Saint Joseph’s University
Haub School of Business Working Paper Series
No: 11-4
Pricing Food Quality Attributes: Dissecting Cereal and
Salty Snack Products
Christopher Shanahan
Neal Hooker (Saint Joseph’s University)
Tekle Atalay
Pricing Food Quality Attributes:
Dissecting Cereal and Salty Snack Products
By
Christopher Shanahan, Neal H. Hooker* and Tekle Atalay
*Contact Author
Neal H. Hooker, CJ McNutt Professor
Department of Food Marketing
Saint Joseph’s University
Mandeville 27
278, 5600 City Avenue
Philadelphia PA, 19131
Email: nhooker@sju.edu
Tel: (610) 660-3481
Fax: (610) 660-1997
Department of Food Marketing
April, 2011
Pricing Food Quality Attributes:
Dissecting Cereal and Salty Snack Products
Paper presented at
Price Measurement Workshop Center for Applied Economic Research
Australian School of Business, University of New South Wales
Sydney, Australia. December 10, 2008
Abstract
Food businesses are using a range of promotional messages and positioning claims to keep up
with increasingly complex consumer concerns about nutrition, health and well being, provenance,
and the ethical and environmental impacts of their ingredient choices. Such efforts may also
promote novel pricing strategies. This study uses hedonic pricing models to explore the marginal
implicit price of various quality attributes in two food categories. Results indicate that nutrient
content levels and the practice of second-degree price discrimination are nonlinearly related to
the price per serving for cereal and salty snack product lines. Organic quality claims enjoy an
appreciable premium (3-7 cents per serving). Private label products and innovative products have
significant price discounts (5-13 cents per serving). The findings of this study indicate that offer
prices per serving vary based on the design specification of the products. To be effective such
pricing strategies must recognize relationships between key food quality attributes and consumer
willingness to pay.
Keywords: Hedonic Pricing Model, Organic, Location Association, Health and Nutrition
Marketing Claims
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Introduction
The use of value added product claims is an integral part of food manufacturer’s differentiation
strategy decisions (Shanahan, Sporleder and Hooker, 2008). Many firms recognize that they can
create a competitive advantage by discovering new combinations of value added product claims
in order to differentiate their product relative to the competition (Nagle and Holden, 2002). As
such, a firm’s choice of a competitive advantage strategy is an innovative act (Stanford, 2007).
An innovative act occurs when two or more previously unrelated elements are combined in order
to create a new “qualitative distinct whole” where the new product has a function that is different
from the individual antecedent parts (Harper and Liecht, 2002). Such product strategies can be
successful when rivals fail to respond (no imitation). Porter (1991) suggests that the maintenance
of competitive advantage involves the dynamic management of a given firm’s value chain rather
than treating each quality attribute creation activity as a collection of separate parts. Thus,
creating a competitive advantage becomes a function of the interaction of food quality attributes,
not simply the sum of each activity’s contribution.
Hedonic pricing models are often used to explore individual quality attribute differentiation
strategies across industries, including food and beverage. Specifically, the hedonic model
specification relates the selling price of a product to a set of product and quality characteristics
possessed by the product and provides insight into which attributes have the greatest correlation
with a product’s price. With respect to food and beverage products, these models can incorporate
a rich variety of quality characteristics, including detailed nutrition content and the presence of
various product positioning messages such as health claims, provenance and production methods.
In addition, the impact of brand type, second-degree price discrimination, and the degree of
product innovation can be incorporated into these pricing models. This model specification can
be used to determine whether marketing communications which describe multiple quality claims
on an individual product offer higher implicit value for the product characteristic or if the
interaction of such messages are or are not synergistic (Teratanavat and Hooker, 2006).
This study explores the use of various food quality claims on cereal and salty snack product
labels. Specifically, this study tests several hypotheses and attempts to answer pressing questions
concerning the increased use of food labeling claims and manufacturer’s willingness to charge
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for new products. Variance in nutritional content is expected to have an impact on the price per
serving that differs across “good” and “bad” characteristics. In addition, the use of nutrition
content, health, provenance, and organic claims are likely to positively impact price. It is
expected that as the number of product claims used by a firm increases, so the marginal impact
on the premium for each quality characteristic will change, and most likely decline.
Other factors are explored in thus study including the impact of increasing the number of
servings per product - expected to negatively impact the price per serving at a decreasing rate if
food manufacturers are practicing second-degree price discrimination (Nagle and Holden, 2002).
This study also controls for the type of brand. Specifically, private label food products are
expected to have a discounted price relative to manufacturer branded food products. Finally, the
study explores if food manufacturers and retailers follow a price penetration strategy (Nagle and
Holden, 2002) when marketing new product lines or new varieties.
