT). Cronin and Taylor, 1992 also established the direct links of SQ and SAT to Behavioural Intentions (BI). The direct link between SQ and BI was established by Cronin and Taylor, 2000 across six industries. The link was also confirmed by Zeithaml, Berry and Parasuraman, 1996 when they found a positive relationship between overall Service Quality (SQ) and price sensitivity (a BI dimension). The authors set out to ascertain whether the indirect effect of Service Quality (SQ) on Behavioural Intentions (BI) is mediated by Customer Satisfaction (SAT). The service industry is competitive and affords a wide range of choices for customers. The ‘red ocean’ (cut-throat competition) necessitates the study of the indirect link of SQ on BI and mediated by SAT. Also of interest to the authors is to assess the significance of the direct effect of Service Quality (SQ) on Behavioural Intentions (BI) if the mediating effect is significant. Therefore the authors propose Proposition 2

Both the direct effect (SQ on BI) and the indirect effect (SQ mediated by SAT on BI) explain customers’ Behavioural Intentions in retailing industry. Research Methodology A pilot study was undertaken to generate the items presented in the questionnaire for this study. The respondents were the customers visiting retail outlets in the South Indian city of Hyderabad. Respondents were explained the purpose of the research and translations were done wherever native languages were the only medium to communicate with respondents. Respondents were approached while shopping to use the walk-through-audit (WTA) approach that could accurately trace customer response to Service Quality in the outlet. As a result of these interviews with respondents of the pilot study, items in the questionnaire were reworded and presented to the final sample of respondents. Dimensions used in the finalized research instrument were “Tangibles” (that include physical setting, layout, assortments, and appearance of sales staff), “Responsiveness” (the promptness or readiness of


205

sales staff or employees to serve customers and provide requisite information), “Knowledge” (competencies and skills that staff possess in order to serve customers better), “Reliability” (the extent to which customers can depend on the quality of products on display in the outlet, delivery of promised offers and promotions), “Accessibility and Flexibility” (the ability of the retail outlet to design and deliver the service that matches customer expectations by managing its operating hours, layout, systems, recreational and other value added facilities), and “Recovery” (the extent to which remedial measures are initiated when there is a complaint or suggestion). Each item in the questionnaire was rated by respondents on a seven-point Likert scale ranging from Strongly Disagree (1) to Strongly Agree (7). The dimensions Responsiveness, Accessibility and Flexibility, and Knowledge are in congruence with Zeithaml, Berry and Parasuramans’ Attitude and Behaviour, Empathy and Assurance (1988).

Lapeirre’s (1996) study confirmed the dimensions “Reliability” and “Reliability”. Customer Satisfaction was measured using four items with the same 7-point Likert type scale and Behavioural Intentions with three items in the questionnaire.

The study is also confined to three retailing formats. Format I: Department Stores with broad variety, deep assortments, high service, low to high

prices, located as regional malls with an average of more than 30,000 SKUs. Format II: Specialty Stores with narrow variety, very deep assortment, high service, high

prices, located as standalone malls with an average of 15000 SKUs. Format III: This format is confined to the food products, pulses and other grocery items. It

also has its offerings vegetables, fruits, and bakery items. The researcher selected a retailer operating successfully in all the three formats to assess the

influence of SERVQUAL dimensions on Customer Satisfaction and Repurchase Intention. Also covered under this study was the assessment of the impact of Customer Satisfaction and Repurchase Intention on positive word of mouth communication. The organization selected was Pantaloon’s retail. Further, the researcher selected competitors to Pantaloon’s in each format to carry out a comparative study. Pantaloons’ and Competitor brands of retailers chosen under each format are

Format I: Big Bazar, Vishal Mega Mart and Wah Magna Format II: Pantaloons, Brand Factory, Central, Shoppers’ Stop, Life Style Format III: Food Bazar, Spencers, Reliance Fresh, More, Magna (Retail outlets in italicized are Pantaloons’ outlets, and others are competitors) The researcher found that Pantaloons’ had its highest number of outlets in all the three formats

operational in the South Indian city of Hyderabad. For carrying out a study of SERVQUAL in Retailing for Pantaloon’s , it is obvious that Hyderabad is the right choice to conduct the survey for determining the influences of SERVQUAL dimensions on Customer Satisfaction and Repurchase Intention, and the influence of Customer Satisfaction and Repurchase Intention on Positive word of mouth. The researcher divided the geographical region of Hyderabad and selected a stratified random sample as shown in Table 3.

