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7/26/2019 Barry L Bayus, Sachin Gupta
1/11
257
An empirical analysis
of consumer durable
Barry L. Bayus
Uni uersiry of Nort h Carolina Chapel Hil l N C 27599 USA
Sachin Gupta
Cornell Vniuersi@ I thaca NY 14853 USA
Final version received April 1992
Despite the dominant role of replacement purchases in
many durable categories, previous research has not empha-
sized modeling replacement behavior. In this paper, we de-
velop a descriptive model for replacement intentions based on
variables associated with product and household characteris-
tics, and empirically estimate this model with cross sectional
data for a set of home appliances. Results indicate that the
perceived condition of the currently owned unit and its age
are significantly related to replacement intentions. Whether
or not a spouse is working and expected future household
financial situation are also significant explanatory variables.
Implications and directions for future research are also dis-
cussed.
Corr espondence t o: B.L. Bayus, Kenan-Flagler Business
School, University of North Carolina, Chapel Hill, NC 27599,
USA
* This research was conducted while the first author was a
faculty member at Cornell University. Funding for the data
collection was provided by the Johnson Schools Institute
for Research in Marketing at Cornell University. Special
thanks are extended to Peter Dickson, Dick Wittink, the
editor and two reviewers for their comments on an earlier
draft.
replacement intentions *
1 Introduction
The high penetration of consumer durables
such as refrigerators, clothes washers, vac-
uum cleaners, and coffee makers implies that
a large portion of currently observed sales
are due to replacement purchases. For exam-
ple, according to industry sources, in 1985
replacements accounted for 88% of refriger-
ator sales, 78% of washer sales, 77% of vac-
uum cleaner sales, and 67% of coffee maker
sales in the US (Merchandising, 1986). As
the installed base of products ages over time,
replacement sales are also expected to in-
crease. Thus, a better understanding of the
durable replacement process can be impor-
tant for areas such as sales forecasting, mar-
keting new and existing products, and pro-
duction planning. Knowledge of the impor-
tant variables related to the replacement de-
cision may also enable manufacturers to de-
velop more precise targeting strategies
through the use of database marketing tech-
niques (Bayus, 1991b).
Intern. J. of Research in Marketing 9 (1992) 257-267
North-Holland
Despite the dominant role of replacement
purchases in many durable categories, previ-
ous research has not paid much attention to
the replacement decision. The aggregate de-
mand for consumer durables has, however,
received considerable research effort (e.g.,
see the reviews in Dickson and Wilkie, 1978;
Pickering, 1981). Studies have empirically in-
vestigated variables related to ownership of
durables (e.g., Nickels and Fox, 1983; Kim,
19891, durable expenditures (e.g., Strober and
Weinberg, 1977; Weinberg and Winer, 1983;
Van Raaij and Gianotten, 19901, and proba-
bility of purchase (e.g., Winer, 1985b). Al-
though demand for durable goods is concep-
tualized to come from first time purchases,
0167-8116/92/ 05.00 0 1992 - Elsevier Science Publishers B.V. All rights reserved
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B.L. Bayus S. Gupra / Consumer dura ble replacement i nt ent ions
replacements, and purchases of additional
units (e.g., Pickering, 1981; Winer, 1985a),
few empirical studies have considered re-
placement behavior. Attention has tended to
focus on the influence of household charac-
teristics (e.g., income, working wife) on pur-
chase, while product characteristics (e.g.,
condition of a currently owned unit) are gen-
erally ignored. By and large, matching empir-
ical data with econometric models has re-
sulted in disappointing results in terms of
overall statistical fits (e.g., Winer, 1985b) and
predictive ability (e.g., McNeil, 1974). An
exception is Bayus et al. (1989) in which very
good forecasting results for color televisions
were achieved by modeling the separate de-
mand components of sales.
In this paper, we (1) develop a descriptive
model for replacement intentions based on
variables representing product and house-
hold characteristics, and (2) use cross sec-
tional data for a set of home appliances to
empirically estimate this replacement model.
Our results indicate that the perceived con-
dition of the currently owned unit and its age
are significantly related to replacement in-
tentions. Whether or not a spouse is working
and the expected future household financial
situation are also significant explanatory
variables. Importantly, we find no intrinsic
product specific effects (i.e., the product spe-
cific intercepts in a logit model are not statis-
tically significant).
