Consumer response to price changes in higher-priced brands

Abstract and Figures

Price elasticity is a widely used measure of consumers’ willingness or ability to pay for goods and services. This research examines the price elasticity of high-priced brands. We define high-priced brands as those that sell at or above the price point at which consumers begin to consider that product to be luxurious or premium in the category (Kapferer et al., 2014; Sjostrom et al., 2016). More specifically, we use high-priced wine brands as the context for this research. Wine is an ideal product to use because it has a wide price range, and can be purchased for various consumption situations. When prices are high we anticipate that elasticities may no longer function as they do in everyday consumer packaged goods markets. Instead, they might become smaller or possibly even positive if consumers are prepared to pay for the quality they desire. We employ stated choice experiments to investigate how Situational Factors, Consumer Factors and Contextual Factors influence price elasticities for high-priced wine brands in Australia. Results are that price elasticity estimates for the high-priced brands in this study are −1.8 on average. This is lower than the commonly reported figure of −2.6 for brands in general; however, in one part of the experiment respondents chose for a ‘high-importance’ occasion. Smaller price elasticities were found when (1) the perceived importance of the consumption situation was high, (2) among regular high-priced wine buyers, and (3) among brands with a higher initial price position. These results demonstrate that the patterns of price elasticity for high-priced products are mainly similar to that for other FMCG products, but consumers are slightly less responsive.
Content may be subject to copyright.
Consumer Response to Price Changes in Higher-Priced Brands
Price elasticity is a widely used measure of consumers’ willingness or ability to pay for goods
and services. This research examines the price elasticity of high-priced brands. We define
high-priced brands as those that sell at or above the price point at which consumers begin to
consider that product to be luxurious or premium in the category (Kapferer et al., 2014,
Sjostrom et al., 2013b). More specifically, we use high-priced wine brands as the context for
this research. Wine is an ideal product to use because it has a wide price range, and can be
purchased for various consumption situations. When prices are high we anticipate that
elasticities may no longer function as they do in everyday consumer packaged goods markets.
Instead, they might become smaller or possibly even positive if consumers are prepared to
pay for the quality they desire. We employ stated choice experiments to investigate how
Situational Factors, Consumer Factors and Contextual Factors influence price elasticities for
high-priced wine brands in Australia. Results are that price elasticity estimates for the high-
priced brands in this study are -1.8 on average. This is lower than the commonly reported
figure of -2.6 for brands in general; however, in one part of the experiment respondents chose
for a ‘high-importance’ occasion. Smaller price elasticities were found when (1) the
perceived importance of the consumption situation was high, (2) among regular high-priced
wine buyers, and (3) among brands with a higher initial price position. These results
demonstrate that the patterns of price elasticity for high-priced products are mainly similar to
that for other FMCG products, but consumers are slightly less responsive.
Key words: Price, Elasticity, Experiment, Consumer
1. Introduction
Price is an important consideration in the consumer decision-making process (Monroe, 2003).
It shapes consumer perceptions of a brand, and changes in price can markedly change
demand for the brand. Correct pricing strategy is thus crucial to a brand’s success (Lehmann
and Winer, 2005).
The most widely used measure of consumer response to price changes is price elasticity
(Schindler, 2012), which is the percentage change in demand for a one-percent change in
price. Price elasticity is the numerical representation of consumer’s price sensitivity towards
a particular brand (or product). In turn, price sensitivity is the extent to which individual
consumers perceive and respond to changes in price for products or services (Wakefield and
Inman, 2003). Whilst individual consumers differ in their price sensitivity, their aggregate
level response to price changes for a brand can be represented by the price elasticity for that
brand. The focus of this study is price elasticity, and specifically for high-priced brands. A
choice experiment is the method used for the study.
Price elasticity for consumer packaged goods brands is approximately -2.6 on average
(Scriven and Ehrenberg, 2004, Tellis, 1988). That said, price elasticity for any specific brand
has been found to vary across different situations (e.g., Dunn et al., 2013, Scriven and
Ehrenberg, 2004). For example, the same brand of instant coffee was reported to have a price
elasticity varying from -1.0 to -4.0 across ten studies (Scriven and Ehrenberg, 2004)
depending on situational factors such as the direction of the price change. If brand price
elasticities can vary, this means that consumers do not always necessarily have the same
response to price changes. This phenomenon has led researchers to question why there are
such differences, and whether any consistent factors underlie them.
Price elasticity is generally negative; when a brand’s price increases, sales decrease, and vice
versa. However, price elasticities can also be positive in rare cases. Positive elasticities are
sometimes reported for luxury products that sell at very high prices (Kapferer, 2012),
although the evidence tends to be anecdotal. For FMCGs, the price gap between the cheapest
and most expensive brands may only be a few dollars, but at the same time the most
expensive brand may be twice the price of the cheapest (as is the case for commonly bought
categories such as instant coffee, toothpaste, toilet paper and pasta sauce).
By contrast, product categories such as wine, chocolate, or cosmetics have more dispersed
prices. For example, many wine brands sell for under $10 per bottle, yet many others sell for
five or ten times that price or even higher (Romaniuk and Dawes, 2005). Therefore, prices
vary proportionally by several hundred percent, and in absolute terms in multiples of $10,
indeed some brands in categories such as wine are hundreds of dollars more expensive than
Little research has been conducted to test consumers’ responses to price changes for such
brands that sell at higher price levels (Lockshin and Corsi, 2012). Therefore, not a great deal
is known about consumer price sensitivity, and therefore the price elasticity, of high-priced
goods. Knowledge about price response for higher priced items would be useful for
marketers, given the growth in the upper price tier in many markets (Britner, 2016, Stilinovic,
Moreover, while some studies have examined how factors such as the direction of the price
change (e.g. Scriven and Ehrenberg, 2004) or situation (Wakefield and Inman, 2003) is
linked to price response, little is known about how such factors might work in the context of
higher-priced brands. This provides an initial rationale for the present study.
Studies of factors affecting brand price elasticity use three main methods: historical sales
analysis (e.g., Bell et al., 1999, Bolton, 1989b, Danaher and Brodie, 2000), controlled in-store
experiments (Anderson et al., 2009, Bemmaor and Mouchoux, 1991, Litvack et al., 1985) and
choice experiments (Dunn et al., 2013, Ehrenberg and England, 1990, Scriven and
Ehrenberg, 2004, Woodside and Ozcan, 2009). Although each method has advantages and
disadvantages, the choice experiment approach was considered the most suitable for this
research for several reasons. First, we wished to examine the impact of situational factors on
price elasticity, and an experiment allows us to collect consumer choices made under various
situational scenarios. Second, the use of an experiment avoids extraneous influences such as
shelf position and in-store promotions. Third, we wish to include in the analysis factors such
as the respondent’s prior purchasing behavior, which can readily be obtained as part of a
stated choice experiment.
The category chosen for the investigation is wine, because it has many brands that sell at
higher prices. In addition, the demand for premium wine is increasing among consumers, and
the industry is suggested to introduce more premium wines to adapt to this consumer shift
(Corsi and Rowley, 2016). The intended contribution of this study is therefore to progress
knowledge about price elasticity, specifically in the context of high-priced brands, and to
incorporate both situational factors and buyer characteristics to determine how these affect
price elasticity.
The remainder of this paper is divided into four major sections. First, we review previous
studies and highlight gaps in the literature for further investigation resulting in research
questions. Second, we present the data collection method and analysis approach. Third, we
present the results of each research question. Lastly, we provide a general discussion of the
findings, as well as limitations of the study and directions of future research.
2. Literature Review
2.1 Price elasticity
Price elasticity is almost always reported to be negative (Monroe, 2003): lower price means
higher unit sales, and vice versa. However, in rare cases it is reportedly positive (Moore and
Pareek, 2010, Tellis, 1988 p. 337), although the possible reasons are elusive. High-priced
luxury goods are said to be an exception to the usual demand–price relationship. These
products are relatively price insensitive, meaning that demand does not necessarily decline if
price increases, and may actually increase when the price increases (Moore and Pareek,
2010). Products such as these are classified by economists as ‘Veblen goods’. Veblen (1899)
argued that most levels of consumption by the upper classes are merely ways of displaying
wealth and social status. Expensive perfume and wine are good examples of items that
economists would consider Veblen goods, as people who cannot easily tell the quality of a
perfume or wine may use price as an indicator of quality instead. Therefore, the higher the
price (within limits), the more likely it is that luxury-oriented consumers will buy that brand
(Moore and Pareek, 2010). Many studies have supported the existence of the category of
Veblen goods, and stated that consumers of high-priced brands respond to price changes
differently from consumers of regular brands (Dolan and Hermann, 1996, Kapferer, 2012,
Moore and Pareek, 2010). However, none of these studies have estimated actual price
elasticities to support this claim. Furthermore, Veblen’s theory was developed more than a
century ago, when luxury consumption was only accessible to a very limited number of
people. In more than 100 years of development, society has changed, and luxury goods have
become more accessible to the general public. Indeed, in recent years many manufacturers of
luxury goods have launched lower-priced items, and there has been the emergence of the
concept of ‘affordable luxury’ (Mundel et al., 2017). Such changes lead to doubt regarding
whether Veblen’s theory is still applicable to contemporary society. That said, this study does
not set out to specifically examine goods that may be classified as luxury. ‘Premium’ and
‘luxury’ are terms that are difficult to define. The characteristics of luxury brands are
excellent quality, a prestige image, an element of uniqueness and exclusivity, and high prices
(Kapferer, 2001). In fact, consumers shift their perceptions of a product from regular to
luxury/premium at certain price points (Kapferer et al., 2014, Sjostrom et al., 2013a). This
price point varies across different product categories. For example, for wine and spirits, the
shift occurs at AUD50, for watches at AUD400 and for perfume at AUD150 (Sjostrom et al.
