the role of expectations in the determination of consumer satisfaction

The Role of Expectations in The Determination of Consumer Satisfaction

Ved Prakash, D.B.A. Florida International University

and

John W. Lounsbury, Ph.D. The University of Tennessee, Knoxville

A recent paradigm advanced for understanding consumer satisfaction focuses on the concept of confirmation of expectations (e.g., Howard and Sheth 1969; Oliver 1980; Engel and Blackwell 1982). In brief, the confirmation of expectations paradigm involves identification of a set of expectations which may lead to a purchase decision and can be finally assessed in terms of postpurchase evaluation resulting either in confirmation or disconfirmation of expectations. Confirmation of expectations is viewed as a determinant of satisfaction which may eventually lead to repurchase of the product. This conceptualization of the prepotent role of expectations in determining satisfaction has roots in Helson's adaptation level theory as well as in Thibaut and Kelley's (1959) comparison level theory. Further- more, some authors (Day 1976) believe that the confirmation paradigm offers a good basis to test the reliability and validity of the process by which satisfaction is achieved.

While the confirmation paradigm has enjoyed popularity in consumer satisfaction research and it appears on the surface to be an internally con- sistent theoretical model, it is not without some limitations. First, it is not clear what type of expectations should be confirmed in the determination

�9 1984, Academy of Marketing Science, Journal of the Academy of Marketing Science Summer, 1984, Vol. 12, No. 3 1-17

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THE ROLE OF EXPECTATIONS IN THE DETERMINATION OF CONSUMER SATISFACTION

of satisfaction. Secondly, there are some as yet unrecognized, or at least unappreciated, problems in the measurement of confirmation. This paper addresses both of these concerns in an attempt to refine the paradigm of confirmation of expectations.

Turning first to the question of the type of expectations to be studied, references can be found in the satisfaction literature to three major types of expectations. The most frequently studied type is predictive expectations, which refers to consumer beliefs about how a brand is likely to perform on certain attributes (e.g., Miller 1976; Olson and Dover 1976; Granbois and Summers 1977; Swan and Trawick 1979a). These beliefs may be formed on the basis of past experience, information from advertising, and opinions of other people. The major advantage of this type of expectations is that they deal with realistic brand expectations. Also, there is substantial evidence to support the view that predictive expectations can be measured reliably and validly (cf. Fishbein and Ajzen 1975). A second major category is normative expectations. Many different names have been given to these expectations, including desired expectations (Miller 1976), and normative expectations (Granbois and Summers 1977). The basic idea underlying these terms is a norm or a standard that should be met in order for a consumer to be satisfied. The major disadvantage of this approach is that it involves considerations of equity, personal values, cultural norms, soci- oeconomic and political philosophy, and quality of life. A third major type is comparative expectations, which refers to expectations about a brand as compared to similar other brands. If the performance of the brand purchased is better than expectations about similar brands, there would be satisfaction with the purchase. Latour and Peat (1980) suggest that satisfaction in this context is an additive function of previous experience with similar brands.

Unfortunately, most studies of consumer satisfaction have not delineated or compared the different types of expectations reviewed above. An excep- tion is a study by Swan and Trawick (1979a) in which it was found that normative expectations are better correlates of satisfaction than predictive expectations. This study, however, did not compare the normative expectations with comparative expectations. Woodruff et. al (1983) suggest the desirability of using experience-based norms in the determination of satisfaction. So far these authors have only provided a conceptual model of this relationship. They have not yet reported any empirical data on the efficacy of these norms. Therefore, further studies comparing the relative efficacy of the three types of expectations in leading to confirmation and disconfirmation, and thus to satisfaction are needed.

