The Diffusion of Organic Food Products: Toward a … Diffusion of Organic Food Products: Toward a...

The Diffusion of Organic Food Products: Towarda Theory of Adoption

Christopher J. ShanahanDepartment of Agricultural, Environmental and Development Economics, TheOhio State University, 323 Ag Admin, 2120 Fyffe Road, Columbus, OH,43210– 1067Neal H. HookerDepartment of Agricultural, Environmental and Development Economics, TheOhio State University, 323 Ag Admin, 2120 Fyffe Road, Columbus, OH,43210– 1067. E-mail: [email protected]

Thomas L. SporlederDepartment of Agricultural, Environmental and Development Economics, TheOhio State University, 323 Ag Admin, 2120 Fyffe Road, Columbus, OH,43210– 1067

ABSTRACT

This study explores drivers influencing food processors’ decisions to adopt organic practicesand the constraints which may limit the availability of food products using the NationalOrganic Program (NOP) organic seal as a marketing tool. A constrained diffusion model isapplied to assess seal qualified adoption across food categories. A second model exploresmarket forces that influence variations in the diffusion process. Results suggest that externalfactors, including new regulation, impact diffusion rates. Future adoption of organic practiceswill require enhanced informational and physical access for potential adopters. [EconLitcitations O330, Q130, Q160]. r 2008 Wiley Periodicals, Inc.

INTRODUCTION

Many consumers in the United States demand that the food they consume have atleast a minimum standard of perceived physiological safety. U.S. consumers tend torely on the U.S. Department of Agriculture (USDA) and the Food and DrugAdministration (FDA) to enforce food safety standards. Consumer uncertainty ofthe perceived level of safety of food products is the primary driver of the growth ofthe U.S. organic food market (Mintel International Group, 2006). More than everbefore, media attention has focused on food scares and food-safety issues, which inturn are escalating consumer doubt in the safety of food supply chains. Certainconsumers demand above-minimum standards of perceived safety or credencequality. These consumers are not satisfied with conventionally produced food thatmay contain objectionable remnants of chemical-based pesticides, antibiotics, orother additives. Certain consumers are concerned about food produced withbioengineered organisms as ingredients (Mintel International Group, 2006). Some ofthese same consumers view organically produced food products as a means ofsubstantiating the integrity of food supply chains and avoiding these undesirableexternalities of conventional food production.

Agribusiness, Vol. 24 (3) 369–387 (2008) rr 2008 Wiley Periodicals, Inc.

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/agr.20164

369

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, WholeFoods Market, the largest natural-foods supermarket in the United States, conducteda consumer survey that found over 50% of their consumers believe organic food isbetter for the environment, better at supporting small and local farmers, and betterfor their health. The survey also found that 32% believed that organic food tastesbetter and that 42% believed that organic food is of a higher quality relative tononorganic food products (Whole Foods Market, 2004). Mirroring this increasingdemand, a market-support infrastructure is developing, including retail outlets, new-product development, and a more standardized quality-assurance regulatory system(Klonsky & Greene, 2005). The continued adoption of organic products amongconsumers depends on increasing retail access, the number of new-product offerings,branding, market entry of established mainstream food processors, and increasedexport 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 morelucrative than conventional crops and livestock because the market is segmented andbecause certain consumers are willing to pay premiums for differentiated and/orspecialty products. However, the vast majority of U.S. farmers and food processorsare still hesitant about organic production and the inherently greater uncertaintyassociated with these production systems. This, in turn, is having adverse effects onthe U.S.’s ability to supply, at stable prices, the increasing demand for organicproducts in domestic and international markets. Consequently, U.S. organic foodprocessors are increasingly looking abroad to fulfill their input needs. At the currentgrowth rates in consumption and production of organic foods, consumer prices willremain high relative to conventionally produced foods, and imports will more thanlikely supplement the shortfalls of domestic production of organic multi-ingredientgoods (Batte et al., 2007). Thus, both the power of farmers and organic consumersare 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 October2002, some producers claimed to have conformed to the obligations tied to usingorganic production practices when they did not. It was later determined by interestedconsumer and producer groups that accountability measures were needed toeffectively satisfy consumer’s credence quality requirements. Multiple and similarschemes were developed by consumer and producer associations and stategovernments to standardize the meaning of organic through the use of third-partyauditors and state-sponsored regulatory bodies, yet evidence has suggested thatlittle mutual recognition of these schemes occurred. This created more confusionamong 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 quality-assurance certification process that notarizes the product (both primary ingredientsand finished food products) as NOP-certified organic. Qualification is based on thefirms’ ability to fulfill the production or processing obligations and is certified by athird-party agency accredited by the USDA. The policy goal of the NOP is to

