The Diffusion of Organic Food Products: Toward
a Theory of Adoption
Christopher J. Shanahan
Department of Agricultural, Environmental and Development Economics, The
Ohio State University, 323 Ag Admin, 2120 Fyffe Road, Columbus, OH,
43210– 1067
Neal H. Hooker
Department of Agricultural, Environmental and Development Economics, The
Ohio State University, 323 Ag Admin, 2120 Fyffe Road, Columbus, OH,
43210– 1067. E-mail: hooker.27@osu.edu
Thomas L. Sporleder
Department of Agricultural, Environmental and Development Economics, The
Ohio State University, 323 Ag Admin, 2120 Fyffe Road, Columbus, OH,
43210– 1067
ABSTRACT
This study explores drivers influencing food processors’ decisions to adopt organic practices
and the constraints which may limit the availability of food products using the National
Organic Program (NOP) organic seal as a marketing tool. A constrained diffusion model is
applied to assess seal qualified adoption across food categories. A second model explores
market forces that influence variations in the diffusion process. Results suggest that external
factors, including new regulation, impact diffusion rates. Future adoption of organic practices
will require enhanced informational and physical access for potential adopters. [EconLit
citations O330, Q130, Q160]. r 2008 Wiley Periodicals, Inc.
INTRODUCTION
Many consumers in the United States demand that the food they consume have at
least a minimum standard of perceived physiological safety. U.S. consumers tend to
rely on the U.S. Department of Agriculture (USDA) and the Food and Drug
Administration (FDA) to enforce food safety standards. Consumer uncertainty of
the perceived level of safety of food products is the primary driver of the growth of
the U.S. organic food market (Mintel International Group, 2006). More than ever
before, media attention has focused on food scares and food-safety issues, which in
turn are escalating consumer doubt in the safety of food supply chains. Certain
consumers demand above-minimum standards of perceived safety or credence
quality. These consumers are not satisfied with conventionally produced food that
may contain objectionable remnants of chemical-based pesticides, antibiotics, or
other additives. Certain consumers are concerned about food produced with
bioengineered organisms as ingredients (Mintel International Group, 2006). Some of
these same consumers view organically produced food products as a means of
substantiating the integrity of food supply chains and avoiding these undesirable
externalities of conventional food production.
r 2008 Wiley Periodicals, Inc.
Agribusiness, Vol. 24 (3) 369–387 (2008)
Published online in Wiley InterScience (www.interscience.wiley.com).
DOI: 10.1002/agr.20164
369
370 SHANAHAN, HOOKER, AND SPORLEDER
The market for U.S. organic foods has been growing at 20% per year since 1990
(Dimitri & Green, 2002). Total sales more than doubled between 1998 ($5.5 billion)
and 2003 ($13 billion) (Batte, Hooker, Haab, & Beaverson, 2007). In 2004, Whole
Foods Market, the largest natural-foods supermarket in the United States, conducted
a consumer survey that found over 50% of their consumers believe organic food is
better for the environment, better at supporting small and local farmers, and better
for their health. The survey also found that 32% believed that organic food tastes
better and that 42% believed that organic food is of a higher quality relative to
nonorganic food products (Whole Foods Market, 2004). Mirroring this increasing
demand, a market-support infrastructure is developing, including retail outlets, newproduct development, and a more standardized quality-assurance regulatory system
(Klonsky & Greene, 2005). The continued adoption of organic products among
consumers depends on increasing retail access, the number of new-product offerings,
branding, market entry of established mainstream food processors, and increased
export opportunities (Dobbs, 2006; Klonsky & Greene, 2005).
Consumer demand for organic food drives the adoption of organic production
practices. Many producers see organic (and other kinds of specialty farming) as more
lucrative than conventional crops and livestock because the market is segmented and
because certain consumers are willing to pay premiums for differentiated and/or
specialty products. However, the vast majority of U.S. farmers and food processors
are still hesitant about organic production and the inherently greater uncertainty
associated with these production systems. This, in turn, is having adverse effects on
the U.S.’s ability to supply, at stable prices, the increasing demand for organic
products in domestic and international markets. Consequently, U.S. organic food
processors are increasingly looking abroad to fulfill their input needs. At the current
growth rates in consumption and production of organic foods, consumer prices will
remain high relative to conventionally produced foods, and imports will more than
likely supplement the shortfalls of domestic production of organic multi-ingredient
goods (Batte et al., 2007). Thus, both the power of farmers and organic consumers
are constrained by the slow growth of domestic organic production.
Adding to the uncertainty, the exact meaning of organic initially was unclear. Prior
to implementation of the USDA’s National Organic Program (NOP) in October
2002, some producers claimed to have conformed to the obligations tied to using
organic production practices when they did not. It was later determined by interested
consumer and producer groups that accountability measures were needed to
effectively satisfy consumer’s credence quality requirements. Multiple and similar
schemes were developed by consumer and producer associations and state
governments to standardize the meaning of organic through the use of third-party
auditors and state-sponsored regulatory bodies, yet evidence has suggested that
little mutual recognition of these schemes occurred. This created more confusion
among both consumers and producers as the number of quality standards increased
(Fetter & Caswell, 2001).
The NOP established a national-level organic quality standard and classified
exactly what it means to be organic. The NOP is a third-party, voluntary qualityassurance certification process that notarizes the product (both primary ingredients
and finished food products) as NOP-certified organic. Qualification is based on the
firms’ ability to fulfill the production or processing obligations and is certified by a
third-party agency accredited by the USDA. The policy goal of the NOP is to
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DIFFUSION OF ORGANIC FOOD PRODUCTS 371
substantiate and standardize organic labeling to give all agents in the market an
assurance of the credence quality possessed by products with an organic claim. The
NOP also substantiates the certification of multi-ingredient processed goods using a
four-tiered labeling system that encodes the relevant product by its content of
organic ingredients (100% Organic, Organic (contains at least 95% organic), Made
with organic ingredients (contains at least 70% organic), and Some Organic
Ingredients (contains o70%)). Only the first two levels can use the official USDA
Organic seal on the front of the label, and the last level of NOP certification forbids
the use of the word organic on the front panel of the product (for more detail, see
Atalay, Shanahan, & Hooker, 2006; Batte et al., 2007).