Literature Review
Consumer’s price sensitivity for a given product varies over the product’s life cycle as they
accumulate knowledge about the value adding qualities the product possesses (Nagle and Holden,
2002) With respect to product innovations, consumers initially tend to be relatively price
insensitive due to a lack of information about the product (Nagle and Holden, 2002). Sensitivity
increases over time as consumers learn about product attributes and compare to product
alternatives. Initially, consumers use the product offer price as a gauge of the product’s perceived
quality particularly when there are few substitutes with similar quality bundles. Manufacturers of
new food and beverage products, typically experience goods, often price new introductions, relaunches or new varieties at a discount to penetrate the market and overcome disadvantages of
weak appropriability (Sporleder, et al., 2008). Building on these observations, Nagle and Holden
(2002) suggest that an innovation’s price should incorporate the product value while recognizing
the role of a reference price on consumer’s perceived level of quality (Dawer and Parker, 1994;
Nagle and Holden, 2002).
Specific to the food and beverage industry, concerns over nutrition, health and well-being,
provenance, and the ethical and environment impact of food choices are growing and in response,
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retailers and manufacturers are providing products with a range of new functional ingredients
and promotional claims (Duram, 2005; Richards and Padilla, 2007). Accordingly, food label
regulation is also increasing in intricacy, attempting to ensure that the information consumers
receive is reliable and sufficient to facilitate purchase decisions (Mojduszka and Caswell, 2000).
Processed food sold in the US, for example, must have a name signaling the product’s identity,
information about who makes it, a correct and standard measure of its ingredient content, and a
nutrition facts label (FDA, 1999). In addition, any added-value claims used by food
manufacturers that address the relative nutritional quality of the product, claims that relate to a
health benefit, and claims concerning a food’s organic identity are standardized by regulations in
order to protect consumers from frivolous or deceptive marketing.
The presentation of nutritional information on food labels was standardized by the
implementation of the Nutrition Labeling and Education Act (NLEA) under the authority of the
Food and Drug Administration (FDA). Under the NLEA, all processed foods are required to
display in a standardized format the nutritional content of the product, presented in the Nutrition
Facts statement. Food manufacturers must make available information on fifteen key nutrients
and four macronutrients regardless of whether significant levels are present including calories,
total fat, sodium, total carbohydrates and protein (See the FDA’s A Food Labeling Guide for the
full list of required nutrients). The ten other nutrients may be omitted if content levels are
insignificant. Other nutrients may be included on a voluntary basis, but are also defined by the
NLEA and must be listed in a specific order (FDA, 1999). Macronutrients, except for sugar and
protein, must also state the "Percent Daily Value" (DV) relative to a 2000 calorie diet to provide
consumers an easy gauge to compare the nutritional quality of various product alternatives.
Vitamin and mineral content are always presented in DV if included.
In addition to the Nutrition Facts statement, NLEA also regulates the use of nutrient content
claims and health claims (FDA, 1999). Nutrient content claims declare the level of a nutrient in a
food, health claims link the nutrient content of a given product to a specific health condition.
Claims such as "low fat" or "low in sodium", which are usually placed on the front panel of the
package, are recognized as a form of promotion that highlights the relative nutritional content of
the product. Nutrient content claims can be voluntarily used by processors, but if used, the
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NLEA specifies how they may be used, including certain product composition standards.
Manufacturers also have the option to use health claims if they can provide sufficient scientific
evidence that the nutritional content of their product lowers the risk of a specific health condition
(Ross, 2000). FDA carefully regulates health claims recognizing the dynamic nature of the
science of functional foods. Currently, health claims are hierarchal based on the amount of
supporting scientific research underlying the correlation between lowered potential risk of a
specific health malady and a given nutrient (Hooker and Teratanavat, 2008).
A location association claim (LAC) is a quality attribute which specifies the geographic origin of
production (or processing). Locales vary by specific market and environmental factors which
directly influence the level of quality, such as culture, climate and natural resources. Many
locales, and by association products from those locales, have obtained valuable reputations and it
has been shown that consumers are willing to pay premiums for knowledge of a product’s
geographic origin (Atalay, Hooker and Shanahan, 2007). As such, incentives exist for
manufacturers marketing products with location association claims to push for the protection of
their exclusive use of LAC claims (and restrict use by firms outside the locale) through
government regulation, much like a trademark. Examples from Europe are common (Protected
Denomination of Origin/Geographical Indications – see, e.g., Souza Monteiro and Caswell,
2008). Similar claims are appearing in North America (Atalay, Hooker and Shanahan, 2007).
Unlike a trademark, however, the exclusive use of a LAC claim is afforded to all producers or
manufacturers located within the locale.
When a food manufacturer declares that their product is organic, this implies that the ingredients,
and final product, were produced under a specific set of practices that emphasize renewable
resources and conservation of soil and water (AMS, 2000). If firms want to use the organic claim,
their production facilities are required to be notarized by the USDA under the National Organic
Program (NOP), a national-level organic quality standard which requires a government-approved
third-party certifier to inspect the facilities (AMS, 2000). Similar to health claims, a hierarchal
labeling system exists for multiple-ingredient organic food based on the content of organic
ingredients. The levels in descending order are 100% Organic, Organic (contains at least 95%
organic content), Made with organic ingredients (contains at least 70% organic content) and
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Some Organic Ingredients (contains less than 70% organic content) 1 . Only products which
qualify for the top two levels can use the official USDA Organic seal on the front panel of their
product label. Batte, et al. (2007) found that the price premium for an organic quality claim may
have increased following the implementation of the US Department of Agriculture’s national
standard for organic certification (NOP) in 2002.
