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0732-2399/99/1801/0077$05.00
Copyright q1999, Institute for Operations Research
and the Management Sciences
Marketing Science/Vol. 18, No. 1, 1999
pp. 77–92
What You Don’t Know About Customer-
Perceived Quality: The Role of Customer
Expectation Distributions
Roland T. Rust • J. Jeffrey Inman • Jianmin Jia • Anthony Zahorik
Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee 37203,
roland.rust@owen.vanderbilt.edu
School of Business, University of Wisconsin, Madison, Wisconsin 53706, jinman@bus.wisc.edu
Department of Marketing, Chinese University of Hong Kong, NT, Hong Kong, jia@baf.msmail.cuhk.edu.hk
ACNielsen Burke Institute, Nashville, Tennessee 37212, zahoriktny@aol.com
Abstract
We show that some of the most common beliefs about
customer-perceived quality are wrong. For example, 1) it is
not necessary to exceed customer expectations to increase
preference, 2) receiving an expected level of bad service does
not reduce preference, 3) rational customers may rationally
choose an option with lower expected quality, even if all non-
quality attributes are equal, and 4) paying more attention to
loyal, experienced customers can sometimes be counter-
productive. These surprising findings make sense in retro-
spect, once customer expectations are viewed as distribu-
tions, rather than simple point expectations. That is, each
customer has a probability density function that describes the
relative likelihood that a particular quality outcome will be
experienced. Customers form these expectation distributions
based on their cumulative experience with the good or ser-
vice. A customer’s cumulative expectation distribution may
be conceptualized as being a predictive density for the next
transaction.
When combined with a diminishing returns (i.e., concave)
utility function, this Bayesian theoretical framework results
in predictions of: (a) how consumers will behave over time,
and (b) how their perceptions and evaluations will change.
In managerial terms, we conclude that customers consider
not only expected quality, but also risk. This may help ex-
plain why current measures of customer satisfaction (which
is highly related to expected quality) only partially predict
future behavior. We find that most of the predictions of our
theoretical model are borne out by empirical evidence from
two experiments. Thus, we conclude that our approach pro-
vides a useful simplification of reality that successfully pre-
dicts many aspects of the dynamics of consumer response to
quality.
These findings are relevant to both academics and man-
agers. Academics in the area of customer satisfaction and
service quality need to be aware that it may be insufficient
to measure only the point expectation, as has always been
the standard practice. Instead it may be necessary to measure
the uncertainty that the customer has with respect tothe level
of service that will be received. Due to questionnaire length
constraints, it may not be practical for managers to include
uncertainty questions on customer satisfaction surveys. Nev-
ertheless it is possible to build a proxy for uncertainty by
measuring the extent of experience with the service/good,
and this proxy can be used to partially control for uncertainty
effects.
The findings of the study were obtained using 1) an ana-
lytical model of customer expectation updating, based on a
set of assumptions that are well-supported in the academic
literature, and 2) two behavioral experiments using human
subjects: a cross-sectional experiment, and a longitudinal ex-
periment. Both the analytical model and the behavioral ex-
periments were designed to investigate the effects that dis-
tributions of expectations might have, and especially the
effects that might deviate from the predictions that would
arise from a traditional point expectation model. The behav-
ioral experiments largely confirmed the predictions of the
analytical model. As it turned out, the analytical model cor-
rectly (in most cases) predicted behavioral effects that con-
tradict some of the best-accepted “truisms” of customer
satisfaction.
It is now clear that a more sophisticated view of customer
expectations is required—one that considers not only the
point expectation but also the likelihood across the entire
distribution of possible outcomes. This distinction is not “just
academic,” because it results in predictable behavior that de-
viates significantly from that which was traditionally ex-
pected based on simpler models.
(Quality;Customer Satisfaction Measurement;Customer Expec-
tations;Customer Retention;Bayesian Updating;Customer Life-
time Value)
WHAT YOU DON’T KNOW ABOUT CUSTOMER-PERCEIVED QUALITY:
THE ROLE OF CUSTOMER EXPECTATION DISTRIBUTIONS
78 Marketing Science/Vol. 18, No. 1, 1999
1. Introduction
The trade literature in quality and customer satisfac-
tion abounds with rarely questioned platitudes. Some
of the most often repeated and/or noncontroversial
are:
•“It is necessary to exceed customer expectations.”
•“If a customer expects a bad level of quality and receives
it, he/she will reduce his/her level of preference for the
brand.”
•“Given two equally-priced options, the customer will
choose the one with the higher expected quality.”
•“Management should always focus on its most loyal
customers.”
We will show, using both an analytical model and
behavioral experiments, that all of these truisms are
flawed. Management must instead adopt a more so-
phisticated view of how customer expectations are up-
dated, and how customer expectation updating relates
to preference and future choice behavior.
1.1. What’s Wrong with Customer Satisfaction
Measures?
Managers routinely conduct customer satisfaction sur-
veys and use that information to produce explanatory
and predictive models of customer repurchase inten-
tion, word-of-mouth intention, customer repurchase
behavior, and market share (Bolton and Drew 1991a,
1991b; Boulding et al. 1993; Cronin and Taylor 1992,
1994; Fornell 1992; Oliver and DeSarbo 1988;
Parasuraman et al. 1985; Rust and Zahorik 1993; Teas
1993).
1
Such models may be used to evaluate the pro-
jected profit impact of service improvement programs
(Rust et al. 1995). Recently, some authors have ques-
tioned the predictive ability of customer satisfaction
measures (Gale 1997, Reichheld 1996). They have com-
plained that many customers who report being “very
satisfied” or perceive quality to be “excellent” never-
theless subsequently switch to a competitor. In general,
while customer satisfaction measures and/or per-
ceived quality measures are important predictors of
intentions and behavior, they often explain only 30–
50% of the variance.
We argue that one of the reasons for the apparently
1
Strictly speaking, many customer satisfaction surveys actually mea-
sure perceived quality.
weak satisfaction-behavior link may be that customers
respond according to the perceived variance in service,
in addition to expected quality. In other words, cus-
tomers’ certainty about quality has an important im-
pact. This perceived risk can be related to the variance
of a Bayesian predictive distribution of quality out-
comes, resulting in a more multidimensional view of
customer expectations. This viewpoint results in some
apparently counter-intuitive insights that have some
important managerial implications.
1.2. Dynamics and the Decision Theory
Perspective
Because customer relationships unfold over time, it is
important to clearly understand the dynamics of how
quality perceptions are formed and updated as well as
how such perceptions influence customer retention
over time. With few exceptions (Anderson and
Sullivan 1993, Bolton and Drew 1991a, Boulding et al.
