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Technical complexity and consumer knowledge as moderators of service quality evaluation in the automobile service industry
Technical Complexity and Consumer Knowledge as Moderators of Service Quality Evaluation in the Automobile Service Industry SYED SAAD ANDALEEB Penn State Erie
AMIYA K. BASU Syracuse
University
The relationship between a customer’s assessment of the service quality of an automobile service/repairfacility andfive factors--(l) perceivedfairness of the facility and its personnel, (2) empathy, (3) responsiveness, (4) reliability. and (5) convenience-was examined. Perceived fairness was found to be an important determinant of service quality evaluation and its importance depended on the complexity of the task involved and the customer’s knowledge ofautomobile repairs. In particular, when the task was complexand the customer did notfeel knowledgeable, perceivedfairness was found to be significantly more important than any of the otherfourfactors.
Academics and industry practitioners have begun to recognize that customer satisfaction with service encounters is critical for the success of an organization (Peters and Austin 1985). Clearly, it is important for service providers to understand how consumers judge service quality. The literature on this topic can be traced back to the early fifties (Fisk, Brown, and Bitner 1993). However, the pace of research in the area has accelerated only recently. These studies have examined various industries including retail banking, repair and maintenance services, long-distance telephone, credit cards, physicians, hotels, business school placement centers, hospitals, and tire stores (Parasuraman, Zeithaml, and Berry-henceforth PZB1988; Carman 1990; Reidenbach and Sandifer-Smallwood 1990; Boulding, Kalra, Staelin, and Zeithaml 1993). The present research addresses service quality in a unique environment, automobile service and repair (henceforth ASR), which is considered to be among the most unpleasant experiences faced by American consumers (Harvard Business School Case 5-690-062,
Syed Saad Andalccb, Penn State Erie, School of Business, Station Road, Erie, PA 16563-1400. Amiya K. Basu, Syracuse University, School of Management, Department of Marketing, Suite 400, Syracuse, NY 13244-2130. Journal of Retailing, Volume 70, Number 4, pp. 367-381, ISSN 0022-4359 Copyright 0 1994 by New York University. All rights of reproduction in any form reserved.
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For this class of services, the cost can be high. Moreover, the technical nature of the services can be complex, which the customer may not be able to monitor or evaluate with regard to the precise input of the service provider. ASRs can therefore be differentiated from, say, hotel services where the customer can clearly evaluate the service provided and its cost. Because of the potentially complex nature of ASR, customers are likely to feel vulnerable. As the complexity of the service increases, their sense of vulnerability is likely to increase because of the uncertainty associated with the outcome they receive relative to their inputs. This raises a concern about equity (Bagozzi 1975) and, thereby, perceived fairness (Oliver and. Swan 1989a) that may influence the evaluation of service quality. The sense of vulnerability may, however, be moderated by the extent of knowledge that a customer has about ASR. With higher levels of knowledge, customers are likely to feel less vulnerable and fairness may become less important, especially for complex services. Thus, we introduce perceived fairness as a separate construct and examine its impact on service quality evaluation in the context of the technical complexity of the service received and the extent of the customer’s knowledge of ASR. In addition to introducing these important variables to the service quality literature, our approach differs from most other studies on service quality. Rather than use an expectancy discon.rmation framework, which focuses on the gaps between expectations and perceptions (PZB 1988), we examine how customer satisfaction depends on their perceptions of the actual service received. There are two reasons for this departure. First, our data were collected subsequent to the service encounter, and questions about service expectations prior to the encounter would have relied on the customers’ memory. Second, while the expectancy disconfirmation approach is intuitively appealing, recent work in the area (Brown, Churchill, Peter 1993; Peter, Churchill, Brown 1993) has identified measurement problems in the use of difference scores and advised against using a gap approach to evaluate service quality. 1990).
Also, Cronin and Taylor (1992) suggest that service quality can be predicted adequately by using perceptions of the service dimensions alone. To summarize, we analyze a service context where the customer may find it difficult to monitor or evaluate the work performed by the service provider, leading to uncertainty about the service received. Consequently, the perceived fairness with which the customer has been treated may become important in the evaluation of service quality. The feeling of uncertainty is likely to be heightened by the technical complexity of the service situation which may vary widely, from a routine oil change to a major overhaul of the transmission system. However, if the customer feels Thus, we attempt to examine customers’ knowledge about quality. The research questions 1. 2.
knowledgeable about ASR, (s)he will feel less uncertainty. how the technical complexity of the service sought and automobile repairs moderate their evaluations of service addressed, therefore, are:
What factors are important in customers’ evaluation of service quality in the automotive service and repair industry? To what extent do the technical complexity of the service sought and customers’ knowledge of such services moderate customers’ evaluation of service quality?
Quality ~va~uafio~in the Automobile Service industry
369
CONCEPTUAL FRAMEWORK
The current growth in the literature on service quality was sparked by the comprehensive framework developed by Parasuraman, Zeithaml, and Berry (1985), followed by the introduction of SERVQUAL as a generic measure of service quality (PZB 1988). SERVQUAL, later refined by the original researchers (Parasuraman, Berry, and Zeithaml-hereafter PBZ-199 l), uses twenty-two matched pairs of items to measure the gaps between customer expectations and customer perceptions on five dimensions of a service experience-tangibles, reliability, responsiveness, assurance, and empathy. The sum of the gaps represents the deviation from the ideal and is used to assess service quality. The SERVQUAL measure has been used widely in the industry (Brown, Churchill, and Peter 1993). However, several researchers have suggested that in specific service situations, it may be necessary to delete or modify some of the SERVQUAL dimensions, or even introduce new dimensions (Carman 1990; Finn and Lamb 1991). Our preliminary research using focus-group interviews (discussed subsequently) with subjects who recently experienced ASRs led to the identi~c~ion of five factors that were relevant for ASR. The domains of the constructs were similar to the scales proposed by PZB (1988) and PBZ (1991). The major difference was that the “tangibles” dimension did not appear to be critical. Instead, the perceived fairness of the facility and its employees was found to be very important to individuals assessing the service quality of ASR. It is interesting to note that fairness was not entirely overlooked by PZB (I 988) and PBZ (1991). For example, the modified SERVQUAL ins~ment presented in PBZ (1991) has three pairs of items corresponding to fairness and trust (items P14 & E14, P15 & El5 and P21& E2 1). However, these items weredivided among the scales for assurance and empathy. The other four factors that were identified as important at the preliminary stage were empathy, responsiveness, reliability, and convenience. These five factors are the independent variables in our research. We also assume that a higher evaluation of service quality always leads to greater customer satisfaction which can be measured directly. Thus, to understand how the overall evaluation of service quality depends on the factors identified, hypotheses relating these factors to customer satisfaction are now proposed.
t-l ypotheses
As discussed earlier, perceived fairness was expected to have a major effect on service quality evaluation in the present context. We first discuss the precise meaning offairness as applied to ASR and present the corresponding hypotheses. The concept of fairness has been developed in exchange theory and is grounded in the concept of equity (Blau 1964). This concept has been applied to marketing contexts and found to be an important determinant of customer satisfaction with an exchange (Huppertz, Arenson, and Evans 1978; Oliver and Swan 1989a; 1989b). As discussed in Oliver and Swan (1989a), the notion of fairness implies distributive justice where each party in an exchange gets what (s)he deserves. According to Homans (1961), distributive justice (and hence fairness) implies that all parties receive benefits that are proportional to their investments.
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In the context of ASR, the relationship between the customer and the service provider is asymmetric as the latter can generally determine its own input and outcome. Thus, our position is that the customer will not be concerned with whether the service provider has received just compensation for its efforts. Instead, as we define fairness, (s)he will focus on whether (s)he has got what (s)he deserved, i.e., whether her/his own input (time spent, price paid) is commensurate with the outcome received (e.g., the perceived quality of the repair). From this perspective, the perception of fairness should diminish if the customer feels that (s)he has been charged too much, or that the service is not adequate. This position is consistent with the findings of Oliver and Swan (1989b) who observed a similar egocentric perception of fairness in their study of a customer’s relationship with the dealer during automobile purchase. We therefore posit that: Hl:
A higher level of perceived fairness will lead to greater satisfaction with the service performed.