There are also many other factors that impact food and beverage manufacturer pricing decisions
that are indirectly related to the product’s quality. For example, many marketers tend to follow
two primary pricing strategies that are independent of the product’s quality attributes when they
are introducing a product into the market place: price skimming and penetration pricing (Roa,
1984). Price skimming usually involves a high introductory price matched with heavy promotion,
with the price gradually decreasing over the product’s life cycle. This price strategy is most
effective when the firm correctly assumes that initial demand is relatively price insensitive and
decreases overtime (Nagle and Holden, 2002). When a firm wants to establish a large market
share, price penetration may be an optimal strategy (Nagle and Holden, 2002). The basic
penetration price strategy implies a low introductory price which slowly rises as demand grows.
Then price stabilizes during the maturity stage of the product’s life cycle. Firms introducing new
nondurable goods, such as food products, tend to use a price penetration strategy but may use a
price skimming strategy depending on the relative amount of new quality attributes (relative to
the category) the product possesses (Kerin et al., 2006).
Food and beverage manufacturers also employ quantity discount pricing as a differentiation
strategy (Nagle and Holden, 2002). Specifically, larger product packages are often less costly per
unit (serving) relative to smaller packages. This is the same phenomenon as bulk purchasing
when a wholesaler purchases products in large quantities at a lower price per item, or unit price,
from a manufacturer. Evidence of such second degree price discrimination is explored below.
The type of company that owns the brand (including manufacturer-owned brands or retailerowned private label brands) can also impact pricing strategies. National or manufacturer’s
1
Batte, et al. (2007) provides the first empirical evidence of consumer willingness to pay for various levels of
organic content under the NOP labeling scheme.
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brands often exert heavy promotional effort in order to build consumer loyalty, attract new
consumers and enhance the prestige of their products and the retailers who sell their product.
Also, manufacturer’s brands tend to have more extensive distribution networks and private
brands tend to exert relatively less promotional and distribution effort compared to
manufacture’s brands (Richards, Hamilton, and Patterson, 2007). A private brand also affords
retailers some protection from manufacturer’s decisions to withdraw a product line and guard
against possible up-stream competition from manufacturers (Richards, Hamilton, and Patterson,
2007). In addition, retailers have little control over the magnitude of distribution of
manufacturer’s branded products compared to their own private brands. The primary
consequence of the differences between manufacturer’s brands and private brands is that private
brands tend to be lower in price relative to manufacturer’s brands, which may influence a buyer’s
perception that private brands are of a lower quality relative to manufacturer’s brands (Richards,
Hamilton, and Patterson, 2007). However, it is possible that private brands are likely to benefit in
the current economic downturn as consumers increasingly switch away from value-added food
products toward relatively more affordable alternatives due to increasingly constrained food
budgets.
In summary, many food and beverage manufacturers recognize that consumers derive utility
from various combinations of quality characteristics and therefore price products accordingly.
The retail recommended price of a new food product is the manufacturer’s willingness to accept
or charge for varying design specifications at time of launch of the food product given their
expectations of consumer willingness to pay for varying bundles of quality attributes (Rosen,
1974). A firm’s optimal choice of which quality characteristic bundle and what to charge, its
differentiation strategy, is determined by selecting the quality attribute bundle that maximizes
long-run profit.
Methodology
The hedonic price model is based on the theory that consumers derive utility from some
combination of valued services that a particular product is able to provide (Stanley and
Tschirhart, 1991). In turn, each service is a functional combination of product characteristics that
the product exhibits. Thus, consumer utility is a function of a product’s bundle of quality
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characteristics:
(1)
U (Qi , X )
(2)
Qi = s m ( z n ), n = 1..N , m = 1..M
where sm are the m number of services product Qi provides and zn is the n number of quality
characteristics possessed by product Qi. Depending on the level of zn (which may be binary,
scalar or continuous), each quality characteristic can positively or negatively impact the value of
the service provided by product Qi and thus utility. For example, the total amount of fat or sugar
can positively impact the taste service of product Qi but negatively impact nutritional or health
services (Stanley and Tschirhart, 1991). Consumers maximize their total utility subject to their
total available budget I, where I = ∑PiQi and Pi is the price of product Qi.
Given increasing marginal costs of providing an increasing number of zn quality characteristics
in order to deliver an increasing number of services sm, the expected marginal revenue of
providing Qi also increases in order to ensure optimal production levels (Rosen, 1974). In other
words, the supply-side hedonic price model determines variance in Pi across varying design
specifications of Qi where Pi is expected to be an increasing function of the total number of
services sm provided by Qi. Also, as noted above, through their market intelligence gathering
activities food and beverage manufacturers recognize that the amount of each quality
characteristic zn can positively or negatively impact the amount of consumer valued service sm
provided by product Qi. Revealed pricing strategies should account for this. Thus, it is expected
that the amount of each zn included in design specification Qi is some nonlinear function of the
producer’s offer price Pi. Given this theoretical background, a manufacturer’s willingness to
charge function can be constructed.