PRAKASH AND LOUNSBURY

No matter what type of expectations are chosen for the study of confirmation, there still remains the problem of the actual measurement of the construct of confirmation. One could simply ask a consumer whether an expectation has been met after the decision to purchase has been made; and in fact, such a technique has been employed by Swan and Combs (1976). However, this strategy invites a bias toward "post-decision dissonance re- duction" (Festinger 1956) wherein the subject rationalizes his or her decision by stating that it was (at least partially) in line with prior expectations. The other strategy for measuring confirmation, and the one most typically used, involves computation of the difference between post-purchase evaluations and pre-purchase expectations on a set of common dimensions (Oliver 1979b). In this approach, a small discrepancy between pre-purchase expectations on an attribute such as quality and post-purchase evaluation of quality would denote confirmation of expectations. Most often pre/postpurchase differences are computed across a set of meaningful dimensions and summed to form a composite index of confirmation (Oliver 1979b; Swan and Trawick 1979a).

The fundamental problem with any approach which uses difference scores is that of lack of reliability. This conclusion has been virtually sanctified in the psychometric literature (see, for example, Cronbach and Furby 1970; Lord 1963; Magnusson 1965) in psychology to the point where difference scores are rarely used at all. As Cronbach and Furby summarize it in their discussion of gain (i.e. difference) scores, investigators "would ordinarily be better advised to frame their questions in other ways" (p. 80). There are several factors contributing to the unreliability of difference scores. First, difference score unreliability increases as the unreliability of either scores on which it is computed increases. Second, the relative mag- nitude of difference score unreliability is increased as the correlation between the two measures on which it is based increases. Third, when the two component variables are measured at different points in time, difference score unreliability capitalizes on any effects of regression-to-the mean over time. Such regression effects lower the reliability of the difference score. The following formula for estimating the reliability of a difference score given by Lord (1963) exemplifies the major sources of unreliability of difference scores:

" S 2 S ~ r ' - 2S S r + r ' y yy y x xy x xx

rdd ' = S 2 - 2S S r + S 2 y y x xy x

THE ROLE OF EXPECTATIONS IN THE DETERMINATION OF CONSUMER SATISFACTION

Where: rdd' = reliability of difference scores formed by subtracting the scores

on one variable from the scores on another variable (y - x)

ryy, = the reliability coefficient of the one variable--y (e.g., pre-purchase expectations)

�9 xx ' = the reliability of a second variable--x (e.g., post-purchase evaluation)

'xy = correlation coefficient between the two variables

S~y = variance of the y variable

S~ = variance of the x variable

As can be seen in the above formula, not only does the reliability of a difference score decrease as the reliability of either of the two component measures decreases, but also it decreases as the correlation between the two component measures increases. Thus, if one wanted to increase difference score reliability, one might attempt to lower the correlation between, say, a pre-purchase expectation and a post-purchase evaluation for a given attribute. But this would lead to a paradox, since with lower correlations between the two measures, one would have less assurance that the same attribute was in fact being assessed.

Since empirical estimates of the reliability of difference-score confirmation measures have been conspicuously lacking in the literature to date, the second purpose of the present study was to examine the reliability of difference scores computed for the three types of (pre-purchase) expectations in relation to post-purchase evaluations. In this regard, low reliabilities of the confirmation measures would indicate an upper bound for validity estimates and provide one reason why the role of confirmation of expectations has not been more effective in accounting for explained variance in consumer satisfaction or purchase decisions.

METHOD

Subjects. Data were collected from 402 students in Business Administra- tion at a large Southeastern university. These subjects were chosen for this purpose as they themselves were consumers of the products in the study.

Procedures. Data were collected in two stages, once for pre-purchase expectations and, later, for post-purchase evaluation with an interval of three weeks between them so as to allow time for actual purchase and consumption of the products. Two products were used for the study Fast Food Hamburger Restaurants (FFHR) and Beer.