1993), the research literature has not produced dy-
namic models of how these changes occur. Researchers
have begun to argue that a decision theory framework
offers a powerful way of conceptualizing the dynamics
of quality and customer retention (Anderson and
Sullivan 1993, Boulding et al. 1993). Bayesian decision
theory (Berger 1985, Zellner 1971) provides a way of
understanding customer retention that has thus far
been underutilized. This literature provides well-
explored methods of describing how people incorpo-
rate new information and form new expectations over
time. Thus, it can be used to generate testable predic-
tions of behavior over time (Kahneman and Tversky
1972).
Essentially there are two major issues in the field of
behavioral decision research-risky choice behavior and
probability judgments. Expected utility can be deter-
mined by mean and variance if the distribution is nor-
mal or if the utility function is quadratic (Markowitz
1959, 1987; Meyer 1987). Behavioral research on risky
choice has revealed discrepancies between actual
choice behavior and the prescriptions of the classic von
Neumann and Morgenstern (1944) utility axioms. For
example, Payne (1973) argued that factors other than
mean and variance (or even higher moments) are
needed to capture risk perceptions and risky choice
behavior. Empirical studies have challenged the tra-
ditional utility theory on several grounds (e.g.,
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
Marketing Science/Vol. 18, No. 1, 1999 79
Kahneman and Tversky 1979; Bell 1982, 1985; Machina
1987; Inman et al. 1997). This has resulted in a much
deeper understanding of risky choice behavior, while
maintaining much of the mathematical formalism of
the normative approach (see Fishburn (1988) for a com-
prehensive review of generalizations of expected util-
ity theory).
Behavioral research on probability judgments has
produced mixed results regarding the descriptive
power of Bayesian decision theory. In studies of prob-
ability judgments, prior probability (or base-rate) in-
formation is often ignored by subjects (Kahneman and
Tversky 1973), but sometimes it is utilized appropri-
ately (Gigerenzer et al. 1988). Moreover, people have
a tendency to seek confirmatory evidence in aggregat-
ing various pieces of information, which may lead to
the failure to change one’s opinion in the face of non-
supporting evidence (Wason 1960, Doherty et al. 1979).
Another relevant issue is the underestimation of pos-
terior probability, called “conservatism” (Edwards
1968, Slovic and Lichtenstein 1971). That is, upon re-
ceipt of new information, subjects revise their posterior
probability estimates in the same direction as the
Bayesian model, but revisions are typically much less
extreme than those calculated from the Bayesian per-
spective. In general, probabilistic judgments involve
contingent processing and heuristics, which some-
times yield reasonable results and sometimes lead to
systematic biases (Tversky and Kahneman 1974,
Fischhoff and Beyth-Marom 1983). People often utilize
a variety of rules for making probabilistic inference in
different environments, including both statistical and
nonstatistical methods (Ginossar and Trope 1987).
Although previous research has generated several
empirical findings of risky choice behavior and prob-
ability judgments, there is surprisingly little empirical
evidence regarding a dynamic decision model that
uses Bayesian updating for revising expectation distri-
butions upon receipt of new information. In particular,
previous research on probability judgments based on
Bayesian updating has used discrete probability infor-
mation and/or nonbusiness contexts. In contrast, our
study focuses on the descriptive validity of a Bayesian
decision model using continuous probability infor-
mation in the area of quality perceptions and customer
retention.
1.3. Comparison with Previous Models
Beginning with SERVQUAL (Parasuraman et al. 1988)
service quality measurement instruments have univer-
sally focused on a point estimate of quality. The im-
plicit assumption has always been that a customer’s
“perceived quality” or “expected quality” was a single
point. This viewpoint has also been shared by the re-
cent dynamic models of customer satisfaction. Such
models involve the updating of expectations and qual-
ity perceptions. For example, Boulding et al. (1993) em-
ploy a linear updating scheme by which expectations
and cumulative quality perceptions are updated ac-
cording to the most recent transaction quality percep-
tion. Their model is analogous to an exponential
smoothing model. Likewise, Bolton and Drew
(1991a,b) employ linear updating functions. Only
Anderson and Sullivan (1993) suggest (but do not de-
velop) an updating approach that involves a distribu-
tion rather than only a point estimate.
Our model is different in that it involves a fully de-
veloped Bayesian updating scheme, in conjunction
with a diminishing returns utility function. We will see
that the behavioral predictions arising from our model
are quite different in some cases from the predictions
arising from linear updating models. Our insights arise
from the observation that experience with a brand
makes knowledge of the brand’s quality more com-
plete, thereby reducing the customer’s risk. Because
the customer’s utility function is concave (i.e., dimin-
ishing returns), reduced risk is always good and in-
creases the customer’s preference for the brand. This
results in some seemingly counter-intuitive results that
are nevertheless quite logical under closer inspection.
Importantly, these results have key consequences for
managerial practice.
1.4. Plan of the Paper
In the next section we develop a theoretical framework
that can be used to describe the relationship between
customer retention and quality perceptions over time,
based on the concepts of expected utility maximization
and Bayesian updating. This framework results in sev-
eral propositions about how customers should behave
over time. We then present two behavioral experi-
ments to test whether or not the theoretical proposi-
tions hold up on actual behavior. Finally, we discuss
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
80 Marketing Science/Vol. 18, No. 1, 1999
the implications of our results, as well as limitations
and directions for future research.
2. Model Development
In this section we present the relationship between
quality and customer retention as a model in which
retention is based on the distribution of customer ex-
pectations. Importantly, we allow the distribution to
be updated over time according to the quality per-
ceived in a particular transaction. While previous re-
search (e.g., Anderson and Sullivan 1993, Boulding et
al. 1993) has pointed in this direction, this is the first
attempt to formulate these phenomena in a fully
Bayesian framework, complete with operationalized
prior and posterior distributions of quality. We begin
by presenting the assumptions on which our theoreti-
cal model is based. We then present the theoretical for-
mulation in formal detail. Finally, we provide a list of
nontrivial testable propositions that arise from the
model.
2.1. Assumptions
We assume a scenario in which an individual with ex-
isting expectations chooses a particular option, expe-
riences an outcome, and then changes his/her expec-
tations based on the outcome.
Assumption 1. Utility as a function of perceived quality
is continuous, twice differentiable, increasing and concave.
This implies that customers suffer more from not having
their expectations met than they benefit from an equivalent
positive disconfirmation.
This assumption is borne out by prior empirical re-
search in customer satisfaction and service quality
(Anderson and Sullivan 1993, DeSarbo et al. 1994,
Inman et al. 1997, Rust et al. 1995), and is also consis-
tent with assumptions traditionally made in decision
theory (Keeney and Raiffa 1976). This particular shape
of the utility function, well-documented in a variety of
academic and proprietary studies, combines with the
Bayesian framework to produce interesting and man-
agerially relevant, testable conclusions.
Assumption 2. The customer has a predictive distribu-
tion of outcomes that reflects the relative likelihood that each
outcome will occur.