2013). The focus of our study is high-priced wine brands. We do not explicitly define these
as luxury or affordable luxury brands. However, it is likely the brands we incorporate are
considered luxury, or affordable luxury by many consumers due to them being priced above
AUD50. We further explain our rationale for this price range later in the paper.
2.2. Price elasticity magnitude and direction
The literature on price elasticity has found a reasonable degree of consistency in average
price elasticity, with multiple studies producing similar figures of approximately -2.6 (Tellis,
1988; Scriven and Ehrenberg 2004; Bijmolt et al., 2005; Dunn et al., 2013). Knowing the
likely range of price elasticity is of great value to marketing practitioners. However, those
estimates are reported for brands in general and do not specifically examine if price elasticity
is different for high-priced brands.
One of the very few studies that clearly indicates a price elasticity direction for high-priced
brands is by Mandhachitara and Lockshin (2004) who conducted a study on high-priced
Scotch whisky in Thai department stores. They found a negative elasticity, that is, when price
was decreased, sales increased). This finding is in contrast to current belief about high-priced
brands. However, the authors also found that the highest sales occurred during weeks in
which there were no price change at all, which suggests there were other factors at play.
A literature search found only one publication that has reported a smaller elasticity for a high-
priced brand, Dolan and Simon (1996). These authors compared price elasticity for two
different types of automobiles, and reported an absolute price elasticity of 0.7 to 1.5 for
luxury automobiles, and an absolute price elasticity of larger than 1.5 for normal automobiles
(Dolan and Simon 1996, p. 77). This result supports the claim that high-priced brands have
smaller price elasticities. However, the study does not reveal the source of the data or its
analytical approach. This makes it difficult to evaluate the generalisability of the findings.
Furthermore, the price elasticities were reported in absolute value, making it impossible to
determine the direction of how consumers responded to price changes.
In summary, the direction of price elasticity is generally negative. In addition, there is a
reasonable degree of consistency in average price elasticity, with the most frequently
occurring average magnitude being -2.6. High-priced brands are believed to be an exception
to the demand–price relationship, and to have smaller price elasticities; however, there is a
lack of empirical evidence to support such claims. This leads to the first set of research
questions, as follows:
RQ1a: Does the direction (sign) of price elasticity differ for high-priced brands, compared to
regular brands?
RQ1b: Does the magnitude of price elasticity differ for high-priced brands, compared to
regular brands?
2.3 Factors that influence Price Elasticity
Price elasticity for specific brands has been found to vary considerably according to certain
contextual factors (Dunn et al., 2013; Ehrenberg, 2004). If price elasticities vary, this means
that consumers may not always have the same responses to changes in the price of a brand.
This has led researchers to question why there are such different responses, and whether any
consistent factors underlie them. After decades of research, an understanding of the factors
affecting price elasticity has emerged, yet it is far from comprehensive. Factors that have
been widely shown to correlate with larger price elasticities include: brands with smaller
market shares (e.g. Bolton, 1989a, Guadagni and Little, 1983, Scriven and Ehrenberg, 2004);
goods that can be stockpiled (e.g. Bell et al., 1999, Danaher and Brodie, 2000); and retailer
support, such as in-store displays and feature advertising (e.g. Bemmaor and Mouchoux,
1991, Huber et al., 1986, van Heerde et al., 2001).
However, while a number of factors have been found to affect price elasticity, there has been
considerable inconsistency between studies, and therefore there is no broad agreement on the
effect of other factors. In addition, past studies on this area have predominately focused on
examining FMCGs, which are typically low-priced; thus, it is uncertain whether the findings
can be generalised for high-priced brands. This gap leads to the second overarching research
question for this research: What factors influence price elasticity for high-priced brands?
Trying to model all the factors that influence price elasticity is complex and the total number
of factors could make the design of such an experiment much too large to be practical. The
literature review highlights that some of the most important factors that influence price
elasticity are: situational factors, consumer factors and contextual factors. Therefore, this
research focuses on investigating how these three factors influence price elasticity for high-
priced brands.
Situational Factors: Any investigation of consumer behaviour that ignores situational
effects is likely to provide unreliable results (Belk, 1974). Accordingly, it is crucial to
identify the ‘product-use situation’ of an item, because this changes the relative importance
of product factors (Fennell, 1978) such as price quality or features. Consumers are likely to
select products or brands that are suitable for consumption on particular occasions (Dickson,
1982) or situations (Stoltman et al., 1999). For example, when purchasing wine for important
occasions, such as business dinners, consumers consider the quality of wine to be an
important attribute, and high price drives quality perceptions for such occasions (Hall et al.,
2001). Studies such as the ones cited highlight the importance of price as a quality indicator
for special situations, yet do not discuss how consumers respond to price changes in such
situations. There remains a lack of understanding of how situational factors influence brand
price elasticity. The published literature indicates that price differences between brands have
the greatest effect when consumers’ attention is focused on price (Lichtenstein et al., 1993). It
is anticipated that price elasticity will be lower when consumers are focused on factors other
than price, such as the perceived importance of the occasion they are buying for, and the
social environment connected to it. These findings have been further supported by many
studies (Agnoli et al., 2011, Barber, 2009, d'Astous and Saint-Louis, 2005, Dodd et al., 2005,
Orth, 2005, Wakefield and Inman, 2003). The above studies have examined consumer’s price
sensitivity in relation to different situations; however, none of them reported an actual price
elasticity estimate. Furthermore, none of these studies examined high-priced brands. The
literature highlights the importance of price in relation to different situations, yet there
remains a gap in understanding how situational factors such as the importance of occasion
influence price elasticity for brands, especially high-priced brands. This leads to the
following research question:
RQ2a: Does a more important consumption situation result in lower price elasticity for high-
priced brands?
Consumer Factors: There is a large body of marketing literature that focuses on studying
buyers based on the frequency and volume of products that they purchase or consume (e.g.
Chrysochou et al., 2011, Scriven and Ehrenberg, 2004, Twedt, 1964). The literature indicates
that these different groups of consumers may respond to price changes differently. Therefore,
having a better understanding of the differences between heavy and light category buyers can
assist in developing more effective pricing strategies. Many studies have found that heavy
users of a brand are more responsive to price changes than light users. Kalyanaram and Little
(1994) found that consumers with higher purchase frequencies are more sensitive to price
changes because they are more aware of the range of price distributions. However, the focus
of that study was on analysing the latitude of price acceptance in consumer packaged goods,
rather than investigating how consumers respond to price changes. Kim and Rossi (1994)
found that consumers with high purchase frequency were much more price-sensitive than
consumers with low purchase frequency. A third study examining price effects for new
nondurables found that promotional price sensitivities are generally higher for repeat
purchasers than for trial purchasers, and that heavy users in the product class are more price-
sensitive than light users at the trial stage (Helsen and Schmittlein, 1994). However, other
studies have found different results. Scriven and Ehrenberg (2004) reported no relationship
between self-reported category usage and price elasticity. Dunn et al. (2013) found smaller
brand price elasticities among heavy brand users. Given the lack of consensus in past work,
further investigation is required to clarify the relationship between brand usage and price
elasticity. Moreover, these studies mainly focused on low-priced products, such as FMCGs,
and there is a gap in knowledge relating to the interplay between usage and price elasticity
for high-priced brands. These points lead to the following research question:
RQ2b: Is price elasticity (for high priced brands) lower among light buyers of the category
compared to heavy buyers of the category?
Buyers can be light, medium or heavy buyers of the product category (e.g.Twedt, 1964) but
also could buy mostly from the high-price tier of that product category, or perhaps only
occasionally, or not at all at that high tier. Of interest here is whether the tendency to buy
from the high tier relates to sensitivity to price changes for brands in that tier. Arguably,
greater familiarity with brands and prices in high tiers implies a lower perception of risk in
choosing amongst them (e.g. Mieres et al., 2006). Therefore, the buyer has a greater tendency
to switch to take advantage of a better price offered by comparable alternatives, leading to
higher elasticity among regular high-tier buyers. Likewise, just as more regular buyers of a
product category tend to have larger repertoires of brands (Banelis et al., 2013), regular
buyers of the high price tier will likely have bought a larger variety of brands from that tier.
Therefore, they are more familiar with alternatives and so switching between them is easier.
A counter-argument can also be raised – buyers who tend to purchase expensive wine are
likely to have higher income, which would likely make them less price-sensitive. Second,
buyers of high-priced wine are likely to derive more hedonic value from their purchases,
which may also make them less price-sensitive. However, there is little evidence specifically
pertaining to these aspects of price response for high-price brands. Therefore, the next RQ is:
RQ2c: Is price elasticity (for high priced brands) lower among light buyers of high-priced
brands compared to heavy buyers of high-priced brands?