Some food processors also are increasingly looking at organic products as viable
ventures. Yet, it is unclear whether the adoption of the voluntary NOP quality
standards (discussed later) facilitates the flow of market information, effectively
substantiates the quality of products, or even for which firms it is most appropriate.
Certain products may command a price premium and be perceived by consumers to
be ‘‘officially’’ organic even though the NOP certification process was bypassed. This
type of activity is expected to increase in an evermore dynamic organic market which
is itself increasing in size and product-offering depth, categories and brands,
customer base and consumer attitudes, motivation, and behavior. Thus, food
processors may minimize competitive rivalry by developing organic products. Food
processors are relatively larger than individual primary producers, most exert
relatively more effort towards product differentiation than other agents within the
industry, and they may possess a high degree of spatial buyer power because they
purchase a large volume of primary product in thin spatial (regional) markets.
The objective of this study is to focus on the temporal innovation diffusion process
and the specification of a diffusion process based on a constrained linear model. The
model is specified using external factors that impact the rate of NOP-qualified
adoption across food categories following key concepts compiled in the economic,
sociology, and consumer-marketing literatures. A two-stage modeling approach is
undertaken where estimated rates of innovation diffusion for each food category are
estimated applying methods of Bass (1995) and Jain, Mahajan, and Muller (1991).
Then, a second-stage model empirically explores possible market drivers that
influence the variation in the constrained rates of organic adoption diffusion across
food categories. The combination of perspectives from various paradigms on the
innovation diffusion process offers valuable insight into how constrained social
networks determine the net effect of a process innovation’s temporal diffusion
through an industry.
THEORETICAL BACKGROUND
Firms create competitive advantages (CA) by perceiving or discovering new
strategies, or value-added activities, to differentiate relative to rivals. A competitive
advantage occurs when a firm’s earnings exceed their costs and potential rivals are
not able to drive earnings to perfectly competitive levels (Porter, 1983). In instances
where perfect market information is available, competitive advantage cannot be
sustained over a period of time. A firm creates a sustainable competitive advantage
(SCA) by developing value-creating processes that cannot be duplicated or imitated
easily by others. SCA creation also is the implicit avoidance of the perceived
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372 SHANAHAN, HOOKER, AND SPORLEDER
competitive threats that reduce expected supranormal performance (Barney, 2006;
Stanford University, 2007; Wiggins & Ruefli, 2002). Firms that are proactive in
guarding against these threats of competitive rivalry will be more effective in
achieving SCA (Porter, 1983).
Sustaining a competitive advantage requires the firm to develop a process of
continuous organizational learning and the systematic adaptation to changes in the
firm’s external environment and internal capabilities (Barney, 2006). Through the
identification of quality attributes and by assigning price premiums based on
perceived consumer willingness to pay, firms are able to differentiate their respective
product offers and focus on targeting customers, thereby reducing the degree of
competition within the industry. The difference between consumer value derived
from a firm’s product offer and the firm’s cost incurred from quality attribute
creation activities is the premium earned by a firm’s differentiation efforts (Stanford
University, 2007). Consumers are willing to pay for value, and firms that
differentiate in accordance to expected consumer demand, given the firm is able to
dampen the other external competitive-parity forces, will create a sustainable
competitive advantage. The firm with the greatest differentiation premium will gain
an SCA if that firm is able to create these strategic quality-attribute activities better,
or at less cost, than its rivals and guard against imitation.
Basically, firms create SCA by perceiving or discovering new marketing strategies
that differentiate product offers relative to rivals, which in turn delivers supranormal
returns. As such, the firm’s choice of a CA strategy is an innovative act (Stanford
University, 2007). Innovative marketing strategies will shift a given firm’s SCA
when rivals fail to respond to the new strategy. Innovations that typically shift CA
include the discovery of new production technologies, buyer needs, industry
segments and changes in input costs, input availability, and government regulations
(Stanford University, 2007). The most effective innovators will be those firms that
have developed the most robust environmental-scanning capabilities and identify an
innovative mix of quality attributes that maximizes a firm’s expected performance
and is hard to replicate by potential competitors (Kerin, Berkowitz, Hartley, &
Rudelius, 2006).
To effectively analyze the role the NOP plays in the competitive strategy of organic
firms, the innovation-adoption process must be modeled in a formal way. An
innovation is any change that is perceived new to a potential adopter (Rogers, 2003)
and occurs when two or more previously unrelated elements are combined to create a
new ‘‘qualitative distinct whole,’’ where the consequential new object has a function
completely different from the individual antecedent parts (Harper & Liecht, 2002).
Necessity often spurs the development and diffusion of innovations within a social
system, which in turn causes society to adapt or change (which can bring about
new necessities). Innovations offer social agents new ways to interpret meanings of
observed social situations, to solve problems, and to reduce social/environmental
uncertainty.
Most of the early research on diffusion theory takes a pragmatic approach to
understanding the temporal adoption process. Rogers (2003) defined innovation
diffusion as the process by which an innovation is communicated and transferred
through a social system over time. The driver of diffusion is the adoption and the
utilization of the innovation by individuals. The decision to accept an innovation by
the receiver, and thus the spread through a population, depends on the perceived
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DIFFUSION OF ORGANIC FOOD PRODUCTS 373
characteristics of the innovation. Rogers broadly defined such characteristics as the
relative advantage of the innovation, compatibility with the needs and desires of the
adopter, the relative complexity of the innovation, and the relative trialability and
observability of the innovation compared to its alternative. The diffusion of an
innovation through a given social system can be thought of as the idea’s spread in a
limited population; the number of adopters initially grows exponentially as the
new technology takes root within the social structure. Then, the number of
adopters reaches a critical mass, and growth begins to slow down as the
last remaining potential adopters accept or reject the innovation. Thus, the diffusion
process can be framed within any s-shaped, unimodal distribution function. The
logistics function is the most popular functional form due to its ease of
use (Fernandez-Cornejo & McBride, 2004). Fig. 1 provides a visual representation
of the NOP diffusion process.
