CARDIFF BUSINESS SCHOOL
WORKING PAPER SERIES
Cardiff Marketing and Strategy
Working Papers
Gordon Foxall, Ji Yan, Victoria James
and Jorge Oliveira-Castro
Brand-related and situational influences on demand elasticity
M2009/2
Cardiff Business School
Cardiff University
Colum Drive
Cardiff CF10 3EU
United Kingdom
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ISSN: 1753-1632
November 2009
This working paper is produced for discussion purpose only. These working papers are expected to be published in
due course, in revised form, and should not be quoted or cited without the author’s written permission.
1
Brand-related and situational influences on demand elasticity
Gordon Foxall1
Ji Yan1
Victoria James1
Jorge Oliveira-Castro2
1
Consumer Behaviour Analysis Research Group, Cardiff University
2
Consuma, University of Brasilia
Address correspondence to Gordon Foxall
Cardiff Business School, Cardiff University,
Aberconway Building, Colum Drive,
Cardiff CF10 3EU, UK.
[email protected]
2
Brand-related and situational influences on demand elasticity
Abstract
The paper reports an investigation of variations in demand elasticity for foods, the aim
of which was to investigate whether price elasticity varies from product to product,
from brand to brand, with differing brand attributes that contribute to combinations of
functional and symbolic benefit to consumers, and for varying price dynamics. Using
panel data for 1500+ British consumers purchasing 4 food products with 2000+ brands
over 52 weeks, the study also examines how factors other than price affect demand
elasticity for brands. Price elasticity differs among products and brands, for price
variations occurring at different speeds. Moreover, differing combinations of the
informational and utilitarian benefits provided by brands influence the level of price
elasticity exhibited by different brand types. These results differ significantly from
those of an earlier study by Ehrenberg and England. The paper discusses reasons for
this discrepancy in terms of brand-related and situational influences on consumer
choice.
Keywords: elasticity of demand; brand choice; utilitarian benefit; informational
benefit; food products
3
Brand-related and situational influences on demand elasticity
Marketing scholars and practitioners often claim that brands have a characteristic
price-elasticity. Evidence for this comes from experimental investigations (e.g. Urban
& Hauser, 1980, Narasimhan & Sen, 1983; Shoker & Hall, 1986; Mahajan & Wind,
1986) which report that a brand or product has its own elasticity, a finding that
corroborates demand theory (Telser, 1962; Broadbent, 1980; Roberts, 1980; Nagle,
1987; Gabor, 1988). Price is not the sole factor affecting elasticity, of course (e.g.
Scriven & Ehrenberg, 1999), but consumers’ price sensitivities make a central input to
marketing strategy and tactics (Anderson & Simester, 2009; Ratchford, 2009). Any
study which generates contrary results and which remains influential some 20 years
after its initial publication therefore deserves respect and attention. Ehrenberg and
England (1990) reported, on the basis of an experimental study, that elasticity for
foods does not differ significantly across brands and products, even when prices are
rising or falling and doing so at different speeds. This paper examines these claims by
describing an investigation based on direct observation of consumer choice which
assesses elasticity across food products and brands. The findings indicate that
elasticity for such products and brands is dynamic to an extent not identified by
Ehrenberg and England and that the functional and symbolic characteristics of brands
are systematically related to patterns of elasticity.
Most research into factors that influence elasticity focuses on consumer-related
characteristics, demographics and psychographics, but shows little consensus (cf. Trier
et al., 1960; Gabor & Granger, 1961; Coe, 1971; Murphy, 1978; Zeithaml & Fuerst,
1983; McGoldrick & Marks, 1987; Sirvanci, 1993; Hoch et al., 1995; Mazumdar &
Papatla, 1995; Dillon & Gupta, 1996; George et al., 1996; Jones & Mustiful, 1996;
Dhar & Hoch, 1997; Kallyanam & Putler, 1997; Ainslie & Rossi, 1998; Mulhern et al.,
1998; Bell et al., 1999; Kim et al., 1999; Sethuraman & Cole, 1999; Erdem et al., 2001;
Estelami & Lehmann, 2001; Kenesei and Todd, 2003; Ailawadi & Keller, 2004;
Boatwright et al., 2004; Rosa-Diaz, 2004; Scriven & Ehrenberg, 2004; Rao, 2009).