Contextual Factors: A reference price is often defined as an internal price to which
consumers compare observed prices (Lowengart, 2002). However, another conceptualisation
is an external reference price. Consumers are reported to use other brands’ prices as reference
points to assess the focal brand’s offer, in addition to comparing a brand’s current price with
its former price (Rajendran and Tellis, 1994). As a result, the initial price level of a brand
relative to its competitors is anticipated to have a significant effect on its price elasticity –
that is, is the normal price of the brand below most competitors, at the average, or priced at a
premium? Some past research examining grocery brands found lower promotional price
elasticities for high price tier brands (Zenor et al., 1998). However, other work has found
high price brands have larger promotion elasticities (Danaher and Brodie, 2000, Fok et al.,
2006), but they should be less elastic for increases. The reason for expecting this asymmetric
response to price changes is that a price increase takes the brand further away from
competitor prices, hence the price response should be concave in line with prospect theory
(Kahneman and Tversky, 1979). Moreover, a price increase for a high priced brand should
pass fewer competitors compared to a price decrease. Evidence suggests price passing
magnifies price elasticities (Scriven and Ehrenberg, 2004).
Studies have also indicated that consumers are less sensitive to price increases for FMCG
brands with relatively high prices than they are to price increases of brands with relatively
low prices (Woodside and Ozcan, 2009). In contrast to the above studies, which focused on
two ends of the price spectrum, Scriven and Ehrenberg’s (2004) analysis of regular price
changes found larger elasticities among brands with prices closest to the average price of all
brands. There is clear evidence from past studies that the relative position of an item’s initial
price within a price tier can have a significant effect on price elasticities. However, there
were no high-priced brands used in the study by Woodside and Ozcan (2009), and it is
difficult to determine whether particularly high-priced brands were used in the study by
Scriven and Ehrenberg (2004). Therefore, it is unclear whether the generalisations from those
studies can be applied to high-priced brands. This leads to the following research question:
RQ2d: How does the relative position of a brand’s initial price (i.e. being the low-priced,
mid-priced or high-priced brand in the high-price tier) affect price elasticity for high-priced
A pictorial representation of the concepts and hypotheses is shown below as Figure 1.
Figure 1: Framework of the Study
3. Data Collection Method and Analysis approach
An online choice experiment was designed to test consumer responses to price changes for
brands. To create a realistic choice environment, all brands presented in the choice
experiment were real brands that were currently stocked by local wine retailers. The
respondents were presented with 14 different choice sets, that each consisted of six brands.
The prices of the brands were manipulated across the choice sets. At the beginning of the
experiment, each respondent was shown the six brands at their normal in-market price under
a specific situation and asked:
“Which one of the following wines would you be MOST likely to purchase?”. This was
followed by a validation question, “Would you actually purchase the wine you just chose in
real life?”. This validation question was asked for each choice set. We use only the stated
choices that were confirmed by a positive response to this validation question.
Each respondent was shown the same six brands in different choice sets, with the price of one
brand changed by either a price decrease or increase. At the end of the survey the original
choice set with all the brands at their ‘normal’ in-market price was again shown to the
respondents. To minimise any order effects, a William Design (Wang et al., 2009) was used
to control the presenting order of choice set 2 to choice set 13, and the presenting order of the
six brands within each choice set. Once the choice experiment was completed, the
respondents were given a series of post-experiment questions, which included questions
Changes to !
Brand’s price
Regular !
High-Priced !
Price !
Price elasticity different
for High-Priced brands ?
Important consumption
occasion lowers Price
elasticity ? RQ 2a
Buyers’ !
Different for
Heavy / Light
buyers ?
RQ2b, 2c
tier Price elasticity depends
on position in high-price
tier ? RQ 2d
related to prior purchases and demographics. One might argue that a weakness of the
research is that it is based on a simulated choice experiment, rather than actual purchasing.
However, there is support for using this method from past studies (e.g. Dunn et al., 2013,
Woodside and Ozcan, 2009, Scriven and Ehrenberg, 2004). Moreover, several procedures
ensured the conditions surrounding data collection were conducive to the results being valid.
First, several questions were designed as manipulation checks to make sure the participants
had purchased high priced wines and that they paid attention to the prices in the experiment.
Also, the sample of participants was sourced from a professional online provider. We
obtained a high-quality sample, and presented a realistic task that was not onerous to
respondents. The total sample was 652, but was reduced to 541 after deleting respondents
who failed the manipulation checks.
The final sample (n=541) encompassed Australian residents aged over 18 years, not currently
working in the wine industry and had purchased wine over $30/bottle in the last 12 months
for off-premise consumption.
All wine brands chosen for the choice experiment were Shiraz from Australia. Using a
common product type avoided introducing potential confounds into the results. To make the
choice experiment more realistic, all brands presented to participants were real brands that
are currently available in most leading wine retailers. Likewise, the attributes associated with
each individual brand (i.e. grape variety, region, ratings) were all real information. The price
levels of the brands ranged between $50 and $100 per bottle. There were three reasons for
using this $50-$100 price range. First, Sjostrom et al. (2013b) reported that consumers shift
their perceptions of wines from regular to luxury/premium at $50. Second, it is common
practice in Australia to group wines priced between $50 and $100 as a price category: most
leading Australian wine retailers (such as Dan Murphy’s, David Jones and Vintage Cellars)
use this range as a category in their price tiers. Third, wine priced above $50 represents the
most expensive 4-5% of wine sold in the Australian market; therefore $50-$100 wine
qualifies as high-priced. Note that while the lowest price in the actual experiment was $50,
the criteria for participants to be included was that they had bought wine at $30 or more in
the last 12 months. The reasons for this criterion were (a) it made it feasible to get a large
sample, and (2) consumers who have bought $30 can realistically be thought of as potential
customers for wine priced at $50 or more. Moreover, we also examined elasticities among
those who had, or had not bought wine at or above the $50 price point.
Each brand’s prices were manipulated across three levels: (1) a ‘base’ or normal price; (2)
decreased price; and (3) increased price. The magnitude of the price change was 15% for
increases or decreases. The ±15% price change level was chosen based on the precedent in
past pricing studies (e.g., Dunn et al., 2013; Ehrenberg, 1990; Scriven and Ehrenberg, 2004).
However, as the prices presented to the respondents were rounded to two digits, the actual
change ranged from 14 to 16% across the different brands. In ten of the choice sets, the price
change took a brand past one or more of the prices of its competitors. In two others, the price
change involved no passing.
All respondents were administered all 14 choice sets, but were randomly assigned to one of
the three usage situations replicated from a study by Quester and Smart (1998). In that study,
these situations were formulated in conjunction with wine experts and retailers, and designed
to represent different levels of perceived risk/importance. The level of perceived
risk/importance of the three situations in ascending order was as follows:
Situation 1, to drink at home during the week over dinner;
Situation 2, to take to a dinner party at a friend’s house; and
Situation 3, to give to a person you highly respect as a gift for their 50th birthday.
The variation in the importance of these situations serves as a segmentation base to answer
RQ2a, pertaining to how the importance of the consumption situation influences price
elasticity. The sample comprised a reasonable cross-section of the population in terms of age
and gender as shown in Appendix 1.
To answer RQ2b and RQ2c, buyers of the product category were divided into segments
created from the self-reported category purchase data. Respondents were asked how often
they purchased wine, using response categories ranging from 1=less than once per year to
6=Once per week or more often. The respondents who selected this most-frequent category
were classified as heavy buyers (n=147). We calculated that this heavy-buyer group, which
comprises 27% of the sample, would account for approximately 60% of all purchases made
by the total sample. This concentration of purchasing is approximately similar to
Schmittlein, Morrison and Cooper (1993) who reported the concentration of purchases
among the top 20% of buyers tends to be approximately 60%, rather than the oft-quoted
Pareto 80/20 concentration.
High-Price Tier Buyers: Respondents were also asked about their purchasing of wine at
various price levels. Respondents who had not purchased wines priced over $50/bottle were
defined as non-high-priced buyers of the product category. Respondents also reported what
percentage of the wines they had purchased in the previous 12 months was in the $51 to $100
price category. They were also asked to report how many bottles of wine priced at over
$50/bottle they had purchased during the previous 12 months. The percentage of wines
purchased in the $51 to $100 price category, and the quantity of bottles purchased over
$50/bottle, were combined to achieve a balanced weight of purchases in the high-priced
category. A total of 243 respondents were classified as heavy buyers of high-priced wine.
This figure is larger than the number of heavy buyers of wine generally, but it reflects that
some lighter or medium buyers of wine generally may tend to confine their purchases to
high-priced wine brands.
Each choice set yielded a count of respondents who chose each particular wine brand. From
these counts, we calculated the proportion of choices given to each brand in each choice set.