In the next section, the related literature
for consumer durable demand is reviewed. A
model of durable replacement intentions is
then developed. The sample and data col-
lected are next described, and the dependent
and independent variables are defined. Re-
sults based on multivariate logistic regression
analyses are then discussed. Finally, implica-
tions of these findings and suggestions for
further research are discussed.
2. Related literature
Major research thrusts in the consumer
durables area have included the following
topics: (1) information search and decision
making (e.g., see the review in Beatty and
Smith, 19871, (2) planning of purchases and
the acquisition sequence of durables (e.g.,
Kasulis et al., 1979; Dickson et al., 1983;
Bayus and Rao, 1989), and (3) post-purchase
behavior (the disposition of durables-e.g.,
Jacoby et al., 1977; DeBell and Dardis, 1979;
consumer dissatisfaction and complaint be-
havior-e.g., Tse and Wilton, 1988). None of
these efforts, however, explicitly studies con-
sumer replacement behavior.
Two recent efforts investigate the timing
of replacement purchases for single durable
products. Antonides (1990) empirically stud-
ies the replacement behavior (conditional on
a failure) for washers and finds that income
and household size are positively related to
replacements (as opposed to repairing the
item). Bayus (1991a) reports that demo-
graphic characteristics, attitudes and percep-
tions, and search behavior of consumers
trading in an automobile are significant ex-
planatory variables of the timing of automo-
bile replacements.
Replacement demand is included in the
general conceptual model for consumer
durable demand proposed by Pickering
(1981) and revised by Winer (1985a). Based
on a review of the major efforts concerned
with predicting durable demand, Pickering
(1981) proposes a behavioral model in which
demand is a function of purchase expecta-
tions (i.e., intentions). Purchase expectations
in turn are modeled as a function of personal
financial circumstances and expectations
(which is related to consumer confidence),
personal circumstances (e.g., household
move, marriage), orders of acquisition of new
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B.L. Bayus S. Gupt a / Consumer durabl e replacement int ent ions 259
durables, rates of depreciation of existing
units, and current and future expectations of
product characteristics. Dynamic and feed-
back effects are also considered by allowing
unanticipated events (e.g., product failure,
special price/ promotion, financial windfall,
unavailability of desired product) to affect
the demand for a particular durable.
As conceptualized in Pickering (1981), re-
placement demand involves an implicit com-
parison of the utility likely to be derived
from the purchase of a new item as com-
pared with the utility obtained from the ex-
isting unit. He suggests that this comparison
will depend on the age of the existing unit,
its reliability, an assessment of whether it is
likely to break down or require replacement
in the near future, and to a lesser extent, the
perceived attractiveness of new, more up to
date units available in the marketplace.
However, no detailed operationalization of
variables which influence replacement pur-
chases is given, nor are we aware of any
study which has empirically estimated the
influence of these variables on replacement
demand.
3.
A model of durable replacement inten
tions
In this section, a descriptive model of re-
placement intentions for a consumer durable
is developed. Variables associated with prod-
uct and household characteristics are consid-
ered as predictors. Intentions are expected
to be an important indicator of replacements
since durable purchases are considered a
planned purchase (Pickering, 1984). Empiri-
cal studies by Morrison (1979), Kalwani and
Silk (19821, and Jamieson and Bass (1989)
also demonstrate a strong relationship be-
tween stated intentions and actual purchases
of durables. Kalwani and Silk (1982) further
find that durable purchase intentions are lin-
early related to purchase behavior.
We can specify the functional relationship
for replacement intentions as
R(X) =g(X) +e, (1)
where g(X) is the deterministic component
of replacement intentions and is dependent
on the set of explanatory variables X. Here,
E is the error term (stochastic component) of
intentions. The set of variables in X include
characteristics of the product currently
owned and household characteristics. Fol-
lowing the conceptual model proposed by
Pickering (1981), possible effects due to the
stock of durables owned on replacement in-
tentions for a particular product are not con-
sidered (i.e., we assume the indirect utilities
of product replacement are separable). Re-
laxing this assumption is left for future re-
search.