This assumption says that the customer considers
not only the outcome s/he expects on average (i.e., the
point expectation), but also the entire distribution of
possible outcomes.
Assumption 3. In any purchase situation, the cus-
tomer’s preference for an option increases with its expected
utility, and probability of choosing an option increases with
preference for that option.
The assumption is consistent with the economic the-
ory of utility and the consumer viewpoint (Luce 1959,
Manrai 1995, McFadden 1976) with randomness aris-
ing from the variability of the predictive distribution.
Assumption 4. The customer has a prior distribution of
average quality for the product or service.
Through experience, a customer learns what quality
can be expected on average. This prior distribution
may be formed on the basis of experience with other
brands, on the basis of prior experience with the brand
under consideration, or both. Similar assumptions are
commonly made in a variety of Bayesian applications
(e.g., Little 1966).
Assumption 5. The customer updates the prior distri-
bution based on the perceived quality of the transaction us-
ing a Bayesian updating process.
Such an updating mechanism has been suggested by
previous research in the service quality area (Anderson
and Sullivan 1993, Boulding et al. 1993, Kopalle and
Lehmann 1995).
2
Assumption 6. Perceived quality varies randomly across
transactions.
Even in a highly controlled environment such as a
manufacturing assembly line, there are numerous un-
controllable factors that can produce variability in the
quality of the output (Burr 1976). It is notable that in
service, which is a growing majority of every devel-
oped economy, variability is typically much greater
2
We tested Assumption 5 in our first experiment by estimating the
relative weight subjects placed on prior perceptions versus the
weight placed on new information in updating the performance ex-
pectation. The weight on new information was significantly positive.
Thus, consistent with Assumption 5, subjects used the information
to update their perceptions of the expected performance.
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
Marketing Science/Vol. 18, No. 1, 1999 81
than in manufacturing. While an entire field of study,
statistical process control (Juran and Gryna 1980), has
arisen to identify and attempt to control the sources of
variation in quality, some variability inevitably re-
mains even in a process that is “under control”
(Deming 1986). Another source of variation is percep-
tion error on the part of the customer (Thurstone 1927).
We recognize that prior research indicates that vari-
ables such as prior expectations may influence the
quality perception itself (e.g. Boulding et al. 1998,
Oliver 1997), but to keep this initial model simple
enough to provide maximum insight, we suppress sec-
ondary effects here.
2.2. Mathematical Formulation
We now build a formal mathematical structure that
incorporates the preceding assumptions. Let the cus-
tomer have a known prior distribution p(Q) of the av-
erage quality (Q) of the brand. Let p(Q) have a normal
distribution with mean land variance s
2
.0, reflecting
the customer’s degree of uncertainty. Further, for a
particular transaction, we will denote the customer’s
perceived quality as X. We assume that Xis distributed
normally, with mean Qand variance r
2
.0 repre-
senting random variation arising from both variability
of quality and errors in perception. It is clear from stan-
dard Bayesian analysis (Berger 1985, p. 127) that the
joint density of Qand Xis:
1122
h(x,Q)4(2prs) exp{11/2 [((Q1l)/s)
22
`((x1Q)/r)]} (1)
from which we can get the predictive (marginal distri-
bution) of X:
`
p(x)4h(x,Q)dQ
#
1`
11/2 112
4(2pq)(rs) exp{1(l1x)
22
/[2(r`s)]} (2)
where q4(r
2
`s
2
)/(r
2
s
2
). If we scale the units (with-
out loss of generality) such that r
2
`s
2
41, then the
predictive distribution becomes
11/2 2
p(x)4(2p) exp{1(l1x) /2} (3)
which is immediately seen as being normal with mean
land variance one.
We now address what happens when a level of qual-
ity, x
t
, is observed on the next transaction. We also de-
fine the disconfirmation to be D
t
4x
t
1l, following the
traditional definition (Oliver 1980). (It is worth noting
that the disconfirmation, although conceptualized as a
difference, is not usually calculated mathematically,
but rather observed, and measured, directly.) Again
from standard Bayesian updating, the posterior distri-
bution of Q,p(Q|x
t
), is calculated as:
p(Q|x)4h(x,Q)/p(x)
ttt
1/2
4(q/2p) exp{1(q/2)[Q1(1/q)
222
((l/s)`(x/r))] } (4)
t
which is a normal distribution with mean l`Ds
2
and
variance q
11
4r
2
s
2
. Thus, the posterior mean in-
creases (decreases) when the disconfirmation, D,is
positive (negative). Also the customer’s uncertainty
decreases, regardless of the outcome, since q
11
,s
2
.
The predictive density of the quality of the next trans-
action, x
t`1
, given observed quality level x
t
,is
`
p(x|x)4f(x|Q)p(Q|x)dQ (5)
t`1tt`1t
#
1`
where f(x
t`1
|Q) is normal with mean land variance
r
2
. This predictive density is easily shown to be normal
with mean l`Ds
2
and variance r
2
`r
2
s
2
. The pre-
dictive density’s new mean is bigger (smaller) if the
disconfirmation, D, is positive (negative), and its vari-
ance is smaller regardless (since r
2
`r
2
s
2
#1), reflect-
ing the greater certainty created by experience.
From (4) we can see that the mean of the posterior
distribution exceeds that of the prior distribution
whenever the disconfirmation, D, is positive and is
lower when Dis negative. The same holds for the pre-
dictive distributions. In other words, the expectations
(and future predictions) move in the direction of the
perceived level of quality. Note also from (4) that the
variance is reduced, as makes sense from having more
experience. From Assumption 1, we assume a contin-
uous, twice differentiable utility function U(x), with U8
.0 and U9,0. From Assumption 3, we assume an
expected utility V, that is equal to *U(x)p(x)dx, where
p(x) is the predictive distribution of x. The expected
utility can be expressed in terms of mean and standard
deviation since the predictive distribution is normal.
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
82 Marketing Science/Vol. 18, No. 1, 1999
For example, if we assume an exponential utility func-
tion, such as is popularly used in modeling risky
choice problems, then the expected utility can be de-
termined by a tradeoff between expected value and
risk (Jia and Dyer 1996):
2
V4
a
`
a
l1
a
r(6)
i1i2ii 3ii
where V
i
is the preference measure of brand i,l
i
is the
expected performance of brand i, is the variance
2
r
i
measuring perceived risk or uncertainty of brand i’s
performance, and
a
1i
and
a
21
,
a
3i
.0 are constants.
This is a positive linear transformation of the certainty
equivalent form of the exponential expected utility.
The ratio
a
2i
/
a
3i
measures customers’ risk tolerance,
reflecting a tradeoff between the mean and variance in
determining preference.