customer
While perceived fairness should always have a positive influence on service quality evaluation, we argue that the relative influence of perceived fairness for a given customer depends on two factors which moderate that customer’s ability to monitor or evaluate the exact input of the service provider: (1) how knowledgeable the customer felt about ASR, i.e., her/his subjective knowledge of ASR (Bucks 1983, and (2) the technical complexity of the service (i.e., how complex the customer felt the service was). We first draw on the consumer behavior literature to clarify what we mean by knowledge in the present context. Alba and Hutchinson (1987) proposed that consumer knowledge has two dimensions: (1) familiarity, defined as the number of product related experiences accumulated by the consumer, and (2) expertise, defined as the ability of the consumer to perform product related tasks. For simple tasks, familiarity may imply expertise and it is therefore not necessary to distinguish between these factors in the development of hypotheses involving knowledge (e.g., Rao and Monroe 1988). However, ASR has two important differences from, say, the evaluation of women’s blazers used by Rao and Monroe (1988). First, ASR encounters tend to be varied and hence not habit forming. Second, the service may involve complex technology and is performed by experts. Thus, our position is that in the case of ASR, familiarity does not necessarily give the customer either the confidence or the ability to judge the input of the service provider. For example, a customer who is familiar with ASR through multiple encounters in the recent past may still not be able to determine whether the service provider has repaired the fuel injection system properly. Also, we feel that if a customer is just familiar with ASR but does not have expertise, (s)he will not consider (her)himself “knowledgeable” about ASR. Thus, in developing the hypotheses, we refer only to the expertise dimension of knowledge and assume that in their self-assessment of knowledge, the respondents also do the same (the latter conjecture is tested formally in the scale purification section). While knowledge of ASR is clearly a continuous variable, to facilitate interpretation we dichotomize knowledge and place customers into two groups: (1) not knowledgeable about ASR, and (2) knowledgeable about ASR. Consider first a customer who is not knowledgeable. We posit that such a customer can only assess a few superficial aspects of the service (e.g., the prices charged for parts and labor) to form her/his perception of fairness. For
Quality Evaluationin the Automobile Service Industry
371
example, if a customer who is having the fuel injection system repaired and does not feel knowledgeable about the intricacies of the technology thinks (s)he has been charged an unfairly high labor rate or finds that the service provider has overestimated the time spent on the job, (s)he is likely to feel that (s)he has been overcharged for every other aspect of the service. The extent to which the customer makes such inferences should increase with the technical complexity of the service as more elements of the service will not be understood by the customer. (The assessment of the technical complexiry of a particular service is discussed in the Measurement section.) Thus, with an increase in technical complexity, the customer will rely more heavily on perceived fairness to judge the overall quality of the service. Hence, we posit that: H2:
If a customer is not knowledgeable about automobile service and repairs, then the importance of perceived fairness will increase with the technical complexity of the service provided.
The role of perceived fairness is different for a customer who feels knowledgeable about ASR. Such a customer has confidence in her/his ability to evaluate all aspects of a service encounter directly (e.g., whether the service has been rendered properly, whether the price is fair, etc.) and does not feel a need to rely on the goodwill of the service provider. Thus, an increase in technical complexity should not magnify the importance of perceived fairness for the knowledgeable customer. Note, however, that the evaluation of all aspects of a service encounter is likely to be time consuming even for a sophisticated customer. If this customer evaluates a few aspects of the service and observes fairness, (s)he will not be inclined to monitor all aspects of the service. Thus, perceived fairness is desirable even to the knowledgeable customer. We posit that: H3:
If a customer is knowledgeable about automobile service and repairs, then the importance of perceived fairness is the same at all levels of technical complexity.
Regarding the other four factors identified in the previous section (empathy, responsiveness, reliability, and convenience), the SERVQUAL literature is based on the premise that a higher score on any of these traits will be reflected in a higher evaluation score (and hence greater customer satisfaction). To corroborate that position, the following hypotheses were also tested: H4:
A higher level of perceived empathy will lead to greater customer satisfaction.
H5:
A higher level ofperceived responsiveness will lead togreater customer satisfaction.
H6:
A higher level of perceived reliability will lead to greater customer satisfaction.
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Journal of Retailing Vol. 70, No. 4 1994 H7:
A higher level of perceived
convenience
will lead to greater customer
satisfaction.
RESEARCH
METHOD
Measurement
The first research task was to identify the factors which are likely to influence the evaluation of service quality of ASRs. Based on past research and our personal experience of ASRs, we expected that the SERVQUAL dimensions would be a useful starting point and that perceived fairness would have to be introduced as a separate factor. To obtain an independent confirmation of this position, a focus-group interview was conducted with twelve executive MBA students who had a recent ASR encounter. The participants were exposed to neither the service quality literature nor the SERVQUAL scale. The interview resulted in identifying a list of items which the participants felt were important to them in assessing service quality in the present context. This list included items related to empathy, reliability, responsiveness, and convenience. In addition, several items were related to the fairness of the service provider, i.e., whether the customers received outputs that were commensurate with their inputs. Thus our position that fairness should be introduced as a separate factor was supported. Using the five factors that were identified, we began to construct the measurement scales using the procedures recommended by Churchill (1979). The initial measures were tested on a convenience sample of twenty-five individuals who had required repairs or servicing on their automobiles. The fairness scale included several new measures in addition to the three items proposed by PBZ (1991). Each subject was asked to comment on the clarity of the scale items. The questions identified as problematic were modified and tested once more on another convenience sample of twenty five respondents before finalizing the version used for data collection. The final scale consisted of 27 items to measure the five factors identified and is presented in the appendix. A two-item scale was also developed to measure the customer’s satisfaction with the service directly. Another two-item scale was developed to measure thecustomer’s behavioral intention regarding the use of the facility in the future. These scales, also presented in the appendix, were used to test the hypotheses and conduct validity checks. To measure technical complexity, a convenience sample of 27 subjects, chosen from faculty, staff and graduate students, was used. The subjects were given a detailed list of services and asked to rate the technical complexity of each service on a seven point scale. For each service, the average for the sample was computed. For routine inspection, putting on new tires, oil change, and tire rotation, the averages were 3 or less, and these services were classified as “simple.” For brake repair/check, transmission repair/check, body work, heater repair/check, electrical system repair/check, and exhaust repair/check, the averages were 4 or more, and these services were classified as “complex.”
Quality Evaluation in the Automobile
Service Industry
373
To measure the customer’s subjective knowledge, we followed Brucks (1985) who used a two-item scale to measure an individual’s knowledge of sewing machines where the respondent (a female subject) was asked to rate (1) how knowledgeable she was compared to an average woman, and (2) how familiar she was with sewing machines. As argued earlier, we did not consider familiarity to be an appropriatemeasure of knowledge in present context. Thus, we used a single five-point item-labeled poor, fair, average, good, and excellent-to obtain the customers’ self-ratings of the knowledge of ASR. The main body of the questionnaire consisted of items to measure the five factors (perceived fairness, empathy, responsiveness, reliability, and convenience), the direct assessment of satisfaction with the service rendered, and behavioral intention regarding the use of the service facility in the future. The questions referred to the respondent’s most recent visit to an automobile service facility. Similar to PZB (1988). a seven point scale ranging from “Strongly Agree” to “Strongly Disagree” was used to measure the response to each item. There were no verbal labels for the scale points 2 through 6. Multiple items were used to measure each construct. A multiple-choice question with an “other (please specify)” option was used to record the type of service the customer received.
Data Collection
Data were collected through a mail survey conducted in two fairly large cities in Northeastern United States. The population selected was adults of age 18 years or more. The sampling frame was the telephone directory of each city and the sampling units were households to which surveys were mailed. Sampling elements were the actual people who owned cars and had experienced ASR. A total of 550 surveys were mailed and 133 surveys returned for a response rate of 24%.
Scale Purification
According to convention, we assessed the measurement properties of our scales by first examining their unidimensionality and reliability. Item-to-total correlations for each of the proposed scales were examined. The correlations ranged from .71 to .87 for fairness, .59 to .89 for empathy, from .64 to .87 for responsiveness, from .80 to .85 for reliability, and from .48 to -69 for convenience. Scale items measuring each construct were then factor analyzed separately. In each case, only one factor emerged with an eigenvalue exceeding 1, and the null hypothesis of unidimensionality could not be rejected at a = .lO using the Bartlett sphericity test. The measures were next tested for internal consistency. The two items measuring satisfaction had a Pearson correlation of .892 @ c .OOOl) and the two items measuring behavioral intentions had a Pearson correlation of .946 @ c .OOOl), indicating internal consistency. The Cronbach’s alpha values for the constructs of fairness, empathy, responsiveness, reliability and convenience are presented in Table 1. The alpha value exceeded .9 in four of the five
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TABLE1
Zero Order Correlations 17) Fairness (1) Empathy
Responsiveness
Notes:
t
(3)
(4)
Convenience
(31
(4)
0)
.933+ o&54*
(2)
Reliability
1.3
(5)
.945+
0.632*
0.621*
0.730*
0.753*
0.687*
.901+
0.404*
0.362*
0.435*
.927+ 0.418*
.761+
+ Figures in the diagonal represent Croobach’s alpha for standardized variables.