Later, Lawrence Brown (1975) extended Rogers’ (2003) idea that information
transfer is the primary factor that influences adoption rates by noting that the
innovation propagator ultimately controls the innovation’s informational and
physical distribution to adopters. In other words, innovation propagators have
control over the potential adopter’s market access to the innovation. Specifically,
innovation propagators control access by determining where and how many
diffusion agencies to establish within the targeted social system, the extent of the
innovation’s distribution infrastructure, the innovation’s price, and the level of
promotional and marketing effort exerted. Increasing the adopter’s access to the
innovation entails the supplier being able to minimize physical and informational
distributional constraints. Along similar lines, Bröring, Leker, and Röhmer (2006)
suggested that suppliers (i.e., established innovative firms) may differ substantially in
their ability to innovate because of internal organizational design.
Figure 1
A Two-Stage Adoption Model.
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374 SHANAHAN, HOOKER, AND SPORLEDER
Organic adoption by food processors (i.e., process innovation adoption) can be
observed by examining the population of all new processed food-product lines
released into a given market and determining which product lines are using an
organic quality claim (as determined by the informational content of its product
label). Use of an organic promotional claim on a new product line implies that the
agribusiness’s product/brand manager made a decision to adopt organic practices.
The demand for a process innovation is derived from the demand for the foodquality attribute by consumers.
Adoption rates are a function of the characteristics of the adopter. The expected
benefits and the anticipated costs from process adoption vary across food industries
and sectors, and also are influenced by market structure, consumer demand, and the
power of suppliers. Expectations and anticipations change over time, yet since
information diffuses through food categories and industry sectors at different rates
due to external market factors, expectations and anticipations also will vary. Path
dependency and industry learning networks fuel the variety of adoption rates, and
hence the cumulative number of adopters; however, the nature of this impact is
expected to decrease over time. Expectations about potential earnings and
anticipated costs or uncertainty usually decrease over time due to learning effects
and the accumulating nature of information.
METHODOLOGY: CHARACTERIZING ADOPTION AND DIFFUSION
One possible logistic functional form that describes a Rogers’ diffusion model is the
Bass (1995) model, and is based on the simple conjecture that the first adopters of an
innovation communicate to, interact with, and influence other potential adopters in
subsequent periods. The Bass model links the number of adoptions in Time t given
that they have not yet adopted to some fraction of the total number of innovation
adopters that already have adopted in Time t1 (Jain et al., 1991). Thus,
nt ¼
dNðtÞ
¼ p½M Nðt 1Þ
dt
q
þ Nðt 1Þ½M Nðt 1Þ
M
ð1Þ
where nt 5 the number of innovation adoptions in Time t, p 5 the coefficient of
diffusion due to external influence, q 5 the coefficient of diffusion due to interaction
with other adopters, M 5 the cumulative number of potential adopters at Time t 5 0,
N(t) 5 the cumulative number of innovation adoptions in Time t, and
[MN(t)] 5 the cumulative number of potential adopters in Time t.
As N(t) increases and approaches M, the rate of innovation adoption decreases. In
addition, the rate of diffusion tends to zero as time approaches infinity. The
coefficient of diffusion due to imitation is a measure of the accelerative force created
by the interaction of adopters of the innovation and potential adopters through time
on the speed at which an innovation diffuses (i.e., travels) through a social system
(i.e., category or industry), often assumed to change at a constant rate (i.e., constant
acceleration). This permits the model to consider the possibility of social interaction
between potential adopters and previous adopters, which in turn creates the
necessary social network to emerge and spread the idea of the innovation through
the population like a contagion (Fernandez-Cornejo & McBride, 2004). Thus,
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DIFFUSION OF ORGANIC FOOD PRODUCTS 375
market interactions between adopters and nonadopters must occur to increase the
rate of adoption. Note that the number of adoptions at Time t per change in the
coefficient of innovation:
@nt =@q ¼ Nðt 1ÞM 1 ½M Nðt 1Þ
ð2Þ
In response to criticism about the construct validity of the model and alternative
logistic-based specifications of the diffusion process, Bass (1995) conceded that the
model was an example of an empirical generalization or ‘‘a pattern or regularity that
repeats over different circumstances and that can be described simply by
mathematical, graphical or symbolic method’’ (p. G6). Thus, the model’s purpose
is not to entirely capture all of the theoretically true factors impacting the diffusion
process but to be used as a pragmatic, cost-saving, problem-solving tool for anyone
desiring to forecast future-innovation adoption. Despite this virtue and Bass’ (1995)
redefinition of the model’s purpose, the forecasting tool still does not escape the
assumption of temporal regularity: that the diffusion process observed today will be
the same tomorrow. In fact, this assumption is essential for the general Bass model to
effectively proxy the complex innovation diffusion process.