We then calculated the proportional change in choices for the brand arising from the price
changes. From that method we then derived the price elasticity for each brand using the point
elasticity formula: (e.g., see Ehrenberg and England, 1990)
!"#$%&'&%( ) *
+,-#.%&%( /0#$1*,-#.%&%(2
+34&'1 / 0#$1*34&'12
The point elasticity formula can be used to calculate the elasticity from a base price to a
higher price, or to a lower price. Using this formula we calculated the average elasticity for
price increases and decreases, as has been done in similar previous studies (e.g., Scriven and
Ehrenberg, 2004); by consumption situation, by user group and so on. The elasticities are
based on the proportion of respondents choosing a brand, hence standard errors for
proportions were computed using the standard formula 5! 3 ) 6+7862
9*; and from these we
calculated if any differences in elasticities were statistically significant at the p=0.05 level.
4. Results
Figure 1 shows the choice share for each brand of wine in the choice experiment at different
price levels. It clearly shows that all six brands had their highest choice share when sold at a
decreased price, and the lowest share when sold at an increased price. All the brands
exhibited negative price elasticity. Therefore the answer to RQ1a is, the direction or sign of
price elasticity is the same for these high-priced brands as has been reported for brands
generally (e.g. Tellis, 1988).
Figure 2: Choice Share for Each Brand at Different Price Levels
Next we examined the size of the average elasticity. We found the average elasticity for these
brands to be -1.8. This is somewhat smaller than other studies or meta-analyses that have
reported figures of -2.6 (e.g. Bijmolt et al., 2005, Danaher and Brodie, 2000). However, this
result is partly due to the fact that 1/3 of the sample made their choices given the scenario
they were buying a gift for a person’s 50th birthday. This group exhibited a lower elasticity of
-0.8, as discussed in detail later. Using the average elasticity of the other two more
‘everyday’ buying occasions the average elasticity is -2.7. We conclude that these high priced
brands exhibit approximately similar price elasticities as everyday brands, answering RQ1b.
This means that price increases for high-priced wine brands will result in a reduction in unit
sales that in percentage terms is larger than the price change. The same can be said for
decreases – high priced wine brands, if they decrease their price relative to others will exhibit
heightened demand that is larger in percentage terms than the price decrease.
RQ2a posed the question as to whether price elasticity is lower for important consumption
situations. To address this, we calculated the price elasticity according to the three
consumption situations, which varied by importance. Table 1 indicates that price elasticity
decreased in line with the perceived importance of the consumption situation. The most
important occasion, a gift, yielded an elasticity of -0.8, lower than the other two less
important occasions (significantly different at p<0.05). However, whilst the elasticity for the
medium-importance occasion was lower than for the least important occasion (-2.2 vs -2.7)
the difference between them was not statistically significant (p=0.16). Therefore, the answer
to RQ2a is a qualified yes, price elasticity is lower for more important consumption
Note there is a somewhat anomalous result for the price elasticity of price increase for a
dinner party of -1.4, compared to the elasticity for price decrease for a dinner party of -2.9.
The lower elasticity for the increase is partly because one brand exhibited higher demand
when its price was increased. If we remove that one brand from the results, the elasticity for
a price increase for a dinner party is -2.0, and the overall elasticity for a dinner party is -2.5.
The same pattern of declining elasticity for more important occasions would then be
exhibited: -2.7 (drinking at home), -2.5 (dinner party), -0.8 (gift), with that one unusual
observation removed. Table 1 shows the elasticity results.
Table 1 Price Elasticity Results
* statistically significant difference to at least one other factor at p<=0.05 level
RQ2b asked if light category buyers exhibit less price elasticity. To test this, we calculated
the price elasticity for two groups of respondents: light and heavy buyers of wine (wine
generally, not specifically high-priced wine). Results are shown in Table 1. There was not a
statistically significant difference between the elasticity among light wine buyers (-1.7) and
heavy wine buyers (-2.1) with the p-value of the difference in proportions being p=0.22.
Next, we examined RQ2c, price elasticity among light to heavy buyers of high-priced wine.
As shown in Table 1, price elasticity is lowest among heavy buyers of high-priced wine at -
0.7 (difference to either non or light-buyers significant at p<0.01). Interestingly, price
elasticity is highest among light buyers of high-priced wine, at -3.6, compared to non-buyers’
-2.8 (significant difference at p=0.06). Elasticities follow this same pattern for price increases
and decreases, being highest among light buyers and lowest among heavy buyers. The lower
elasticity among heavy high-price buyers is counter to other results in the literature, but it
could potentially be because of the unique nature of the product under investigation. A
potential explanation is that there is a higher level of discernment among heavy buyers of
expensive wine, therefore more fixed preferences, leading to lower price elasticity.
Furthermore, light buyers who have at least some experience of buying high-priced wine are
more price sensitive than non-buyers because they have more confidence to switch between
brands to take advantage of favourable price changes.
The next question to be addressed is RQ2d, pertaining to initial price position and price
elasticity. The results for RQ2d, as shown in Table 1, indicate that the magnitude of price
for Price
Elasticity for
RQ2a: Situation Factors
Drinking at home (n=185)
Dinner party (n=178)
Gift for a 50th birthday (n=178)
RQ2b: Consumer Factors ( All Wine Buyers )
Light buyers of wine (n=394)
Heavy buyers of wine (n=147)
RQ2c: Consumer Factors ( High-Price Tier
Buyers )
Non-buyers of high-priced wine (n=129)
Light buyers of high-priced wine (n=169)
-2.5 *
Heavy buyers of high-priced wine (n=243)
RQ2d: Initial Position in the Price Category
Brands with low initial price position
Brands with mid initial price position
Brands with high initial price position
elasticity decreases according to the brand’s position in the price category. That is, the lower
the initial price position, the larger the price elasticity. The elasticity for the brands with a
low initial price position in the set was -2.5, significantly different (p<0.01) to the -1.5 for
brands with a mid initial price position and -1.3 for brands with a high initial price position.
Whilst the directional difference between brands with mid and high initial positions was
consistent with the notion of lower elasticity as the price point gets higher, the difference
between -1.5 and -1.3 for brands with mid and high initial positions was not statistically
significant. We also see that the pattern in declining elasticity is consistent for increases and
decreases, as shown in Table 1. A summary of the research question. and results is shown in
Table 2.
Table 2 Summary of Results
Research Questions
Does the direction (sign) of price
elasticity differ for high-priced
brands, compared to regular
No. Price elasticity for these high-
priced brands was negative, as is the
case for regular brands.
Does the magnitude of price
elasticity differ for high-priced
brands, compared to regular
No. The magnitude of price elasticity
for high-priced brands was found to
be similar to what has been reported
for regular brands.
Does a more important
consumption situation result in
lower price elasticity for high-
priced brands?
Yes. Price elasticity was found to be
lower for an important consumption
Is price elasticity for high-priced
brands lower among light buyers
of the category compared to
heavy buyers of the category ?
No. The difference in price elasticity
between light and heavy wine
category buyers was not statistically
Is price elasticity for high-priced
brands lower among light buyers
of high-priced wine compared to
heavy buyers of high-priced
No. In contrast to the hypothesis,
price elasticity was found to be
higher among non-and light buyers
of high priced wine.
How does the relative position of
a brand’s initial price affect price
elasticity for high-priced brands?
Brands with a low initial price
position (in a range of high priced
brands) had larger elasticities.
5. Discussion
RQ1 examined whether the direction (RQ1a) and magnitude (RQ1b) of price elasticity differs
for high-priced brands compared to regular brands. The results from this experiment indicate
that, at the aggregate level, all brands involved in this experiment have negative elasticity, the
same as regular brands.
Second, in answer to RQ1b the average elasticity in the study was -1.8 across three varying
conditions of purchase occasion importance; or -2.3 using only the two more ‘everyday’
purchase occasions. However, given the complex nature of this research question, it is
difficult to draw a generalised conclusion based on the results of a single experiment. The
argument might arise that price elasticity estimates for high-priced brands from experiments
may be smaller than those in real-world situations because participants are not completing
real purchases with their own money. This may lead to them show less price sensitivity than
in a real purchase situation (Schindler, 2012). On the other hand, price elasticities from such
choice experiments might also be greater than that in real-world situations because
participants may give the prices presented in the experiment more attention than they
otherwise would (Schindler, 2012). That said, the elasticity results from this experiment are
relatively similar to the results from other price experiments (e.g. Dunn, 2014, Scriven and
Ehrenberg, 2004) that used everyday brands. Whilst the sample in this study is different to
other experimental price studies, the similarity in results suggests some commonality in the
magnitude of consumer response to price changes across various conditions. Indeed, the
pricing experiments cited above have yielded similar elasticities to studies using in-market
data (e.g. Bijmolt et al., 2005). However, more work is needed to examine the external
validity of elasticities from stated choice experiments.
RQ2a examined the three main factors of: situational factors, consumer factors and
contextual factors. The results across different usage situations indicate that price elasticity
decreased significantly when the perceived importance of the consumption situation
increased. This result aligns with the findings from prior research on the topic (d'Astous and
Saint-Louis, 2005, Hall et al., 2001, Quester and Smart, 1998, Wakefield and Inman, 2003).
The differences between elasticities indicate that consumers are more tolerant of price
increases for high-priced brands when they are buying for more important situations. This
result supports prior findings that consumers usually move up one or two price points for
gifts and special occasions (Lockshin, 2015). In conclusion, a more important consumption
situation does result in lower price elasticity.