A pilot study was carried out on a random basis for 30 respondents and the characteristic taken in the pilot study was whether the respondents visited Pantaloons’ retail outlets or not. Table : Pilot Study

Visited atleast once Never visited Total Respondents 18 12 30

P = Percentage of population who have visited Pantaloon’s outlets as one of their choice P = Visited atleast once/Total pilot study respondents = 18/30 = 0.60 = 60% Sample size determination ( For Infinite Population ) n = {Z2 * (P) * (1-P )}/ C2

Z = 1.96 (For 95% confidence levels)


206

P = Percentage of population with a particular choice C = Confidence levels expressed as a decimal Taking 95% confidence levels and P = 0.6 (based on the pilot study) n = {1.96*1.96*(0.6)*(1-0.6)}/0.05*0.05

= {3.8416*0.6*0.4}/0.0025 = 0.921984/0.0025 = 368.79

n ≃ 369 As Pantaloons has 4 major formats in retailing, the researcher has divided the sample as

indicated below in Table A. Table A: Sample Strata

Types of outlet Geographical Location No. Of. Respondents Total

1. Big Bazar a.) Abids 31

93 b.) Ameerpet 31 c.) L B Nagar 31

2. Brand Factory a.) Abids 46 92 b.) Banjara Hills 46 3. Central a.) Panjagutta X Roads 92 92 4. Pantaloons a.) Himayat Nagar 92 92

TOTAL 369 Analysis and Inferences The researchers first carried out a factor analysis of Sample 1 (S1) comprising of a third (123) of the bigger sample (369) which is the Sample 2 (S2) for this study. An iterated factor analysis was done with commonalities for items estimated from squared multiple correlations and the method of estimation employed was maximum likelihood. The method yielded four factors that were rotated with a promax algorithm. Items with loading smaller than 0.4 on any factor were deleted. Also, factors that showed cross-loadings of more than 0.4 on more than one factor were deleted as they are not pure measures for the construct. Kaiser’s (1960) eigen values and the scree test was used to identify the factors. For assessing the dimensions of the newly developed scale, confirmatory factor analysis (CFA) was used on the broader Sample 2 (S2) as CFA is a more rigorous measure of dimensionality than the exploratory factor analysis. The reason for having used two different samples for the exploratory factor analysis and the confirmatory factor analysis was to reduce the probability of capitalizing the factors on chance characteristics. The iterated factor analysis is presented in Table II. Item 25 was removed with a lower than 0.3 factor loading for factor 2. Also, item 18 and item 19 were removed as they were associated with factor 3 in sample 1 and at the same time were associated with factor 4 in sample 2. Item 7 was found not to fit well with the assigned factor “Knowledge” and is therefore deleted Table II: Factor Loadings for Service Quality Dimensions

Noa F1 F2 F3 F4 Tangibles Recovery Responsiveness Knowledge

2 0.911 2.0227 3 0.804 4 0.802 1 0.707 0.260 5 0.638

26 0.981 27 0.772 29 0.723


207

25 0.680 28 0.593 10 1.100 11 0.847 18 0.560 9 0.540 8 0.281 0.487

19 0.299 0.400 16 0.849 15 0.753 17 0.672 7 0.616

Eigen value 10.58 1.59 1.20 1.07 Cumulative Percent

of Explained variance 58.87 60.78 66.73 72.08

Cronbach alpha 0.93 0.81 0.91 0.78 Exploratory factor analysis on sample S1 a factor loadings less than 0.20 are not shown