*-+p<.o1.
cases and always exceeded .75. As the alpha values were above the threshold of .7 suggested by Nunnally (1978), these results indicate strong internal consistency of the measures. To assess distinct validity, Gaski and Nevin (1985) and Gaski (1986) suggest that if the correlation between one scale and another is not as high as the coefficient alpha of each scale, then discriminant validity is present, The results of Table 1 show that for the five constructs, the alpha values are systematically higher. However, we also find that the scales are strongly correlated, indicating that the constructs have domains that overlap. Consequently, the 27 scale items were factor analyzed with varimax rotation using the correlation matrix as input. The factor analysis identity five signi~cant factors which explained 75.77% of the variance. Table 2 presents the rotated factor loadings and, for each factor, highlights the scale items strongly related to that factor. Note that each factor is related strongly to the items in exactly one scale. This suggests that the rotated factors are actually measuring empathy, fairness, responsiveness, reliability and convenience, respectively, after removing the overlap among the scales. We used the s~nd~~z~ rotated factors, which are uncorrelated with one another by cons~uction, as measures of empathy (EMPATHY), perceived fairness (FAIR), responsiveness (RESP), reliability (REL) and convenience (CONV). (The regression analysis discussed later was also performed using the original scales. The results are almost identical to what is reported here.) To test the validity of our position that, in rating themselves on their knowledge of ASR, customers focus on expertise rather than familiarity obtained through usage experience, the Pearson correlation between the self-rating of knowledge and the number of ASR encounters the respondent had in the past year (as a proxy of familiarity) waqcomputed. The computed correlation was -.02369 (p = .7867). As this is statistically insignificant, our position is supported. Finally, the two items me~u~ng customer satisfaction were combined to create a customer satisfaction score SAT, the two items measuring behavioral intention were combined to create a behavioral intention score BINT, and the Pearson correlation of SAT and BINT was computed. The computed correlation was ,946 Ip < .OOOl), which establishes that the measure of customer satisfaction behaves as expected. This lends support to the nomological validity of the measure of customer satisfaction.
Qualify
Evaiuarjon
in the Automobile
375
Service ln~u5~ry
TABLE2 Rotated Factor Pattern Factor1 ~~MFAT~Y~
Factor 2 {FAiRI
Factor 3 {REP)
Factor 4 IREL)
factor 5 ICoN1/)
Xl
0.18042
0.88008
0.08746
0.20234
0.10422
.867
x2
0.19743
0.67285
0.24421
0.23021
0.02786
.605
x3
0.21591
0.82854
0.24445
0.10647
0.13080
.a21
x4
0.28662
0.78457
0.24705
-0.00999
0.07191
,764
X5
0.15225
0.71487
0.43495
0.23163
0.14771
.799
X6
0.35800
0.59367
0.11549
0.43198
0.09717
.690
x7
0.47607
0.57401
0.14610
0.38313
0.24665
.785 .756
x8
0.80905
0.26597
0.11924
0.10358
0.07416
x9
0.76113
0.26865
0.06963
0.03595
-0.04267
.660
NO
0.72137
0.21213
0.23044
0.31409
0.00024
,717
x11
0.67713
-0.05529
0.17785
0.13053
0.12917
,527
x12
0.80645
0.26861
0.28593
0.21597
0.10168
,861
x13
0.76127
0.26911
0.31297
0.24176
0.13445
.826
x14
0.74875
0.29533
0.29976
0.24301
0.18129
.a30
x15
0.76615
0.26238
0.29959
0.25992
0.17665
.844
x16
0.29271
0.31212
0.78716
0.13448
0.16634
.a49
x17
0.22161
0.22748
0.75865
0.10720
0.25544
.753
xl8
0.19540
0.07180
0.67205
0.32448
0.08942
.608
x19
0.21695
0.21036
0.75903
0.19654
0.04826
.708
x20
0.31399
0.38115
0.72245
0.04374
0.04296
.770
X21
0.42316
0.26781
0.21775
0.71945
0.16011
.841
x22
0.37914
0.30830
0.40523
0.60161
0.07790
.771
x23
0.40122
0.36068
0.27737
0.62254
0.10169
.766
X24
0.44452
0.41752
0.35195
0.54686
0.04206
.797
x25
0.21071
0.27643
0.09857
-0.03988
0.81629
‘799
X26
0.06602
0.03993
0.10096
0.09409
0.89480
.826
x27
0.04828
0.05638
0.33825
0.41148
0.57506
,620
Variance
6.220
5.110
4.167
2.773
2.189
Total= 20.459
Explained
(23.03%)
(18.93%)
(15.43%)
(10.27%)
(8.11%)
(75.77%)
RESULTS
Multiple regression analysis was used to test the hypotheses regarding how customer satisfaction, as measured by SAT, is related to the five factors identified earlier, and how these relationships are moderated by technical complexity and knowledge (Ht -H7). The customer’s subjective knowledge was introduced as a dichotomous variable K, defined to be zero when the customer’s self-rating on the five point scale was less than the sample average (3.24), and one otherwise. Thus, customers with self ratings of 3 or less on the 5 point scale had K = 0 and customers with ratings of 4 or more had K = 1. The technical
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journal of Retailing Vol. 70, No. 4 1994
complexity of the service was also introduced as a dichotomous variable T (T= 0 if simple, T = 1 if complex). The following model, developed to allow the effect of FAIR on SAT to be moderated by technical complexity and subjective knowledge, was estimated first: SAT = PO+ PIFAIR + PzEMPATHY + j33RESP+ fi4REL + /3$ZONV + fiisFAIR*K+ P7FAIR*T + PgFAIR*K*T + E.
This model had an R* of .814 (adjusted R2 = ,802) and all independent variables were significant at p < .Ol. Since the omission of a significant predictor might bias the estimates of the coefficients of retained predictors, afull model was also estimated which included all the following regressors: FAIR, EMPATHY, RESP, REL, CONV, K, T,K*T,FAJR*K, EMPATHY*K, RESP*K, REL*K, CONV*K, FAIR*T, EMPATHY*T, RESP*T, REL*T, CONV*T, FAIR*K*T, EMPATHY*K*T, RESP*K*T, REL*K*T, andCONV*K*T. For the full model, R2 = .833 (adjusted R* = ,798). Using the F-test, the null hypothesis, that no variable in the full model other than those included in the restricted model identified earlier is significant, cannot be rejected at a = .05. Thus, we conclude that the restricted model is adequate to estimate the parameters of FAIR, EMPATHY, RESP, REL, CONV, FAlR*K, FAIR*T, and FAIR*K*T. The results are presented in Table 3. To facilitate interpretation, the estimated regression equation is presented separately for the four cases: (1) K = 0 and T =0;(2)K=OandT=1;(3)K=1andT=0;and(4)K=1andT=1.Thus,thelevelof knowledge is low in Cases 1 and 2, and high in Cases 3 and 4. From Table 3, it is clear that everything else being the same, customer satisfaction is a strictly increasing function of EMPATHY, RESP, REL and CONV. Thus, H4, HS, H6 and H7 are supported. Consider now the hypotheses regarding FAIR, i.e., Hl - H3. To test Hl, it is necessary to test whether the coefficient of FAIR is significantly positive for all four combinations of knowledge and technology under consideration: (1) K = 0,T = 0;(2) K= 0,T = 1; (3) K = 1, T = 0;and (4) K = 1, T = 1. In these four cases, the regression coefficients of FAIR are I, (pt + p7), (PI +p6), and (pt +p6 + p7 + ps , res 1.15 (bt + k) = 2.208 (fit + fib) = 2.22, and (B r+ fi6+ /!7+ /?etively’ s)= 1.505.Also,fromTable From Tab1e 3’ ” = 3, p ; .Ol for Bt. Using the estimated covariance matrix of the parameter estimates (these request), it can be shown that p < .Ol for (81 + b7), (fin+ b6) and as well.Thus,Hl issupported. According to H2, if K = 0,the coefficient of FAIR should be greater when T= 1 than when T= 0,i.e., (pt + p7) > pt, or, equivalently, p7 > 0. Thus, to test H2, it is necessary to test the null hypothesis p7 IO. From Table 3, the null hypothesis p7 S 0 can be rejected at a = .Ol, thereby supporting H2. Finally, according to H3, if K = 1, the coefficient of FAIR should be the same for T = 0 and T = 1, i.e., @t 436) = (PI +/%j + p7 + ps) or, equivalently, (p7 + fis)= 0. From Table 3, (cj7 + 11s) = 1.058 - 1.773 = -.7 15. Using the estimated covariance matrix of parameter estimates (not presented), it can be shown that the standard deviation of (87 + fis) is .45 14, resulting in a t-statistic of -1.584 which has ap value greater than .lO. While H3 cannot be rejected at a 90% level of confidence based on the data available, the results raise the intriguing possibility that (p7 + ps) is actually negative, i.e., to a knowledgeable CUStOmer, fairness is more important when technical complexity is low. A possible reason for this may
Quality Evaluation
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377
Service Industry
TABLE 3
Regression Estimation with Restricted Model Model : SAT = PO + PlFAlR + PzEMPATHY
+ PjRESP + P4REL + j35CONV
+ PbFAIR*K+
P7FAIR*T+
bFAlR*K*T+
E
ParameterEstimates Variable
Parameter
INTERCEPT
Estimate
Standard
t-ratio
p value
106.43
0.0001
Error
11.692
0.110
FAIR
1.150
0.160
7.21
0.0001
EMPATHY
1.245
0.110
11.36
0.0001
RESP
0.648
0.119
5.46
0.0001
REL
1 ,139
0.110
10.35
0.0001
CONV
0.590
0.115
5.14
0.0001
FAIR * K
1.07
0.279
3.84
0.0002
FAIR * T
1.058
0.355
2.98
0.0035
FAIR*K* Notes:
T
-1.773
2 = ,814, adjusted !# = ,802, F = 67.94,
-3.01
0.590
0.0032
p < .OOOl.