The results indicate heavy buyers of the wine category exhibit higher price elasticities. This is
consistent with past research into grocery brands (Helsen and Schmittlein, 1994, Kalyanaram
and Little, 1994). It matches theoretical expectations, in that heavy buyers are more price and
quality informed (Woodside and Ozcan, 2009) and have a greater financial incentive to
maximize the value they receive from their expenditure in the category (Danaher and Brodie,
However, what the study also found is that heavier buyers of high-priced wine showed lower
price elasticities as a group. There are two potential explanations for this result. First is that
buyers who tend to buy at the high price range in a category derive more pleasure from
purchasing and consuming the product, e.g. they are more likely to be connoisseurs. They
are therefore are less sensitive to the particular prices on offer. There is indirect evidence to
support this explanation, in that hedonic consumption is linked to lowered price sensitivity
(Wakefield and Inman, 2003). A second potential explanation is that people who can afford
to purchase high-priced wines are more likely to have higher incomes. Higher income
households in turn are likely to exhibit lower price elasticity because there is reduced
household budget pressure to look for savings on purchases (Degeratu et al., 2000).
This finding indicates that the weight of consumers’ level of prior expenditure at the high-
priced level in the product category has more influence on their response to price changes
than their overall purchase frequency for wine. For products with a small range of selling
prices, such as FMCGs, the price gap between the cheapest and most expensive brands may
only be a few dollars. Thus, it may not be necessary to consider segmenting buyers into
different price-level buying groups. However, for products with a wide range of selling
prices, it may be important to consider consumers’ weight of purchase at different price
levels. An additional finding is that consumers do react to price reductions for high-priced
wines, and that light buyers of this price range are the most responsive consumer group. This
finding potentially gives a guide to how promotional activities might work for high-priced
wines and among which groups. This finding should also be heartening to the industry as
light buyers are also the most numerous consumer group in the market (Sharp, 2010).
Next, the results indicate that the magnitude of price elasticity for a high-priced brand
decreases according to the brand’s price position within the high end of the category. The
lower the initial price position (within the range of prices at the high end), the larger the price
elasticity. These results align with research that has previously identified the relationship
between initial price level and price elasticity for regular priced brands (Danaher and Brodie,
2000, Fok et al., 2006, Woodside and Ozcan, 2009). An additional finding here is that the
elasticity for mid-priced brands and high-priced brands (within the high price tier) are very
similar, compared to that of lower-priced brands. This suggests that there may be a threshold
effect within the high-price category, and when the brand’s price reaches a certain level,
consumers become less sensitive to price changes. It is common for a company to have
multiple brands with different price positions. These results indicate that brands at different
price positions respond to price changes differently, and therefore applying a uniform pricing
strategy, such as applying the same percentage of price decrease/increase across an entire
portfolio, may not be the most effective approach to managing prices. Instead, brand
managers should leverage the differences in price response to help overall performance. For
example, if a company must increase prices, it may be more beneficial to substantially
increase the prices of those brands whose consumers are less sensitive to price increase,
rather than spreading the increase across every brand in the portfolio.
6. Conclusions
This research is the first to investigate the direction and magnitude of price elasticity for
high-priced brands, as well as the factors that influence price elasticity at higher price tiers. It
contributes to understanding of the complex nature of pricing, and has yielded several
implications for both academics, and industry practitioners.
For academic pricing research, this research highlights the importance of addressing price
dynamics in a broader context. While past pricing investigations using in-market data
(Kalyanaram and Little, 1994) and experiments (Dunn, 2014, Scriven and Ehrenberg, 2004)
have not been completely dependent on everyday grocery products, such products have
certainly dominated findings to date. This study provides a starting point for future
replication and exploration of the topic. By expanding the research from mainly FMCGs to
high-priced brands, it also contributes new knowledge about how consumers respond to price
changes. The findings from the study also provide marketing practitioners with more accurate
information about how consumers respond to price changes at higher price tiers. This can
assist those marketers in developing more effective pricing strategies, particularly those who
manage high-priced brands in their portfolio.
The results of this research also make a contribution to the area of wine marketing research.
There is a lack of research in the area of premium/luxury wines, and subsequently a lack of
understanding of whether consumer behaviour towards premium/luxury wines is different
from behaviour towards regularly priced products (Lockshin and Corsi, 2012). This study
contributes some knowledge to this research gap, and provides some insight into how
consumers respond to price changes for high-priced goods.
For managers – particularly of high-priced brands in categories such as wine, the study
provides some practical implications. First, high-priced brands are not immune to the inverse
relationship between price and demand; such brands have negatively signed price elasticity
that is approximately similar to normal brands. Therefore, price increases for high priced
brands will dampen demand and price decreases will increase demand. That said, it appears
that brands that sit at the higher price points within the high end are less price-elastic. This
means temporary reductions for them are less likely to generate large sales uplifts than
reductions for somewhat less expensive brands. A second practical implication pertains to
situations: higher importance situations are linked to lower price elasticity. A brand manager
interested in reducing price elasticity for their brand should consider linking it to more
important consumption occasions via marketing communications, such as what the
Champagne producers have done focusing on celebrations. Third, retailers of high-priced
wine can plan targeted offers to lighter or heavier users of wine generally, and high-priced
wine more particularly, knowing that price elasticity is greater among these groups.
7. Limitations & Directions for Future Research
No study is exempt from limitations, and this one is no exception. The study was based on a
simulated choice experiment, rather than actual purchases made in a store. However, there is
considerable support for using the stated choice method in the literature (e.g. Scriven and
Ehrenberg, 2004, Wittnick, 2004). The current study used fairly simple verbal descriptions of
purchase situations that varied in importance to the purchaser. A direction for future research
would be to build purchasing scenarios relating to the importance of the occasion using a
broader range of cues to the respondent, perhaps incorporating not only textual descriptions,
but graphics (pictorial representations of low and high importance events for example).
These would enable a more in-depth investigation of how purchase situation impacts on the
choice of high-priced items.
Another limitation of this research is that it only focused on the off-trade (i.e. retailer) market
for wines selling between $50 and $100 per bottle. Further work that includes the on-trade
market, and wines at even higher price points would provide richer results. Indeed, an
extension of the present study would be to change the purchase situation in which wine is
purchased at a retailer to a restaurant. For example, how is the choice of fine wines on
restaurant wine lists impacted by price; and whether this price-demand relationship is
moderated by the importance of the dining occasion.
Next, this research only included a single product category in a single country. This means it
not possible to confidently generalize to different categories or countries. There are other
potential categories that would be excellent vehicles to further examine questions about high
priced brands, such as leather goods and perfume. Such products lend themselves to
questions about the importance of the purchase occasion, as they can be purchased for
particular occasions, or for others as gifts and those gift occasions may vary in perceived
importance also. Given the rise in popularity for products at the premium end in many
markets (e.g. Deloitte, 2016, Shullman, 2016, Mundel et al., 2017), more findings on these
issues would be valuable to marketing research and practice.
**This research was supported by the Australian Grape and Wine Research and
Development Corporation.
Appendix 1 Sample Composition
Situation 1
(n = 185)
Situation 2
(n = 178)
Situation 3
(n = 178)
(n = 541)
50 or above
Married/de facto
Less than $20,800
$72,801 or above
AGNOLI, L., BEGALLI, D. & CAPITELLO, R. 2011. Generation Y's perception of wine
and consumption situations in a traditional wine-producing region. International
Journal of Wine Business Research, 23, 176-192.
ANDERSON, E. T., CHO, E. K., HARLAM, B. A. & SIMESTER, D. 2009. What Affects
Price and Price Cue Elasticities? Evidence from a Field Experiment. Massachusetts
Institute of Technology.
BANELIS, M., RIEBE, E. & RUNGIE, C. 2013. Empirical evidence of repertoire size.
Australasian Marketing Journal, 21, 59-65.
BARBER, N. 2009. Wine consumers information search: Gender differences and
implications for the hospitality industry. Tourism and Hospitality Research, 9, 250-
BELK, R. W. 1974. An Exploratory Assessment of Situational Effects in Buyer Behavior.
Journal of Marketing Research, 11, 156-163.
BELL, D. R., CHIANG, J. & PADMANABHAN, V. 1999. The Decomposition of
Promotional Response: An Empirical Generalisation. Marketing Science, 18, 504-
BEMMAOR, A. C. & MOUCHOUX, D. 1991. Measuring the short-term Effect of in-store
promotion and retail advertising on brand sales: A factorial experiment. Journal of
Marketing Research, 28, 202-214.
BIJMOLT, T. H. A., VAN HEERDE, H. J. & PIETERS, R. G. M. 2005. New empirical
generalizations on the determinants of price elasticity. Journal of Marketing
Research, XLII, 141-156.
BOLTON, R. N. 1989a. The Relationship Between Market Characteristics and Promotional
Price Elasticities. Marketing Science, 8, 153-169.
BOLTON, R. N. 1989b. The robustness of retail-level price elasticity estimates. Journal of
Retailing, 65, 193-219.
BRITNER, L. 2016. Premiumisation drives US wine import trends - research [Online].