Derived from the utility maximization first order condition, it can be determined that Pi is a
function of the number of quality characteristics possessed by product Qi and is the sum of the
marginal implicit prices (MIP) of all zn quality characteristics (Huang and Lin, 2007). In
functional form, we can describe Pi as
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(3)
Pi = ∑ Pn ( z n )
where Pn ( z n ) is the MIP of the amount of zn quality characteristic possessed by Qi.
Previous hedonic price applications have debated the correct functional form of the price-quality
characteristic function. A simple linear combination model of the number of quality attributes
possessed by the product can be used, but this specification ignores the impact of nonlinearities
of quality interactions. Generalized linear models which allow for the sample data to determine
the correct functional form through the application of Box-Cox transformations have been
developed by Estes (1986) who explored the implicit prices of green pepper quality attributes, by
Bowman and Ethridge (1992) in their study of US cotton fibers and by Stanley and Tschirhart
(1991) in one of the few applications of the hedonic pricing model to multi-ingredient food
(cereal) products. Often, Box-Cox applications determine that either a semi-log or double log
linear model performs better than simple linear specifications (Estes, 1986). Rosen (1974)
theoretically shows that the price of a given good is a nonlinear function of the product’s quality
attributes. Thus, a practical specification for exploring willingness to charge is to assume that the
offer price per serving, PPS, of product Qi is an exponential function of the number of quality
characteristics bundled into Qi or,
A
(4)
PPS = α ∏ e
a =1
β 0 a + β1 a z a
B
∏e
β 0 b + β1b z b + β 2 b z b2
b =1
where A+B = N are the number of quality characteristics, za are the quality characteristics that
are expected to exponentially impact price per serving and zb are quality characteristics that are
expected to impact price per serving over different ranges of zb.
The practically of using this specification stems from its relative ease of estimation using OLS,
straightforward calculations of the MIP per serving for each included quality attribute and its
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ability to test hypotheses about the a priori relationships between price per serving and each of
the quality attributes. Recent hedonic pricing research has employed similar log-linear
approaches including San Martin, Troncoso and Brummer’s (2008) study of the determinants of
Argentinean wine prices exported to the US and Huang and Lin’s (2007) analysis of organic
tomato prices across regional markets. Taking the natural log of both sides of (4) converts the
equation to a log-linear form and reveals its relative ease of estimating the quality attribute
coefficients:
A
(5)
B
ln PPS = φ + ∑ β1a z a + ∑ β1b zb + ∑ β 2b zb2
a =1
A
where
B
b =1
B
φ = ln α + ∑ β0 a + ∑ β 0b
a =1
b =1
is a constant. Note that this model does not assume a
b=1
constant MIP over the full range of quality attribute zn rather the estimated quality attribute
coefficients have a useful interpretation where β1a indicates the percent change in price per
serving given a one unit increase za and β1b + 2∑β2b (the partial derivative of lnPPS with respect
to zb) indicates the percent change in price per serving given a one unit increase zb (Studenmund,
2006). Following this logic, we can calculate the average MIP per serving of each quality
attribute za and the average MIP per serving of each quality attribute zb, respectively, using the
following equations:
(6)
PPS a ( z a ) = PPS * β1a
(7)
PPS b ( z b ) = PPS * ( β1b + 2β 2b ) .
Data Description
Data on new cereal and salty snack products comes from Mintel’s Global New Product Database
(www.gnpd.com). The database contains food product label information for most new product
lines released in the US. Along with product name, company and brand information, each
observation contains retail recommended prices, nutritional facts, quality claims and category.
For each of the observed food categories, a complete sample (in terms of price and a list of
nutrition information) of new products were identified and investigated. The data used covers
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cereal and salty snack product innovations in the database, released between January 1, 2000 and
July 31, 2006 and includes a total of 564 cereal products and 962 salty snack products.
Mintel’s GNPD database contains information about the type and quantity of value-adding
product positioning claims for each innovation, including the use of nutritional, organic and
other promotional claims such as structure-function claims. Also for each innovation, a product
description including health claims is reported. For the purposes of this study, all of these claims
were expected to exponentially impact price per serving. NUTC is defined as the number of
nutrition claims used by each product line; HC is a binary variable indicating if the innovation
makes a health claim as described in the product description field. A search function was
developed that queried strings of words that describe specific health condition risk reductions,
such as heart disease, cancers and hypertension. Observations that claimed a health condition
risk reduction were coded as a health claim. The number of all other value-added differentiating
claims, OPC, was also included in both models. This variable is expected to positively impact
the price per serving.
Tables 1 and 2 present summary statistics for the variables used in this investigation. The
average price per serving PPS for a typical cereal product, whether it is of the cold or hot variety,
is nearly $0.40, and ranges from as low as $0.04 to $4.79. Ranging as low as $0.01 to $9.99 per
serving, salty snack products are on average $0.48 per serving.