Following Parasuraman et al’s (1988) research, these items with reassigned and the factors are shown in Table III. Item-to-total correlations and coefficient alpha were calculated to reassign items or delete a few items. The eigen values and alphas for “Tangibles” (Factor 1) are 10.58 and 0.93 and for “Recovery” (Factor 2) they are 1.59 and 0.81. For “Responsiveness” (Factor 3) the eigen values and Cronbach alpha values are 1.20 and 0.91 and for “Knowledge” (Factor 4) the values are 1.07 and 0.78. It can also been seen that the Cronbach alpha values for all the factors are above the accepted value of 0.7 also indicating decent consistency internally among items. Using the results from Table II a Confirmatory Factor Analysis was done for the sample 2 (S2). Table III: Factor Loadings for the service quality dimensions

Noa F1 F2 F3 F4 Tangibles Recovery Responsiveness Knowledge 3 0.993 2 0.883 4 0.795 1 0.675 5 0.547 0.354

26 0.786 27 0.647 0.273 29 0.564 0.233 28 0.456 0.266 11 0.837 9 0.811

10 0.807 8 0.593 0.318

16 0.665 15 0.645 17 0.518 7b 0.420

Eigen value 8.56 1.84 1.03 0.88 Cumulative Percent

of Explained variance 50.33 61.08 67.06 72.18

Cronbach alpha 0.92 0.80 0.91 0.78 Exploratory factor analysis on sample S2 a Factor loadings less than 0.20 are not shown No.7 which is the adequacy of parking did not fit in with Dimension

“knowledge” and was not included in further analysis.


208

Convergent validity was assessed using t tests for factor loadings. The coefficient for one indicator was fixed at 1.00 for each of the four factors and the metric for the scale was assessed. Excepting for the fixed loadings, items exhibited highly significant t-statistics (p > 0.01) indicating that all variables were good measures to their construct. All the indicators had standard loadings higher than with the highest of 0.9 as shown in Table IV.

Figure 1: The Research Model

Responsive Knowledge Recovery Tangibles

V1 V3 V2 V4 V5 V8 V9 V10 V11 V15 V16 V17 V26 V27 V29 V28

The research model depicted schematically in Figure 1 shows four factors that establish the relationships between variables and their respective factor dimensions. The fit statistics also indicate that the Model A is the accepted measurement model. Composite reliability scores for each factor are also shown in Table IV. The analysis also shows that all factors have composite reliability scores greater than 0.7 which is the accepted norm. The researchers then assess discriminant validity which is the degree to which items used in the construct are distinct. Discriminant Validity is said to satisfy if a 95 percent confident interval of the inter-factor correlation between two constructs does not include an absolute value of one (Anderson and Gerbing, 1988). Correlations for all the constructs are shown in Table V and they were high, the 95 percent intervals for these correlations did not include 1.0. Thus, the interval test is supportive of the discriminant validity of the constructs considered in this work. Chi-square differences test also reaffirms the position that the constructs be perceived distinct (Anderson and Gerbing, 1988).


209

Table IV: Properties of the CFA for SERQUAL

Construct and Indicators Standardized Loading t-statistics Composite Reliability Tangibles (F1) 0.93

1 0.81 13.51* 2 0.86 14.64* 3 0.91 15.49* 4 0.84 14.24* 5 0.82 13.81*

Recovery (F2) 0.81 26 0.74 9.78* 27 0.83 10.50* 28 0.63 8.58* 29 0.66 8.93*

Responsiveness (F3) 0.92 8 0.81 7.31* 9 0.87 7.45*

10 0.87 7.45* 11 0.85 7.40*

Knowledge (F4) 0.84 15 0.84 5.33* 16 0.80 5.32* 17 0.74 5.26*

Notes: Number refers to Table I; analysis is performed on sample S2; * Indicates significance at P > 0.01 level

These results strongly support our proposition P1 that the dominant dimensions for assessing service quality are “Tangibles”, “Responsiveness”, “Recovery”, and “Knowledge”. It may also be noted that the Customer Satisfaction has a Cronbach alpha value of 0.97 and that for Behavioural Intentions it is 0.83 meaning that there is no need for reassigning items under these constructs. Table V: Correlation matrix for all exogenous and endogenous variables