Measures. Data were collected with respect to 11 attributes of FFHR such as: taste of food, having food served the way you like it, food served hot, quality of food, menu variety, speed of service, friendliness of em- ployees, value for price, cleanliness of restaurant, location convenience, and atmosphere/decor, and 7-attributes of Beer such as: good taste, good value for price, pleasant aftertaste, recommendation of friends, good quality of ingredients, and brand reputation. With respect to these attributes, data were collected on three types of expectations: a) Predictive Brand Expec- tations. The subjects were asked to indicate how a brand was likely to perform on each of the attributes. The responses were indicated on 7-point semantic differential scales; b) Normative Expectations. The respondents were asked to indicate on 7-point semantic differential scales how a brand should perform on different attributes in order for them to be completely satisfied; c) Comparative Expectations. The subjects were asked to indicate their expectations from two other brands (than the one actually purchased). These data were also collected on 7-point semantic differential scales. In addition, at the second stage of the experiment data were collected on: d) Postpurchase Evaluation, which called for ratings of the brand actually purchased on 7-point semantic dfferential scales on various attributes; d) Overall Satisfaction, where satisfaction with the brand purchased was measured on a 7-point semantic differential scale ranging from Extremely Dis- satisfied (1) to Extremely Satisfied (7); and f) Intention to Repurchase, based on overall satisfaction, the respondents were asked to indicate the chances of repurchasing the same brand on an 11-point scale ranging from No Chance at all (1) to Absolutely Certain (11). In addition data were derived for three confirmation of expectations measures: g) Confirmation of Predictive Expectations was obtained from the subtraction of predictive brand expectations from postpurchase evaluation for each attribute and then adding them to form a composite score; h) Confirmation of Normative Expectations was derived from the difference between postpurchase evaluation and normative expectations for each attribute and then adding them to form a composite score; i) Confirmation of Comparative Expectations was computed from subtracting comparative expectations from postpurchase evaluation for each attribute and then adding them to form a composite score. The reliability of various scales was estimated by means of a coefficient alpha computed on the composite scales and by means of a test- retest coefficient computed on the single item measures.

THE ROLE OF EXPECTATIONS 1N THE DETERMINATION OF CONSUMER SATISFACTION

RESULTS

Table 1 summarizes the means and standard deviations of the study variables. Table 2 displays the inter-correlations among the study variables. We will consider first the results of the correlations between overall consumer satisfaction and the three types of confirmation of expectation measures, then the correlation between post-purchase evaluation and the three confirmation measures for both fast-food hamburger restaurants and beer.

Determinants of OveraU Satisfaction. In the case of both FFHR and Beer, all the correlations were significant, with post-purchase evaluation showing the highest correlation with overall satisfaction. In both cases, the measure of confirmation of predictive expectations correlated at a lower level (r = .33 and r = .19 for FFHR and Beer, respectively) than the other two confirmation measures. The measure of confirmation of comparative expectations correlated with overall satisfaction at the .48 (FFHR) and .29 (Beer) levels. The measure of confirmation of normative expectations appeared to correlate more highly with overall satisfaction (r = .50 and r = .41, respectively for FFHR and Beer) than the other two confirmation measures. Using a series of pairwise t tests of the difference between correlated coefficients of correlated (see Guilford 1965), the only significant differences between the three types of confirmation of expectation measures correlated with overall satisfaction were as follows: the confirmation of normative expectations performed significantly better than the confh'mation of predictive expectations for both FFHR and Beer (t = 3.23 and t = 5.77, respectively) at the p < .05 level. The confirmtion of comparative expectations performed significantly better than the confirmation of predictive expectations for both FFHR and Beer (t = 2.56 and t = 2.68, respectively) at p < .05. The difference between the confirmation of normative expectations and the confirmation of comparative expectations were not significant. This indicates a slight relative superiority for the measure of confirmation of normative expectations.