The number of servings per package, SERV, and each of the nutrients are quality characteristics
that are expected to exponentially impact price per serving over a given range. These impacts are
likely to be independent. SERV is expected to negatively impact price per serving at a decreasing
rate. Other per serving nonlinear variables included in the model are the number of calories, KAL;
total fat content in grams, FAT; total carbohydrates in grams, CARB; total protein content in
grams, PROT; total cholesterol in milligrams, CHOL; total sodium content in milligrams, SOD;
total sugars in grams, SUG, and total dietary fiber content in grams, FIB. Also expected to have a
nonlinear relationship with the price per serving is the average vitamin and mineral density per
serving, VITM, as measured by the average daily value of the four primary micronutrients
(vitamin A, vitamin C, calcium and iron). Due to expected nonlinearities, each of these
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independent variables is included as are the square of each variable.
Each product innovation observation also indicates whether it is an organic product or not
(ORG=1 if product makes an organic claim and zero otherwise). This study explores whether
suppliers are willing to charge more for an organic quality claim. The marginal change in the
premium charged by a firm for a particular positive nutrition quality attribute should be positive
when it is used in conjunction with an organic claim. Also, the marginal change in the premium
charged by a firm for a particular location association claim is expected to be positive when it is
used in conjunction with an organic claim. However, bounded rationality (information overload
due to crowded product labels and busy shoppers) may confound a simple linear relationship.
Nearly 11% of cereal products make an organic claim but only 3% of salty snacks. Among those
innovations that did bear the organic claim, the set of NOP seal qualified products, ORGB, were
identified in a previous food label analysis which either confirmed or did not confirm the
observed product line’s possession of the NOP seal (i.e., a minimal 95% organic content, and
launch post-October 2002 – see Atalay, Shanahan and Hooker, 2006).
Firms have the option to use place names in association with their product offerings if they
believe a “local” claim has value. A search function for US state names was developed to
determine the existence of location association claims, LAC, in each observation. A binary
variable was developed to code whether the innovation uses such a claim in the marketing mix
(i.e.: brand, company, or product name). As shown in Tables 1 and 2, the means of the LAC
variables suggest less than one percent of cereal and 2 percent of salty snacks have such claims.
In order to determine whether brand type impacts the price per serving of innovations, a binary
measure indicating whether the product is a private brand or not was developed (PL = 1 when the
brand is privately owned by the retailer, zero otherwise). Twenty eight percent of new cereal
products and 18% of salty snacks carry privately-owned labels. GNPD indicates the degree of
innovation type of each observation. 2 It is hypothesized that firms with new-to-the world
products and new varieties of food products follow a price penetration strategy in order to
2
Innovations are exclusively coded as “new-to-the-world product, new variety of an already existing product, new
packaging or new formulation of an already existing product line.”
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establish their respective products. For the purposes of this study, new-to the-world products and
new varieties, NEW, are coded as one, zero otherwise. If penetration pricing is more frequently
used this variable is expected to have a negative impact on the price per serving of a given cereal
or salty snack product. In order to control for inter-category variance in price per serving, a
binary dummy variable, SC, indicates a cold (SC = 1) or hot (SC = 0) cereal.
Results
Tables 3 and 4 report results of the two hedonic models. The overall fit of the two models is
relatively strong, adjusted R2s for the cereal and salty snack hedonic models are 0.71 and 0.47,
respectively. The F statistics suggest that the likelihood of all of the included variables equaling
zero are significantly below 1 percent. In terms of the statistical significance of the variables
across the food categories, salty snack prices appear to be most responsive in terms of the quality
attributes compared to cereal prices. Specifically, the cereal model has only 8 out of 31 variables
statistically significant at a level of 95% or better, which is indicative of the lower levels of
variance in price per serving within the cereal category relative to the snacks category and the
lower variance in nutritional content across cereals (Stanley and Tschirhart, 1991). The following
discussion investigates the calculated MIPs for a selected set of quality attributes across both
models, suggesting implications for food manufacturers and retailers.
Tables 3 and 4 also report the average MIP per serving for each quality attribute. The average
MIP per serving of a cereal and salty snack product decreases by $0.068 and $0.043, respectively,
per one serving increase. This is consistent with the discussion that firms charge lower per
serving prices for bulk or larger package sizes due to the application of second degree price
discrimination. However, the impact is nonlinear and eventually bottoms out with an increased
number of servings as indicated by the statistically significant positive sign on the square of
servings per package. See Figure 1 for the calculated price per serving for a reference cereal and
salty snack product as the number of servings per product increases.
We anticipated that the nutritional content would impact price per serving over given ranges of
content levels. Many of the expected nonlinear relationships were found to be statistically
significant in both models. However, a priori expectations of the direction of these impacts were
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not obvious - certain nutrients may positively impact one service and negatively impact another.