TAN REC RES KNO SQ SAT BI Tangibles 1.00 Recovery 0.44* 1.00 Responsiveness 0.67* 0.58* 1.00 Knowledge 0.59* 0.66* 0.73* 1.00 Service Quality 0.81* 0.80* 0.90* 0.89* 1.00 Customer Satisfaction 0.58* 0.53* 0.62* 0.59* 0.69* 1.00 Behavioural Intentions 0.55* 0.53* 0.57* 0.56* 0.66* 0.88* 1.00

Note: *Indicates significance at p>0.01 level Table VI: Comparative fit indices among models

X2 /df RMSEA RMSR AGFI CFI NFI NNFI Model A 2.75 0.083 0.077 0.846 0.940 0.910 0.927 Model B 2.97 0.088 0.092 0.838 0.935 0.905 0.918 Model C 2.76 0.083 0.096 0.802 0.929 0.892 0.919

Results shown in Table IV also clearly support the second proposition P that Service Quality

has direct effect (SQ effect on BI) and also the indirect effect (SQ influences SAT and therefore impacts BI) on Behavioural Intentions. The R2 value for BI is 0.93. Also, the standardized coefficients from Service Quality to Customer satisfaction are 0.69 and from Customer Satisfaction to Behavioural Intentions it is 0.88. This clearly indicates the proposed path between Service Quality, Customer Satisfaction and Behavioural Intentions. Therefore the second proposition P2 can be accepted. A


210

statistically significant but smaller standardized regression coefficient (0.10) was observed between Service Quality and Behavioural Intentions, indicating the indirect impact of Service Quality on Behavioural Intentions. The path of influence mediated by Customer Satisfaction is stronger and therefore Customer Satisfaction is the driver for Behavioural Intentions in the retail sector. Conclusions and Discussion The researchers also comprehend the importance of customer satisfaction and customer focus for the retail sector, an attempt is made to study the delivery of quality service in retailing. For achieving this objective of delivering quality service, it is imperative that organizations must understand their customers to say competitive. The sources of customer expectations are marketer controlled factors (such as pricing, advertising, sales promises) as well as factors that the marketer has limited ability to affect (innate personal needs, word of mouth communications, and competitive offerings). These are precisely the areas that the researcher explores; in retailing by constructing a structured questionnaire and administering it to the sample (369) considered for the study. SERVQUAL has been researched a great deal but very little in the retailing context. Further, research in the Indian Scenario is miniscule. These factors influenced the researcher to study SERVQUAL in RETAILING. With customer as the focus, it was obvious that customer expectations and perceptions had to be studied taking the customer Gap as the basis. In a perfect world, expectations and perceptions would be identical. Customers would perceive that they have received what they thought they would and should. However, in practice, these are usually separated by some distance. It is of interest to very marketer, be it in retailing or in any other sector, to bridge this distance.

Desired service is a blend of what the customer believes “can be” and “should be”. What is of great interest to marketers is the extent to which customers are willing to accept a variation in service offered by different providers. SERVPERF varies across providers and necessity to understand the zone of tolerance become crucial for marketers. If service drops below adequate service - the minimum level considered acceptable – customers will be frustrated and their satisfaction with the company will be undermined. If SERVPERF is higher than the Zone of Tolerance at the top end – where performance exceeds desired service – customers will be very pleased and probably quite surprised as well. One might also consider the zone of tolerance as the range or window in which customers do not particularly notices service performance (SERVPERF).

When service falls outside this range and is either very low or very high, the service gets the customers’ attention in either a positive or negative way. An example in the retail sector: Consider the service at a checkout line in a grocery store. Most customers hold a range of acceptable time for this service counter – probably somewhere between 5 and 10 minutes. If service consumes that period of time, customers probably do not pay much attention to the wait. If a customer enters the line and finds sufficient checkout personnel to serve her in the first two or three minutes, she may notice the service and judge it as excellent. On the other hand, if a customer has to wait in line for 15 minutes, she may begin to grumble and look at her watch. The longer the wait is below the Zone of Tolerance, the more frustrated she becomes.