370 SHANAHAN, HOOKER, AND SPORLEDER

Agribusiness DOI 10.1002/agr

substantiate and standardize organic labeling to give all agents in the market anassurance of the credence quality possessed by products with an organic claim. TheNOP also substantiates the certification of multi-ingredient processed goods using afour-tiered labeling system that encodes the relevant product by its content oforganic ingredients (100% Organic, Organic (contains at least 95% organic), Madewith organic ingredients (contains at least 70% organic), and Some OrganicIngredients (contains o70%)). Only the first two levels can use the official USDAOrganic seal on the front of the label, and the last level of NOP certification forbidsthe use of the word organic on the front panel of the product (for more detail, seeAtalay, 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 qualitystandards (discussed later) facilitates the flow of market information, effectivelysubstantiates 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 tobe ‘‘officially’’ organic even though the NOP certification process was bypassed. Thistype of activity is expected to increase in an evermore dynamic organic market whichis itself increasing in size and product-offering depth, categories and brands,customer base and consumer attitudes, motivation, and behavior. Thus, foodprocessors may minimize competitive rivalry by developing organic products. Foodprocessors are relatively larger than individual primary producers, most exertrelatively more effort towards product differentiation than other agents within theindustry, and they may possess a high degree of spatial buyer power because theypurchase 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. Themodel is specified using external factors that impact the rate of NOP-qualifiedadoption across food categories following key concepts compiled in the economic,sociology, and consumer-marketing literatures. A two-stage modeling approach isundertaken where estimated rates of innovation diffusion for each food category areestimated applying methods of Bass (1995) and Jain, Mahajan, and Muller (1991).Then, a second-stage model empirically explores possible market drivers thatinfluence the variation in the constrained rates of organic adoption diffusion acrossfood categories. The combination of perspectives from various paradigms on theinnovation diffusion process offers valuable insight into how constrained socialnetworks determine the net effect of a process innovation’s temporal diffusionthrough an industry.

THEORETICAL BACKGROUND

Firms create competitive advantages (CA) by perceiving or discovering newstrategies, or value-added activities, to differentiate relative to rivals. A competitiveadvantage occurs when a firm’s earnings exceed their costs and potential rivals arenot able to drive earnings to perfectly competitive levels (Porter, 1983). In instanceswhere perfect market information is available, competitive advantage cannot besustained over a period of time. A firm creates a sustainable competitive advantage(SCA) by developing value-creating processes that cannot be duplicated or imitatedeasily by others. SCA creation also is the implicit avoidance of the perceived

371DIFFUSION OF ORGANIC FOOD PRODUCTS


competitive threats that reduce expected supranormal performance (Barney, 2006;Stanford University, 2007; Wiggins & Ruefli, 2002). Firms that are proactive inguarding against these threats of competitive rivalry will be more effective inachieving 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 thefirm’s external environment and internal capabilities (Barney, 2006). Through theidentification of quality attributes and by assigning price premiums based onperceived consumer willingness to pay, firms are able to differentiate their respectiveproduct offers and focus on targeting customers, thereby reducing the degree ofcompetition within the industry. The difference between consumer value derivedfrom a firm’s product offer and the firm’s cost incurred from quality attributecreation activities is the premium earned by a firm’s differentiation efforts (StanfordUniversity, 2007). Consumers are willing to pay for value, and firms thatdifferentiate in accordance to expected consumer demand, given the firm is able todampen the other external competitive-parity forces, will create a sustainablecompetitive advantage. The firm with the greatest differentiation premium will gainan 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 supranormalreturns. As such, the firm’s choice of a CA strategy is an innovative act (StanfordUniversity, 2007). Innovative marketing strategies will shift a given firm’s SCAwhen rivals fail to respond to the new strategy. Innovations that typically shift CAinclude the discovery of new production technologies, buyer needs, industrysegments and changes in input costs, input availability, and government regulations(Stanford University, 2007). The most effective innovators will be those firms thathave developed the most robust environmental-scanning capabilities and identify aninnovative mix of quality attributes that maximizes a firm’s expected performanceand 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. Aninnovation 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 anew ‘‘qualitative distinct whole,’’ where the consequential new object has a functioncompletely different from the individual antecedent parts (Harper & Liecht, 2002).Necessity often spurs the development and diffusion of innovations within a socialsystem, which in turn causes society to adapt or change (which can bring aboutnew necessities). Innovations offer social agents new ways to interpret meanings ofobserved social situations, to solve problems, and to reduce social/environmentaluncertainty.Most of the early research on diffusion theory takes a pragmatic approach to