The actions of innovation suppliers can be easily represented in the Bass model to
account for their impact on the ultimate spatial and temporal diffusion of a given
innovation. The actions of innovation suppliers impact the innovation distribution
in society by splitting the rate of innovation adoption into a camp of supplied (or
qualified) adoptions (at) and a camp of adopters waiting for the supply to
‘‘materialize’’ (or those rejected by the supplier) (bt). Thus, as shown in Fig. 1, the
rate of adoption of an innovation, nt, is split into two flows, with, nt 5 at1bt. The
external factors that impact the overall rate of adoption are demand oriented and are
fueled by interactions of social agents within the system. The factors that impact
whether the adoption is delivered are supply oriented. This means that factors such
as the extent of the informational and physical distribution system and marketing/
pricing practices impact whether an adoption is ‘‘approved’’ for dissemination and,
thus, the ultimate extent of the innovation’s diffusion. Initially, assuming external
influence on adoption is zero, Equation 1 is modified to highlight the split in the
diffusion flow into two parts:
dNðtÞi dAðtÞi dBðtÞi
¼
þ
dt
dt
dt
A
¼fi AðtÞi ½Mi AðtÞi BðtÞi þ fBi BðtÞi ½Mi AðtÞi BðtÞi ;
ð3Þ
dNðtÞi
¼ fN
i NðtÞi ½Mi AðtÞi BðtÞi ;
dt
ð4Þ
dAðtÞi
¼ fA
i AðtÞi ½Mi AðtÞi BðtÞi dt
ð5Þ
dBðtÞi
¼ fBi BðtÞi ½Mi AðtÞi BðtÞi :
dt
ð6Þ
which suggests that
and
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376 SHANAHAN, HOOKER, AND SPORLEDER
Integrating Equation 4 yields the following logistic function:
NðtÞi ¼ Mi =½1 þ eðaf
N
tÞ
ð7Þ
and making a log-linear transformation of Equation 7 yields the following linear
function:
ln½NðtÞi =ðMi NðtÞi Þ ¼ a þ fN t:
ð8Þ
Similarly, integrating Equations 5 and 6 and applying log-linear transformations
yields the following functions for deriving the coefficients of diffusion of seal
qualified and nonseal qualified innovation adoptions:
ln½AðtÞi =ðMi NðtÞi Þ ¼ a þ fA t
ð9Þ
ln½BðtÞi =ðMi NðtÞi Þ ¼ a þ fB t
ð10Þ
and
Using Equation 8, factors that influence the magnitude of the accelerative force
on the flow of innovation diffusion, as represented by fN
i , across predefined
groups of the population of potential adopters are explored. In the empirical
application presented later, the pools of potential adopters are all product-line
or brand managers contained within each food category, and the decision facing
each brand manger is whether to adopt an organic marketing strategy for
their respective new product. As with any decision to adopt an innovation,
innovation demand is influenced by the expected relative advantage from adoption
given external factors. The impact of such environmental factors on relative
diffusion flows across various food categories can be easily examined with a
linear model. Likewise, the factors that influence the relative magnitude
of the restricted flows of innovation diffusion, represented by fA
and fBi
i
and derived from Equations 9 and 10 across predefined groups of the population
of potential adopters, also are of interest. The extent of an innovation’s
supply and ultimate diffusion depend on the supplier’s effort at providing
effective information and physical distribution systems and the supplier’s
marketing and pricing strategies. It is expected that for each of the investigated
independent variables, the impact on the seal qualified innovation diffusion
rate will be opposite, or smaller, than the impact on the constrained flow of
nonseal innovation diffusion rate. To empirically test this hypothesis, each
external factor can be examined using a linear association model that
takes the constrained certified adoption flow for each category and regresses
the resultant dependent variables against possible proxies representing variations
in constraints due to supply distribution, pricing, and marketing strategies for
each category.
The following hypotheses begin to formalize the impact of external factors on the
organic adoption decision and qualification for NOP certification.
The number of product lines in a food category (E), will positively impact the
diffusion rate of organic adoptions across categories. It is expected that the
likelihood of a firm to adopt organic practices is dependent on the level of
market rivalry within a category. The largest food categories will have the most
accumulated production experience, greater access to needed certified inputs,
and will be under more pressure to innovate due to the forces of competitive
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DIFFUSION OF ORGANIC FOOD PRODUCTS 377
rivalry. Thus, it is expected that larger food categories will be more likely to adopt
organic practices and will have a higher probability of NOP seal qualification to
effectively differentiate product lines. Food categories representing a smaller number
of firms may be at a disadvantage in terms of qualifying for the differentiating seal.
Alternatively, seal qualification may not be as necessary because of the lower degree
of rivalry within the category. Increased rivalry and threat of imitation suggests
increased incentives to continually innovate and increased willingness to incur higher
costs for seal qualification.
Market power (MP), calculated as the average number of product lines per
company within each defined food category, will negatively impact the adoption
diffusion rates of organic practices within a food category. The more concentrated is
power within a given subset of an industry, the less innovative the industry will be
(Rogers, 2003). This is because the range of new ideas (i.e., the extent of intraindustry learning and environmental scanning) is constrained when only a few
companies dominate the industry. Note that once the initial decision to adopt is
made within a centralized industry, innovative activity ought to be easier to
implement relative to less-centralized industries among those companies who
dominate their respective industries.
Product complexity, characterized by the average number of ingredients (I) per
food category, suggests a more involved supply chain, making it less likely a firm will
be organic and thus also hindering a food-category’s adoption diffusion rate. It is
expected that product complexity also is negatively related to the odds that the new
organic product will qualify for the NOP seal, which in turn hinders the diffusion of
seal qualified adoptions. It also decreases the likelihood that non-seal qualifying
organic adoptions will occur, but to a lesser extent.
The greater the average number of explicit quality, or value-added, claims
used in the marketing of each food-category’s product (Cl), a measure of
product differentiation, the rate of organic adoptions will increase. It is
expected that firms who put more effort towards differentiation of product
offers will be more likely to adopt organic practices. As a result, the number of
value-added claims used in the marketing of each new food product (Cl) is positively
related to the firm’s demand for organic production technology (O). Aggregating
demand to calculate variance of diffusion rates across food categories should
not change this expected relationship. It also is expected that average differentiating
effort will be greater among food categories with greater diffusion rates of
non-seal qualifying adoptions. This is because firms within these categories are likely
to compensate for the lack of the NOP seal with other value-added product clams to
stay competitive and ensure that their product’s price premiums are substantiated
and guarded.
It is expected that the later the timing of the first seal qualified organic adoption
within a given food category (T), calculated as the number of months after the
implementation of the NOP, the more likely the producer of that good will adopt
organic practices due to the principal of the time value of money and the decreasing
degree of uncertainty about the NOP as it diffuses through the organic market.
Thus, we will see higher rates of diffusion among food categories that wait to see if
the process innovation will be a commercial success. The likelihood that the new
organic product will qualify for the NOP seal also is positively impacted by adoption
timing. Interorganizational learning is cumulative; thus, it is expected that firms will
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378 SHANAHAN, HOOKER, AND SPORLEDER
become more knowledgeable about NOP seal qualification standards and requirements through learning-by-doing. Additional supplies of organic ingredients will
emerge over time. The expected consequence of this learning and access is higher
rates of seal qualified diffusion and lower rates of non-seal qualified diffusion among
food categories that wait to see if the seal will be beneficial.