The estimated regression equation in special cases (the fitted SAT is denoted by SAT): Case 1: low knowledge, low complexity (K = 0, T = 0, 54 observations): SAT = 11.692
+ 1 .15 f FAIR + 1.245 * EMPATHY
+ ,648 * RESP + 1 .139 * REL + .59 * CONV.
Case 2: low knowledge, high complexity (K = 0, T = 1, 23 observations): SAT = 11.692
+ 2.208
Case 3: high knowledge, SAT=
11.692 11.692
low complexity
(K=
high complexity
+ .648 * RESP + 1 .139 * REL + .59 * CONV.
1, T= 0,40
+ 2.22 * FAIR + 1.245 * EMPATHY
Case 4: high knowledge, SAT=
* FAIR + 1.245 * EMPATHY
observations):
+ ,648 * RESP + 1 .139 * REL + .59 * CONV.
(K = 1, T = 1, 16 observations):
+ 1.505 * FAIR + 1.245 * EMPATHY
be that when the core service is not complex,
+ ,648 * RESP + 1 .139 * REL + .59 * CONV.
the knowledgeable
customer
peripheral aspects of the service encounter and tends to detect unfairness. should be explored further in future research.
focuses on
Clearly, this issue
CONCLUSION
We examined how five factors-perceived fairness, empathy, responsiveness, reliability and convenience-affected acustomer’s evaluation of ASRs. The results supported our hypotheses that all these factors generally have a positive effect on service quality evaluation and that the impact of perceived fairness is moderated by the customer’s self assessment of the knowledge of ASR and the technical complexity of the service concerned. In particular, as hypothesized, we found that when the customer is not knowledgeable, the importance of fairness increases significantly with technical complexity. In fact, if the service was technically complex and the customer was not knowledgeable, fairness emerged as, by far, the most important predictor of service quality evaluation.
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Journal of Retailing Vol. 70, No. 4 1994
A comparison of the parameter estimates also indicated that responsiveness and convenience, while always significant, were considerably less important than fairness, empathy, and reliability. A possible explanation of this finding may be that the customer views responsiveness and convenience as peripheral to the core service provided. Future research should examine if customers do indeed decompose the overall service into its core and peripheral aspects. It would also be interesting to determine whether the relative importance of such peripheral aspects of service becomes greater during the actual choice of a service facility rather than during the evaluation of the service after it is performed. While the findings of this study provide additional insights about service quality evaluation, a relatively small sample size of 133 was used due to resource constraints. Consequently, we simplified our analysis by forming four categories through the dichotomization of knowledge and technical complexity. With a larger data set, it should be possible to determine, for example, whether there is a knowledge threshold beyond which the importance of perceived fairness diminishes with additional increases in knowledge. In future research, the questionnaire should also include direct measures of customers’ perceptions of their ability to evaluate the input of the service provider. A causal model can then be used to test the position that knowledge and technical complexity affect the importance of perceived fairness through the customer’s self perception of how well (s)he can evaluate the input of the service provider. Finally, we note that the arguments used to develop the hypotheses regarding the moderating effects of customer knowledge and technical complexity should apply to any service situation where the service provided is technically complex and some customers cannot therefore evaluate the exact input of the service provider. Thus, the validity of our arguments can be tested in future research by examining whether the results regarding perceived fairness continue to hold in other similar contexts such as the purchase of homes, insurance, and certain medical and legal services.
APPENDIX
A.
ITEMS MEASURING
CUSTOMER
SATISFACTION
For both items, the response was on a seven point scale from very unsatisfied to very satisfied: Item 1: Item 2:
B.
Overall, how satisfied were you with this facility? How would you rate the overall service quality you received from this facility?
ITEMS MEASURING
BEHAVIORAL
INTENTION
REGARDING
For both items, the response was on a seven point scale from very unwilling
FUTURE
USE
to very willing.
Quality Evaluation in the Automobile Service Industry
Item 1: Item 2:
How willing would you be to recommend this facility to others? How willing would you be to return to this facility in the future?
C. ITEMS MEASURING FAIRNESS, EMPATHY, RESPONSIVENESS, RELIABILITY AND CONVENIENCE
The response was on a seven point scale from strongly disagree to strongly agree.
1.
Fairness:
x1 + The facility I went to charged a fair price. x2 -_) I felt I was taken advantage of by this facility.* ~3 + The price I paid for labor was fair. ~4 + The price I paid for parts was fair. x5 + I left knowing I was fairly treated. X6 + The service personnel were honest. x7 + The facility had my best interest in mind.
2.
Empathy:
xs + The service personnel x9 + x10 + xl i + x12 + xl3 +
3.
The service The facility The service The service The service
listened to my problem.
personnel did not pay enough attention to me.* understood what I wanted. personnel explained the work to be performed. personnel were respectful. personnel were polite.
x14 + The service personnel
were helpful.
xis + The service personnel
were friendly.
Responsiveness:
X16 + The service was completed in a timely manner. ~17 + The facility had my appointment scheduled promptly. xis + The facility scheduled my appointment near the date I desired. x19 -_) Upon arrival, I was quickly waited on. x20 + My car was ready when promised.
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Journal of Retailing Vol. 70, No. 4 1994 4.
Reliability:
x21 +
The facility did the work that was promised. x22 -+ The service personnel were well trained. x23 + I felt the service was done correctly on the first visit. X24 + The service personnel were competent.
5.
Convenience:
x25 + It was convenient to have my car serviced by this facility. The facility is in an easily accessible location. X27 + The facility had convenient hours.
x26 +
* + The item was reversed. Acknowledgment: The authors wish to thank the editor Charles A. Ingene, three anonyof Retailing reviewers, and Tridib Mazumdar for valuable comments and
mous Journal suggestions.
REFERENCES
Alba, Joseph W. and J. Wesley Hutchinson (1987). “Dimensions of Consumer Expertise,” Journul of Consumer Research, 13(March): 41 l-454. Bagozzi, Richard P. (1975). “Marketing as Exchange,” Journal ofMarketing, 39(0ctober): 32-39. Blau, Peter M. (1964). Exchange and Power in Social Life. New York: Wiley. Boulding, William, Ajay Kalra, Richard Staelin, and Valerie A. Zeithaml(l993). “A Dynamic Process Model of Service Quality: From Expectations to Behavioral Intentions,” Journal of Marketing Research, 30(February): 7-27. Brown, Tom J., Gilbert A. Churchill, Jr., and J. Paul Peter (1993). “Improving the Measurement of Service Quality,” Journal ofRebling, 69(Spring): 127-139. Brucks, Merrie (1985). “The Effects of Product Class Knowledge on Information Search Behavior,” Journal of Consumer Research, lZ(June): l-l 6. Carman, James M. (1990). “Consumer Perceptions of Service Quality: An Assessment of the SERVQUAL Dimensions,” Journal of Retailing, 66(Spring): 33-55. Churchill, Gilbert A., Jr. (1979). “A Paradigm for Developing Better Measures of Marketing Constructs,” Journal of Marketing Research, 1qFebruary): 64-73. Cronin, J. Joseph, Jr. and Steven A. Taylor (1992). “Measuring Service Quality: A Reexamination and Extension,” Journal of Marketing, 56(July): 55-68. Finn, David W. and Charles W. Lamb, Jr. (1991). “An Evaluation of the SERVQUAL Scales in a Retailing Setting.” 483-490 in Advances in Consumer Research, Vol. 18, Rebecca H. Holman and Michael R. Solomon (eds.). Provo, UT: Association for Consumer Research. Fisk, Raymond P., Stephen W. Brown, and Mary Jo Bitner (1993). “Tracking the Evolution of the Services Marketing Literature,” Journal of Retailing, 69(Spring): 61-103.