Based on Assumption 3, the consumer chooses the
brand with the highest expected utility. Thus, a mul-
tinomial logit model (Luce 1959, McFadden 1974) can
be used to estimate the choice likelihood for each
brand:
p4exp(V) exp(V). (7)
ii
o
j
@
j
2.3. Propositions Arising from the Model
From the model above, a number of empirically test-
able propositions arise that warrant further scrutiny.
(Proofs of all of the propositions are available in an
Appendix that is available from the authors on re-
quest.) Following are some of the propositions result-
ing from the model that would seem to have a reason-
able chance of being empirically falsified:
Proposition 1. If an option is chosen and a better than
expected outcome is observed, then the probability of choos-
ing that option will increase.
Intuitively, this proposition follows from the theo-
retical formulation whereby the predictive distribu-
tion’s mean increases (and the predictive distribution
shrinks). Preference improves because quality now
seems better on average, and also less risky.
Proposition 2A. If the preferred option is chosen and
an expected outcome is observed, then the probability of
choosing that option will increase.
The logic here is that if the utility function is con-
cave, reducing the variance of the predictive distribu-
tion will increase the expected utility. Preference im-
proves not because the option seems better, but rather
because it is less risky.
Proposition 2B. If a nonpreferred option is chosen (as
is possible in our probabilistic choice model) and an expected
outcome is observed, then the probability of choosing that
option will increase.
Again, the shrinking predictive distribution in-
creases the expected utility. We separate Propositions
2A and 2B because 2B will not obviously hold even if
Proposition 2A is supported. Shrinking variance
makes it more certain that the nonpreferred option will
be worse, which would seem to suggest a plausible
non-Bayesian basis for which this proposition may not
hold. As before, preference for the option improves be-
cause the option seems less risky.
Proposition 3. A rational consumer may choose an
equally-priced option for which the expected quality is worse.
The intuition behind this proposition is risk aver-
sion. If one option has a larger variance for its predic-
tive distribution, the uncertainty regarding this per-
formance may induce many consumers to choose a
lower expected performance/less variance option. Due
to this, a less risky option can be preferable to a more
risky option with a higher mean.
Proposition 4. A worse than expected quality outcome
may still increase the probability of choice for that option.
The intuition for Proposition 4 is that there are two
countervailing effects at work. The worse than ex-
pected outcome (all other things being equal) will tend
to lower the predictive distribution and the expected
utility. However, at the same time the variance of the
predictive distribution is being reduced which will in-
crease expected utility, given a concave utility func-
tion. For a small enough negative disconfirmation, the
positive effect of the variance reduction will outweigh
the negative effect of the lowered expectation, leading
to an increase in preference.
Proposition 5. A negative disconfirmation will evoke a
greater relative change in preference relative to the case of
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
Marketing Science/Vol. 18, No. 1, 1999 83
zero disconfirmation than will a positive disconfirmation of
equal magnitude.
As mentioned previously, there will be two effects
on preference: a variance reduction effect and a dis-
confirmation effect. Experience always reduces vari-
ance, resulting (all other things being equal) in in-
creased preference. Positive disconfirmation increases
preference, and negative disconfirmation (all other
things being equal) decreases preference. Because of
the concavity of the utility function, increasing abso-
lute disconfirmation will create a disparity between the
absolute effect of a negative disconfirmation and that
of a positive disconfirmation. Proposition 5 refers to
the case of zero disconfirmation as the basis, which
eliminates the variance reduction effect (i.e., all cases
have the same reduced variance). Since there is only a
disconfirmation effect at work, this results in a simple
pattern of asymmetric changes in preference.
Proposition 6: Given diffuse priors, and an equal
historically-observed mean and variance, a sufficiently large
negative disconfirmation will cause a greater preference shift
in a less experienced customer. (Also a positive disconfir-
mation will cause a greater preference shift in a less expe-
rienced customer.)
This effect occurs because more experience produces
less uncertainty, but at a decreasing rate. In other
words, experience has diminishing returns. Therefore
the value gained from experience is large at first, re-
sulting in large changes in preference, but diminishes,
eventually resulting in small changes in preference.
This is consistent with other research that shows that
preference shifts less for more experienced customers
(Bolton 1998, Boulding et al. 1993, 1998).
While the theoretical decision model forms a math-
ematically rigorous way of thinking about quality per-
ceptions and customer retention, it is still only a math-
ematical abstraction. To determine the usefulness of
this approach, we must test whether customers’ be-
havior over time corresponds with the model’s predic-
tions. We now present results of two experiments in
which the propositions were subjected to empirical
test. We then discuss the implications of these results
and directions for future research.
3. Longitudinal Experiment
3.1. Overview
One hundred and sixty undergraduate students at two
large universities participated in a computerized
decision-making exercise in return for extra credit. The
experiment was designed to measure the effect of ex-
pectation distributions on discrete choice and on
choice probability. The exercise consisted of the con-
struction of a history of experiences among three
brands of camera batteries. Disconfirmation was ma-
nipulated, while probability of choice, performance ex-
pectation, and perceived variance in performance for
each brand of battery were measured at several points
in the experiment.
The independent variables are 1) the disconfirma-
tion (D4x
t
1l) between the actual and expected
performance of the chosen battery, 2) the expected per-
formance (l) of each battery, and 3) the perceived var-
iance (r
2
) of each battery. Disconfirmation was manip-
ulated, while expected performance and perceived
variance were measured. Expected performance for
each battery was measured by the question “I’d expect
the next battery (A/B/C) to last hours,” while per-
ceived variance for each battery was captured via sub-
jects’ response to the question “About 95% of the bat-
teries for Brand (A/B/C) last between and hours.”
Five different levels of disconfirmation were used:
ten hours above expected, zero disconfirmation, and
one, three, and ten hours below expected. The com-
puter exercise interactively managed the amount of
disconfirmation so that each subject was exposed to
two of the five different levels of disconfirmation in
experiences 4 and 7. For example, if the subject was to
be exposed to the three hour disconfirmation treat-
ment, the computer provided outcome feedback for
the purchase by subtracting three hours from the sub-
ject’s expectation for that brand.
3
3.2. Method
We used an unbalanced design with more subjects in
the zero and small disconfirmation (negative one and
three hours) conditions to increase statistical power for
those disconfirmation conditions. Sample sizes are
3
The order of exposure to the amount of disconfirmation was coun-
terbalanced.
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
84 Marketing Science/Vol. 18, No. 1, 1999
Table 2 Longitudinal Experiment—Means Across Conditions
Shift in Choice Probability
Disconfirmation Level N
Overall
Shift
Low
Experience
High
Experience
`10 Hours 33 8.70** 14.31 3.41
0 Hours 112 5.21** 5.93 4.44
Preferred option 40 2.88*
Nonpreferred option 72 6.51**
11 Hours 70 0.63 1.44 10.52
13 Hours 72 10.42 0.76 11.41
110 Hours 33 112.09** 113.81 110.47
**Shift statistically different than zero (
p
,0.01).