Each of the aforementioned external factors can be examined using a linear model
and the associated coefficients estimated using ordinary least squares. For example,
previous research has estimated linear models to measure the diffusion of
bioengineered corn among U.S. farmers, optical scanner use among grocery stores,
and automated teller machine (ATM) use within the banking industry (FernandezCornejo & McBride, 2004; Hannan & McDowell, 1984; Levin, Levin, & Meisel,
1992). Earlier work by Romeo (1977) has suggested the use of a log-linear
specification, which implies that the relationship between the factors and the
independent variable are nonlinear over the adoption cycle. This investigation will
use a linear model specification in the second stage, with checks for multicollinearity
among the independent variables using a simple correlation coefficient matrix and
heteroskedasticity by examining estimated residual tables. Evidence of heteroskedasticity will be accounted for using White heteroskedasticity-consistent standard
errors (Studenmund, 2006). The expected relationship between the forces of
competitive rivalry and constrained innovation diffusion is not currently clear;
thus, tests for correlates are sufficient for the purposes of this preliminary
investigation.
Mintel’s Global New Product Database (GNPD; www.gnpd.com) provides one
source of food product label information well suited to this research. GNPD gathers
data on product innovations, which includes product inventions, new brands,
product line-extensions, and product reformulations within consumer packaged
goods markets. The database lists new-product information and label pictures for
goods launched in 49 countries. A search function can separate products using
certain quality claims (including organic) with results including product name,
description, variants (flavors, sizes, etc.), ingredients and nutritional information,
category, company information, distribution channels, and price in local currency
and in euros. Drawing from this population of new food products (in excess of
300,000 annually), 32,434 U.S. new food and beverage products covering all product
innovations released between October 1, 2002 (the start of the NOP program) and
April 30, 2006 were identified and analyzed.
A food category is equivalent to a set of products similar in type and production
and correlates to the product’s originating industry. NOP seal qualifiers (a), NOP
non-seal qualifiers (b), and nonorganic adopters in 30 mutually exclusive food
categories (containing both organic and nonorganic products) were identified
through a proactive process of food label analysis, where possession of the NOP seal
or an organic claim statement which identifies the product’s level of certified organic
content was either confirmed or not confirmed. This analysis resulted in a complete
sample totaling 19,317 new food products, from which 1,252 claimed to be organic
(n) (see Atalay et al., 2006). The average number of ingredients in a given food
category (I), the average number of promotional claims in each food category (Cl),
the time of market release (T), the average number of product lines per company in
each food category (MP), and the number of new product lines in each food category
(E) also were identified. A description of the external factors is presented in Table 1.
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DIFFUSION OF ORGANIC FOOD PRODUCTS 379
RESULTS
As observed in the calculated category averages, the number of non-seal qualified
adoptions for most categories is greater than the number of seal qualified adoptions
with the exception of fruit, vegetable, soup, breakfast cereal, and sugar (see Table 1).
This may be because these categories have been innovative in the past in offering
organic products and are therefore ahead of other categories in diffusing organic
technologies. Organic practices are relatively new, and the diffusion process for
many of the remaining food categories are still at an early stage. The coefficient of
diffusion of seal qualified adoptions is less than the coefficient of diffusion of nonseal qualified adoptions in the cakes and pastry, cheese, cereal, dessert, and sweetspread categories (the slopes of the respective diffusion curves).
A different picture emerges when the shapes of the resultant diffusion curves from
the first-stage estimation of the 30 food categories are more closely analyzed (see
Table 2). The cumulative number of seal qualified adoptions is increasing at a greater
rate relative to the number of non-seal qualified adoptions (Fig. 2). Thus, seal
qualified product (i.e., 195% organic content) growth in the market relative to nonseal qualified organic food product (i.e., those in the lower two categories, with
o95% organic content) growth is greater in most categories, suggesting that the
NOP seal is an increasingly important component in the marketing strategies of
organic food firms.
Generalized least squares, which controls for possible heteroskedasticity using
consistent standard errors and covariance, was used to estimate the coefficients of
A
diffusion for organic adoptions fN
i , seal qualified adoptions fi , and non-seal
B
qualified adoptions fi for each of the 30 food categories (Table 3). The resultant
parameters are all statistically significant, and the degree of explained variation for
all 90 models is relatively high (3 parameters 30 categories). From Table 3, the
resultant coefficients of diffusion of seal qualified adoptions for most categories are
greater than the coefficients of diffusion of non-seal qualified adoptions. Specifically,
the average coefficient of diffusion for seal qualified adoptions is 0.076, and the
average coefficient of diffusion for non-seal qualified adoptions is 0.049. Thus, the
growth of seal qualified products in the marketplace relative to the growth of nonseal qualified food products is greater in many categories, again suggesting that the
NOP seal is becoming an increasingly important component in marketing multiingredient organic foods. The categories with the largest magnitude of the diffusive
force of seal qualified adoptions are soft drinks, crackers/cookies, cheese, meat, and
sauces. Food categories with relatively low seal qualified diffusion rates include
TABLE 1.
Diffusion Model-Descriptive Statistics
Variable Observations
Sum
M
Max
Mdn
Min
SD
19,317
643.90
1,966
468
109
537.17
N
A
B
E
1,252
598
654
32434
41.73 19.93 21.80 1081.13
129
60
69
5393
42.5
17
18.5
631.5
5
4
1
141
28.68 14.57 17.84 1203.27
MP
I
–
–
3.27 11.90
9.73 23.645
2.72 11.4225
1.45 2.015
2.04 5.26
Agribusiness
CL
T
–
–
1.11
7.33
2.146 30
1.0275 6
0.549
1
0.38
7.33
DOI 10.1002/agr
380 SHANAHAN, HOOKER, AND SPORLEDER
TABLE 2.