We also wondered if the predictability of overall satisfaction could be improved by combining the three confirmation measures. To examine this question we ran two separate stepwise multiple regression analyses, one for FFHR and one for Beer. In each case, the criterion variable was overall satisfaction and the predictor variables were the three confirmation of expectation measures. The results of these analyses showed that in the case of FFHR, 30% of the variance in overall satisfaction was accounted for by two of the measures. The confirmation of normative expectation measure entered the equation first, accounting for 25% of the variance, followed by


Table i

MEAN AND STANDARD DEVIATION OF VARIOUS MEASURES

Predictive Expectations

Normative Expectations

Comparative Expectations

Confirmation of Predictive Expectations

Confirmation of Normative Expectations

Confirmation of Comparative Expectations

Postpurchase Evaluation

Satisfaction

Repurchase

FFHR (n = 300)

Mean

5.57

5.72

5.32

-1.02

2.90

1.75

5.48

5.67

8.54

Standard Deviation

0.55

0.48

0.58

5.07

6.48

7.46

5.60

0.80

2.47

Predictive Expectations

Normative Expectations

Comparative Expectations

Confirmation of Predictive Expectations

Confirmation of Normative Expectations

Confirmation of Comparative Expectations

Postpurchase Evaluation

Overall Satisfaction

Repurchase

BEER (n = 231)

Me an

5.54

5.45

5.44

-0.28

0.42

0.43

5.50

6.01

9.54

Standard Deviation

0.59

0.58

0.56

3.56

4.55

4.11

5,56

0.56

1.89

THE ROLE OF EXPECTATIONS 1N THE DETERMINATION OF CONSUMER SATISFACTION

TABLE 2

INTERCORRELATIONS BETWEEN DIFFER~qT VARIABLES

PE NE CE

PE 1.00

NE .42 1.00

CE .36 .45 1.00

CPE - .40 - .08 - .08

CNE .29 - .45 - .07

CCE .24 - .09 - .61

PP .66 .35 .29

S .39 .17 .08

R ,33 .ii .05

FFHIR (n = 300)

CP__~E CNE

1.00

.47 1.00

.42 .62

.43 .68

.33 .50

.i0 .32

CCE PP S R

1.00

.58 1.00

.48 .66

.31 .42

1.00

.45 I. O0

PE NE CE

PE 1,00

NE .59 1.00

CE .68 .57 1.00

CPE - .50 - .31 - .30

CNE - .008 - .60 - .13

CCE - .08 .22 - .54

PP .61 .35 .45

S .39 ,ii .28

R .13 .04 .17

BEER (n = 231)

CPE CNE

1.00

.60 1.00

.66 .64

.39 .54

,19 .41

.08 .22

CCE PP S R

1.00

.52 1.00

.29 .59 1.00

.25 .31 .26 1.00

PE = Predictive Expectations

NE = Normative Expectations

CE = Comparative Expectations

CPE = Confirmation of Predictive Expectations CNE ~ Confirmation of Normative Expectations CCE = Confirmation of Comparative Expectations PP = Postpurchase Evaluation S = Overall Satisfaction

R = Intention to Repurchase


the confirmation of comparative expectations measure, which accounted for an additional and unique 5% of the variance. In the case of Beer, only the confirmation of normative expectations measure entered the regression equation at a significant level (of at least p < .05), accounting for 17% of the variance in satisfaction. Thus, both in the case of FFHR and Beer the confirmation of normative expectations was the predominant predictor of overall satisfaction.

Determinants of Repurchase. The pattern of correlations between confirmation of expectations and repurchase is similar to the case for overall satisfaction. Namely, the predictive confirmation of expectations measure correlated lowest of the three types with probability of repurchase for both FFHR and Beer (r = .10 and r = .08, respectively). For FFHR the normative confirmation measure correlated. 32 with repurchase probability while the comparative expectation measure correlated .31. For beer, the corresponding correlations were. 22 for the normative measure and .25 for the comparative measure. The only pairs of these correlations which were significantly different from each other as indicated by the t test for correlated correlation coefficients were as follows. In the case of FFHR the confirmation of normative expectations performed significantly better than the confirmation of predictive expectations (t = 3.8) p < .05; also, the confirmation of comparative expectations performed better than the confirmation of predictive expectations (t = 3.4) p < .05. In the case of Beer, none of the three pairs of correlations were significantly different from each other.