Few investigations consider brand-related characteristics as factors affecting elasticity.
Foxall et al. (2004) present evidence that consumer behavior is influenced by
utilitarian (functional) and informational (symbolic) benefits. Utilitarian benefits
include functional outcomes of purchase and consumption, derived from the use of the
product itself. Informational benefit, in contrast, is symbolic, social, and mediated by
the actions and reactions of other people. While utilitarian benefit is related to
economic and functional benefits of products or services, informational benefit is
related to social status and prestige, associated with buying, owning, or using products
or services. In the case of packaged goods, increased utilitarian benefits relate to
product formulations that offer more or better functional attributes, such as baked
beans with sausage vs. plain baked beans, or chocolate chips cookies vs. plain
digestive cookies. These formulations usually have distinct brand identities and differ
in price. Based on earlier research which has identified differences in elasticity
between groups of consumers purchasing brands embodying various combinations of
utilitarian and informational reinforcement (Oliveira-Castro et al., 2005, 2006, 2008a,
4
2008b, 2010), we expected inclusion of these variables in the calculation of elasticity
coefficients would produce results rather different from those reported by Ehrenberg
and England.
A consideration that arises from this involves the rather different contexts in
which our research and that of Ehrenberg and England took place. Utilitarian and
informational outcomes of consumer choice are sources of benefit that have been
conceptualized as post-behavioral controlling consequences of choice within the
Behavioral Perspective Model (BPM; Foxall, 2004) which also includes
pre-behavioral mechanisms for the integration of consumption history and current
stimuli that predict behavioral outcomes. This complex of predictive stimuli is known
as the consumer behavior setting and its scope is determined by the number of
competing choices it signals as available to the consumer. A setting that permits only a
single behavior to be enacted within it or, at best, a few behaviors is known as a closed
setting and is exemplified by being a dental patient: although one is at liberty to leave
the surgery at any time, most people feel constrained to follow the single program of
behaviors that define being a patient. By contrast, an open setting permits numerous
alternative behaviors and is exemplified by a buffet at which the consumer is able to
select among many foods and drinks and combinations of foods and drinks, to move
around more or less at will, to speak to whomever she chooses, and to leave at any
time. The idea of a continuum of consumer behavior settings defined in terms of their
scope is of particular interest in the quest for factors that determine the elasticity of
demand for food products and brands, for elasticity varies with the number of
substitute behaviors (hence, products or brands) available to the consumer. Elasticity is
higher when more rather than fewer substitutes are available. We would predict,
therefore, that elasticity would be more dynamic in the case of the relatively open
settings in which our investigation occurred than for the relatively closed experimental
settings in which Ehrenberg and England’s research was conducted.
The first question is whether there are consistent patterns in elasticity for products
and brands, as prices rise and fall, and as they do so at different speeds. Eqn (1)
measures the relationship between the quantity a consumer buys and the price paid
(Kagel et al., 1995): Log Quantity = a – b (log Price), in which b represents the price
elasticity coefficient. The second question is whether brand related characteristics
affect price elasticity, in particular, considering utilitarian benefit and informational
benefit of brands. Such patterns are measured by decomposing the elasticity into three
coefficients: brand price, informational coefficients and utilitarian coefficients. Eqn (2)
measures the relationships among quantities bought, prices paid, utilitarian benefits of
brands and informational benefits of brands: Log Q = a – b1 (log P) – b2 (log INF) –
b3 (log UTIL), where Q is quantity purchased, P is price, INF is informational benefit
level, and UTIL is utilitarian benefit level. Based on the conceptual proposal and
results in the literature, and especially to compare levels of elasticity in natural
purchase settings with the experimental results reported by Ehrenberg and England,
the following hypotheses were tested: H1: Price elasticity varies across different
brands and products. H2: Price elasticity varies across products and brands when
prices are changing at different speeds. H3: Informational and utilitarian benefits are
5
elements of brands that are embodied in their buyers’ sensitivity to price changes. H4:
Informational benefits are positively correlated with the quantity consumers buy. H5:
Utilitarian benefits are positively correlated with the quantity consumers buy. H6:
Informational benefits account for more variance in quantity bought than utilitarian
benefits. H7: Own price elasticity of a specific brand should account for more
variance in the quantity consumers buy than informational and utilitarian benefits.