What is of further interest to markers is that Zones of Tolerance vary from service dimensions. The more important factor, the narrower the zone of tolerance is likely to be. In general, customers are likely to be less tolerant about unreliable service (broken promises or service errors) than other service deficiencies, which mean that they have higher expectations for this factor. In addition to higher expectations for the most important service dimensions and attributes, customers are likely to be less willing to relax these expectations than those for less important factors, making the Zone of tolerance for the most important service dimension smaller and the desired and adequate service levels higher.

To be able to successfully operate a retail outlet it’s imperative that the dimensions considered for this study be carefully designed and delivered to meet customer expectations. The “Tangibles” could be improved by concentrating on parameters like store design, layout, visible signage,


211

atmospherics, lighting and physical arrangements to name a few. The retail outlet also needs to improve their “Recovery” by being able to act quick, tracking complaints, making the service fail-safe, by being able to give adequate explanation when required, and ensuring fair play in transactions. Retail outlets need to learn from recovery experiences, take feedback and suggestions from customers, train staff in etiquette and empathy in order to bolster customers’ perception of “Responsiveness” at the outlet. There must be made available facilities for the physically challenged, elderly and the children. Outlets should be conveniently situated and new ones opened if neighbourhoods have potential for business expansions. Staff in adequate number should be trained on processing transactions and billing apart from being able to provide needed information to customers. These could go a long way in improving “Accessibility and Flexibility” dimension of Service Quality. Continuous and adequate training is quintessential for staff in utilizing equipment, and have knowledge on assortments, prices, offers, cash wraps, promotions etc. These initiatives could improve the “Knowledge” dimension of Service Quality. Having worked the right strategies for these dimensions, retail outlets can be rest assured of positively influencing Customer Satisfaction and thereby impacting customers’ Behavioural Intentions.


212

References [1] Anderson, E.W. and Sullivan, M.W. (1990), “Customer satisfaction and retention across

firms”, paper presented at the TIMS College of Marketing Special Interest Conference on Services Marketing, Nashville, TN, September.

[2] Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modelling in practice: a review and recommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-23.

[3] Babakus, E. And Boller, G.W. (1992), “An empirical assessment of the SERVQUAL scale”, Journal of Business Research, Vol. 24 No. 3, pp. 253-68.

[4] Babakus, E. And Mangold, W. (1989), “Adapting the SERVQUAL scale to health care environment: an empirical assessment”, in Bloom, AMA, Chicago, IL, p. 195

[5] Bebko, C.P. (2000), “Service intangibility and its impact on consumer expectations of service quality”, Journal of Services Marketing, Vol. 14 No. 1, pp. 9-26.

[6] Brady, M.K. And Robertson, C.J. (2001), “Searching for a consensus on the antecedent role of service quality and satisfaction: an exploratory cross national study”, Journal of Business Research, Vol. 51 No. 1, pp. 53-60.

[7] Brown, T.J., Churchill, G. And Peter, J. (1993), “Research note: improving the measurement of service quality”, Journal of Retailing, Vol. 69 No. 1, pp. 127-39.

[8] Burton, S., Sheather, S. and Roberts, J. (2003), “The effect of actual and perceived performance on satisfaction and behavioural intentions”, Journal of Service Research, Vol. 5 No. 4, pp. 292-302.

[9] Carman, J.M. (1990), “Consumer perceptions of service quality: an assessment of the SERVQUAL dimensions”, Journal of Retailing, Vol. 66 No. 1, pp. 33-55.

[10] Cook, D.P., Goh, C. and Chung, C.H. (1999), “Service typologies: a state of the art survey”, Production and Operations Management, Vol. 8 No. 3, pp. 318-38.

[11] Cronin, J.J. Jr and Taylor, S.A. (1992), “Measuring service quality: a re-examination and extension”, Journal of Marketing, Vol. 56 No. 3, pp. 55-68.