The results of the stepwise multiple regression analysis showed that in the case of FFHR, the confirmation of normative expectations measure accounted for 10% of the variance in repurchase intention, followed by the confirmation of expectations measures, which accounted for an additional unique 2% of the variance. In the case of Beer, the confirmation of comparative expectations measure was the sole significant predictor entering the regression equation, accounting for 6% of the variance in repurchase intention.

Reliability Analysis for Confirmation of Expectation Measures

Using the formula presented earlier it is possible to estimate difference score reliability coefficients for the three types of confirmation of expectation measures. To do so, one must view the X score in the forumula as the summated pre-purchase expectation measure and the Y score as the summated postpurchase evaluation measure, with individual reliabilities for

10 THE ROLE OF EXPECTATIONS IN THE DETERMINATION OF CONSUMER SATISFACTION

each as presented earlier, Doing so yields the resultant difference score reliability coefficients for FFHR and Beer presented in Table 3.

As can be seen from this table, most of the reliability coefficients for the confirmation of expectation measures are fairly low (median rdd' = .48), with values above .60 only in the case of confirmation of normative expectations for FFHR (where rdd' = .68) and comparative expectations from FFHR (where rdd' = .78). In the median case, this means that over half of the variance in observed scores of confirmation of expectation measure is error variance! Since reliability places an upper bound on validity (cf. Nunnally 1970), it is easy to see one explanation why so many of the individual correlation coefficients between confirmation of expectation measures on the one hand and satisfaction and repurchase measures on the other are low.

One might inquire what the observed correlations between confirmation measures and satisfaction or repurchase would be if the reliability of the confirmation measures (as difference scores) were increased. There is a way to estimate these using a formula provided by Lord (1963). The estimated correlation between a "true difference"--in this case the estimated true difference between prepurchase expectation and post-purchase evaluation--and another criterion variable (symbolized in c)--in this case with overall satisfaction with a purchase or repurchase probability--is symbolized as rDc and is related to the reliability of the difference score (roe') and the observed correlation between the difference score (rdc) as shown in the following formula:

-qc S_Dc =

rdd'

Having estimated rDc, or the correlation between a true difference and another variable c, after correcting for the attenuation in the difference measure, it is possible to plug in difference values of rdo', and estimate what the observed values of rdc would be. We have done so for both FFHR and Beer correlations with the results as shown in Tables 3 and 4.

Thus, for example, it can be seen in Table 4 that the starting or observed values are .19 for the reliability of the predictive confirmation of expectation measure and .19 for the correlation between this confirmation measure and the overall satisfaction measure. Using the formula presented above, we see that for Beer purchases the estimated observed correlation between confirmation and satisfaction rises to a value of .42 if the reliability of the confirmation of predictive expectation measure were raised to .95, with

PRAKASH AND LOUNSBURY 11

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intermediate values as shown. Similarly, if the reliability of the confirmation of normative expectations measure were raised from .48 to .95 for Beer purchases and correlations with satisfaction, the estimated observed correlation would rise from .41 to .58. Note also, however, that there is not as much corresponding increase in the correlation values for repurchase. This reflects the fact that it is very difficult to raise a low correlation coefficient by means of increases in the reliability of one of the measures. Generally, the projected values in Tables 3 and 4 suggest the desirability of increasing the reliability of the confirmation of expectation measures, particularly as a means of improving correlations with overall satisfaction. Were the reliabilities for the confirmation measures raised to about the same level as the actual observed value of the satisfaction measures for Beer and FFHR (nearly .95 in both cases), one could expect a median increase of 33% in the correlation coefficients.