Method
TM
Data from the ACNielsen Homescan panel, based on 10,000+ UK households that
used home barcode scanners, including information about four product categories
during 52 weeks from July 2004 were obtained for baked beans, biscuits, fruit juice,
and yellow fats; there was data for 832, 1594, 895, and 1354 households, respectively,
for these products including, for each purchase, the brand, store, item characteristics,
pack size, amount spent, number of items, and date. The methods used to assess brand
characteristics were as follows (Oliveira-Castro et al., 2005, 2008a, 2008b).
Informational benefit (INF) offered by each brand was measured by a questionnaire on
which a convenience sample (n=33) rated each brand in terms, first, of how
well-known they judged it to be: (0 not at all, 1 a little, 2 quite well known, 3 very well
known); and, second, the brand’s perceived quality (0 unknown, 1 low, 2 medium, 3
high). Higher levels of utilitarian benefit (UTIL) manifest in additional attributes
which are considered to have value-adding qualities for the product or its consumption;
they are visibly declared on the package or are part of the product name, and ultimately
justify higher prices. Moreover, in most cases, several general brands offer product
varieties with and witho8ut these attributes. UTIL was assessed by adopting the same
ranking procedure used in previous studies (Oliveira-Castro et al., 2005). Plain
formulations of items were ranked as having a UTIL of 1, whereas more sophisticated
formulations were ranked as having a UTIL of 2). Brands were classified into groups,
derived from the combination of 2 UTIL levels and 3 INF levels: (1) INF 1, UTIL 1;
(2): INF 1, UTIL; (3) INF 2, UTIL 1; (4) INF 2, UTIL 2; (5) INF 3, UTIL 1; (6): INF 3,
UTIL 2.
Results and Discussion
Results for Eqn (1) are shown in Tables 1 and 2. All regressions are significant; all
elasticity coefficients for groups are negative; all fall between 0 and -1.0, indicating
inelastic demand. Absolute coefficient values (Figure 1(a)) are lower for groups 1-3
than the others, indicating brands with lower functional and symbolic attributes show
higher price responsiveness. Elasticity for each brand group within each product
differs from the price elasticity for each brand group across all products (Table 2, Fig
1(b)). Absolute elasticity differs among brand groups within each product category,
among the same brand groups across products, among different brand brands across
product categories, and with the overall elasticity of each product. All but one of the
absolute elasticity values across products and brand groups fall between 0 and 1. The
highest inelasticity groups for each product are shown in Table 2. Absolute elasticity
(Figure 2) varies substantially by group. H1 is accepted.
Different groups show lowest inelasticity across brands within each product
category. Unit prices for each product rose and fell at different speeds. Figure 2
6
indicates lack of consistency in price changes across the seasons for the products. All
slopes within each product and for the brand groups differ, indicating that the price
dynamics of each product and brand group differ with the season, i.e. average unit
prices rose or fell over the seasons at different speeds. Chow-tests (Chow, 1960) for
regressions for products based on Eqn (1) are shown in Tables 3; all are significant: the
independent variables have different impacts on the dependent for the various
subgroups. Unit price impacts differently on quantity bought in each season by
products and brand groups. Elasticity for each product thus differs when prices rise
and fall at different speeds. H2 is accepted.