Assuming that the error term and is iid
according to a Type I extreme value distribu-
tion, (1) can be transformed into the familiar
logistic function. Letting P denote the prob-
ability of replacing a durable, the model is
P=l/.[l+exp(-CY-X/3)],
(2)
where X is the vector of explanatory vari-
ables, j3 is the coefficient vector, and a is an
intercept term. We note that the marginal
impact on the replacement probability due to
changes in an explanatory variable (say xi) is
pjP l - PI. Because P is a function of sev-
eral explanatory variables, the marginal im-
pact of a single variable thus depends on the
values of the other variables. In other words,
the interactive effects of several explanatory
variables are implicitly included in (2). Since
the dependent variable we will analyze is
binary (likely or not likely to replace),
As part of the empirical study described later in this paper
replacement purchase data for refrigerators and coffee
makers were also collected along with intentions data. Re-
sults not reported in this paper indicate that the same set of
explanatory variables for replacement intentions and pur-
chase were signif icant for both these products.
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260 B.L . Bayus, S. Gupta / Consumer durable replacement intentions
maximum likelihood methods can be used to
estimate the parameters of (2); the logistic
regression package LOGIST) implemented in
SAS is used in our analyses (Harrell, 1986).
We next discuss the specific variables we
use to account for product and household
characteristics.
3.1 Product characteristics
The perceived condition of the existing
unit is expected to be an important variable
for replacement intentions (e.g., households
owning a unit in poor condition will usually
have higher replacement intentions than
those owning a unit in good working condi-
tion). Current unit condition is a function of
physical wear and tear (i.e., usage), mainte-
nance and repair efforts, and the quality of
the original brand purchased.
We also expect that the age of the cur-
rently owned unit will be an important indi-
cator of product obsolescence. This obso-
lescence may be due to the desire for new
technology and/or features, image or styling
preference changes (Bayus, 1991a), and
changes in price expectations (Winer, 1985b).
In order to describe this aspect of consumer
durables, we consider the product specific
hazard rate. The duration time (i.e., time
between purchases or age of the existing
unit) is assumed to have some p.d.f. f t) and
c.d.f. F t). The hazard rate h t) =f t>/[l -
F t)] is the likelihood that a replacement
purchase is made for a unit of age t, given
that it was not replaced in (0,
t).
For durable
purchases, an increasing hazard rate is ex-
pected (i.e., the probability of a replacement
purchase increases as the units age in-
creases). As discussed in Schmittlein and
Helsen (1990), the Weibull distribution f t)
=y/ y tY-
exp[ - ( t/f3)Y]> captures several
possible hazard forms, including concave in-
creasing hazards (1 < y < 2), linearly increas-
ing hazards (y = 2), and convex increasing
hazards (y > 2). Based on empirically fitted
replacement distributions for several
durables, Weibull distributions with y close
to 2 provide very good fits (Bayus, 1988;
1991a). Thus, the Rayleigh distribution (i.e.,
a Weibull with y = 2) is used to model
durable hazard rates. For the Rayleigh, h t)
= 2et.
3.2 Household characteristics
Although there is some disagreement over
the empirical significance of specific mea-
sures, previous studies indicate that the gen-
eral variables of stage in the family life
cycle and need for convenience are im-
portant determinants of durable purchases.
Strober and Weinberg (1977) and Weinberg
and Winer (19831, for example, find that
younger households are more likely to be in
the durable acquisition stage (since they tend
to own fewer durables). Winer (1985b) finds
that age of household head exhibits a nonlin-
ear relationship (i.e., U-shaped) to purchase
probability. His results suggest that replace-
ment demand is associated with older house-
holds. Households in the later life cycle
stage are more likely to need a replacement
due to cumulated usage over time and/or
may be better able to afford a replacement
(since young children are generally not pre-
sent in the household). Contrary to Strober
and Weinberg (19771, Weinberg and Winer
(19831, and Winer (1985b), Kim (1989) finds
that wifes employment is significantly re-
lated to durable ownership, even after con-
trolling for income and life cycle effects.
Finally, there is general agreement that a
recent move is associated with durable pur-
chases (e.g., Winer, 1985b; Wilkie and Dick-
son, 1985). Based on this literature, we hy-
pothesize that older households, households
with working wives, and households that have
recently moved are more likely to have posi-
tive replacement intentions for currently
owned durables.