[12] Dabholkar, P.A., Shepherd, C.D. And Thorpe, D.I. (2000), “A conceptual framework for service quality: an investigation of critical conceptual and measurement issues through a longitudinal study”, Journal of Retailing, Vol. 76 No. 2, pp. 139-73.

[13] Fitzsimmons, J.A. And Fitzsimmons, M.J. (2004), Service Management: Operations, Strategy, and Infor mation Technology, 4th ed., Irwin McGraw-Hill, New York, NY.

[14] Gro¨ nroos, C. (1988), “Service quality: the six criteria of good perceived service quality”, Review of Business, Vol. 9 No. 3, pp. 10-13.

[15] Kaiser, H.F. (1960), “The application of electronic computers to factor analysis”, Educational and Psychological Measurement, Vol. 20, pp. 141-51.

[16] Kellogg, D.L. And Chase, R.B. (1995), “Constructing an empirically derived measure for customer contact”, Management Science, Vol. 41 No. 11, pp. 1734-49.

[17] Lapierre, J. (1996), “Service quality: the construct, its dimensionality, and its measurement”, in Swartz, T.A., Bowen, D.E. And Brown, S.W. (Eds), Advances in Services Marketing and Management, Vol. 5, JAI Press Inc., Greenwich, CT, pp. 45-70.

[18] Levitt, T. (1981), “Marketing intangible products and product intangibles”, Harvard Business Review, Vol. 59 No. 3, pp. 94-102.

[19] Llosa, S., Chandon, J. and Orsingher, C. (1998), “An empirical study of SERVQUAL’s dimensionality”, The Service Industries Journal, Vol. 18 No. 2, pp. 16-44.

[20] Lovelock, C.H. (1983), “Classifying services to gain strategic marketing insights”, Journal of Marketing, Vol. 47 No. 3, pp. 9-20.

[21] McDougal, G. and Levesque, T. (1992), “The measurement of service quality: some methodology issue”, Proceedings of the Second International Research Conference on the Management of Service Activities, pp. 411-430.


213

[22] Mills, P.K. and Marguiles, N. (1980), “Toward a core typology of service organizations”, Academy of Management Review, Vol. 5 No. 2, pp. 255-65.

[23] Oliver, R.L. (1997), A Behavioural Perspective on the Consumer, McGraw-Hill, New York, NY.

[24] Olsen, S.O. (2002), “Comparative evaluation and the relationship between quality, satisfaction, and repurchase loyalty”, Journal of the Academy of Marketing Science, Vol. 30 No. 3, pp. 240-9.

[25] Parasuraman, A., Zeithaml, V.A. And Berry, L.L. (1988), “SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality”, Jour nal of Retailing, Vol. 64 No. 1, pp. 12-40.

[26] Peter, J., Churchill, G. and Brown, T. (1993), “Caution in the use of difference scores in consumer research”, Journal of Consumer Research, Vol. 19 No. 4, pp. 655-62.

[27] Reichheld, F. and Sasser, W. (1990), “Zero defections: quality comes to services”, Harvard Business Review, Vol. 68 No. 5, pp. 105-11.

[28] Rust, R.T. And Oliver, R.L. (1994), “Service quality: insights and managerial implications from the Frontier”, in Rust, R.T. And Oliver, R.L. (Eds), Service Quality: New Directions in Theory and Practice, Sage Publications, Thousand Oaks, CA, pp. 72-94.

[29] Schmenner, R.W. (1986), “How can service businesses survive and prosper?” Sloan Management Review, Vol. 27 No. 3, pp. 21-32.

[30] Westbrook, R.A. And Oliver, R.L. (1991), “The dimensionality of consumption emotion patterns and consumer satisfaction”, Journal of Consumer Research, Vol. 18 No. 1, pp. 84-91.

[31] Zeithaml, V.A., Berry, L.L. and Parasuraman, A. (1996), “The behavioral consequences of service quality”, Journal of Marketing, Vol. 60 No. 2, pp. 31-46.

crm 5

Documents

south indian city

provide requisite information

include physical setting

harvard business review

cronbach alpha values

exploratory factor analysis

iterated factor analysis

confirmatory factor analysis