barry l bayus, sachin gupta

7/26/2019 Barry L Bayus, Sachin Gupta

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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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258

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


10/11

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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