Bromsgrove, United Kingdom: Just Drinks. Available: just-
research_id120055.aspx [Accessed October 2016].
behaviour of heavy and light wine buyers differ? 6th AWBR International
CORSI, A. M. & ROWLEY, M. 2016. The only way is up: Adapting to the consumer shift to
more premium wine. Wine and Viticulture journal, 31, 59-61.
D'ASTOUS, A. & SAINT-LOUIS, O. 2005. National versus store brand effects on consumer
evaluation of a garment. Journal of Fashion Marketing and Management, 9, 306-317.
DANAHER, P. J. & BRODIE, R. J. 2000. Understanding the Characteristics of Price
Elasticities for Frequently Purchased Packaged Goods. Journal of Marketing
Management, 16, 917-936.
DEGERATU, A. M., RANGASWAMY, A. & JIANAN, W. 2000. Consumer choice
behavior in online and traditional supermarkets: The effects of brand name, price, and
other search attributes. International Journal of Research in Marketing, 17, 55-78.
DELOITTE 2016. Global powers of luxury goods 2016: Disciplined innovation. Machelen,
Belgium: Deloitte.
DICKSON, P. R. 1982. The impact of enriching case and statistical information on consumer
judgments. Journal of Consumer Research, 8, 398-406.
DODD, T. H., LAVERIE, D. A., WILCOX, J. F. & DUHAN, D. F. 2005. Differential effects
of experience, subjective knowledge, and objective knowledge on sources of
information used in consumer wine purchasing. Journal of Hospitality and Tourism
Research, 29, 3-19.
DOLAN, R. J. & HERMANN, S. 1996. Power Pricing: how managing price transforms the
bottom line, New York, The Free Press.
DUNN, S. 2014. How do consumers respond to price changes? An examination of
competitive context and prior purchase experience effects. Master of Business
(Research, Marketing) Masters, University of South Australia.
DUNN, S., BOGOMOLOVA, S. & DAWES, J. 2013. An investigation into the effect of
competitive context on brand price elasticities. International Choice Modeling
Conference. Sydney.
EHRENBERG, A. & ENGLAND, L. R. 1990. Generalising a pricing effect. Journal of
Industrial Economics, 39, 47-68.
FENNELL, G. 1978. Consumers' Perceptions of the Product - Use Situation. Journal of
Marketing, 38-47.
FOK, D., HORVATH, C., PAAP, R. & FRANSES, P. H. 2006. A hierarchical bayes error
correction model to explain dynamic effects of price changes. Journal of Marketing
Research, 43, 443-461.
GUADAGNI, P. M. & LITTLE, J. D. C. 1983. A logit model of brand choice calibrated on
scanner data. Marketing Science, 2, 203-238.
HALL, J., LOCKSHIN, L. & O'MAHONY, G. B. 2001. Exploring the links between wine
choice and dining occasions: factors of influence. International of Wine Marketing,
13, 36-53.
HELSEN, K. & SCHMITTLEIN, D. 1994. Understanding price effects for new nondurables:
How price responsiveness varies across depth-of-repeat classes and types of
consumers. European Journal of Operational Research, 76, 359-374.
HUBER, J., HOLBROOK, M. B. & KAHN, B. 1986. Effects of competitive context and of
additional information on price sensitivity. Journal of Marketing Research, 250-260.
KAHNEMAN, D. & TVERSKY, A. 1979. Prospect theory: An analysis of decision under
risk. Econometrica, 47, 263-291.
KALYANARAM, G. & LITTLE, J. D. C. 1994. An Empirical Analysis of Latitude of Price
Acceptance in Consumer Package Goods. Journal of Consumer Research, 21.
KAPFERER, J.-N., KLIPPERT, C. & LEPROUX, L. 2014. Does luxury have a minimum
price: An exploratory study into consumers’ psychology of luxury prices. Journal of
Revenue & Pricing Management, 13, 2-11.
KAPFERER, J. N. 2001. Strategic Brand Management: Creating and Sustaining Brand
Equity Long Term, Milford, Kogan Page.
KAPFERER, J. N. 2012. Abundant rarity: The key to luxury growth. Business Horizons, 55,
KIM, B.-D. & ROSSI, P. E. 1994. Purchase frequency, sample selection, and price
sensitivity: The heavy-user bias. Marketing Letters, 5, 57-67.
LEHMANN, D. R. & WINER, R. S. 2005. Product management, 4th ed, New York, Mc-
Graw Hill.
LICHTENSTEIN, D. R., RIDGWAY, N. M. & NETEMEYER, R. G. 1993. Price perceptions
and consumer shopping behaviour: A field study. Journal of Marketing Research, 30,
LITVACK, D. S., CALANTONE, R. J. & WARSHAW, P. R. 1985. An Examination of
Short-Term Retail Grocery Price Effects. Journal of Retailing, 61, 9-25.
LOCKSHIN, L. 2015. Stop discounting. Wine Business Monthly.
LOCKSHIN, L. & CORSI, A. M. 2012. Consumer behaviour for wine 2.0: A review since
2003 and future directions. Wine Economics and Policy, 1, 2-23.
LOWENGART, O. 2002. Reference Price Conceptualisations: An Integrative Framework of
Analysis. Journal of Marketing Management, 18, 145-171.
MANDHACHITARA, R. & LOCKSHIN, L. 2004. Fast moving luxury goods: Positioning
strategies for Scotch whisky in Thai department stores. International Journal of Retail
& Distribution Management, 32, 312-319.
MIERES, C. G., MARTIN, A. M. D. & GUTÍERREZ, J. A. T. 2006. Antecedents of the
difference in perceived risk between store brands and national brands. European
Journal of Marketing, 40, 61-82.
MONROE, K. B. 2003. Pricing: making profitable decisions, New York, NY, McGraw-Hill.
MOORE, K. & PAREEK, N. 2010. Marketing the basis, Abingdon, Oxon, Routledge.
MUNDEL, J., HUDDLESTON, P. & VODERMEIER, M. 2017. An exploratory study of
consumers’ perceptions: What are affordable luxuries? Journal of Retailing and
Consumer Services, 35, 68-75.
ORTH, U. R. 2005. Consumer personality and other factors in situational brand choice
variation. Journal of Brand Management, 13, 115-133.
QUESTER, P. G. & SMART, J. 1998. The influence of consumption situation and product
involvement over consumers' us of product attribute. Journal of Consumer Marketing,
15, 220-238.
RAJENDRAN, K. N. & TELLIS, G. J. 1994. Contextual and Temporal Components of
Reference Price. Journal of Marketing, 58, 22-34.
ROMANIUK, J. & DAWES, J. 2005. Loyalty to price tiers in purchases of bottled wine.
Journal of Product & Brand Management, 14, 57-64.
SCHINDLER, R. M. 2012. Pricing Strategies: A Marketing Approach, California, Sage
Publications, Incorporated.
SCHMITTLEIN, D. C., COOPER, L. G. & MORRISON, D. G. 1993. Truth in Concentration
in the Land of (80/20) Laws. Marketing Science, 12, 167-183.
SCRIVEN, J. & EHRENBERG, A. 2004. Consistent consumer responses to price changes.
Australasian Marketing Journal, 12, 21-39.
SHARP, B. 2010. How Brands Grow, Melbourne, Oxford University Press.
SHULLMAN, B. 2016. Luxury marketing: Demographics of luxury consumption. Admap,
July/August, 18-19.
SJOSTROM, T., CORSI, A. M. & LOCKSHIN, L. 2013a. Do consumers perceive luxury
and premium attributes to belong exclusively to higher-price points? ANZMAC. New
SJOSTROM, T., CORSI, A. M. & LOCKSHIN, L. 2013b. What attributes do consumer
associate with luxury and premium products? ANZMAC. New Zealand.
STILINOVIC, M. 2016. Premiumization: The most affluent retail trend of 2016 [Online].
New York City, New York: Forbes. Available:
trend-of-2016/#63d877a55d07 [Accessed October 2016].
STOLTMAN, J. J., MORGAN, F. W. & ANGLIN, L. W. 1999. An investigation of retail
shopping situations. International Journal of Retail & Distribution Management, 27,
TELLIS, G. J. 1988. The Price Elasticity of Selective Demand: A Meta Analysis of
Econometric Models of Sales. Journal of Marketing Research, 25, 331-341.
TWEDT, D. W. 1964. How Important to Marketing Strategy is the "Heavy User"? Journal of
Marketing, 28, 71-72.
VAN HEERDE, H. J., LEEFLANG, P. S. H. & WITTINK, D. R. 2001. Semiparametric
Analysis to Estimate the Deal Effect Curve. Journal of Marketing Research, 38, 197-
VEBLEN, T. 1899. The Theory of the Leisure, New York, The New American Library.
WAKEFIELD, K. L. & INMAN, J. J. 2003. Situational price sensitivity: the role of
consumption occasion, social context and income. Journal of Retailing, 79, 199-212.
WANG, B.-S., WANG, X.-J. & GONG, L.-K. 2009. The construction of a Williams design
and randomization in cross-over clinical trials using SAS. Journal of Statistical
Software, 29, 1-10.
WITTNICK, D. R. 2004. Brand choice models and evaluations. Marketing Research,
Summer, 42-44.