These patterns may differ across cereals and salty snacks. For example, the average MIP for a
one gram increase in total fat was $0.005 per serving of a salty snack product. However, the
negative sign on the squared total fat variable indicates that the MIP is diminishing. This may
suggest that fat initially enters positively into the taste service of a given product, but negatively
into the nutritional benefit service after a particular taste service threshold is surpassed. Cereal
price per serving for fat increases at a increasing rate ($0.004 average MIP), which may suggest
total fat’s importance in providing taste service in cereal products, but the calculated coefficients
in the model were not statistically significant. See Figure 2 for the calculated price per serving
for a reference cereal and salty snack product as the grams of total fat per product increases. The
same may be said of sodium content in cereal products where the average MIP is $0.001 given a
one milligram increase in sodium, but this is expected to diminish with additional sodium.
However, this affect is not the same among salty snack products where the average MIP is $0.000 per serving given a one milligram increase in sodium.
Among salty snack product nutrients, protein content has the largest positive average MIP of
$0.061. This suggests that salty snacks that offer products with high protein content can earn a
significant premium. Among cereal nutrients, cholesterol content has the largest positive average
MIP of $0.074. This may imply cholesterol content has an important role in a non-nutrient
service initially, but this affect diminishes as indicated by the negative sign on the squared
cholesterol variable.
Average vitamin and mineral content (average percent of daily value) has an unexpected
negative average MIP of -$0.185 in the cereal category. The sign on the vitamin and mineral
variable may be due to the aggregation approach used to code the data. This result requires
further study to determine if increased average vitamin and mineral content negatively impacts
the offer price due to other non-nutrient services. The average vitamin and mineral content does
positively enter into PPS initially among salty snack products. Sugar content impacts the PPS in
a similar manner. In the cereal category, the calculated average MIP is a negative $0.002
compared to positive $0.006 in the salty snack categories.
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The average MIP per serving for an additional nutritional claim, NUTR, for cereal and salty
snack products is $0.003 and -$0.009, respectively. In both models, the estimated parameters are
not statistically significant at a 90% level of confidence. The same can be said for the impact of
using an additional health claim HC on a cereal and salty snack product where the (statistically
insignificant) average MIPs are calculated at -$0.012 and -$0.180, respectively. Further, the use
of a location association claim produces insignificant estimated coefficients with calculated
MIPs per serving for cereal and salty snack products of $0.001 and $0.030, respectively, likely
due to low frequency of actual use.
The average MIP for the use of other promotional claims on salty snack products is statistically
significant at a 95% level of confidence and is $0.053 per serving. However, the opposite impact
occurs within the cereal model where the estimated coefficient is negative and the calculated
average MIP of -$0.008 (significant only at an 80% level of confidence). This suggests that
additional nutritional differentiating effort among cereal producers has minimal impact on price
per serving variance. This may occur because the absolute amount of differentiating effort is
already high among cereal products relative to salty snacks (The mean of OPC = 1 for cereal
compared to 0.21 for salty snacks).
As expected, the average MIP for an organic claim, ORG, on a cereal product is positive $0.038
per serving, but is only statistically significant at an 80% level of confidence. The average MIP
per serving of salty snack products is $0.073 at a 90% level of confidence. Thus, there is a
positive offer premium for the use of an organic claim. For organic products launched after
October 2002 and qualifying for USDA’s organic seal, which substantiates the organic claim and
requires at least 95% organic ingredients. In both models, the estimated coefficients describing
whether the product line bears the NOP seal, ORGB, were positive but neither is statistically
significant. In addition, the organic and nutritional claim interactive terms had unexpected, yet
generally statistically insignificant, negative coefficients in both product models. This may imply
that marketers may not want to use both organic and nutritional content claims because they
perceive these claims to be redundant or even as a source of product label noise. More work is
necessary to explore this phenomenon.
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The impact of brand type on the price per serving was as expected, cereal private brands are on
average $0.118 per serving less expensive relative to other brands and retail-owned salty snack
innovations are less expensive by an average of $0.133 per serving. These parameters are
statistically significant at a 95% level of confidence level in both models. The relative level of
innovation contained in the product has the expected negative relational signs. Negative average
MIPs support the use of penetration pricing strategies for new cereals and salty snacks. However,
the statistical significance varies across the two food categories. The relative newness of a cereal
product is not statistically significant but it is statistically significant at a 95% level for new salty
snack products. Based on these results, firms are willing to offer appreciable discounts to
consumers buying private label products and highly innovative product offers.
Conclusion
The objective of this study was to develop a pricing model that took into account variance in
quality attributes delivered by cereal and salty snack innovations. This study is timely, providing
an assessment of emerging food label claims available to food manufacturers and retailers.
Recognizing that firms coexist in a heterogeneous market where each firm varies in the type and
level of value-added activities and that each activity creates a quality attribute that fulfills a
consumer need, pricing based on value deliverables is critical to the market success of a given
product line. Consumers are willing to pay for value and firms that differentiate in accordance to
consumer demand will create a competitive advantage.
It was shown that the relationship between price and individual quality attributes may not simply
be linear. The nonlinearities identified in these pricing models among cereal and salty snack
innovations can in turn be associated to the pricing strategies of firms interested in releasing new
product lines into these markets. For example, using this model based on all firms’ expected
consumer demand for each quality attribute, optimal prices per serving can be developed given
the particular design specification of a new product using a simple linear programming
procedure.