Model
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Second-Stage Data
Food category
Baking Ingredients/Mixes
Bread/Bread Products
Cakes/Pastries/Sweet Goods
Crackers/Cookies
Soft Drinks
Coffee/Tea
Confectionery
Milk Product
Milk
Cheese
Desserts/Ice Cream
Fruit
Vegetables
Meals
Meat
Sauces
Pasta Sauces
Seasonings
Rice
Pasta
Potato Products
Side Dishes
Snacks
Snack/Cereal/Energy Bars
Fruit Snacks
Soup
Savory Spreads
Sweet Spreads
Sweeteners/Sugar
Cereal
Figure 2
Agribusiness
M
N
A
B
E
MP
I
CL
761 25 11 14 1011 2.914 12.872 0.807
607 48 17 31 774 2.735 16.703 1.235
667 17 5 12 943 2.893 20.814 0.882
1393 90 34 56 1737 3.000 13.804 0.921
236 10 5 5 313 2.630 7.794 1.039
547 81 19 62 823 2.084 3.13 0.761
1966 52 27 25 2886 4.327 10.412 0.89
402 44 18 26 1391 9.727 11.974 1.601
186 47 25 22 247 2.148 9.651 2.028
473 25 10 15 678 2.745 6.994 0.81
1287 33 13 20 3072 8.853 17.569 1.193
234 23 19 4 318 2.134 4.032 0.755
478 57 43 14 630 2.551 6.434 0.888
1626 56 17 39 1904 4.086 21.022 1.27
1157 41 12 29 1681 3.062 12.644 1.002
1785 129 60 69 3440 3.543 10.961 0.679
125 19 12 7 165 1.528 10.669 0.764
398 11 5 6 592 1.941 9.26 0.549
181 19 12 7 224 2.055 11.155 1.213
463 63 30 33 564 2.564 9.399 1.275
241
5 4 1 295 2.706 13.261 1.016
128 19 8 11 148 1.644 10.265 1.318
1419 72 26 46 5393 7.105 14.823 1.055
643 54 26 28 776 3.129 23.645 1.695
165
9 8 1 183 2.577 12.756 1.539
443 50 30 20 548 2.796 17.274 1.247
222 17 9 8 354 1.566 11.69 0.731
342 45 26 19 570 1.810 6.185 0.977
109 14 8 6 141 1.454 2.015 1.119
633 77 59 18 633 5.861 17.848 2.146
T
2
6
18
8
30
4
3
9
7
18
3
4
1
6
12
1
1
27
1
6
12
7
3
6
9
4
4
1
6
1
Innovation Diffusion Paths Among Organic Adopters—All Food Categories.
DOI 10.1002/agr
DIFFUSION OF ORGANIC FOOD PRODUCTS 381
TABLE 3.
Model
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
First-Stage Results of Diffusion Rates per Food Subcategory
Food category
Baking Ingredients/Mixes
Bread/Bread Products
Cakes/Pastries/Sweet
Goods
Crackers/Cookies
Soft Drinks
Coffee/Tea
Confectionery
Milk Product
Milk
Cheese
Desserts/Ice Cream
Fruit
Vegetables
Meals
Meat
Sauces
Pasta Sauces
Seasonings
Rice
Pasta
Potato Products
Side Dishes
Snacks
Snack/Cereal/Energy Bars
Fruit Snacks
Soup
Savory Spreads
Sweet Spreads
Sweeteners/Sugar
Cereal
M
Max
Mdn
Min
SD
(fn) Adj. R2 fn (fa) Adj. R2 fa (fb)
Adj. R2 fb
0.057
0.060
0.038
0.890
0.889
0.750
0.072
0.062
0.086
0.906
0.884
0.915
0.047
0.051
0.027
0.873
0.902
0.565
0.079
0.046
0.053
0.080
0.063
0.096
0.064
0.058
0.061
0.053
0.077
0.068
0.081
0.057
0.083
0.060
0.076
0.064
0.064
0.072
0.076
0.062
0.056
0.065
0.074
0.076
0.068
0.066
0.096
0.064
0.038
0.012
0.911
0.901
0.852
0.845
0.841
0.845
0.896
0.767
0.739
0.954
0.895
0.887
0.921
0.968
0.842
0.808
0.818
0.854
0.894
0.830
0.847
0.937
0.878
0.952
0.938
0.932
0.925
–
–
–
–
–
0.100
0.118
0.040
0.096
0.067
0.080
0.123
0.063
0.057
0.083
0.077
0.107
0.100
0.078
0.057
0.049
0.090
0.053
0.060
0.066
0.067
0.075
0.072
0.065
0.085
0.072
0.072
0.076
0.123
0.072
0.040
0.020
0.877
0.904
0.956
0.912
0.951
0.890
0.911
0.912
0.730
0.955
0.847
0.886
0.912
0.882
0.633
0.815
0.883
0.901
0.890
0.946
0.920
0.913
0.870
0.919
0.954
0.854
0.907
–
–
–
–
–
0.065
0.029
0.050
0.065
0.049
0.075
0.050
0.049
0.030
0.024
0.070
0.059
0.071
0.036
0.064
0.049
0.060
0.000
0.054
0.072
0.065
0.000
0.035
0.059
0.061
0.052
0.059
0.049
0.075
0.051
0.000
0.019
0.894
0.827
0.887
0.783
0.784
0.832
0.787
0.705
0.791
0.743
0.909
0.834
0.900
0.897
0.727
0.788
0.756
0.000
0.898
0.797
0.889
0.000
0.842
0.931
0.902
0.758
0.879
–
–
–
–
–
coffee/tea, fruit, rice products, potato products, and seasonings. Note that many
products within these food categories are relatively low in product-ingredient count,
suggesting that particular ingredients in products within these food categories may
be more difficult to source in terms of critical organic variants.