*Shift statistically different that zero (
p
,0.05).
Table 1 Longitudinal Experiment—Performance Feedback Across
Brands
Brands
Exp
1
Exp
2
Exp
3
Exp
4
Exp
5
Exp
6
Exp
7
Exp
8
Exp
9
Exp
10
1 725967
E
13
–
D
1
74 57
E
16
–
D
2
72 56 69
2 636960
E
23
–
D
1
59 68
E
26
–
D
2
67 64 65
3 666064
E
33
–
D
1
65 67
E
36
–D
2
59 67 63
E
ir
4Subject’s performance expectation for Brand
i
after round
r
.
D
j
4Disconfirmation level for condition
j
.
shown in Table 2. Further, the brand labels were coun-
terbalanced across subjects to control for order effects.
Each subject was asked to imagine that s/he had re-
cently purchased a brand of camera which used a spe-
cial type of battery. A special type of battery was used
in order to mitigate effects of category familiarity (i.e.,
so that each subject began the procedure with a diffuse
expectation). The subject was told that s/he had sam-
pled each brand of battery three times and was ac-
cordingly shown the first series of three experiences
(see Table 1, columns 1–3). The subject was asked to
provide his/her choice probability for each battery and
to give his/her performance expectation and perceived
variance for each battery. At the beginning of the
fourth experience, the subject was told that s/he had
purchased one of the three brands
4
(randomized across
subjects) and was exposed to the first disconfirmation
condition. Choice probability was then remeasured.
The outcomes for the other two brands were then
given and expected performance and perceived vari-
ance in performance were measured.
Following the fourth experience, subjects completed
a brief distractor task and were then given outcome
feedback on two more purchases of each brand (see
Table 1, columns 5 and 6). Performance expectations
and perceived variance were remeasured following the
sixth experience. At the beginning of the seventh ex-
perience, the subject was again told that s/he had pur-
chased one of the three brands (different from the
4
Subjects were told to imagine that they had purchased the brand,
“perhaps because it was on sale or because you had a coupon for
it.”
brand in the fourth experience) and was given out-
come feedback for this brand. Choice probability was
remeasured. The subject was then given outcome feed-
back on the other two brands to complete the seventh
experience. Following a second distractor task, subjects
were provided outcome feedback for three more pur-
chases of each battery, yielding a final history of ten
purchases of each battery. Finally, expected perfor-
mance and perceived variance were measured and the
subject was asked which battery she would choose.
3.3. Results
Table 2 shows the means for each experimental con-
dition. Most of our propositions regard shifts in choice
probability at specific levels of disconfirmation. Prop-
osition 1 predicts that the probability of choosing an
option will increase when the observed outcome is
greater than expected. This proposition is supported,
as probability of choice increased by an average of 8.7
points for the chosen option when the observed out-
come exceeded the expected performance (t
32
44.76,
p,0.01).
5
As predicted, when consumers experienced
positive disconfirmation, their probability of choosing
the option increased.
Proposition 2A predicts that if an expected outcome
is observed (i.e., zero disconfirmation) for the most
5
Although most of our propositions are directional, to be conserva-
tive we use a two-tailed test in our analysis.
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
Marketing Science/Vol. 18, No. 1, 1999 85
preferred option, the probability of choosing the op-
tion will increase. Proposition 2B makes a similar pre-
diction, but for a nonpreferred option. Both proposi-
tions are supported. The probability of choosing the
option increased when the option was the most pre-
ferred option, shifting almost three points (t
39
41.73,
p,0.10), while the probability of choosing the option
when it was not the most preferred option increased
over six points (t
71
45.26, p,0.01).
Proposition 3 predicts that subjects will not neces-
sarily choose the brand with the highest expected per-
formance. We tested Proposition 3 by asking subjects
to choose a brand after observing the ten experiences
with each brand. Using the multinomial logit model in
(6) and (7), as one would anticipate, the expected per-
formance exerts a significant effect on choice (
a
2
4
0.235, p,0.01). Importantly, consistent with Proposi-
tion 3, the perceived variance also exerts a significant
impact on subjects’ choice (
a
3
410.383, p,0.01).
Thus, a brand’s choice probability increases as the ex-
pected performance increases and decreases as the per-
ceived variance increases.
Proposition 4 predicts that an outcome that is worse
than expected may still increase the probability of
choosing the option. We test this proposition by ex-
amining the effect on choice probability of disconfir-
mation levels of one, three, and ten hours less than
expected. This proposition is not supported. The av-
erage probability of choosing the option increased
slightly (i.e., in the expected direction) in the negative
one hour disconfirmation condition, but the increase is
not statistically significant (t
69
40.50, NS). In the neg-
ative three hour disconfirmation condition, the aver-
age probability of choosing the option decreased
slightly, but again this decrease is not statistically sig-
nificant (t
71
410.34, NS).
Proposition 5 predicts that a negative disconfirma-
tion will have a greater impact on preference than a
positive disconfirmation of equal magnitude. We test
this by contrasting the 110 and `10 hour disconfir-
mation groups. Let X
1
be the difference between `10
and zero disconfirmation effects and X
2
be the differ-
ence between zero and 110 disconfirmation effects.
The variance of X
1
and X
2
is the sum of the variance
of the `10 and zero and 110 and zero disconfirmation
effects, respectively, and the variance of X
1
1X
2
is the
sum of the variances of X
1
and X
2
. The t-test of the
difference between X
1
and X
2
is statistically significant
(t
65
44.69, p,0.01), supporting Proposition 5.
Since Proposition 6 states that an equivalent discon-
firmation level results in a smaller shift in preference
for more experienced customers, we conducted an AN-
OVA with experience level (high/low), disconfirma-
tion, and their interaction as independent variables
and the shift in preference as the dependent variable.
6
As predicted by Proposition 6, the effect of experience
was significant (F
1,310
43.99, p,0.05), with smaller
shifts at the higher experience level. Specifically, choice
probability shifted by 2.7 points when experience was
relatively low (four experiences) and shifted only 0.3
points when experience was higher (seven experi-
ences). Not surprisingly, the shift in choice probability
was an increasing function of the amount of disconfir-
mation (F
4,310
422.09, p,0.01). Further, the interac-
tion between experience level and disconfirmation was
marginally significant (F
4,310
42.02, p,0.10).
In sum, we find support for Propositions 1, 2A, 2B,
3, 5, and 6. Subjects’ probability of choosing an option
increased if performance for that option met (Propo-
sitions 2A and 2B) or exceeded (Proposition 1) the ex-
pected outcome. Per Proposition 3, subjects did not
necessarily choose the brand with the greatest ex-
pected performance. Rather, they balanced the brand’s
expected performance against its variability in perfor-
mance. Further, while subjects’ probability of choosing
an outcome was not adversely affected by an outcome
that was slightly below their prior expectations, their
probability of choice did not increase (significantly) as
predicted by Proposition 4. The preference shifts cre-
ated by negative vs. positive disconfirmations were
greater in the negative disconfirmation direction, as
predicted in Proposition 5. Proposition 6 was sup-
ported as well—at a given disconfirmation level, more
experienced subjects tended to update their expecta-
tion to a lower degree than less experienced subjects.