DISCUSSION

The implications of the present findings are relatively strightforward. First, while all three measures of confirmation of expectations correlated significantly with overall satisfaction and perceived probability of repurchase, both the normative and comparative expectations measures were better individual predictors than the predicted expectation measures. This may suggest the importance of considering expectations about the attributes of a brand or product not in an absolute sense, but in relation to other factors, particularly how the consumer believes the product or brand should perform on a set of attributes and how its attributes perform compared to other brands or products. But the differential results for the three confirmation measures may simply reflect differences in their reliabilities, as the higher correlations were generally associated with higher reliabilities computed on the difference scores. The findings of the multiple regression analyses generally suggest that researchers should use both the normative and comparative confirmation measures in predicting satisfaction and repurchase. Both of these types of confirmation of expectation measures appear to tap some unique sources of variance in satisfaciton and repurchase in the case of FFHR.

The chief problem confronting researchers who want to employ a difference score method to compute confirmation of expectation measures is the low reliability of such measures. Low reliabilities, such as the median value of .48 observed in the present context, are not acceptable. Low reliability reduces the power of statistical tests, lowers estimates of effect sizes, and


more generally clouds our understanding of the construct validity of theo- retically relevent variables. Low reliability likewise impedes our ability to predict important outcomes such as purchases in the applied domain.

If the confirmation of expectations construct is to continue to serve as a subject of study in this area, we believe that either efforts be made (suc- cessfully) to improve their reliability when measurement is based on difference scores, or that other, more reliable approaches be used to measure confirmation of expectations which do not involve the use of difference scores. With respect to the first option, two of the more reasonable strate- gies might be to: 1) increase the reliability of the two component variables (although the reliabilities of satisfaction and repurchase variables in this study are already quite high), which could be accomplished by increasing the number of items or increasing the homogeneity of items in a component measure; 2) attempt to use multiple types of confirmation of expectations measures. Even with relatively low reliabilities for each of the individual confirmation mesures, given enough of them, prediction can be substan- tially improved.

With respect to the other option of searching for more reliable measures of confirmation of expectations, it is possible to measure the degree to which confirmation of expectations has been realized directly through the following type of scale:

How well did Product X perform on "attribute A"? "Much Worse "Worse than "The Same or About "Better than than expected Expected" the Same as Expected" Expected"

"Much Better than Expected"

This approach avoids the use of difference scores and has, in fact, been used by Aiello et. al. (1977), Linda and Oliver (1979), Oliver (1977, 1979b) and Swan and Trawick (1980). These studies have shown that this type of scale is highly correlated with satisfaction. But these studies have not reported on any reliability estimates; also, these studies have used the comprehensive scale only at the overall level, but not at the level of attributes to compute a summated measure of satisfaction. Also, as we men- tioned earlier, this approach suffers from a bias toward the subject rationalizing his or her pre-purchase expectations. To help reduce such a bias, we suggest coupling this approach with the following procedure: Measure and record pre-purchase expectations for each subject and allow the subject to review these expectations immediately prior to making ratings on the degree to which confirmations have been realized. Such an approach would force the subject to attend to his or her actual pre-purchase expec-


tations and it could be modified to request the subject to justify actual confirmations and disconfirmations of expectations.

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ABOUTTHEAUTHORS

VED PRAKASH is Assistant Professor of Marketing at Florida Interna- tional University. He received his Doctor's degree in Business Administra- tion from the University of Tennessee. His articles have appeared in the Journal of the Academy of Marketing Science and in the proceedings of Association for Consumer Research, National AIDS, Academy of Marketing Science, and in a book on Personal Values. The research topics that he is currently involved in are: Mood States, Involvement, Product Familiarity,


Psychological Gender Differences and Advertising Format, and Negative Information.

JOHN LOUNSBURY is Associate Professor of Psychology, the University of Tennessee, Knoxville. He reveived his Ph.D. in Psychology from Mich- igan State University. His research and teaching interests are in the areas of Applied Psychological Measurement, Job Motivation, and the Relation- ship Between Work and Leisure. Most recently, he has conducted research on factors associated with Vacation Satisfaction and on the Unobtrusive Measurement of Company Morale.

the role of expectations in the determination of consumer satisfaction

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