It was shown that second-degree price discrimination is a common practice among the observed
firms. Further, private labels more often use discounts in their pricing strategies. Also, firms
15
releasing new-to-the-world products and new varieties of already existing product lines are more
apt to offer a lower price per serving in order to establish their respective product lines. On the
other end of the spectrum, firms that offer specialty product lines, such as organic alternatives,
are more wiling to charge a premium for the added specialty. Also, offer prices rise when firms
use increasing numbers of differentiating claims. However, the impact of “promotional noise”
may dampen these effects – demonstrated well by the cereal hedonic model. Firms’ efforts to
control for these events are essential to ensure a final selling price close to the optimal price for a
given innovation.
The hedonic pricing model has once again been proven to be a successfully pragmatic model
specification well suited to describe the relationship between the offer price of a product and its
set of product and quality characteristics. However, future work should incorporate quantitative
data on consumer behavior. This study is based on the firm’s expectation of consumer demand
and the particular revelation of the (initial) pricing strategy (the offer price or manufacturers’
recommended selling price). Retailer or home scanner data are candidates for strengthening this
analysis. Such data might also allow for a (product or category) life-cycle analysis. Retail sales
data can yield consumer willingness to pay measures for the organic identity quality attribute,
nutritional, health claims, other quality attributes explored in study and more. Comparative
statistics describing product adoption conditions and the market shares of firms using each of the
quality attributes can lend further insight into the use-value of each of the claims on processed
food products. Demand elasticities can be determined and integrated into an individual firm’s
pricing strategy. Few firms have access to such rich product attribute information and those firms
that do possess this information consider it to be a competitive advantage. The primary objective
of future work should focus on working through the above limiting issues, bridging the factors
that induce consumer demand for product innovations and the firms' derived demand for process
and product innovation. This will promote a better understanding of the function of market
transactions between innovation buyers and innovation sellers.
16
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19
Table 1: Cereal Data Description
n = 564 Observations
Variable
Mean
Std. Dev.
Maximum
Minimum
Price per serving
PPS ($)
$0.40
$0.47
$4.79
$0.04
Number of servings per package
SERV
10.861
5.23
46
1
Calories
KAL (Kcal)
146.253
46.17
300
10
Total fat
FAT (g)
1.997
1.93
15
0
Carbohydrates
CARB (g)
30.037
8.87
60
0
Protein
PROT (g)
3.870
3.07
26
1
Cholesterol
CHOL (mg)
0.021
0.43
10
0
Sodium
SOD (mg)
157.156
84.89
440
0
Sugars
SUG (g)
10.373
5.16
22
0
Dietary fiber
FIB (g)
3.160
2.88
25
0
Average percent daily value (vitamin A, C, calcium and iron)
VITM (%)
0.138
0.12
1.61
0.00
Number of nutrition marketing claims
NUTC
0.904
1.08
5
0
Presence of health claim (1=yes)
HC
0.048
--
1
0
Number of other product positioning claims
OPC
1.000
0.95
5
0
Presence of organic claim (1=yes)
ORG
0.108
--
1
0
Presence of USDA organic seal (organic content 95%+) (1=yes)
ORGB
0.067
--
1
0
Private label product (1=yes)
PL
0.282
--
1
0
Degree of product innovation (1=”new”)
NEW
0.739
--
1
0
Presence of a location association claim (1=yes)
LAC
0.009
--
1
0
Hot or cold cereal (0=hot; 1=cold)
SC
0.807
--
1
0
Nutrition information (per serving)
Measurement
20
Table 2: Salty Snacks Data Description
n = 962 Observations
Variable
Mean
Std. Dev.