While these results are encouraging, our first exper-
iment is flawed in two respects. First, the longitudinal
6
An analysis using prior preference as a covariate and preference as
the dependent variable produced almost identical results. For ease
of exposition, we discuss the analysis with shift in preference as the
dependent variable.
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
86 Marketing Science/Vol. 18, No. 1, 1999
nature of the design may have increased the chance
that subjects saw that two factors were being manip-
ulated and inferred the appropriate responses. How-
ever, it is difficult to see how this can explain our asym-
metry results. Second, we did not clearly demonstrate
that, holding satisfaction/quality fixed, choice is influ-
enced by the variance around this construct. Our sec-
ond experiment addresses both of these concerns.
4. Cross-Sectional Experiment
4.1. Overview
The second experiment was a 2 (high/low amount of
experience) 22 (zero/negative disconfirmation)
between-subjects design. Two hundred and twenty
three undergraduates participated in the experiment
in return for course credit. Three subjects misunder-
stood the directions and were eliminated from the
analysis, leaving a sample of 220 subjects.
4.2. Independent and Dependent Measures
As in the first experiment, our independent variables
are experience level (a proxy for s
2
) and disconfirma-
tion (D). High experience subjects were exposed to 20
experiences, while low experience subjects were ex-
posed to only three experiences. In the zero disconfir-
mation condition, subjects were shown an additional
outcome that was equal to the mean of the previous
experiences. Subjects in the negative disconfirmation
condition were shown an outcome that was two stan-
dard deviations below the mean of their previous
experiences.
Our primary dependent variable is perceived qual-
ity. We adopted the measures of perceived quality
used by Boulding et al. (1993), a 100-point scale an-
chored by “Very unfavorable” and “Very favorable.”
This measure was taken immediately following the ex-
perience manipulation (l) and again following the spe-
cific visit outcome manipulation (l`Ds
2
). We also
used the measures developed by Boulding et al. (1993)
to assess purchase intentions and likelihood to spread
word of mouth, 100-point scales anchored by “Very
unlikely” and “Very likely.” Like Boulding et al.
(1993), we find that these two measures are highly cor-
related (
a
40.86), so we combine them in our analysis.
As manipulation checks, we took several measures
following the experience manipulation.
7
First, we as-
sessed perceived variability of the service and per-
ceived consistency of the service. The first measure
asked subjects to rate how variable they had found the
service to be, while the second asked them to rate how
consistent the service had been. We then asked subjects
to rate how sure they were of the average level of qual-
ity of Cafe´ Au Lait and whether they were an occa-
sional customer or a regular customer. Following the
specific visit manipulation, we asked subjects how
closely the experience on that occasion matched their
expectations to assess subjective disconfirmation. This
was accomplished using a standard disconfirmation
question, “Overall, how closely did your experience
with Cafe Au Lait on this occasion match your expec-
tations?,” measured on a 100-point “Much Worse Than
Expected” to “Much Better Than Expected” scale. We
also measured subjects’ perceptions of how interesting
and realistic they found the study, as well as their gen-
der, age, and how many times they had stopped at a
coffee shop in the last 30 days.
4.3. Method
Subjects were shown a scenario describing a coffee
shop, the Cafe´ Au Lait. They were told that the coffee
shop had recently opened near campus and that they
had visited it (a) a few times in the low experience
condition, or (b) every day for the past four weeks in
the high experience condition. They then read a verbal
and graphical description of their series of experiences.
In the low experience condition, subjects examined a
graph of three experiences. In the high experience con-
dition, subjects examined four graphs with one graph
on each page. The first graph showed the first week,
the second graph showed the first two weeks, and so
on, so that by in the fourth graph the subjects could
see the entire series of 20 past experiences. We were
careful to construct the graphs so that the mean (83)
and the standard deviation (5.5) were identical be-
tween the low and high experience conditions.
Subjects were run in groups so that we could control
the amount of time (40 seconds) that each page was
7
All of the manipulation checks suggest that the experience manip-
ulation was successful and that service variability perceptions were
equivalent across groups. These are available from the authors.
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
Marketing Science/Vol. 18, No. 1, 1999 87
Figure 1 Cross-sectional Experiment Shifts in Perceived Quality by
Condition
Table 3 Cross-Sectional Experiment—Means Across Conditions
(Standard Deviation in Parentheses)
Experimental
Condition
Initial
Quality
Percep-
tions
Updated
Quality
Percep-
tions
Shift in
Quality
Percep-
tions
Subjective
Discon-
firmation
Behavioral
Intentions
High Experience/
Zero Disconfirmation
81.2
(6.9)
79.4
(8.5)
11.8 57.9
(10.5)
174.5
(22.5)
High Experience/
Negative Disconfirmation
80.2
(7.2)
68.3
(10.6)
111.9 36.3
(12.2)
145.1
(29.3)
Low Experience/
Zero Disconfirmation
79.0
(7.6)
78.2
(7.3)
10.9 57.1
(11.5)
154.6
(24.0)
Low Experience/
Negative Disconfirmation
78.7
(7.7)
62.3
(13.0)
116.4 40.7
(11.5)
119.8
(31.6)
viewed. For the experience manipulation to be suc-
cessful, it is critical that the high experience subjects
internalize the past experiences. Thus, we ensured that
the high experience subjects examined each graph for
a specified time to prevent them from speeding
through the survey. After subjects examined the
graphs, they completed a series of measures described
in the next section. Following the first set of measures,
subjects were exposed to the disconfirmation condi-
tion. As before, they were timed so that each subject
examined this stimulus for the same amount of time
(25 seconds). They were then instructed to complete
the remainder of the survey at their own pace, were
thanked, and released.
4.4. Results
4.4.1. Updated Quality Perceptions. To analyze
our data we use analysis of covariance with updated
quality perceptions as the dependent variable and ini-
tial quality perceptions and subjective disconfirmation
as covariates (Cronbach and Furby 1970, Lord 1958).
8
The fit for the model is relatively good, with an R
2
of
0.51 (see Table 3 for means). As predicted, the level of
experience exerts a main effect on updated quality per-
ceptions (t
214
42.21, p,0.05). Specifically, quality
8
Importantly, the groups were equivalent in terms of both initial
quality perceptions and subjective disconfirmation.
perceptions for high experience subjects updated less
than those of low experience subjects (16.5 versus
18.7 for high and low experience subjects, respec-
tively. Importantly, effects of prior perceptions of qual-
ity are controlled for in the analysis via the covariate
(t
214
48.64, p,0.01).