understanding the temporal adoption process. Rogers (2003) defined innovationdiffusion as the process by which an innovation is communicated and transferredthrough a social system over time. The driver of diffusion is the adoption and theutilization of the innovation by individuals. The decision to accept an innovation bythe receiver, and thus the spread through a population, depends on the perceived



characteristics of the innovation. Rogers broadly defined such characteristics as therelative advantage of the innovation, compatibility with the needs and desires of theadopter, the relative complexity of the innovation, and the relative trialability andobservability of the innovation compared to its alternative. The diffusion of aninnovation through a given social system can be thought of as the idea’s spread in alimited population; the number of adopters initially grows exponentially as thenew technology takes root within the social structure. Then, the number ofadopters reaches a critical mass, and growth begins to slow down as thelast remaining potential adopters accept or reject the innovation. Thus, the diffusionprocess can be framed within any s-shaped, unimodal distribution function. Thelogistics function is the most popular functional form due to its ease ofuse (Fernandez-Cornejo & McBride, 2004). Fig. 1 provides a visual representationof 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 theinnovation propagator ultimately controls the innovation’s informational andphysical distribution to adopters. In other words, innovation propagators havecontrol over the potential adopter’s market access to the innovation. Specifically,innovation propagators control access by determining where and how manydiffusion agencies to establish within the targeted social system, the extent of theinnovation’s distribution infrastructure, the innovation’s price, and the level ofpromotional and marketing effort exerted. Increasing the adopter’s access to theinnovation entails the supplier being able to minimize physical and informationaldistributional constraints. Along similar lines, Broring, Leker, and Rohmer (2006)suggested that suppliers (i.e., established innovative firms) may differ substantially intheir ability to innovate because of internal organizational design.

Figure 1 A Two-Stage Adoption Model.



Organic adoption by food processors (i.e., process innovation adoption) can beobserved by examining the population of all new processed food-product linesreleased into a given market and determining which product lines are using anorganic quality claim (as determined by the informational content of its productlabel). Use of an organic promotional claim on a new product line implies that theagribusiness’s product/brand manager made a decision to adopt organic practices.The demand for a process innovation is derived from the demand for the food-quality 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 industriesand sectors, and also are influenced by market structure, consumer demand, and thepower of suppliers. Expectations and anticipations change over time, yet sinceinformation diffuses through food categories and industry sectors at different ratesdue to external market factors, expectations and anticipations also will vary. Pathdependency and industry learning networks fuel the variety of adoption rates, andhence the cumulative number of adopters; however, the nature of this impact isexpected to decrease over time. Expectations about potential earnings andanticipated costs or uncertainty usually decrease over time due to learning effectsand the accumulating nature of information.