Maximum
Minimum
Price per serving
PPS ($)
$0.48
$0.80
$9.99
$0.01
Number of servings per package
SERV
8.49
6.78
90
1
Calories
KAL (Kcal)
141.937
40.89
510
5
Total fat
FAT (g)
7.06
4.01
45
0
Carbohydrates
CARB (g)
17.46
5.55
57
1
Protein
PROT (g)
2.81
2.15
19
0
Cholesterol
CHOL (mg)
1.11
4.07
50
0
Sodium
SOD (mg)
230.10
137.93
1430
0
Sugars
SUG (g)
2.11
3.86
26
0
Dietary fiber
FIB (g)
1.49
1.35
11
0
Average percent daily value (vitamin A, C, calcium and iron)
VITM (%)
0.06
0.89
0.26
0
Number of nutrition marketing claims
NUTC
0.44
0.78
5
0
Presence of health claim (1=yes)
HC
0.00
--
1
0
Number of other product positioning claims
OPC
0.21
0.45
3
0
Presence of organic claim (1=yes)
ORG
0.07
--
1
0
Presence of USDA organic seal (organic content 95%+) (1=yes)
ORGB
0.02
--
1
0
Private label product (1=yes)
PL
0.18
--
1
0
Degree of product innovation (1=”new”)
NEW
0.84
--
1
0
Presence of a location association claim (1=yes)
LAC
0.02
--
1
0
Nutrition information (per serving)
Measurement
21
Table 3: Cereal Hedonic Pricing Model Results
Variable
Coefficient
0.8105
Constant
-0.1780
SERV
0.0031
SERV2
0.0038
KAL
0.0000
KAL2
0.0082
FAT
2
0.0015
FAT
-0.0532
CARB
0.0008
CARB2
0.0005
SOD
-0.0014
SOD2
0.0085
PROT
2
-0.0005
PROT
-0.0081
FIB
0.0004
FIB2
0.0758
VITM
-0.2716
VITM2
-0.0059
SUG
2
0.0000
SUG
0.2359
CHOL
-0.0239
CHOL2
0.0076
NUTC
-0.0303
HC
-0.0203
OPC
0.0948
ORG
0.0619
ORGB
-0.2977
PL
-0.0092
NEW
0.0243
LAC
-0.0145
ORG*NUTC
-0.1079
ORG*LAC
-0.0160
SC
Adjusted R-squared
Sum squared residuals
F-statistic
Prob (F-statistic)
t-Stat
SIG
3.94
-20.70
13.56
1.69
-1.35
0.33
0.52
-4.29
3.98
2.39
-2.39
0.41
-0.50
-0.45
0.39
0.24
-0.50
-0.51
-0.01
1.02
-1.02
0.46
-0.41
-1.22
0.95
0.62
-8.11
-0.24
0.12
-0.35
-0.32
-0.33
-***
***
***
**
MIP PPS
--$0.0679
-$0.0015
-$0.0044
--$0.0204
--$0.0009
-$0.0030
--$0.0029
--$0.1849
--$0.0023
-$0.0744
-$0.0030
-$0.0120
-$0.0080
$0.0375
$0.0245
-$0.1177
-$0.0036
$0.0096
-$0.0057
-$0.0427
-$0.0063
***
***
***
***
*
*
*
*
***
22
MIP PPS = 1
--$0.1718
-$0.0038
-$0.0112
--$0.0516
--$0.0023
-$0.0075
--$0.0073
--$0.4674
--$0.0059
-$0.1881
-$0.0076
-$0.0303
-$0.0203
$0.0948
$0.0619
-$0.2977
-$0.0092
$0.0243
-$0.0145
-$0.1079
-$0.0160
0.705
66.36
44.05
0.00
Table 4: Salty Snack Hedonic Pricing Model Results
Variable
Coefficient
-0.5241
Constant
-0.0913
SERV
0.0009
SERV2
-0.0049
KAL
0.0000
KAL2
0.0104
FAT
2
-0.0005
FAT
0.0137
CARB
-0.0003
CARB2
-0.0003
SOD
-0.0002
SOD2
0.1366
PROT
2
-0.0044
PROT
0.0061
FIB
0.0051
FIB2
0.0108
VITM
-0.0003
VITM2
0.0410
SUG
2
-0.0017
SUG
0.0049
CHOL
0.0003
CHOL2
-0.0194
NUTC
-0.3747
HC
0.1044
OPC
0.1517
ORG
0.0272
ORGB
-0.2764
PL
-0.1117
NEW
0.0622
LAC
-0.1299
ORG*NUTC
0.1713
ORG*LAC
Adjusted R-squared
Sum squared residuals
F-statistic
Prob (F-statistic)
t-Stat
-2.93
-17.80
9.48
-2.40
3.19
0.63
-1.01
1.07
-1.10
-0.88
-0.43
4.76
-2.11
0.19
1.24
0.76
-0.45
2.66
-1.85
0.48
1.25
-0.81
-0.67
2.55
1.57
0.17
-5.71
-2.20
0.44
-1.35
0.40
MIP PPS
--$0.0430
--$0.0024
-$0.0045
-$0.0063
--$0.0003
-$0.0613
-$0.0078
-$0.0049
-$0.0180
-$0.0026
--$0.0093
-$0.1799
$0.0501
$0.0728
$0.0131
-$0.1327
-$0.0536
$0.0299
-$0.0624
$0.0822
SIG
-***
***
***
***
*
*
*
***
***
*
***
***
*
***
**
***
***
**
23
MIP PPS = 1
--$0.0895
--$0.0049
-$0.0094
-$0.0131
--$0.0007
-$0.1278
-$0.0163
-$0.0102
-$0.0376
-$0.0055
--$0.0194
-$0.3747
$0.1044
$0.1517
$0.0272
-$0.2764
-$0.1117
$0.0622
-$0.1299
$0.1713
0.471
287.12
2.57
0.00
Figure 1: Calculated Price per Serving by Number of Servings for a Reference Cereal and
Salty Snack Product
Price Per Serving and Number of Servings
1.6
Price $ per Serving
1.4
1.2
1
0.8
Cereal
0.6
Snacks
0.4
0.2
0
0
2
4
6
8
Servings
24
10
12
14
Figure 2: Calculated Price per Serving by Fat Content for a Reference Cereal and Salty
Snack Product
Price Per Serving and Fat Content
Price $ per Serving
0.6
0.5
Cereal
0.4
0.3
Snacks
0.2
0.1
0
2
4
6
8
Fat (g)
25
10
12
14