WOODSIDE, A. G. & OZCAN, T. 2009. Customer choices of manufacturer versus retailer
brands in alternative price and usage contexts. Journal of Retailing and Consumer
Services, 16, 100-108.
ZENOR, M. J., BRONNENBERG, B. J. & MCALISTER, L. 1998. The impact of marketing
policy on promotional price elasticities and baseline sales. Journal of retailing and
consumer services, 5, 25-32.
Title: Consumer Response to Price Changes in Higher-Priced Brands
Author names and affiliation:
Ava Huang
John Dawes, PhD
Larry Lockshin, PhD
Luke Greenacre, PhD
Corresponding author:
Ava Huang
Ehrenberg-Bass Institute, School of Marketing, University of South Australia Adelaide, SA,
Present/permanent address:
Ehrenberg-Bass Institute, School of Marketing, University of South Australia,
Level 4, 70 North Terrace, Adelaide, SA, AUSTRALIA, SA 5000
... Panic stockpiling also impacted consumers' price sensitivity. Previous studies (e.g., Huang et al. [43]; Pantano et al. [44]) have examined the predominance of determining consumer price elasticity. Thus, it is not surprising that many suppliers have raised prices by up to 300% for certain product segments during emergencies. ...
... Finally, seven variables (consumer behaviour, food stress, food price, food availability, food quality and safety, food insecurity, future perceptions on food crisis) were selected, involving 18 items measured on an ordinal scale that were used to represent nonmathematical ideas such as frequency, satisfaction, enjoyment, and degree of stress [66]. Thus, an ordinal scale was used to determine stress on consumer buying behaviour [43]. To design the measurement-related question, a 5-point Likert scale was applied to independent and dependent variables, where 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. ...
Full-text available
Since COVID-19 was confirmed in Bangladesh in March 2020, the government have enacted stringent measures to prevent the spread of the coronavirus, which has had a significant impact on people’s lives. Food consumption habits of consumers have shifted as a result of declining grocery shopping frequency, negative income shock, and food prices shooting up. This paper aims to explore Bangladeshi consumers’ buying behaviour in association with the stress generated from a food supply shortage during the COVID-19 pandemic and the post-outbreak perception of the food industry, using a dataset with 540 online samples collected between July and August 2021. A two-stage cluster sampling method and self-administrated questionnaire techniques were adopted for collecting the data during the third wave of COVID-19. Using partial least squares path modelling (PLS-PM) and multivariate multiple ordered logit regression (MVORD) to reveal the pertinent structure between all the blocks, this study provides two key findings. First, a higher intensity of COVID-19 impact translates into higher food stress associated with income reduction and higher food prices. Second, food stress directly affects consumer buying and consumption behaviour. We strongly recommend connecting consumers with local producers and collective use of shared warehouses through institutions, policies, and reforms to prevent disruption in the food supply chain and to keep food prices stable. Additionally, food producers, distributors, stakeholders, and policy planners should strengthen the food supply chain to stabilize food security.
... During emergency situations such as the Covid-19 pandemic, panic stockpiling does influence consumers` price sensitivity (Huang et al., 2017). In some cases, consumers may accept a price hike of up to 300 per cent during such emergency situations (Pantano et al., 2020). ...
Full-text available
Purpose of the study: the purpose of this article is to provide an initial impact assessment of Covid-19 on the South African retail sector. Design/methodology/approach: The study follows descriptive research design. It employs quantitative analysis of data obtained from Statistics South Africa (Stats SA) over 16-month period (01.2020-04.2021). Findings: The study found that the impact was uneven across all retail categories. Of the seven categories that showed significant contraction were retailers that fall in the category of textiles, clothing, footwear and leather goods, retailers in household furniture, appliances and equipment, retailers in hardware, paint and glass, and all other retailers that did not fall in the other categories. Retailers that were least affected included retail categories that fall in the category of pharmaceutical and medical goods, cosmetics and toiletries followed by general dealers, and retailers of food, beverages and tobacco in specialised stores. Recommendations/value: Retailers response should be equal to the changing consumer demand and choices. More and more consumers are changing their shopping and consumption behaviour, and are willing for home-deliveries, store pick-up, and cashless transactions. Managerial implications: There is no doubt the Covid-19 pandemic is accelerating the adoption of online retailing, and those who will succeed will be the ones that embrace it. Pivoting on existing products and business processes should be seen as a norm in surviving and excelling going forward.
... At this point, what should not be overlooked is that this behavior pattern can continue after panic periods (Pizzi & Scarpi, 2013). In this respect, it would not be wrong to expect that the stocking trend for these products will continue after COVID-19 due to the lack of stock in some product groups in the first days of the epidemic (Huang, Dawes et al., 2017). During panic times, there may be exceptional approaches to price sensitivity. ...
Full-text available
Purpose of the research; in this study, the relationship between consumer decision-making styles and in-store consumer behavior in the Covid-19 process was examined. In addition, the effect of consumer decision-making styles sub-dimensions on in-store consumer behavior was investigated. In the study, correlational research technique, one of the quantitative research methods, was used to examine the relationship between consumer decision making styles and in-store consumer behavior. Target population of the study consists of consumers residing in Konya (Turkey) and shopping in the retail sector. Since it is not possible to reach all of these consumers within the scope of the research, the research was carried out with the convenience sampling method, one of the non-probabilistic sampling types. In this context, by using the data of the research conducted online with 469 participants, the relationships between variables were tested and the hypotheses put forward theoretically were tested. In the research, a questionnaire that was created and developed through consumer decision making styles and in-store consumer behavior variables was used. Statistical package programs were used to analyze the data and descriptive statistics, confirmatory factor analysis and structural equation modeling (SEM) were performed. In line with the findings of the research, it was determined that three (Perfectionist high quality conscious, novelty fashion conscious, price-value conscious) of the eight sub-dimensions of consumer decision-making styles have a statistically significant (p<,001) and a moderate effect on in-store consumer behavior. In line with the empirical results, it was determined that the perfectionist high quality conscious, novelty fashion conscious and price-value conscious sub-dimensions of consumer decision-making styles in the Covid-19 process had a significant effect on in-store consumer behavior. On the other hand, it has been determined that there is no statistically significant (p>,005) effect of brand-conscious, recreational/hedonistic, acting with impulsivity, confused by over choice and loyal consumer decision-making styles on in-store consumer behavior.
... It has already been argued that the multidimensional aspects of consumerism are important. However, it should be noted that there are also "sub-dimensions" or further minor indicators, which shows the varying compositions of the major aspects of consumerism: Practical indicator, Price indicator, and Institutional indicator (Chen et al., 2017;Huang et al., 2017;Rohani et al., 2012;Kurenlahti et al., 2018;Victor et al., 2018). For example, the cultural aspect of food consumption due to religious principles also involve the price of food, the institutional influence of advertisement, and the consumer's everyday habit of food consumption. ...
Full-text available
This study focuses on the phenomenon of consumerism as a component of sustainable development. Sustainable development assumes the balance of production and consumption in the economy. The notion of consumption is framed to be a balanced and sustainable approach by considering the economic, social, and cultural aspect of food consumption. This multidimension aspect of food consumption incorporates other forms of non-economic principles in lubricating and prolonging its economic sustainability. This research focuses on the city of Bandung, as Indonesia’s major urban area which contributes to national economy and also one that maintains traditional norms that constitutes the multidimensional dynamic of food consumption. The testing and measurement of the consumerism index are done through a random sampling survey of 150 respondents distributed in two districts, having a confidence interval of 92.7%. This research shows that advertisement and distribution of global food products, together with the participation of friends and families in consuming food tend to be more influential in causing stronger behavior of food consumption. This confirms the novelty of this research that the multidimensionality of consumption remains relevant, thus a consumerism index based on the variety of aspects are recommended for future development in the study of sustainability and consumption.
... This approach has been applied to understand individuals' price sensitivity in different domains, such as retail market [22], telecommunications [30], healthcare [31], and energy [32,33]. Although consumers vary in their price sensitivity, their overall degree of reaction to the price change of a product/service can be represented by price elasticity [34]. It should be noted that consumers are sensitive to the price of both tangible and intangible products. ...
Full-text available
For the sustainable development of the Korean Professional Baseball League (a.k.a., KBO League), it is critical to propose profitable and efficient pricing strategies for each team in the KBO League. Therefore, the purpose of this study was to understand KBO League fans’ perceptions of price sensitivity. More specifically, this study explored how fans’ perceptions of price sensitivity and latitude of price acceptance vary between different loyalty groups. A total of 213 valid responses were recruited from KBO League fans by using a convenience sampling approach. As a result, based on the levels of attitudinal loyalty and behavioral loyalty, fans were classified into four categories: loyal fans, latent fans, spurious fans, and indifferent fans. Moreover, loyal fans were found to be less sensitive to the ticket price increase of different levels (i.e., 10%, 20%, and 30%), followed by latent fans, spurious fans, and indifferent fans. Meanwhile, loyal fans have the highest level of the latitude of price acceptance, followed by latent fans, spurious fan, and indifferent fans. The findings of this study contribute to a better understanding of sport fans’ responses to the change of ticket price and their perceptions of the accepted price range. Meanwhile, this study offers practical implications for marketers of KBO League teams to develop tailored pricing strategies for their fans by considering different loyalty levels.