METHODOLOGY: CHARACTERIZING ADOPTION AND DIFFUSION

One possible logistic functional form that describes a Rogers’ diffusion model is theBass (1995) model, and is based on the simple conjecture that the first adopters of aninnovation communicate to, interact with, and influence other potential adopters insubsequent periods. The Bass model links the number of adoptions in Time t giventhat they have not yet adopted to some fraction of the total number of innovationadopters that already have adopted in Time t�1 (Jain et al., 1991). Thus,

nt ¼dNðtÞ

dt¼ p½M �Nðt� 1Þ�

þq

MNðt� 1Þ½M �Nðt� 1Þ� ð1Þ

where nt 5 the number of innovation adoptions in Time t, p5 the coefficient ofdiffusion due to external influence, q5 the coefficient of diffusion due to interactionwith other adopters,M5 the cumulative number of potential adopters at Time t5 0,N(t)5 the cumulative number of innovation adoptions in Time t, and[M�N(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. Thecoefficient of diffusion due to imitation is a measure of the accelerative force createdby the interaction of adopters of the innovation and potential adopters through timeon 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., constantacceleration). This permits the model to consider the possibility of social interactionbetween potential adopters and previous adopters, which in turn creates thenecessary social network to emerge and spread the idea of the innovation throughthe population like a contagion (Fernandez-Cornejo & McBride, 2004). Thus,



market interactions between adopters and nonadopters must occur to increase therate of adoption. Note that the number of adoptions at Time t per change in thecoefficient of innovation:

@nt=@q ¼ Nðt� 1ÞM�1½M �Nðt� 1Þ� ð2Þ

In response to criticism about the construct validity of the model and alternativelogistic-based specifications of the diffusion process, Bass (1995) conceded that themodel was an example of an empirical generalization or ‘‘a pattern or regularity thatrepeats over different circumstances and that can be described simply bymathematical, graphical or symbolic method’’ (p. G6). Thus, the model’s purposeis not to entirely capture all of the theoretically true factors impacting the diffusionprocess but to be used as a pragmatic, cost-saving, problem-solving tool for anyonedesiring to forecast future-innovation adoption. Despite this virtue and Bass’ (1995)redefinition of the model’s purpose, the forecasting tool still does not escape theassumption of temporal regularity: that the diffusion process observed today will bethe same tomorrow. In fact, this assumption is essential for the general Bass model toeffectively 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 giveninnovation. The actions of innovation suppliers impact the innovation distributionin society by splitting the rate of innovation adoption into a camp of supplied (orqualified) 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, therate of adoption of an innovation, nt, is split into two flows, with, nt 5 at1bt. Theexternal factors that impact the overall rate of adoption are demand oriented and arefueled by interactions of social agents within the system. The factors that impactwhether the adoption is delivered are supply oriented. This means that factors suchas 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 externalinfluence on adoption is zero, Equation 1 is modified to highlight the split in thediffusion flow into two parts:

dNðtÞidt¼

dAðtÞidtþ

dBðtÞidt

¼fAi AðtÞi½Mi � AðtÞi � BðtÞi�

þ fBi BðtÞi½Mi � AðtÞi � BðtÞi�; ð3Þ

which suggests that

dNðtÞidt¼ fN

i NðtÞi½Mi � AðtÞi � BðtÞi�; ð4Þ

dAðtÞidt¼ fA

i AðtÞi½Mi � AðtÞi � BðtÞi� ð5Þ

and

dBðtÞidt¼ fB

i BðtÞi½Mi � AðtÞi � BðtÞi�: ð6Þ



Integrating Equation 4 yields the following logistic function:

NðtÞi ¼Mi=½1þ eð�a�fN�tÞ� ð7Þ

and making a log-linear transformation of Equation 7 yields the following linearfunction:

ln½NðtÞi=ðMi �NðtÞiÞ� ¼ aþ fN� t: ð8Þ

Similarly, integrating Equations 5 and 6 and applying log-linear transformationsyields the following functions for deriving the coefficients of diffusion of sealqualified and nonseal qualified innovation adoptions:

ln½AðtÞi=ðMi �NðtÞiÞ� ¼ aþ fA� t ð9Þ

and

ln½BðtÞi=ðMi �NðtÞiÞ� ¼ aþ fB� t ð10Þ

Using Equation 8, factors that influence the magnitude of the accelerative forceon the flow of innovation diffusion, as represented by fN

i , across predefinedgroups of the population of potential adopters are explored. In the empiricalapplication presented later, the pools of potential adopters are all product-lineor brand managers contained within each food category, and the decision facingeach brand manger is whether to adopt an organic marketing strategy fortheir respective new product. As with any decision to adopt an innovation,innovation demand is influenced by the expected relative advantage from adoptiongiven external factors. The impact of such environmental factors on relativediffusion flows across various food categories can be easily examined with alinear model. Likewise, the factors that influence the relative magnitudeof the restricted flows of innovation diffusion, represented by fA