Quality Evaluation
in the Automobile Service Industry
381
Gaski, John F. (1986). “Interrelations among a Channel Entity’s Power Sources: Impact of the Exercise of Reward andcoercion onExpert, Referant, and Legitimate Power Sources,” Journal ofMarketing Research, 23(February): 62-71. Gaski, John F. and John R. Nevin (1985). “The Differential Effects of Exercised and Unexercised Power Sources in a Marketing Channel,” Journal of Marketing Research, 22(May): 130-142. Harvard Business School (1990). Ford Motor Company: Dealer Sales and Service, Teaching Note, No. 5-690-062. Homans, G. (1961). Social Behavior: Its Elementary Forms. New York: Harcourt. Huppertz, John W., Sidney J. Arenson, and Richard H. Evans (1978). “An Application of Equity Theory to Buyer-Seller Exchange Situations,” Journal of Marketing Research, lS(May): 250-260. Nunnally, Jum C. (1978). Psychometric Theory, 2nd ed. New York: McGraw Hill. Oliver, Richard L. and John E. Swan (1989a). “Consumer Perceptions of Interpersonal Equity and Satisfaction in Transactions: A Field Survey Approach,” Journal of Marketing, 53(April): 21-35. (1989b). “Equity and Disconfirmation Perceptions as Influences on Merchant and Product Satisfaction,” Journal of Consumer Research, 16(December): 372-383. Parasuraman, A., Valerie Zeithaml, and Leonard L. Berry (1985). “A Conceptual Model of Service Quality and Its Implications for Future Research,” Journal of Marketing, 49(Fall): 41-50. (1988). “SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality,” Journal of Retailing, 64(Spring): 12-10. Parasuraman, A., Leonard L. Berry, and Valerie Zeithaml(l991). “Refinement and Reassessment of the SERVQUAL Scale,” Journal ofRetailing, 67(Winter): 420-450. Peter, J. Paul, Gilbert A. Churchill, Jr., and Tom J. Brown (1993). “Caution in the Use of Difference Scores in Consumer Research,” Journal of Consumer Research, 19(March): 655-662. Peters, Thomas J. and Nancy Austin (1985). A Passion for Excellence: The Leadership Difference. New York: Random House. Rao, Akshay R. and Kent B. Monroe (1988). “The Moderating Effect of Prior Knowledge on Cue Utilization in Product Evaluations,” Journal of Consumer Research, lS(September): 253-264. Reidenbach, R. Eric and Beverly Sandifer-Smallwood (1990). “Exploring Perceptions of Hospital Operation by a Modified SERVQUAL Approach,” Journal of Health Care Marketing, lO(Decembet): 47-55.
Received May 1994; Revised December 1994
AMIYA K. BASU Syracuse
University
The relationship between a customer’s assessment of the service quality of an automobile service/repairfacility andfive factors--(l) perceivedfairness of the facility and its personnel, (2) empathy, (3) responsiveness, (4) reliability. and (5) convenience-was examined. Perceived fairness was found to be an important determinant of service quality evaluation and its importance depended on the complexity of the task involved and the customer’s knowledge ofautomobile repairs. In particular, when the task was complexand the customer did notfeel knowledgeable, perceivedfairness was found to be significantly more important than any of the otherfourfactors.
Academics and industry practitioners have begun to recognize that customer satisfaction with service encounters is critical for the success of an organization (Peters and Austin 1985). Clearly, it is important for service providers to understand how consumers judge service quality. The literature on this topic can be traced back to the early fifties (Fisk, Brown, and Bitner 1993). However, the pace of research in the area has accelerated only recently. These studies have examined various industries including retail banking, repair and maintenance services, long-distance telephone, credit cards, physicians, hotels, business school placement centers, hospitals, and tire stores (Parasuraman, Zeithaml, and Berry-henceforth PZB1988; Carman 1990; Reidenbach and Sandifer-Smallwood 1990; Boulding, Kalra, Staelin, and Zeithaml 1993). The present research addresses service quality in a unique environment, automobile service and repair (henceforth ASR), which is considered to be among the most unpleasant experiences faced by American consumers (Harvard Business School Case 5-690-062,
Syed Saad Andalccb, Penn State Erie, School of Business, Station Road, Erie, PA 16563-1400. Amiya K. Basu, Syracuse University, School of Management, Department of Marketing, Suite 400, Syracuse, NY 13244-2130. Journal of Retailing, Volume 70, Number 4, pp. 367-381, ISSN 0022-4359 Copyright 0 1994 by New York University. All rights of reproduction in any form reserved.
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1994
For this class of services, the cost can be high. Moreover, the technical nature of the services can be complex, which the customer may not be able to monitor or evaluate with regard to the precise input of the service provider. ASRs can therefore be differentiated from, say, hotel services where the customer can clearly evaluate the service provided and its cost. Because of the potentially complex nature of ASR, customers are likely to feel vulnerable. As the complexity of the service increases, their sense of vulnerability is likely to increase because of the uncertainty associated with the outcome they receive relative to their inputs. This raises a concern about equity (Bagozzi 1975) and, thereby, perceived fairness (Oliver and. Swan 1989a) that may influence the evaluation of service quality. The sense of vulnerability may, however, be moderated by the extent of knowledge that a customer has about ASR. With higher levels of knowledge, customers are likely to feel less vulnerable and fairness may become less important, especially for complex services. Thus, we introduce perceived fairness as a separate construct and examine its impact on service quality evaluation in the context of the technical complexity of the service received and the extent of the customer’s knowledge of ASR. In addition to introducing these important variables to the service quality literature, our approach differs from most other studies on service quality. Rather than use an expectancy discon.rmation framework, which focuses on the gaps between expectations and perceptions (PZB 1988), we examine how customer satisfaction depends on their perceptions of the actual service received. There are two reasons for this departure. First, our data were collected subsequent to the service encounter, and questions about service expectations prior to the encounter would have relied on the customers’ memory. Second, while the expectancy disconfirmation approach is intuitively appealing, recent work in the area (Brown, Churchill, Peter 1993; Peter, Churchill, Brown 1993) has identified measurement problems in the use of difference scores and advised against using a gap approach to evaluate service quality. 1990).
Also, Cronin and Taylor (1992) suggest that service quality can be predicted adequately by using perceptions of the service dimensions alone. To summarize, we analyze a service context where the customer may find it difficult to monitor or evaluate the work performed by the service provider, leading to uncertainty about the service received. Consequently, the perceived fairness with which the customer has been treated may become important in the evaluation of service quality. The feeling of uncertainty is likely to be heightened by the technical complexity of the service situation which may vary widely, from a routine oil change to a major overhaul of the transmission system. However, if the customer feels Thus, we attempt to examine customers’ knowledge about quality. The research questions 1. 2.
knowledgeable about ASR, (s)he will feel less uncertainty. how the technical complexity of the service sought and automobile repairs moderate their evaluations of service addressed, therefore, are:
What factors are important in customers’ evaluation of service quality in the automotive service and repair industry? To what extent do the technical complexity of the service sought and customers’ knowledge of such services moderate customers’ evaluation of service quality?
Quality ~va~uafio~in the Automobile Service industry
369
CONCEPTUAL FRAMEWORK
The current growth in the literature on service quality was sparked by the comprehensive framework developed by Parasuraman, Zeithaml, and Berry (1985), followed by the introduction of SERVQUAL as a generic measure of service quality (PZB 1988). SERVQUAL, later refined by the original researchers (Parasuraman, Berry, and Zeithaml-hereafter PBZ-199 l), uses twenty-two matched pairs of items to measure the gaps between customer expectations and customer perceptions on five dimensions of a service experience-tangibles, reliability, responsiveness, assurance, and empathy. The sum of the gaps represents the deviation from the ideal and is used to assess service quality. The SERVQUAL measure has been used widely in the industry (Brown, Churchill, and Peter 1993). However, several researchers have suggested that in specific service situations, it may be necessary to delete or modify some of the SERVQUAL dimensions, or even introduce new dimensions (Carman 1990; Finn and Lamb 1991). Our preliminary research using focus-group interviews (discussed subsequently) with subjects who recently experienced ASRs led to the identi~c~ion of five factors that were relevant for ASR. The domains of the constructs were similar to the scales proposed by PZB (1988) and PBZ (1991). The major difference was that the “tangibles” dimension did not appear to be critical. Instead, the perceived fairness of the facility and its employees was found to be very important to individuals assessing the service quality of ASR. It is interesting to note that fairness was not entirely overlooked by PZB (I 988) and PBZ (1991). For example, the modified SERVQUAL ins~ment presented in PBZ (1991) has three pairs of items corresponding to fairness and trust (items P14 & E14, P15 & El5 and P21& E2 1). However, these items weredivided among the scales for assurance and empathy. The other four factors that were identified as important at the preliminary stage were empathy, responsiveness, reliability, and convenience. These five factors are the independent variables in our research. We also assume that a higher evaluation of service quality always leads to greater customer satisfaction which can be measured directly. Thus, to understand how the overall evaluation of service quality depends on the factors identified, hypotheses relating these factors to customer satisfaction are now proposed.
t-l ypotheses
As discussed earlier, perceived fairness was expected to have a major effect on service quality evaluation in the present context. We first discuss the precise meaning offairness as applied to ASR and present the corresponding hypotheses. The concept of fairness has been developed in exchange theory and is grounded in the concept of equity (Blau 1964). This concept has been applied to marketing contexts and found to be an important determinant of customer satisfaction with an exchange (Huppertz, Arenson, and Evans 1978; Oliver and Swan 1989a; 1989b). As discussed in Oliver and Swan (1989a), the notion of fairness implies distributive justice where each party in an exchange gets what (s)he deserves. According to Homans (1961), distributive justice (and hence fairness) implies that all parties receive benefits that are proportional to their investments.