All Eqn (2) regressions are significant (Table 4). Adjusted-R2 is improved by
adding UTIL and INF variables in all cases (Table 5), albeit marginally. Nevertheless,
UTIL and INF enhance the explanation of price responsiveness. H3 is accepted. B1,
B2, and B3 are significant for all products, indicating that consumers respond to all the
benefit attributes in Eqn (2). Values of B1 are all negative indicating that increases in
price were associated with decreases in quantity demanded. B3 is negative for baked
beans, biscuits and juice; indicating that increases in UTIL are associated with
decreases in quantity demanded for the three products. B2 values are all positive across
products: increases in INF are significantly related to increases in quantity demanded.
H4 is accepted. B3 values are positive in biscuits showing that UTIL positively
influences Q. Hence, H5 is partially accepted. B1 is larger than B2 and B3 in all cases,
so H7 is accepted. B2 values are larger than B3 for baked beans and biscuits; B2
smaller than B3 for biscuits. H6 is rejected since UTIL accounts for more variance in
quantity bought than INF.
Q varies with changes in prices and benefits that occur within and across products
and within and across brands. Price elasticity varies across products and brands, contra
Ehrenberg and England. These findings corroborate evidence that price elasticity
differs among products and brands. Q varies also as price rises and falls, and with the
speed with which it does so. Price elasticity explains a larger portion of variance in
quantity bought than do informational and utilitarian benefits, and does so within and
across products and across brands. However, UTIL and INF do contribute to the
explanation of variance: they individually influence Q. Brand related characteristics
can, therefore, contribute to consumers’ price responsiveness.
Although an obvious source of discrepancy between Ehrenberg and England’s
results and ours lies in the experimental vs. real-world contexts, there is a particularly
significant element of this methodological diversity which bears on the discrepancy:
the number of substitutes available in our study. In their experimental study, despite
the fact that consumers could buy at any store, the closeness of the experimental shop,
which offered few brands, could in part explain the constant elasticity found. One
could argue, nevertheless, that their closed setting should decrease elasticity
coefficients but not make them constant, but the scope of the consumer behavior
settings involved in these studies appears to be an important source of the differences
in elasticity (cf. Gijsbrechts, 1993). Economic theory suggests that the presence of
substitutes for a target commodity increases the price elasticity of demand for it. Since
open settings by definition permit a number of competing behaviors to be available to
7
the consumer, we would expect the price elasticity of demand for any commodity for
which the consumer is in a state of deprivation to be greater than would be the case in a
closed setting where, at the extreme, only one reinforcer is available and there is only
one behavioral route to its delivery. The scope of the consumer behavior setting is
related to Hursh’s (1980) concept of open and closed experimental economies (Foxall
and Schrezenmaier, 2003). In the former, participants are allowed to supplement the
reinforcers they obtain during experiments by means of post-sessional consumption.
In the latter, the experimental sessions are the only source of the reinforcer. Demand
for a commodity shows greater price elasticity when the economy is open: the
availability of an alternative source of the reinforcer makes the respondent less likely
to work for it during the experiment. The discrepancy between Ehrenberg and
England’s results and our findings thus elucidates the theoretical basis of the BPM.
The more open setting, which featured a wider range of brands available in a range of
retail settings at prices that differed according to the competitive pressures of natural
marketing settings, notably an extensive spectrum of complete marketing mixes,
exhibited a more dynamic interaction of price elasticity among products and brands
than did the experimental context.
Table 1. Parameters of Eqn (1) calculated for each brand group, the significance level of the regression (P), and the standard
error of the estimate of b.
Brand
R-Square
B
S.E.