i and fBi

and derived from Equations 9 and 10 across predefined groups of the populationof potential adopters, also are of interest. The extent of an innovation’ssupply and ultimate diffusion depend on the supplier’s effort at providingeffective information and physical distribution systems and the supplier’smarketing and pricing strategies. It is expected that for each of the investigatedindependent variables, the impact on the seal qualified innovation diffusionrate will be opposite, or smaller, than the impact on the constrained flow ofnonseal innovation diffusion rate. To empirically test this hypothesis, eachexternal factor can be examined using a linear association model thattakes the constrained certified adoption flow for each category and regressesthe resultant dependent variables against possible proxies representing variationsin constraints due to supply distribution, pricing, and marketing strategies foreach 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 thelikelihood of a firm to adopt organic practices is dependent on the level ofmarket rivalry within a category. The largest food categories will have the mostaccumulated production experience, greater access to needed certified inputs,and will be under more pressure to innovate due to the forces of competitive



rivalry. Thus, it is expected that larger food categories will be more likely to adoptorganic practices and will have a higher probability of NOP seal qualification toeffectively differentiate product lines. Food categories representing a smaller numberof 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 degreeof rivalry within the category. Increased rivalry and threat of imitation suggestsincreased incentives to continually innovate and increased willingness to incur highercosts 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 adoptiondiffusion rates of organic practices within a food category. The more concentrated ispower 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 intra-industry learning and environmental scanning) is constrained when only a fewcompanies dominate the industry. Note that once the initial decision to adopt ismade within a centralized industry, innovative activity ought to be easier toimplement relative to less-centralized industries among those companies whodominate 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 willbe organic and thus also hindering a food-category’s adoption diffusion rate. It isexpected that product complexity also is negatively related to the odds that the neworganic product will qualify for the NOP seal, which in turn hinders the diffusion ofseal qualified adoptions. It also decreases the likelihood that non-seal qualifyingorganic 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 ofproduct differentiation, the rate of organic adoptions will increase. It isexpected that firms who put more effort towards differentiation of productoffers will be more likely to adopt organic practices. As a result, the number ofvalue-added claims used in the marketing of each new food product (Cl) is positivelyrelated to the firm’s demand for organic production technology (O). Aggregatingdemand to calculate variance of diffusion rates across food categories shouldnot change this expected relationship. It also is expected that average differentiatingeffort will be greater among food categories with greater diffusion rates ofnon-seal qualifying adoptions. This is because firms within these categories are likelyto compensate for the lack of the NOP seal with other value-added product clams tostay competitive and ensure that their product’s price premiums are substantiatedand 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 theimplementation of the NOP, the more likely the producer of that good will adoptorganic practices due to the principal of the time value of money and the decreasingdegree 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 ifthe process innovation will be a commercial success. The likelihood that the neworganic product will qualify for the NOP seal also is positively impacted by adoptiontiming. Interorganizational learning is cumulative; thus, it is expected that firms will



become more knowledgeable about NOP seal qualification standards and require-ments through learning-by-doing. Additional supplies of organic ingredients willemerge over time. The expected consequence of this learning and access is higherrates of seal qualified diffusion and lower rates of non-seal qualified diffusion amongfood 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 ofbioengineered corn among U.S. farmers, optical scanner use among grocery stores,and automated teller machine (ATM) use within the banking industry (Fernandez-Cornejo & McBride, 2004; Hannan & McDowell, 1984; Levin, Levin, & Meisel,1992). Earlier work by Romeo (1977) has suggested the use of a log-linearspecification, which implies that the relationship between the factors and theindependent variable are nonlinear over the adoption cycle. This investigation willuse a linear model specification in the second stage, with checks for multicollinearityamong the independent variables using a simple correlation coefficient matrix andheteroskedasticity by examining estimated residual tables. Evidence of heteroske-dasticity will be accounted for using White heteroskedasticity-consistent standarderrors (Studenmund, 2006). The expected relationship between the forces ofcompetitive rivalry and constrained innovation diffusion is not currently clear;thus, tests for correlates are sufficient for the purposes of this preliminaryinvestigation.Mintel’s Global New Product Database (GNPD; www.gnpd.com) provides one