... Second, the present research did not consider the kinds of price that influence consumers' purchase behavior. For example, the size of the price number , price elasticity (Huang et al., 2017), pricing tactics (Shirai, 2017), strikethrough price (Schmidbauer and Stock, 2018), and the style of price promotion (Kim, 2019) can influence consumer evaluation. Besides, the price of offline and online channels (Homburg et al., 2019), advance sales price (Wu et al., 2019), and price discount (Park et al., 2018) may impact the results of price presentation. ...
Full-text available
Retailers like to use different colors to present the sale price and original price when they are presenting a promotion price. How does the inconsistent color presentation of the prices influence consumers’ purchase likelihood? The extant research does not consider this question. This article will address this question. Drawing on incongruence theory and the persuasion knowledge model (PKM), this article proposes that when the color of the sale price is inconsistent (vs. consistent) with that of the original price, consumers show less preference for the sale price. The reason is that consumers perceive the price as being less trustworthy, which leads to a lower purchase likelihood. Furthermore, this effect is affected by the brand awareness of products. Specifically, when products are less-known brands, the inconsistent (vs. consistent) colors of the sale price and original price will lead to a lower purchase likelihood. In contrast, when products are well-known brands, the inconsistent (vs. consistent) colors of the sale price and original price will lead to a high purchase likelihood. In this article, four studies are used to verify these hypotheses, and implications of theory and practice of the present research are discussed.
... A higher price acceptance increases the likelihood that a consumer is more willing to pay a premium for organic wine. In the literature, the findings are mixed regarding the importance of price for buying wine (Huang et al. 2017); however, our results are in line with the studies of Schäufele and Hamm (2018) and Di Vita et al. (2019) who reported that, for the majority of consumers, price is the pivotal driver of wine choices. Another ...
Full-text available
Consumer behavior is key in shifts towards organic products. A diversity of factors influences consumer preferences, driving planned, impulsive, and unplanned purchasing decisions. We study choices among organic and conventional wine using an extensive survey among Australian consumers (N = 1003). We integrate five behavioral theories in the survey design, and use supervised and unsupervised machine learning algorithms for analysis. We quantify a gap between intention and behavior, and emphasize the importance of cognitive factors. Findings go beyond correlation to the causation of behavior when combining predictive prowess with explanatory power. Results reveal that affective factors and normative cues may prompt unplanned and spontaneous purchasing behavior, causing consumers to act against their beliefs.
This research aims to examine how consumers’ consumption behaviors change in response to food supply chain disruptions and food security crisis during COVID-19. A new conceptual framework of consumer food stress and food consumption paradigms for crises is proposed based on the pillars of food security. This study also investigates the mediating effects of food prices and food stress on the proposed framework between pillars of food security and consumption behavior. A range of quantitative techniques was adopted for collecting the data during the COVID-19, with the sample of Bangladeshi consumers (n = 1150) generating a precise measurement. The study applied PLS-SEM (partial least squares-structural equation modeling) to predict food consumption behavior model and expose relevant relationships between all the constructs. Findings reveal that food access, food quality and safety, and food pricing directly impact consumer food stress and consumption behavior. Also, consumers’ consumption behavior is ultimately influenced by food stress. However, food availability has no significant influence on food stress. This research is one of the few empirical studies exploring a range of food security variables that helps explain why consumers are so anxious about food during COVID-19. This study discussed theoretical and practical implications that help food producers, suppliers, marketers, and policymakers maintain a stable food supply chain management and manage food crises during national emergencies.
Remanufactured products belong to the category of environmentally friendly products. Here, we develop models and strategies for collaborating and co-branding of luxury brands with other companies for producing remanufactured products of luxury brands. Then, we examine whether the strategies can coordinate them for producing remanufactured products or not, because of small market share of luxury brands, they should co-brand with a company which has large market share for expanding the market share and selling the remanufactured product. We use a specific name for a brand of remanufactured product because of preventing change of viewpoint of customers to the luxury brand and avoiding brand switching. Producing a new type product should not hurt the status of the luxury brand. Thus, all the possible circumstances should be considered in the process of introduction of a new brand. After development of remanufactured products, we describe different ways and models of advertising for expanding sales by employing differential game models. Overall, we describe the complete activities that are necessary for development of a new brand. As a specific case, we choose the production of remanufactured products by luxury brands which can be a new subject for future research. We find expansion of luxury brand’s market share a new issue. Moreover, producing remanufactured and environmentally friendly products with secondhand materials by luxury brands is a new subject. In the last part, we perform sensitive analysis to examine the effect of parameters on decision variables and presenting managerial insights.
E-commerce platforms have pioneered many innovative business models based on their strong big data capabilities and consumer profile technology. This paper studies the design of the two-tier price membership mechanism in e-commerce platforms, i.e., the price discrimination in membership fees for different consumer groups. We analyze this mechanism considering and not considering certain consumer behaviors, respectively. We surprisingly find that the entry thresholds of the two-tier price mechanism in four different scenarios coincide at the critical point of whether consumers will buy the membership. The entry threshold provided by the platform distinguishes high-value consumers. When not considering the reference price effect, the two-tier price mechanism will degenerate into a uniform-price mechanism. The high membership fee only acts as a reference price for high-value consumers. Finally, we discuss social welfare and shows that a triple-win scenario can occur among the platform, members, and merchants when the cross-buying effect is strong enough.
Full-text available
Purpose – This study aims to test whether the attributes developed via qualitative or conceptual approaches link to the concept of luxury when measured using a quantitative approach. Given the critical role price has in the definition and identification of luxury products, this research measures whether the use of different attributes is exclusively associated with the highest price points in each category or whether there is some level of sharing with lower price points. Design/methodology/approach – A total of 431 respondents sociodemographically representative of the Australian population were screened for familiarity with the category and then randomly assigned to one of three product categories (wine, spirits and perfume). Best–worst scaling was used to measure the associations between different attributes and the concept of luxury, while the pick-any method was used to measure the association of different attributes to different price points. Findings – The findings are consistent across the three categories investigated, i.e. “premium quality”, “authentic/trustworthy brand” and “good brand reputation/status”, are much more associated with luxury than with regular brands. “Luxury”, “premium”, “antique/old vintage”, “limited production/edition” and “premium price” consistently cluster around the highest price point in each category, while the other attributes tested did not. Originality/value – Despite the plethora of research about attributes associated with the luxury concept, this is the first study attempting to measure the size of the association. The consistency of the results across the three product categories is encouraging in terms of the generalisability of the results for future research.
Specialty media outlets such as The Wall Street Journal, Forbes, and Business Insider have increasingly featured articles that stress the growth of the affordable luxuries market. However, “affordable” and “luxury” are two terms that do not conform to luxury goods literature. While the concept of luxury has been traditionally associated with expensive, difficult to find, and exclusive products, the aforementioned business periodicals seem to suggest that a number of products such as specialty coffee, chocolate, and other commodities can be considered affordable luxuries. We conducted an exploratory investigation to determine whether millennial consumers differentiate between the terms “luxury” and “affordable luxury,” which products they perceive to be affordable luxuries, and the price range they are willing to pay for affordable luxuries. Our exploratory study (1) shows that consumers hold similar quality expectations for luxury and affordable luxury products, (2) reveals differentiating descriptors for luxury and affordable luxury products, (3) suggests that consumers see these products as a way to enhance one's image, and (4) offers pricing guidelines for such products.
This study compares estimates of store-level, brand sales, price elasticities from three alternative functional forms of sales equations and assesses the robustness of the estimates. The estimates are derived from 114 sales equations describing brands sold at different stores. Although the price elasticity estimates from the alternative functional forms initially appear very similar, closer investigation indicates that they exhibit substantial differences. On average, across multiple stores, these differences are often statistically significant. They are also sufficiently large to have an important impact on retailers' decisions. Thus, the researcher who is interested in obtaining price elasticity estimates should test alternative functional forms of brand sales equations.
Previous marketing research on the effects of price has tended to ignore the role of such moderating variables as competitive context and the availability of additional information. The authors presented subjects with forced-choice decisions in several product categories. Using a multinomial logit formulation to measure price sensitivity, they find relatively lower price sensitivity (1) when a brand is placed at the upper price-quality boundary of a choice set rather than in the middle, (2) when only brand names are provided as opposed to only quality ratings, and (3) when quality information is added to available brand-name information. They discuss the theoretical and managerial implications of these findings.
A theoretical framework that integrates all individual product benefits into seven general motivation classes, for the purpose of formulating brand strategy.
In a replicated in-store factorial experiment with 12 national brands in six non-perishable consumer goods categories, the authors find price deal elasticities in the [2-11] range, with larger values for smaller brands. Those elasticities increase 20% to 180% when deals are advertised by the retailer; the rates of increase are smaller for the leading brands. The price deal cross-elasticities of the higher priced brands are found to be smaller than those of the other brands; they are in the [2-2.7] range. Optimal retail deal rates are shown to be robust to model specification.
Circumstances, contexts, or situations are often cited but rarely examined as explanations for unpredicted behaviors. This article reports an exploration of the amounts and patterns of variance in selected purchase decisions as a function of consumption and purchase contexts. Results for the two product categories studied reveal sizable situational effects.