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Journal of Retailing Vol. 70, No. 4 1994
In the context of ASR, the relationship between the customer and the service provider is asymmetric as the latter can generally determine its own input and outcome. Thus, our position is that the customer will not be concerned with whether the service provider has received just compensation for its efforts. Instead, as we define fairness, (s)he will focus on whether (s)he has got what (s)he deserved, i.e., whether her/his own input (time spent, price paid) is commensurate with the outcome received (e.g., the perceived quality of the repair). From this perspective, the perception of fairness should diminish if the customer feels that (s)he has been charged too much, or that the service is not adequate. This position is consistent with the findings of Oliver and Swan (1989b) who observed a similar egocentric perception of fairness in their study of a customer’s relationship with the dealer during automobile purchase. We therefore posit that: Hl:
A higher level of perceived fairness will lead to greater satisfaction with the service performed.
customer
While perceived fairness should always have a positive influence on service quality evaluation, we argue that the relative influence of perceived fairness for a given customer depends on two factors which moderate that customer’s ability to monitor or evaluate the exact input of the service provider: (1) how knowledgeable the customer felt about ASR, i.e., her/his subjective knowledge of ASR (Bucks 1983, and (2) the technical complexity of the service (i.e., how complex the customer felt the service was). We first draw on the consumer behavior literature to clarify what we mean by knowledge in the present context. Alba and Hutchinson (1987) proposed that consumer knowledge has two dimensions: (1) familiarity, defined as the number of product related experiences accumulated by the consumer, and (2) expertise, defined as the ability of the consumer to perform product related tasks. For simple tasks, familiarity may imply expertise and it is therefore not necessary to distinguish between these factors in the development of hypotheses involving knowledge (e.g., Rao and Monroe 1988). However, ASR has two important differences from, say, the evaluation of women’s blazers used by Rao and Monroe (1988). First, ASR encounters tend to be varied and hence not habit forming. Second, the service may involve complex technology and is performed by experts. Thus, our position is that in the case of ASR, familiarity does not necessarily give the customer either the confidence or the ability to judge the input of the service provider. For example, a customer who is familiar with ASR through multiple encounters in the recent past may still not be able to determine whether the service provider has repaired the fuel injection system properly. Also, we feel that if a customer is just familiar with ASR but does not have expertise, (s)he will not consider (her)himself “knowledgeable” about ASR. Thus, in developing the hypotheses, we refer only to the expertise dimension of knowledge and assume that in their self-assessment of knowledge, the respondents also do the same (the latter conjecture is tested formally in the scale purification section). While knowledge of ASR is clearly a continuous variable, to facilitate interpretation we dichotomize knowledge and place customers into two groups: (1) not knowledgeable about ASR, and (2) knowledgeable about ASR. Consider first a customer who is not knowledgeable. We posit that such a customer can only assess a few superficial aspects of the service (e.g., the prices charged for parts and labor) to form her/his perception of fairness. For
Quality Evaluationin the Automobile Service Industry
371
example, if a customer who is having the fuel injection system repaired and does not feel knowledgeable about the intricacies of the technology thinks (s)he has been charged an unfairly high labor rate or finds that the service provider has overestimated the time spent on the job, (s)he is likely to feel that (s)he has been overcharged for every other aspect of the service. The extent to which the customer makes such inferences should increase with the technical complexity of the service as more elements of the service will not be understood by the customer. (The assessment of the technical complexiry of a particular service is discussed in the Measurement section.) Thus, with an increase in technical complexity, the customer will rely more heavily on perceived fairness to judge the overall quality of the service. Hence, we posit that: H2:
If a customer is not knowledgeable about automobile service and repairs, then the importance of perceived fairness will increase with the technical complexity of the service provided.
The role of perceived fairness is different for a customer who feels knowledgeable about ASR. Such a customer has confidence in her/his ability to evaluate all aspects of a service encounter directly (e.g., whether the service has been rendered properly, whether the price is fair, etc.) and does not feel a need to rely on the goodwill of the service provider. Thus, an increase in technical complexity should not magnify the importance of perceived fairness for the knowledgeable customer. Note, however, that the evaluation of all aspects of a service encounter is likely to be time consuming even for a sophisticated customer. If this customer evaluates a few aspects of the service and observes fairness, (s)he will not be inclined to monitor all aspects of the service. Thus, perceived fairness is desirable even to the knowledgeable customer. We posit that: H3:
If a customer is knowledgeable about automobile service and repairs, then the importance of perceived fairness is the same at all levels of technical complexity.
Regarding the other four factors identified in the previous section (empathy, responsiveness, reliability, and convenience), the SERVQUAL literature is based on the premise that a higher score on any of these traits will be reflected in a higher evaluation score (and hence greater customer satisfaction). To corroborate that position, the following hypotheses were also tested: H4:
A higher level of perceived empathy will lead to greater customer satisfaction.
H5:
A higher level ofperceived responsiveness will lead togreater customer satisfaction.
H6:
A higher level of perceived reliability will lead to greater customer satisfaction.
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Journal of Retailing Vol. 70, No. 4 1994 H7:
A higher level of perceived
convenience
will lead to greater customer
satisfaction.
RESEARCH
METHOD
Measurement
The first research task was to identify the factors which are likely to influence the evaluation of service quality of ASRs. Based on past research and our personal experience of ASRs, we expected that the SERVQUAL dimensions would be a useful starting point and that perceived fairness would have to be introduced as a separate factor. To obtain an independent confirmation of this position, a focus-group interview was conducted with twelve executive MBA students who had a recent ASR encounter. The participants were exposed to neither the service quality literature nor the SERVQUAL scale. The interview resulted in identifying a list of items which the participants felt were important to them in assessing service quality in the present context. This list included items related to empathy, reliability, responsiveness, and convenience. In addition, several items were related to the fairness of the service provider, i.e., whether the customers received outputs that were commensurate with their inputs. Thus our position that fairness should be introduced as a separate factor was supported. Using the five factors that were identified, we began to construct the measurement scales using the procedures recommended by Churchill (1979). The initial measures were tested on a convenience sample of twenty-five individuals who had required repairs or servicing on their automobiles. The fairness scale included several new measures in addition to the three items proposed by PBZ (1991). Each subject was asked to comment on the clarity of the scale items. The questions identified as problematic were modified and tested once more on another convenience sample of twenty five respondents before finalizing the version used for data collection. The final scale consisted of 27 items to measure the five factors identified and is presented in the appendix. A two-item scale was also developed to measure the customer’s satisfaction with the service directly. Another two-item scale was developed to measure thecustomer’s behavioral intention regarding the use of the facility in the future. These scales, also presented in the appendix, were used to test the hypotheses and conduct validity checks. To measure technical complexity, a convenience sample of 27 subjects, chosen from faculty, staff and graduate students, was used. The subjects were given a detailed list of services and asked to rate the technical complexity of each service on a seven point scale. For each service, the average for the sample was computed. For routine inspection, putting on new tires, oil change, and tire rotation, the averages were 3 or less, and these services were classified as “simple.” For brake repair/check, transmission repair/check, body work, heater repair/check, electrical system repair/check, and exhaust repair/check, the averages were 4 or more, and these services were classified as “complex.”
Quality Evaluation in the Automobile
Service Industry
373
To measure the customer’s subjective knowledge, we followed Brucks (1985) who used a two-item scale to measure an individual’s knowledge of sewing machines where the respondent (a female subject) was asked to rate (1) how knowledgeable she was compared to an average woman, and (2) how familiar she was with sewing machines. As argued earlier, we did not consider familiarity to be an appropriatemeasure of knowledge in present context. Thus, we used a single five-point item-labeled poor, fair, average, good, and excellent-to obtain the customers’ self-ratings of the knowledge of ASR. The main body of the questionnaire consisted of items to measure the five factors (perceived fairness, empathy, responsiveness, reliability, and convenience), the direct assessment of satisfaction with the service rendered, and behavioral intention regarding the use of the service facility in the future. The questions referred to the respondent’s most recent visit to an automobile service facility. Similar to PZB (1988). a seven point scale ranging from “Strongly Agree” to “Strongly Disagree” was used to measure the response to each item. There were no verbal labels for the scale points 2 through 6. Multiple items were used to measure each construct. A multiple-choice question with an “other (please specify)” option was used to record the type of service the customer received.