source of food product label information well suited to this research. GNPD gathersdata on product innovations, which includes product inventions, new brands,product line-extensions, and product reformulations within consumer packagedgoods markets. The database lists new-product information and label pictures forgoods launched in 49 countries. A search function can separate products usingcertain 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 currencyand in euros. Drawing from this population of new food products (in excess of300,000 annually), 32,434 U.S. new food and beverage products covering all productinnovations released between October 1, 2002 (the start of the NOP program) andApril 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), NOPnon-seal qualifiers (b), and nonorganic adopters in 30 mutually exclusive foodcategories (containing both organic and nonorganic products) were identifiedthrough a proactive process of food label analysis, where possession of the NOP sealor an organic claim statement which identifies the product’s level of certified organiccontent was either confirmed or not confirmed. This analysis resulted in a completesample 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 foodcategory (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 ineach 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.



RESULTS

As observed in the calculated category averages, the number of non-seal qualifiedadoptions for most categories is greater than the number of seal qualified adoptionswith 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 offeringorganic products and are therefore ahead of other categories in diffusing organictechnologies. Organic practices are relatively new, and the diffusion process formany of the remaining food categories are still at an early stage. The coefficient ofdiffusion of seal qualified adoptions is less than the coefficient of diffusion of non-seal qualified adoptions in the cakes and pastry, cheese, cereal, dessert, and sweet-spread 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 (seeTable 2). The cumulative number of seal qualified adoptions is increasing at a greaterrate relative to the number of non-seal qualified adoptions (Fig. 2). Thus, sealqualified product (i.e., 195% organic content) growth in the market relative to non-seal qualified organic food product (i.e., those in the lower two categories, witho95% organic content) growth is greater in most categories, suggesting that theNOP seal is an increasingly important component in the marketing strategies oforganic food firms.Generalized least squares, which controls for possible heteroskedasticity using

consistent standard errors and covariance, was used to estimate the coefficients ofdiffusion for organic adoptions fN

i , seal qualified adoptions fAi , and non-seal

qualified adoptions fBi for each of the 30 food categories (Table 3). The resultant

parameters are all statistically significant, and the degree of explained variation forall 90 models is relatively high (3 parameters � 30 categories). From Table 3, theresultant coefficients of diffusion of seal qualified adoptions for most categories aregreater than the coefficients of diffusion of non-seal qualified adoptions. Specifically,the average coefficient of diffusion for seal qualified adoptions is 0.076, and theaverage coefficient of diffusion for non-seal qualified adoptions is 0.049. Thus, thegrowth of seal qualified products in the marketplace relative to the growth of non-seal qualified food products is greater in many categories, again suggesting that theNOP seal is becoming an increasingly important component in marketing multi-ingredient organic foods. The categories with the largest magnitude of the diffusiveforce of seal qualified adoptions are soft drinks, crackers/cookies, cheese, meat, andsauces. Food categories with relatively low seal qualified diffusion rates include