To explore the stated hypotheses about the external factors expected to impact the
diffusion of organic marketing strategies, the reported coefficients of diffusion are
assumed to depend on some linear combination of market factors. In the second
stage, for each set of the coefficients of diffusion for all food categories, including all
organic adoptions, seal qualified adoptions and non-seal qualified adoptions, the five
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DOI 10.1002/agr
382 SHANAHAN, HOOKER, AND SPORLEDER
external proxy variables (E, MP, I, CL, and T) were regressed to estimate marginal
impacts and combined explanatory power. Model 1 represents the following
proposition: The magnitude of total organic adoption diffusion across food
categories is a linear combination of external factors. Likewise, Models 2 and 3
test the validity of the following proposition: The magnitude of seal qualified and
non-seal qualified organic adoption diffusion across food categories is linear
combinations of external factors. Results are shown in Table 4.
All three models have relatively low coefficients of determination (Model 1
reported an R2 of 0.24, Model 2 reported 0.06, and Model 3 reported 0.17.) F
statistics for the three models suggest that none of the models are statistically
significant at a 99% level. Model 1 is statistically significant at the 95% level
confidence, Model 3 is statistically significant at a 90% level, and Model 2 is not
statistically significant. As expected, the insignificance of Model 2 suggests that
external factors representing competitive rivalry and thus adoption diffusion
constraints appear to have little impact on intracategory variance in diffusion rates
of seal qualified adoptions. The combination of entry threats, intercategory market
power, product complexity, promotional effort, and adoption timing has little
impact on the adoption decision among those firms that are able to have their
respective product lines approved for the NOP seal. There is not strong evidence of
possible multicollinearity among the independent variables. Exploration of the
residual tables revealed that some heteroskedasticity may be present; this is
controlled for using heteroskedasticity-consistent standard errors.
In terms of the statistical significance for each of the independent variables, the
results are mixed. The number of potential adopting product lines (E), meant to
proxy potential competitive rivalry and market-entry threat, is found to be a
statistically significant, positive determinant in all three models. However, the impact
of E on organic adoptions appears to vary by whether the product is approved to use
the organic seal. The magnitude of E’s estimated parameter is greater and has a
greater level of statistical significance in Model 3 relative to Model 2. Consequently,
entry threats appear to contribute to certain firms facing the decision to innovate
without qualifying for the NOP seal to stay competitive. MP, as proxied by the
TABLE 4. Empirical Results of Second-Stage Diffusion Model, White HeteroskedasticityConsistent SEs
Parameter
Model 1
fn
p
Model 2
fa
p
Model 3
fb
P
Constant
E
MP
I
CL
T
Adj.R2
F
P
0.0525
0.0008
0.0038
0.0007
0.0225
0.0001
0.242
2.856
0.037
0.000
0.001
0.001
0.082
0.002
0.421
–
–
–
0.0645
0.0008
0.0033
0.0004
0.0093
0.0012
0.058
1.359
0.274
0.000
0.056
0.008
0.237
0.134
0.056
–
–
–
0.0318
0.0013
0.0040
0.0005
0.0204
0.0001
0.171
2.193
0.088
0.008
0.001
0.021
0.205
0.048
0.438
–
–
–
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DIFFUSION OF ORGANIC FOOD PRODUCTS 383
average number product lines per company in each of the defined food categories, is
statistically negative in all three models as expected. As with E, the magnitude of
MP’s estimated parameter varies by whether the product is approved to use the
organic seal. MP’s estimated parameter is greater in magnitude and has a greater
level of statistical significance in Model 3 relative to Model 2. This implies that firms
in food categories where each firm has on average a high number of product lines are
under more pressure to innovate without NOP seal qualification to stay competitive
or that firms with greater market power have little incentive to acquire seal
certification given the additional costs.
Product complexity, proxied by the average number of ingredients (I) per food
category and expected to hinder a food category’s adoption diffusion rate, is found
to negatively impact diffusion rates in all three models. The estimated coefficient for
the ingredient variable is statistically significant at the 90% level in Model 1 and is
not significant in either Models 2 or 3. The results of this test imply that the initial
decision to adopt organic practices is constrained by the complexity of input supply
chains, but has little impact on whether a given product line is approved to use
the NOP seal. A better measure for product complexity which accounts for the
type of ingredients used in a given product line ought to be explored in
future empirical research.
The average number of promotional claims used in the marketing of each foodcategory’s product (Cl) positively impacts innovation diffusion rate variance in all
three models, and the estimated parameters are statistically significant in Models 1
and 3 at a 95% level of confidence. Differentiating effort impacts the diffusion of
non-seal qualified adoptions more than the diffusion of seal qualified adoptions, as
expected. This implies that the diffusion of non-seal qualified adoptions is occurring
at a greater rate among food categories that on average use more differentiating
effort to market their product lines relative to other categories. This also implies that
there may be a trade-off between the organic quality of a given product and other
possible differentiating qualities that brand managers must consider when they are
debating the adoption of organic marketing practices.
The timing of the first NOP adoption (T) in a food category in months since the
inception of the NOP was expected to increase the likelihood that producers will
adopt organic practices and be approved to use the NOP seal. This is based on the
principal of the time value of money and the decreasing degree of uncertainty about
the NOP as it diffuses through the organic market. Accordingly, it was expected that
higher rates of diffusion among food categories that wait to see if the process
innovation will be a commercial success would be observed. In Model 1, T is found
to have the expected relational sign, yet the estimated parameter is not statistically
significant. T is an even more important determinant for seal qualified adoptions,
evident in the greater magnitude of the positive parameter relative to the other
models and its greater level of statistical significance. In the third model, T is not
statistically significant, and the estimated relational parameter has a negative sign.
This suggests that adopters are learning over time that having the organic seal is a
beneficial and necessary component within the organic marketing strategy. It also
may suggest that the addition of T is irrelevant in determining the relative magnitude
of non-seal qualified organic innovation diffusion across food categories in this
instance due to the length of time observed, the observed potential adopters are
relatively short-sighted in their choice of marketing strategies, or that the perceived
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DOI 10.1002/agr
384 SHANAHAN, HOOKER, AND SPORLEDER
threat of imitation is high, and waiting to see if the innovative marketing strategy
will be a commercial success is not an option.