P
VIF
D-W
N
Group 1
.245
-.527
.002
.000
1.000
.989
16466
Group 2
.302
-.511
.007
.000
1.000
1.207
5641
Group 3
.258
-.497
.010
.000
1.000
.828
39539
Group 4
.311
-.558
.006
.000
1.000
.925
21451
Group 5
.322
-.605
.005
.000
1.000
.981
33023
Group 6
.241
-.533
.006
.000
1.000
1.131
24790
Group
Table 2. Absolute values of the price elasticity of demand across products and brand groups
All
|Ed|
Beans
Butter
Biscuits
Juice
products
Overall
0.818
0.409
0.570
0.468
0.539
Group 1
0.309
0.525
0.587
0.529
0.527
Group 2
0.699
0.589
0.477
0.578
0.511
Group 3
0.348
0.493
0.584
0.437
0.497
Group 4
0.555
0.583
0.563
0.565
0.558
Group 5
1.86
0.476
0.569
0.5
0.605
Group 6
0.879
0.024
0.66
0.077
0.533
Largest absolute values
5 and 6
2 and 4
1 and 6
2 and 4
4 and 5
Smallest absolute values
1 and 3
5 and 6
2 and 4
3 and 6
2 and 3
Table 3. Results from performing Chow-test for regressions based on 4 product categories and 6 Brand
Groups
8
Tests of Between-Subjects Effects
Dependent variable: Log Quantity; Independent variable X: Log (Price)
Source
Df
F
Sig.
df
F
Sig.
4
13761.924
.000
6
9064.843
.000
Intercept
1
39725.828
.000
Intercept
1
39667.845
.000
Log( Price)
1
23162.144
.000
Log (Price)
1
36338.542
.000
Product * X
3
277.942
.000
Group * X
5
71.593
.000
Error
140905
Error
140903
Total
140910
Total
140910
Corrected Total
140909
Corrected Total
140909
Corrected
Model
Source
Corrected
Model
a. R Squared = .281
a. R Squared = .279
Table 4. Regression of quantity bought (Q) of products on unit price of products (P), informational benefit (INF) and utilitarian
benefit (UTIL) for all purchases and shopping trips
All purchases on all shopping trips
R2=.235
D-W= .561
n=13671
R2=.388
D-W=1.170 n=30538
Value
Error
Sig.
Value
Error
Sig.
Constant
4.744
.037
.000
Constant
4.792
.005
<.000
B1 (P)
-.676
.012
.000
B1 (P)
-.570
.003
<.000
B2 (INF)
.192
.008
.000
B2 (INF)
.047
.003
<.000
B3 (UTIL)
-.027
R2=.210
.021
D-W= .787
.198
n=74936
B3 (UTIL) .179
Juice
R2=.138
Value
Error
Sig.
Constant
5.531
.010
<.000
B1 (P)
-.421
.005
B2 (INF)
.184
B3 (UTIL)
-.148
Baked Beans
Butter
Biscuits
<.000
.005
D-W= .883 n=21247
Value
Error
Sig.
Constant
6.238
.022
<.000
<.000
B1 (P)
-.416
.008
<.000
.005
<.000
B2 (INF)
.068
.005
<.000
.011
<.000
B3 (UTIL) -.096
.019
<.000
Table 5. Adjusted R2 changing patterns according to Model 1 with brand price coefficients; Model 2 by adding informational
coefficients based on Model 1; Model 3 by adding utilitarian coefficients based on Model 2.
Model
Beans
Butter
Biscuits
Juice
1
.203
.175
.376
.130
2
.235
.205
.380
.137
3
.235
.210
.388
.138
9
Price Elasticity Coefficients
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Group 1
Group 2
Group 3
Group 4
Group 5
Group 6
Informational/Utilitarian Brands Group
(a)
(b)
Figure 1. Price elasticity coefficients calculated (a) for each brand group, (b) across products and brands
groups
(a) Unit prices changed across quarters, baked beans, butter, biscuits and juice, respectively from l to r.
(b) Unit prices changed across quarters, Brand Groups 1, 2 and 3, respectively from l to r.
(c) Unit price changes across quarters, Brand Groups 4, 5 and 6, respectively from l to r.
Figure 2. Quarterly analyses of price variations.
10
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