Not surprisingly, the effect of disconfirmation on
updated quality perceptions is significant (t
214
46.66,
p,0.01). Subjects who experienced no disconfirma-
tion updated their quality perceptions less (11.8) than
subjects who experienced negative disconfirmation
(114.2). Further, subjective disconfirmation exerted a
significant effect on updated quality perceptions (t
214
42.81, p,0.01), replicating recent findings in the
service quality literature (e.g., Anderson and Sullivan
1993, Boulding et al. 1993, Inman et al. 1997).
The interaction between experience and disconfir-
mation is significant (t
214
42.57, p,0.01). The shift
in quality perceptions across the four conditions is
shown in Figure 1. Both high and low experience sub-
jects updated their quality perceptions minimally
when the outcome was equal to the mean of past ex-
periences (11.8 for the high experience group and
10.8 for the low experience group). However, the low
experience group lowered their quality perceptions
significantly more than the high experience group
(116.4 versus 111.9 for the low and high experience
group, respectively) when the outcome was worse
than expected. Thus, consistent with prediction, ex-
perience appears to “inoculate” consumers to some ex-
tent against a single substandard outcome.
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
88 Marketing Science/Vol. 18, No. 1, 1999
4.4.2 Behavioral Intentions. In examining the ef-
fects of experience level and disconfirmation on be-
havioral intentions, it is unclear whether or not their
impact is mediated by quality perceptions. To test for
potential mediation, we use the method outlined by
Baron and Kenny (1986). Specifically, we examine the
effect of experience (as a proxy for uncertainty) and
disconfirmation on the proposed mediator, updated
quality perceptions, and the dependent variable, be-
havioral intentions, both with and without incorporat-
ing the effect of the mediator. Perfect mediation is
demonstrated if the independent variable exerts a sig-
nificant effect on the mediator as well as the dependent
variable but this effect becomes nonsignificant when
the mediating variable is incorporated as a covariate.
If the effect remains significant but the effect size sig-
nificantly reduces, partial mediation is demonstrated.
We already presented evidence of the significant ef-
fects of both experience and disconfirmation on up-
dated quality perceptions. In the second ANOVA, us-
ing behavioral intentions as the dependent variable,
both experience (t
216
46.22, p,0.01) and disconfir-
mation (t
216
48.82, p,0.01) demonstrate significant
main effects on behavioral intentions. Specifically, high
experience subjects state a much greater intention to
visit the service again and to tell others than do low
experience subjects (160.9 vs. 137.2 for the high and
low experience groups, respectively). However, when
updated quality perceptions are added to the model,
the effect of experience is undiminished (t
215
45.74, p
,0.01), while that of disconfirmation is greatly re-
duced (t
215
42.74, p,0.01). Thus, these results sug-
gest that cumulative experience exerts a direct influence on
behavioral intentions while the effect of disconfirmation is
largely mediated by updated quality perceptions.
5. Discussion and Future Research
5.1. Management Implications
Both our analytical model and our empirical results
shoot holes in some seemingly reasonable quality max-
ims. Let us consider in particular the truisms from the
Introduction:
•“It is necessary to exceed customer expectations.”
Both our analytical model and the longitudinal exper-
iment contradict this (although the cross-sectional ex-
periment, which is perhaps less sensitive to this effect,
does not). The longitudinal experiment showed signifi-
cant positive preference shifts if customer expectations
were exactly met. The reason, based on the analytical
model, is that experience causes a shrinkage in the var-
iance of the predictive distribution for the next trans-
action. That is, experience with a brand leads to de-
creased risk, and decreased risk leads to greater
preference. So in fact, meeting expectations should un-
ambiguously result in higher preference. The analyti-
cal model also suggests that even not quite meeting ex-
pectations might still increase preference, although this
effect was not significant in the longitudinal experi-
ment. Of course, exceeding customer expectations will
still be required if the company seeks to induce cus-
tomer delight (Oliver et al. 1997), but lower levels of
performance may also produce positive results.
•“If a customer expects a bad level of quality and receives
it, he/she will reduce his/her level of preference for the
brand.”
The analytical model and both experiments contradict
this. In the cross-sectional experiment, subjects did not
lower their quality perceptions of the service when
their expectations were met. The longitudinal experi-
ment showed significant positive preference shifts even
for the nonpreferred option, indicating that even when
expectations were low, meeting expectations raised
preference. The analytical model explains why this
preference shift can occur. Again experience shrinks
the variance of the predictive distribution and reduces
risk, thereby increasing preference.
•“Given two equally-priced options, the customer will
choose the one with the higher expected quality.”
Although this statement seems obviously true, it also
is contradicted by both the analytical model and the
longitudinal experiment. The reason, again, is risk. A
higher expected quality can be outweighed by greater
perceived variability. Based on the logit analysis of the
data from the longitudinal experiment, perceived var-
iance has a significant, negative impact on choice. This
resulted in nearly half of our subjects choosing an op-
tion with a lower expected quality.
•“Management should always focus on its most loyal
customers.”
Given that the most loyal customers are the most ex-
perienced (which is consistent with the most typical
behavioral definitions of loyalty), our research casts
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
Marketing Science/Vol. 18, No. 1, 1999 89
doubt on this seemingly self-evident maxim. The cross-
sectional experiment shows that disconfirmation has
the biggest preference impact on less-experienced cus-
tomers, and this phenomenon is supported theoreti-
cally by the analytical model, for any nonnegative dis-
confirmation, and for any sufficiently large negative
disconfirmation.
9
This would seem to imply that man-
agement should pay more attention to its newer (pre-
sumably less loyal) customers, because those are the
customers for which quality differences will have the
greatest impact. In other words, less experienced
(loyal) customers are easier to lose, while more expe-
rienced (loyal) customers are harder to lose, all other
things being equal.
Several other managerial implications arise from
this work. First, customer satisfaction and quality mea-
surement surveys would benefit from including an ex-
perience variable. This is because the degree to which
preferences shift is dependent upon the degree of ex-
perience, with more experienced customers being
more difficult to shift. All other things being equal, for
maximum shift of preference, less experienced custom-
ers should be targeted. Further, in addition to measur-
ing perceived quality, perceived variability and/or
consistency in quality is important to capture as well.
Both factors influence overall quality perceptions and
behavioral intentions.
Our results suggest that it is insufficient for a good
or service to be perceived as better (i.e., a higher ex-
pected quality) than its competitor. For example, new
Brand A, even though it has a higher expected value
than Brand B, may not be preferred to Brand B because
of greater perceived variability resulting in its per-
ceived value being worse. The implication to manage-
ment launching a new product is that actual trial may
be more powerful than supplying information through
such methods as advertising. This suggests that cou-
pons, promotions, and other ways to induce trial may
be necessary in the early stages of a product launch,
even if a positive brand image has been already created
through advertising.