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B.L. Bayus, S. Gupta / Consumer durable replacement intentions
261
Generally speaking, household income is a
significant and positive variable in studies of
durable ownership (e.g., Nickels and Fox,
19831, durable expenditures (e.g., Strober and
Weinberg, 1977; Weinberg and Winer, 1983;
Van Raaij and Gianotten, 1990), and proba-
bility of purchase (e.g., Winer, 1985b). In a
study of the multidimensional measure of
consumer confidence (including current and
expected evaluations of the general eco-
nomic situation, household finances, price
increases, and savings), Van Raaij and Gian-
otten (1990) conclude that income is the most
important determinant of consumer durable
spending. A factor called household finan-
cial situation (composed of perceived cur-
rent and expected household finances) was
also significant, while another factor termed
development of the general economic situa-
tion (composed of perceived current and
expected general economic situation, price
increases, and unemployment) was not a sig-
nificant explanatory variable of durable
spending. Based on these and other related
studies, we hypothesize that the effects of
household income and expected future
household financial situation on replacement
intentions are positive.
3.3 Summary
Based on the previous discussion, the re-
sultant set of eight explanatory variables and
their hypothesized direction of influence on
durable replacement intentions is summa-
rized in Table 1.
4.
The empirical study
4 1 Data
An empirical analysis of durable replace-
ments requires that consumers have experi-
ence with (e.g., currently own) the products
being considered. Due to their relatively long
Table 1
Explanatory variables and hypotheses
Variable
Definition Hypothesized
direction
of influence
CONDITION
reported condition of
unit currently owned
negative
HAZARD
SPOUSE
HHAGE
HHAGE
MOVED
INCOME
EXP_
FINANCES
calculated hazard rate
of unit currently owned
positive
whether or not household
positive
has a working wife
reported age of
household head
negative
squared age of
household head
positive
whether or not household
positive
has recently moved
reported gross annual
household income
positive
expected future
household financial
situation
positive
lifetimes, consumer durables also tend to be
purchased infrequently. These inherent
product and decision characteristics imply
that relatively large samples are needed to
investigate the replacement process (see also
Cox et al., 1983).
Unfortunately, publicly available panel
data sets (e.g., Surveys of Consumer Finances
University of Illinois Survey Research Cen-
ter) do not collect data on key aspects associ-
ated with the replacement decision (e.g., age
and condition of currently owned units).
However, an opportunity to collect cross sec-
tional information on the variables in Table
1 was provided by the Arkansas Household
Research Panel, organized and maintained
by the University of Arkansas in the US.
Households were mailed a four-page ques-
tionnaire in January 1990 concerning their
ownership and twelve month purchase inten-
tions (four-point scale) for several home ap-
pliances: stove, refrigerator, washer, color
television, video cassette recorder, vacuum
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262
Table 2
Sample profile
B.L. Bayus S. Gupt a / Consumer durabl e replacement int ent ions
Respondents Non-
respondents
Total
Mean age HH head
> High school education
Gross annual income
< 35K
35K- 6SK
> 65K
Married
Spouse employed
Sample size
56 years
59%
64%
31%
5%
76%
30%
407
53 years
57%
62%
31%
7%
80%
40%
154
55 years
58%
64%
31%
5%
77%
33%
561
cleaner, and coffee maker. This set of appli-
ances was selected to represent a range of
product categories. Prior research (Pickering
et al., 1973; Bayus and Carlstrom, 1990) indi-
cates that these appliances can be separated
into three groups based on perceptual mea-
sures: major home appliances (stove, re-
frigerator, washer, vacuum cleaner), house-
wares (coffee maker), and entertainment
items (color TV, VCR).
Information on the age (in years) and con-
dition (three-point scale: good, fair, poor) of
currently owned units was also collected. De-
mographic data such as age of household
head (in years), income (eleven-point or-
dered scale), and whether a spouse was em-
ployed (if married) were also available. Fi-
nally, information on the length of residence
at the current address and expected future
household financial situation was collected
(three-point scale: better, same, worse than
now). *
Completed questionnaires were received
from 407 households owning their home or
condominium, representing a response rate
of over 70%. The percentage of households
2
We note that Van Raaij and Gianotten (1990) use a five-
Since replacement intentions were collected using a four-
point scale (much better to much worse than now) to
point scale, estimation procedures such as multinomial logit
measure future household financial situation. In their analy-
or probit, or ordered logit could also be used. However,
ses this scale is assumed to have equal interval properties.
other analyses (using an ordered logit model) not reported
Since the true nature of such a scale has not been estab-
in this paper indicate that the main conclusions remain
lished, we use a three-point ordered scale, but only assume
unchanged. Thus, for ease of interpretation we report re-
ordinal properties.
sults based on a binary dependent variable.
owning these eight home appliances was gen-
erally over 70%. Demographic profiles of the
entire panel and the samples of respondents
and nonrespondents are given in Table 2.