Data Collection
Data were collected through a mail survey conducted in two fairly large cities in Northeastern United States. The population selected was adults of age 18 years or more. The sampling frame was the telephone directory of each city and the sampling units were households to which surveys were mailed. Sampling elements were the actual people who owned cars and had experienced ASR. A total of 550 surveys were mailed and 133 surveys returned for a response rate of 24%.
Scale Purification
According to convention, we assessed the measurement properties of our scales by first examining their unidimensionality and reliability. Item-to-total correlations for each of the proposed scales were examined. The correlations ranged from .71 to .87 for fairness, .59 to .89 for empathy, from .64 to .87 for responsiveness, from .80 to .85 for reliability, and from .48 to -69 for convenience. Scale items measuring each construct were then factor analyzed separately. In each case, only one factor emerged with an eigenvalue exceeding 1, and the null hypothesis of unidimensionality could not be rejected at a = .lO using the Bartlett sphericity test. The measures were next tested for internal consistency. The two items measuring satisfaction had a Pearson correlation of .892 @ c .OOOl) and the two items measuring behavioral intentions had a Pearson correlation of .946 @ c .OOOl), indicating internal consistency. The Cronbach’s alpha values for the constructs of fairness, empathy, responsiveness, reliability and convenience are presented in Table 1. The alpha value exceeded .9 in four of the five
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Journal of Retailing Vol. 70, No. 4 1994
TABLE1
Zero Order Correlations 17) Fairness (1) Empathy
Responsiveness
Notes:
t
(3)
(4)
Convenience
(31
(4)
0)
.933+ o&54*
(2)
Reliability
1.3
(5)
.945+
0.632*
0.621*
0.730*
0.753*
0.687*
.901+
0.404*
0.362*
0.435*
.927+ 0.418*
.761+
+ Figures in the diagonal represent Croobach’s alpha for standardized variables.
*-+p<.o1.
cases and always exceeded .75. As the alpha values were above the threshold of .7 suggested by Nunnally (1978), these results indicate strong internal consistency of the measures. To assess distinct validity, Gaski and Nevin (1985) and Gaski (1986) suggest that if the correlation between one scale and another is not as high as the coefficient alpha of each scale, then discriminant validity is present, The results of Table 1 show that for the five constructs, the alpha values are systematically higher. However, we also find that the scales are strongly correlated, indicating that the constructs have domains that overlap. Consequently, the 27 scale items were factor analyzed with varimax rotation using the correlation matrix as input. The factor analysis identity five signi~cant factors which explained 75.77% of the variance. Table 2 presents the rotated factor loadings and, for each factor, highlights the scale items strongly related to that factor. Note that each factor is related strongly to the items in exactly one scale. This suggests that the rotated factors are actually measuring empathy, fairness, responsiveness, reliability and convenience, respectively, after removing the overlap among the scales. We used the s~nd~~z~ rotated factors, which are uncorrelated with one another by cons~uction, as measures of empathy (EMPATHY), perceived fairness (FAIR), responsiveness (RESP), reliability (REL) and convenience (CONV). (The regression analysis discussed later was also performed using the original scales. The results are almost identical to what is reported here.) To test the validity of our position that, in rating themselves on their knowledge of ASR, customers focus on expertise rather than familiarity obtained through usage experience, the Pearson correlation between the self-rating of knowledge and the number of ASR encounters the respondent had in the past year (as a proxy of familiarity) waqcomputed. The computed correlation was -.02369 (p = .7867). As this is statistically insignificant, our position is supported. Finally, the two items me~u~ng customer satisfaction were combined to create a customer satisfaction score SAT, the two items measuring behavioral intention were combined to create a behavioral intention score BINT, and the Pearson correlation of SAT and BINT was computed. The computed correlation was ,946 Ip < .OOOl), which establishes that the measure of customer satisfaction behaves as expected. This lends support to the nomological validity of the measure of customer satisfaction.
Qualify
Evaiuarjon
in the Automobile
375
Service ln~u5~ry
TABLE2 Rotated Factor Pattern Factor1 ~~MFAT~Y~
Factor 2 {FAiRI
Factor 3 {REP)
Factor 4 IREL)
factor 5 ICoN1/)
Xl
0.18042
0.88008
0.08746
0.20234
0.10422
.867
x2
0.19743
0.67285
0.24421
0.23021
0.02786
.605
x3
0.21591
0.82854
0.24445
0.10647
0.13080
.a21
x4
0.28662
0.78457
0.24705
-0.00999
0.07191
,764
X5
0.15225
0.71487
0.43495
0.23163
0.14771
.799
X6
0.35800
0.59367
0.11549
0.43198
0.09717
.690
x7
0.47607
0.57401
0.14610
0.38313
0.24665
.785 .756
x8
0.80905
0.26597
0.11924
0.10358
0.07416
x9
0.76113
0.26865
0.06963
0.03595
-0.04267
.660
NO
0.72137
0.21213
0.23044
0.31409
0.00024
,717
x11
0.67713
-0.05529
0.17785
0.13053
0.12917
,527
x12
0.80645
0.26861
0.28593
0.21597
0.10168
,861
x13
0.76127
0.26911
0.31297
0.24176
0.13445
.826
x14
0.74875
0.29533
0.29976
0.24301
0.18129
.a30
x15
0.76615
0.26238
0.29959
0.25992
0.17665
.844
x16
0.29271
0.31212
0.78716
0.13448
0.16634
.a49
x17
0.22161
0.22748
0.75865
0.10720
0.25544
.753
xl8
0.19540
0.07180
0.67205
0.32448
0.08942
.608
x19
0.21695
0.21036
0.75903
0.19654
0.04826
.708
x20
0.31399
0.38115
0.72245
0.04374
0.04296
.770
X21
0.42316
0.26781
0.21775
0.71945
0.16011
.841
x22
0.37914
0.30830
0.40523
0.60161
0.07790
.771
x23
0.40122
0.36068
0.27737
0.62254
0.10169
.766
X24
0.44452
0.41752
0.35195
0.54686
0.04206
.797
x25
0.21071
0.27643
0.09857
-0.03988
0.81629
‘799
X26
0.06602
0.03993
0.10096
0.09409
0.89480
.826
x27
0.04828
0.05638
0.33825
0.41148
0.57506
,620
Variance
6.220
5.110
4.167
2.773
2.189
Total= 20.459
Explained
(23.03%)
(18.93%)
(15.43%)
(10.27%)
(8.11%)
(75.77%)
RESULTS
Multiple regression analysis was used to test the hypotheses regarding how customer satisfaction, as measured by SAT, is related to the five factors identified earlier, and how these relationships are moderated by technical complexity and knowledge (Ht -H7). The customer’s subjective knowledge was introduced as a dichotomous variable K, defined to be zero when the customer’s self-rating on the five point scale was less than the sample average (3.24), and one otherwise. Thus, customers with self ratings of 3 or less on the 5 point scale had K = 0 and customers with ratings of 4 or more had K = 1. The technical
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journal of Retailing Vol. 70, No. 4 1994
complexity of the service was also introduced as a dichotomous variable T (T= 0 if simple, T = 1 if complex). The following model, developed to allow the effect of FAIR on SAT to be moderated by technical complexity and subjective knowledge, was estimated first: SAT = PO+ PIFAIR + PzEMPATHY + j33RESP+ fi4REL + /3$ZONV + fiisFAIR*K+ P7FAIR*T + PgFAIR*K*T + E.