9
Interestingly, there is an interval for which a negative disconfir-
mation produces a smaller shift in preference for the less experienced
customer—whenever that customer’s positive preference shift from
variance reduction is roughly equivalent to the negative preference
shift from mean reduction.
Finally, worse-than-expected quality hurts more
than better-than-expected quality helps. This replicates
previous findings by several researchers (e.g.
Anderson and Sullivan 1993, DeSarbo et al. 1994, Rust
et al. 1995). The managerial takeaway is that problems
should be addressed first, and then positive opportu-
nities. In other words, process improvement initiatives
should first focus on eliminating unsatisfactory service
encounters (i.e., providing consistent quality) before
trying to delight customers.
5.2. Contributions to the Quality Literature
Our results contribute to the quality literature in four
respects. First, they suggest that consumers are sensi-
tive to not only the average performance of a product,
but also to its variability around this mean. In other
words, it is not sufficient for a brand to strive to in-
crease its overall quality—it must also strive to reduce
the risk of an outcome deviating from this performance
expectation. Second, consumers are more likely to re-
choose a brand (i.e., retention is increased) if the brand
performs as expected. Contrary to the traditional sat-
isfaction model, meeting expectations can sometimes
be interpreted by consumers as a favorable outcome,
as the experience provides more information about the
brand and reduces the perceived variance of the
brand’s future performance. Third, probability of
choice is not necessarily adversely affected by an out-
come that is slightly below expected. This is apparently
because the customer becomes less concerned with
downside risk if the disconfirmation is relatively mi-
nor. Finally, if negative disconfirmation is relatively
substantial, quality perceptions and behavioral inten-
tions are negatively affected, but this effect is moder-
ated by the amount of prior experience.
Figure 2 shows a graph of the mean shifts in choice
probability across the five disconfirmation conditions
in the longitudinal experiment. One notes that con-
sumers’ reaction to disconfirmation is nonlinear and
they appear more sensitive to negative disconfirmation
than to positive disconfirmation. While this curve is
similar to the effects one typically expects under
Kahneman and Tversky’s prospect theory (1979), our
theoretical perspective and empirical results suggest
that the curve may be “choice-neutral”
10
at a value less
10
We call the curve “choice-neutral” at the point where observed
quality results (on average) in unchanged probability of choice.
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
90 Marketing Science/Vol. 18, No. 1, 1999
Figure 2 Shift in Choice Probability in Response to Disconfirmation
than zero disconfirmation. Specifically, the curve is
choice-neutral at a negative disconfirmation of about
two hours below expected, which is equivalent to ap-
proximately one half of a standard deviation from the
mean. Importantly, the direction of this deviation is
consistent with our prediction (Proposition 4) that a
small negative disconfirmation can produce an in-
crease in preference.
Our results suggest that the most important time for
a brand to establish its quality perceptions in the minds
of consumers is at the time that consumers have little
prior experience with the category. In Bayesian terms,
this is at the point when consumers hold a more diffuse
prior expectation regarding the brand’s performance.
Ironically, the time where many consumers are inex-
perienced with the product is precisely the point in the
product life cycle where the manufacturer is often
struggling with maintaining consistent product qual-
ity. This may explain the Golder and Tellis (1993) find-
ings regarding the failure of many pioneers. If the pio-
neer struggles with quality, a subsequent (more
consistent quality) entrant can gain a perceived quality
advantage with consumers. Alternatively, once the
pioneer has established a relatively high prior expec-
tation of performance and a low perceived variability
(through knowledge gained by consumption experi-
ence), this imposes a barrier to later entrants into the
market since the pioneer finds customers are much eas-
ier to retain. A subsequent entrant must have a signifi-
cantly higher level of expected quality to counteract its
higher perceived variability and risk.
5.3. Future Research and Limitations
There are many interesting ways in which this research
might be extended. For example, the model might be
made richer (albeit more complicated) by the inclusion
of effects not currently accounted for, such as allowing
for variables that affect perceived quality, or otherwise
change (bias) the perception and updating process. In-
teresting work in this regard is already underway
(Boulding et al. 1998). The existence of threshold effects
is another promising area for research. In other words,
updating may only take place when absolute discon-
firmation exceeds a certain threshold. Otherwise the
observation is simply viewed as an expected outcome.
It is also possible that those thresholds may vary across
customers. That might explain the fact that many of
our subjects did not update their preferences.
While our results suggest that, on average, consum-
ers update their expectations in a largely Bayesian
manner, some consumers are probably more Bayesian
than others. In the first experiment, choice probability
for most subjects increased in the 10 hour disconfir-
mation condition and decreased in the 110 hour dis-
confirmation condition. However, in the zero, negative
one hour, and negative three hour disconfirmation
conditions, the modal subjective probability shift was
zero. Approximately half the subjects in both the zero
and negative one hour conditions reported that their
choice probability would be unaffected, while a third
of the subjects in the negative three hour condition
gave this response. Even allowing for measurement er-
ror, some “updating heterogeneity” appears evident
among consumers.
Additional research is warranted to explore the vari-
ables that account for the heterogeneity in consumers’
updating processes. For instance, the Boulding et al.
(1993) notion of “should” expectations could be ex-
tended to expectations of variability in quality. Further,
individual differences in need for cognition (Cacioppo
and Petty 1982) or tendency to engage in post-purchase
regret (e.g., Inman and Zeelenberg 1998) might be im-
portant moderators of perception updating and
changes in probability of choice in response to discon-
firmation. On the other hand, it is important to explore
the contexts in which consumers tend to form expec-
tations of quality that are resistant to change.
Limitations of our study deserve mention. First, as
in all research, one must be careful in overgeneralizing
results. However, we tested our updating model in
two quite different experimental designs and found
RUST, INMAN, JIA, AND ZAHORIK
The Role of Customer Expectation Distributions
Marketing Science/Vol. 18, No. 1, 1999 91
consistent support for our thesis. Second, we tested our
propositions in lab contexts and our results are based
on self-reports. It is important to replicate this research
in other contexts to provide greater confidence in our
results. For example, our subjects observed several it-
erations of outcomes over a short period of time. In a
field context, consumers’ priors might become more
diffuse (due to forgetting) as the interpurchase time
increases or become less diffuse as additional infor-
mation is obtained from advertising or word of mouth.
Finally we should note the assumption of normal dis-
tributions. If customers’ priors and likelihood distri-
butions are not normally distributed, then a more com-
plicated Bayesian formulation would result. This
research provides the basis for subsequent work in the
important area of how consumers dynamically update
their quality perceptions and their preferences.
11
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This paper was received January 2, 1996, and has been with the authors 26 months for 2 revisions; processed by William Boulding.