Generally speaking, the sample of respon-
dents is a little older and has a smaller
percentage of households with a working
spouse than the nonrespondents. No statisti-
cally significant differences exist in terms of
household income, education of the house-
hold head, and the percentage of married
households.
Most of the variables in Table 1 have
natural definitions based on the survey ques-
tions.
Replacement intentions were di-
chotomized into positive (1 = definitely or
likely) and negative (0 = not likely or defi-
nitely not) intentions by combining response
categories. 3 Since unit condition was re-
ported using a three-point scale, two dummy
variables are used to represent perceived unit
condition (GOOD = 1 if unit condition is
good, 0 otherwise; POOR = 1 if unit condi-
tion is poor, 0 otherwise). Similarly, expected
future household financial situation is repre-
sented by two dummy variables BETTER =
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B.L . Bayus, S. Gupta / Consumer durable replacement intentions
263
1 if expected financial situation is better than
now, 0 otherwise; WORSE = 1 if expected
financial situation is worse than now, 0 oth-
erwise). Relating these dummy variables to
Table 1,
POOR
and
BETTER
are hypothe-
sized to have positive coefficient signs, and
GOOD and WORSE are hypothesized to be
negatively related to replacement intentions
and purchase. MOVED is defined as 1 if the
household has lived at the present address
for less than one year, 0 otherwise. The
HAZARD rate for product i is calculated as
28 t i j where tij is the age of product i for
household j and the parameter Bi is found
using the overall mean replacement age of
product i (CL= i(rr/0)/). Estimates of
mean product replacement ages are available
in trade publications and are given in Table
3.
4.2 Analysis method
In order to estimate the replacement in-
tention model, we pooled the data for the
407 respondent households across the seven
products. Aftering deleting observations with
missing values, the total sample size available
for analysis is 2132. Supporting this decision
are two factors: (1) due to the nature of
consumer durables and the replacement de-
cision, a relatively small number of house-
holds indicated a positive replacement inten-
Table 3
Product characteristics for home appliances studied
tion for each of the eight products consid-
ered individually; and (2) the product charac-
teristics summarized in Table 3 suggest that
the expected relationship between unit age,
unit condition, and intentions are similar for
each separate product. Although the model
developed in Section 3 does not consider
differing effects by product category, we al-
low for intrinsic product specific effects in
the logistic regression model (i.e., product
specific constants; see Chintagunta, 1992) by
defining six appropriate dummy variables:
STOKE (1 if stove, 0 otherwise), FRlDGE (1
if refrigerator, 0 otherwise), WASHER (1 if
clothes washer, 0 otherwise), CW (1 if color
TV, 0 otherwise),
VCR
(1 if VCR, 0 other-
wise), and VACUUM (1 if vacuum cleaner, 0
otherwise). Here, coffee maker is assumed to
be the base product (i.e., if all product
dummy variables equal zero). Everything else
being equal, if a particular appliance has
some inherent replacement importance or
priority in the household, then we expect
the dummy variable for that product to be
statistically significant.
5. Resdts
5.1 M odel fit
The overall fit of the logistic regression
model is very good. Classification results for
Stove Refrigerator Washer Color
VCR
Vacuum
Coffee
TV
cleaner maker
Mean replacement (years) a
ge
15 13 12 8 7 11 3
Mean unit age (years)
Positive replacement intentions 15.8 14.5 11.5 9.6 4.5 11.2 4.8
Negative replacement intentions 11.0 9.0 8.0 5.7 2.9 7.2 3.8
Positire replacement intentions ( )
Unit in good condition 4b 2 3 5 3 4 0
Unit in poor condition 64 30 100 80 100 77 100
Sample size 371 360 368 373 259 366 35
a From Appliance, September 1984.
Of all households owning a stove in good condition. 4% had positive replacement intentions.