CONCLUSIONS
Agribusinesses coexist in a heterogeneous market where each firm varies in the type
and level of strategic value-added activities. New combinations of these activities can
create innovative quality attributes that fulfill consumer needs. Consumers are
willing to pay for that newness. Firms that differentiate in accordance to consumer
demand can create a competitive advantage; however, sustaining the competitive
advantage requires the development of a process of continuous organizational
learning and systematic adaptation to changes in the firm’s external environment and
internal capabilities. Constant scanning of the external environment, learning, and
adaptation to changing consumer desires are the keys to creating a sustainable
competitive advantage.
The adoption of organic food-production practices is one type of
marketing strategy available to agribusinesses in the United States; however, the
implementation of the NOP in October 2002 impacted the adoption decision.
Current market structure at a category level is influenced by the power of buyers
and suppliers and the extent of product differentiation. This, in turn, affects firm’s
decisions to adopt a given quality standard (the NOP) and thus the diffusion
of an innovative quality standard through the industry. By combining several
paradigms, a better understanding of the nature of the competitive environment
within the organic market and the factors that influence the adoption of a quality
standard was achieved.
The agribusiness’ decision to adopt organic practices is a function of factors that
maximize the expected benefits from adoption and minimize anticipated costs of
adoption. Adoption also is influenced by certain external factors including expected
consumer demand for the product innovation, the current and future actions of
potential competitors, and the actions of suppliers of the process innovation’s inputs.
Regulation also impacts the decision to adopt.
The combination of both Rogers’s (2003) and Brown’s (1975) perspectives on the
diffusion process offers valuable insight into how constrained social networks
determine the net effect of an innovation’s diffusion through a society. Roger’s
perspective implies that the primary driver of innovation diffusion is the adopter or
demander of the innovation and that firm’s interaction with other adopters. The
primary factor explaining why firms fail to adopt is ignorance of the innovation’s
existence, relative net benefits, and constraints tied to limited accessibility of needed
inputs and marketing outlets. In the case of firms faced with the decision to adopt
organic practices, potential organic adopters need to first know that organic is an
option to consider, that the relative net benefits from adopting organic given
regulatory constraints are greater than other options in their marketing strategy choice
set, and that the firm has access to needed inputs and markets to sell their products.
Thus, adoption propagators and other market facilitators must improve information
about the innovation for firms to make effective adoption decisions. Brown showed
that social change caused by the diffusion of an innovation is due to interactions
between an innovation’s demander and the innovation’s supplier. Accordingly, the
ultimate level of innovation diffusion is a function of the supplier’s ability to improve
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DIFFUSION OF ORGANIC FOOD PRODUCTS 385
the adopter’s informational and physical access to the innovation. According to
Brown’s perspective on innovation diffusion, the act of nonadoption is the fault of the
supplier not being able to provide adequate market access to the innovation. As stated
earlier, the different perspectives on innovation diffusion of Rogers and Brown do not
have to be mutually exclusive. This research suggests that an integrated model can
show the relationship between the supplier’s direct actions toward changing the
adopter’s informational and physical access to the innovation and the adopter’s
decision to adopt a given innovation given social-network constraints.
The second-stage models explored in this study suggest that threats of entry,
adoption timing, market power, product complexity, and differentiation effort
impact the diffusion rates of organic practices across food categories. The impact of
these external factors is more pronounced among those firms not able or willing to
gain NOP seal qualification; however, other factors clearly play a role. It is expected
that a measure of relative benefits and other categorical controls are necessary to
explain more variation in organic adoption diffusion. Further research in matching
food categories to external industry data and sales/scanner data determining a
product innovation’s commercial success is suggested to provide a more robust
second-stage model. In addition, a measure of a given adopter’s inherent competitive
advantage prior to the inception of the NOP also may shed light on how path
dependency may impact the speed at which certain firms within particular food
categories adopt and assimilate the innovation into their product offers.
The empirical results are mixed. Further work is required to specify a model that is
easy to use and to check the viability and verifiability of this empirical model. The
GNPD data can be extended to build other samples able to operationalize
alternative theoretical concepts and the identification of additional sources of
secondary data reporting the nature of the organic food market with respect to the
characteristics of each innovation and the market environment explored.
In the summer of 2007, the USDA’s Agricultural Marketing Service recognized
that ingredient availability inhibited adoption of organic practices. In multiingredient products, an agreed-upon solution was an amendment to the National
List of allowable nonorganic substances. The list of allowable substances increased
from 5 to 38 (USDA, 2007). This action may broaden opportunities for organic
adoption, but impact on consumers’ perceived quality of, and demand for, the
resultant organic output is still unknown and may even be detrimental. Future
research should explore this change in the regulation.
ACKNOWLEDGMENTS
This article was presented at the 1st International EAAE Forum, Innovation
and System Dynamics in Food Networks, in Innsbruck-Igls, Austria, February
14–17, 2007.
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Christopher J. Shanahan completed his BS and MS in Agricultural Economics at OSU and is
now a research analyst with the Frost & Sullivan North America Chemicals, Materials & Foods
Group. He focuses on monitoring and analyzing emerging trends, technologies and market
dynamics in the food, personal care ingredient, and specialty chemical industries in North
America. He has direct experience in market forecast models, modeling product innovation
diffusion, and technology adoption.
Neal H. Hooker received a Ph.D. in Resource Economics from the University of Massachusetts.
He has a MA in Economics from the University of British Columbia (Canada) and a BA (Hons)
in Economics from Essex University (U.K.). Dr. Hooker’s research explores marketing and
management within global food and agribusiness supply chains. He is particularly interested in
how food safety and nutrition attributes are controlled, communicated, and (where appropriate)
certified. He is currently an Associate Professor of Agribusiness at The Ohio State University.
Thomas L. Sporleder received BS, MS, and PhD degrees in Agricultural Economics from The
Ohio State University. Dr. Sporleder’s research explores Agribusiness—value-added agriculture
and the economics of innovation, especially food product innovation; entrepreneurship; and
agricultural cooperatives. He is currently the Farm Income Enhancement Professor at The Ohio
State University.
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