This model had an R* of .814 (adjusted R2 = ,802) and all independent variables were significant at p < .Ol. Since the omission of a significant predictor might bias the estimates of the coefficients of retained predictors, afull model was also estimated which included all the following regressors: FAIR, EMPATHY, RESP, REL, CONV, K, T,K*T,FAJR*K, EMPATHY*K, RESP*K, REL*K, CONV*K, FAIR*T, EMPATHY*T, RESP*T, REL*T, CONV*T, FAIR*K*T, EMPATHY*K*T, RESP*K*T, REL*K*T, andCONV*K*T. For the full model, R2 = .833 (adjusted R* = ,798). Using the F-test, the null hypothesis, that no variable in the full model other than those included in the restricted model identified earlier is significant, cannot be rejected at a = .05. Thus, we conclude that the restricted model is adequate to estimate the parameters of FAIR, EMPATHY, RESP, REL, CONV, FAlR*K, FAIR*T, and FAIR*K*T. The results are presented in Table 3. To facilitate interpretation, the estimated regression equation is presented separately for the four cases: (1) K = 0 and T =0;(2)K=OandT=1;(3)K=1andT=0;and(4)K=1andT=1.Thus,thelevelof knowledge is low in Cases 1 and 2, and high in Cases 3 and 4. From Table 3, it is clear that everything else being the same, customer satisfaction is a strictly increasing function of EMPATHY, RESP, REL and CONV. Thus, H4, HS, H6 and H7 are supported. Consider now the hypotheses regarding FAIR, i.e., Hl - H3. To test Hl, it is necessary to test whether the coefficient of FAIR is significantly positive for all four combinations of knowledge and technology under consideration: (1) K = 0,T = 0;(2) K= 0,T = 1; (3) K = 1, T = 0;and (4) K = 1, T = 1. In these four cases, the regression coefficients of FAIR are I, (pt + p7), (PI +p6), and (pt +p6 + p7 + ps , res 1.15 (bt + k) = 2.208 (fit + fib) = 2.22, and (B r+ fi6+ /!7+ /?etively’ s)= 1.505.Also,fromTable From Tab1e 3’ ” = 3, p ; .Ol for Bt. Using the estimated covariance matrix of the parameter estimates (these request), it can be shown that p < .Ol for (81 + b7), (fin+ b6) and as well.Thus,Hl issupported. According to H2, if K = 0,the coefficient of FAIR should be greater when T= 1 than when T= 0,i.e., (pt + p7) > pt, or, equivalently, p7 > 0. Thus, to test H2, it is necessary to test the null hypothesis p7 IO. From Table 3, the null hypothesis p7 S 0 can be rejected at a = .Ol, thereby supporting H2. Finally, according to H3, if K = 1, the coefficient of FAIR should be the same for T = 0 and T = 1, i.e., @t 436) = (PI +/%j + p7 + ps) or, equivalently, (p7 + fis)= 0. From Table 3, (cj7 + 11s) = 1.058 - 1.773 = -.7 15. Using the estimated covariance matrix of parameter estimates (not presented), it can be shown that the standard deviation of (87 + fis) is .45 14, resulting in a t-statistic of -1.584 which has ap value greater than .lO. While H3 cannot be rejected at a 90% level of confidence based on the data available, the results raise the intriguing possibility that (p7 + ps) is actually negative, i.e., to a knowledgeable CUStOmer, fairness is more important when technical complexity is low. A possible reason for this may
Quality Evaluation
in the Automobile
377
Service Industry
TABLE 3
Regression Estimation with Restricted Model Model : SAT = PO + PlFAlR + PzEMPATHY
+ PjRESP + P4REL + j35CONV
+ PbFAIR*K+
P7FAIR*T+
bFAlR*K*T+
E
ParameterEstimates Variable
Parameter
INTERCEPT
Estimate
Standard
t-ratio
p value
106.43
0.0001
Error
11.692
0.110
FAIR
1.150
0.160
7.21
0.0001
EMPATHY
1.245
0.110
11.36
0.0001
RESP
0.648
0.119
5.46
0.0001
REL
1 ,139
0.110
10.35
0.0001
CONV
0.590
0.115
5.14
0.0001
FAIR * K
1.07
0.279
3.84
0.0002
FAIR * T
1.058
0.355
2.98
0.0035
FAIR*K* Notes:
T
-1.773
2 = ,814, adjusted !# = ,802, F = 67.94,
-3.01
0.590
0.0032
p < .OOOl.
The estimated regression equation in special cases (the fitted SAT is denoted by SAT): Case 1: low knowledge, low complexity (K = 0, T = 0, 54 observations): SAT = 11.692
+ 1 .15 f FAIR + 1.245 * EMPATHY
+ ,648 * RESP + 1 .139 * REL + .59 * CONV.
Case 2: low knowledge, high complexity (K = 0, T = 1, 23 observations): SAT = 11.692
+ 2.208
Case 3: high knowledge, SAT=
11.692 11.692
low complexity
(K=
high complexity
+ .648 * RESP + 1 .139 * REL + .59 * CONV.
1, T= 0,40
+ 2.22 * FAIR + 1.245 * EMPATHY
Case 4: high knowledge, SAT=
* FAIR + 1.245 * EMPATHY
observations):
+ ,648 * RESP + 1 .139 * REL + .59 * CONV.
(K = 1, T = 1, 16 observations):
+ 1.505 * FAIR + 1.245 * EMPATHY
be that when the core service is not complex,
+ ,648 * RESP + 1 .139 * REL + .59 * CONV.
the knowledgeable
customer
peripheral aspects of the service encounter and tends to detect unfairness. should be explored further in future research.
focuses on
Clearly, this issue
CONCLUSION
We examined how five factors-perceived fairness, empathy, responsiveness, reliability and convenience-affected acustomer’s evaluation of ASRs. The results supported our hypotheses that all these factors generally have a positive effect on service quality evaluation and that the impact of perceived fairness is moderated by the customer’s self assessment of the knowledge of ASR and the technical complexity of the service concerned. In particular, as hypothesized, we found that when the customer is not knowledgeable, the importance of fairness increases significantly with technical complexity. In fact, if the service was technically complex and the customer was not knowledgeable, fairness emerged as, by far, the most important predictor of service quality evaluation.
378
Journal of Retailing Vol. 70, No. 4 1994
A comparison of the parameter estimates also indicated that responsiveness and convenience, while always significant, were considerably less important than fairness, empathy, and reliability. A possible explanation of this finding may be that the customer views responsiveness and convenience as peripheral to the core service provided. Future research should examine if customers do indeed decompose the overall service into its core and peripheral aspects. It would also be interesting to determine whether the relative importance of such peripheral aspects of service becomes greater during the actual choice of a service facility rather than during the evaluation of the service after it is performed. While the findings of this study provide additional insights about service quality evaluation, a relatively small sample size of 133 was used due to resource constraints. Consequently, we simplified our analysis by forming four categories through the dichotomization of knowledge and technical complexity. With a larger data set, it should be possible to determine, for example, whether there is a knowledge threshold beyond which the importance of perceived fairness diminishes with additional increases in knowledge. In future research, the questionnaire should also include direct measures of customers’ perceptions of their ability to evaluate the input of the service provider. A causal model can then be used to test the position that knowledge and technical complexity affect the importance of perceived fairness through the customer’s self perception of how well (s)he can evaluate the input of the service provider. Finally, we note that the arguments used to develop the hypotheses regarding the moderating effects of customer knowledge and technical complexity should apply to any service situation where the service provided is technically complex and some customers cannot therefore evaluate the exact input of the service provider. Thus, the validity of our arguments can be tested in future research by examining whether the results regarding perceived fairness continue to hold in other similar contexts such as the purchase of homes, insurance, and certain medical and legal services.
APPENDIX
A.
ITEMS MEASURING
CUSTOMER
SATISFACTION
For both items, the response was on a seven point scale from very unsatisfied to very satisfied: Item 1: Item 2:
B.
Overall, how satisfied were you with this facility? How would you rate the overall service quality you received from this facility?
ITEMS MEASURING
BEHAVIORAL
INTENTION
REGARDING
For both items, the response was on a seven point scale from very unwilling
FUTURE
USE
to very willing.
Quality Evaluation in the Automobile Service Industry
Item 1: Item 2:
How willing would you be to recommend this facility to others? How willing would you be to return to this facility in the future?
C. ITEMS MEASURING FAIRNESS, EMPATHY, RESPONSIVENESS, RELIABILITY AND CONVENIENCE
The response was on a seven point scale from strongly disagree to strongly agree.
1.
Fairness:
x1 + The facility I went to charged a fair price. x2 -_) I felt I was taken advantage of by this facility.* ~3 + The price I paid for labor was fair. ~4 + The price I paid for parts was fair. x5 + I left knowing I was fairly treated. X6 + The service personnel were honest. x7 + The facility had my best interest in mind.
2.
Empathy:
xs + The service personnel x9 + x10 + xl i + x12 + xl3 +
3.
The service The facility The service The service The service
listened to my problem.
personnel did not pay enough attention to me.* understood what I wanted. personnel explained the work to be performed. personnel were respectful. personnel were polite.
x14 + The service personnel
were helpful.
xis + The service personnel
were friendly.
Responsiveness:
X16 + The service was completed in a timely manner. ~17 + The facility had my appointment scheduled promptly. xis + The facility scheduled my appointment near the date I desired. x19 -_) Upon arrival, I was quickly waited on. x20 + My car was ready when promised.
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Journal of Retailing Vol. 70, No. 4 1994 4.
Reliability:
x21 +
The facility did the work that was promised. x22 -+ The service personnel were well trained. x23 + I felt the service was done correctly on the first visit. X24 + The service personnel were competent.
5.
Convenience:
x25 + It was convenient to have my car serviced by this facility. The facility is in an easily accessible location. X27 + The facility had convenient hours.
x26 +
* + The item was reversed. Acknowledgment: The authors wish to thank the editor Charles A. Ingene, three anonyof Retailing reviewers, and Tridib Mazumdar for valuable comments and
mous Journal suggestions.
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Received May 1994; Revised December 1994