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264 B.L. Bayus, S. Gupta / Consumer durable replacement infentions
this model are in Table 4. The percentage of
correctly classified cases is (111 + X308)/
2132 = 90.0%. This is better than a random
proportional chance model, which yields a
hit rate of
p* +
(1 -p)* where p is the
prior probability of positive intentions. Esti-
mating p = 0.131 based on the observed pro-
portion of positive intentions, classification
accuracy for a random model is 77.2%. Al-
though not shown, correct predictions for
individual products were over 85% in all
cases. Due to the relatively small sample
percentage of positive intentions (280/2132
= 13.1%), it is not surprising that the logistic
regression model predicts negative replace-
ment intentions better than positive inten-
tions (98% correct for negative intentions as
opposed to 40% correct for positive inten-
tions). This may also indicate that the model
does not capture all of the important factors
underlying the replacement decision. We dis-
cuss this point in a later section.
5.2 Estimation results
The estimated coefficients for the logistic
model are reported in Table 5. The model
chi-square is highly significant. The coeffi-
cients of the four significant variables are in
the hypothesized direction. The product
characteristics of perceived unit condition
and hazard rate are significant, and show
negative and positive effects, respectively.
Whether a spouse is working and expected
future household financial situation are posi-
tive and significant. None of the product
Table 4
Overall model fit
Actual
replacement
intentions
Positive
Negative
Total
Predicted replace-
ment intentions
Positive Negative
111
169
44 1808
155
1977
Total
280
1852
2132
Table 5
Estimation results
Coefficient
5.88
*-statistic
0.07
Y
STOVE
FRIDGE
WASHER
CTV
VCR
VACUUM
CONDITION
GOOD
POOR
HAZARD
SPOUSE
HHAGE
HhYGE =
MOVED
INCOME
EXP_ FINANCES a
BETTER
WORSE
Model chi-square
(d.f.)
- 7.78
0.12
- 8.44 0.14
- 7.81
0.12
- 7.90
0.12
- 8.08
0.13
- 8.07
0.13
- 2.64
1.68
3.71
0.52
0.00
0.00
0.30
0.02
0.56
-0.31
609.04 *
16)
569.04
13.35 *
5.94 *
0.00
0.15
1.33
0.20
12.73 *
a Chi-square statistic calculated as the difference in model chi
square with and without the two dummy variables repre-
senting this variable (e.g., see Hosmer and Lemeshow, 1989).
* Significant at 0.01 level.
dummy variables are significant, indicating
no intrinsic product specific effects.
Consistent with Kim (19891, we find that
wifes employment status is an important
variable related to replacement intentions.
Supporting the results of Van Raaij and Gi-
anotten (19901, we find that expected house-
hold financial situation is significantly associ-
ated with replacement intentions. Contrary
to the results of Strober and Weinberg (19771,
Weinberg and Winer (19831, and Winer
(1985b) for durable ownership and probabil-
ity of acquisition, we find no significant ef-
fects for age of household head on replace-
ment intentions. Unlike other studies which
find that income is a significant variable for
durable ownership (Nickels and Fox, 19831,
durable expenditures (Strober and Wein-
berg, 1977; Weinberg and Winer, 1983; Van
Raaij and Gianotten, 19901, and purchase
probability (Winer, 1985b), our results do not
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B.L. Bayus S. Gupt a / Consumer durabl e replacement int ent ions 265
show a significant income effect for replace-
ment intentions. However, this result may be
due to the sample analyzed (i.e., respondents
that owned their home) and the inclusion of
the spouses working status. Finally, contrary
to our hypothesis, whether a household has
recently moved is not significantly related to
replacement intentions. We note that this
finding may be due to the fact that very few
households in the sample analyzed reported
a recent move.
5.3 Elasticities
Aside from the statistical significance of
the explanatory variables, it is of interest to
consider the substantive implications of the
model. Elasticities of the significant vari-
ables can be calculated by determining the
percentage change in the probability of posi-
tive replacement intentions for a one unit
change in an explanatory variable. Evaluat-
ing the change in intentions for each house-
hold and averaging across households, the
resulting elasticity estimates are in Table 6.
Considering the product characteristics, a
one year increase in the age of each product
owned increases the probability of positive
replacement intentions by 2.91% and a wors-
ening of the products condition increases
the probability of positive replacement inten-
tions by over 470%. Although the elasticity
estimates of the four variables cannot be
directly compared due to the different un-
Table 6
Elasticity estimates for positive replacement intentions
Variable
HAZARD
Elasticity
(for 1 year increase in unit age)
CONDIT ION
2.91
(from good or fair to poor)
SPOUSE
471.89
(from not working to working)
EXP_ FINANCES
18.95
(from poor or fair to better)
22.70
derlying measurement scales (e.g., it is not
clear that a change in a spouses working
status is the same as a change in product
condition), the values in Table 6 do show the
considerable importance of perceived prod-
uct condition and the hazard rate (which is a
function of the currently owned units age)
on subsequent replacement intentions.
6. Discussion and conclusions
This paper has focused on studying the
nature of consumer durable replacements. A
descriptive model of replacement intentions
was developed and empirically examined for
a set of home appliances.
6.1 Study limitations
Before implications of the results are dis-
cussed, it is important to note some limita-
tions of the study. As always, a general limi-
tation is the use of a specific sample and
single time period (i.e., a sample of Arkansas
households in early 19901, which makes it
difficult to generalize the findings to other
samples or consumers that purchase appli-
ances during other years. In addition, the use
of a mail survey (and potential self-selection
bias in comparison with other methods such
as telephone interviewing) and the potential
inconsistencies associated with self-reported
behaviors limit the generalizability of the
findings.
In this paper, the replacement decision for
a particular product has been studied inde-
pendently of similar replacement decisions
faced by a household and/or first purchase
decisions for other durable products (or ex-
penditure alternatives such as vacations, col-
lege tuition, etc.). Other research (e.g., Ur-
ban and Hauser, 1986) indicates that house-
holds do have budget constraints and thus
make decisions between alternatives. Our use
of cross sectional data limits any conclusions
7/26/2019 Barry L Bayus, Sachin Gupta
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266 B. L. Bayus S. Gupt a / Consumer durabl e replacement int ent ions
about the tradeoffs a household might make
between the set of products owned. Individ-
ual household data, perhaps collected in an
experimental fashion, is needed to study this
issue in greater detail. Studying replace-
ments as a choice among heterogeneous al-
ternatives is also a potential area for future
research (e.g., see Bayus and Rao, 1989).
Although our survey questions were de-
signed to be valid measures of the underlying
constructs in Table 1, due to the nature of
the study (i.e., cross sectional and self-re-
ported) it is possible that the causal direc-
tion of certain relationships is confounded.
This is of particular concern for the measure
of unit condition, i.e., replacement intentions
or a recent purchase might have influenced
the reported unit condition, meaning that
the observed significance of unit condition is
overstated. We examined this possibility and
concluded that such an effect, if operative,
could not entirely account for our findings
concerning unit condition. First, over 85% of
respondents that reported owning a refriger-
ator or a coffee maker in poor condition
specified the reason for making a replace-
ment purchase as old unit broken, ex-
pected old unit to breakdown, or costly
repairs needed. This indicates that the mea-
sure has face validity. Second, across all the
appliances considered, approximately 70% of
respondents stating they would definitely or
were likely to make a replacement purchase
also reported owning an appliance in good or
fair condition. This indicates variability in
the measures of unit condition and inten-
tions.
6.2 Implications
Researchers have suggested that the rea-
son for the poor performance of purchase
intentions in forecasting subsequent sales is
due to measurement and scaling issues (e.g.,
Juster, 1974; Pickering, 1984). Our results
suggest that what is measured is important.
In particular, current unit condition (infor-
mation which is not generally collected) is a
significant explanatory variable of replace-
ment intentions. Since replacements account
for the majority of observed sales of mature
durable categories, including a measure of
unit condition in a model of sales may pro-
vide improved predictive power. Future re-
search might address this question.
Other analyses not reported in this paper
give an initial start in this direction. As dis-
cussed earlier, unit condition is a function of
physical wear and tear, and maintenance and
repair efforts. For six appliances (refrigera-
tor, stove, washer, VCR, color TV, vacuum),
unit age (a proxy for physical deterioration)
is positively and significantly related to re-
ported unit condition, and for four of these
(refrigerator, stove, washer, vacuum> house-
hold size (a proxy for usage) is also positive
and significant. Only in a few cases did a
household in our sample report having made
any expenditures on repairs/ maintenance
for their refrigerator or coffee maker. Future
research needs to refine and extend the
modelling of perceived unit condition across
durable categories.
Finally, our results concerning the impor-
tance of unit age suggest a need to model
and track the age distribution of units in use.
In addition, modelling and estimating the
effects of variables such as price, advertising,
and product enhancements on this underly-
ing age distribution is a promising area for
further research.
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