TABLE 1. Diffusion Model-Descriptive Statistics

Variable Observations N A B E MP I CL T

Sum 19,317 1,252 598 654 32434 – – – –

M 643.90 41.73 19.93 21.80 1081.13 3.27 11.90 1.11 7.33

Max 1,966 129 60 69 5393 9.73 23.645 2.146 30

Mdn 468 42.5 17 18.5 631.5 2.72 11.4225 1.0275 6

Min 109 5 4 1 141 1.45 2.015 0.549 1

SD 537.17 28.68 14.57 17.84 1203.27 2.04 5.26 0.38 7.33



TABLE 2. Second-Stage Data

Model Food category M N A B E MP I CL T

1 Baking Ingredients/Mixes 761 25 11 14 1011 2.914 12.872 0.807 2

2 Bread/Bread Products 607 48 17 31 774 2.735 16.703 1.235 6

3 Cakes/Pastries/Sweet Goods 667 17 5 12 943 2.893 20.814 0.882 18

4 Crackers/Cookies 1393 90 34 56 1737 3.000 13.804 0.921 8

5 Soft Drinks 236 10 5 5 313 2.630 7.794 1.039 30

6 Coffee/Tea 547 81 19 62 823 2.084 3.13 0.761 4

7 Confectionery 1966 52 27 25 2886 4.327 10.412 0.89 3

8 Milk Product 402 44 18 26 1391 9.727 11.974 1.601 9

9 Milk 186 47 25 22 247 2.148 9.651 2.028 7

10 Cheese 473 25 10 15 678 2.745 6.994 0.81 18

11 Desserts/Ice Cream 1287 33 13 20 3072 8.853 17.569 1.193 3

12 Fruit 234 23 19 4 318 2.134 4.032 0.755 4

13 Vegetables 478 57 43 14 630 2.551 6.434 0.888 1

14 Meals 1626 56 17 39 1904 4.086 21.022 1.27 6

15 Meat 1157 41 12 29 1681 3.062 12.644 1.002 12

16 Sauces 1785 129 60 69 3440 3.543 10.961 0.679 1

17 Pasta Sauces 125 19 12 7 165 1.528 10.669 0.764 1

18 Seasonings 398 11 5 6 592 1.941 9.26 0.549 27

19 Rice 181 19 12 7 224 2.055 11.155 1.213 1

20 Pasta 463 63 30 33 564 2.564 9.399 1.275 6

21 Potato Products 241 5 4 1 295 2.706 13.261 1.016 12

22 Side Dishes 128 19 8 11 148 1.644 10.265 1.318 7

23 Snacks 1419 72 26 46 5393 7.105 14.823 1.055 3

24 Snack/Cereal/Energy Bars 643 54 26 28 776 3.129 23.645 1.695 6

25 Fruit Snacks 165 9 8 1 183 2.577 12.756 1.539 9

26 Soup 443 50 30 20 548 2.796 17.274 1.247 4

27 Savory Spreads 222 17 9 8 354 1.566 11.69 0.731 4

28 Sweet Spreads 342 45 26 19 570 1.810 6.185 0.977 1

29 Sweeteners/Sugar 109 14 8 6 141 1.454 2.015 1.119 6

30 Cereal 633 77 59 18 633 5.861 17.848 2.146 1

Figure 2 Innovation Diffusion Paths Among Organic Adopters—All Food Categories.



coffee/tea, fruit, rice products, potato products, and seasonings. Note that manyproducts within these food categories are relatively low in product-ingredient count,suggesting that particular ingredients in products within these food categories maybe 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 areassumed to depend on some linear combination of market factors. In the secondstage, for each set of the coefficients of diffusion for all food categories, including allorganic adoptions, seal qualified adoptions and non-seal qualified adoptions, the five

TABLE 3. First-Stage Results of Diffusion Rates per Food Subcategory

Model Food category (fn) Adj. R2 fn (fa) Adj. R2 fa (fb) Adj. R2 fb

1 Baking Ingredients/Mixes 0.057 0.890 0.072 0.906 0.047 0.873

2 Bread/Bread Products 0.060 0.889 0.062 0.884 0.051 0.902

3 Cakes/Pastries/Sweet

Goods

0.038 0.750 0.086 0.915 0.027 0.565

4 Crackers/Cookies 0.079 0.911 0.100 0.877 0.065 0.894

5 Soft Drinks 0.046 0.901 0.118 0.904 0.029 0.827

6 Coffee/Tea 0.053 0.852 0.040 0.956 0.050 0.887

7 Confectionery 0.080 0.845 0.096 0.912 0.065 0.783

8 Milk Product 0.063 0.841 0.067 0.951 0.049 0.784

9 Milk 0.096 0.845 0.080 0.890 0.075 0.832

10 Cheese 0.064 0.896 0.123 0.911 0.050 0.787

11 Desserts/Ice Cream 0.058 0.767 0.063 0.912 0.049 0.705

12 Fruit 0.061 0.739 0.057 0.730 0.030 0.791

13 Vegetables 0.053 0.954 0.083 0.955 0.024 0.743

14 Meals 0.077 0.895 0.077 0.847 0.070 0.909

15 Meat 0.068 0.887 0.107 0.886 0.059 0.834

16 Sauces 0.081 0.921 0.100 0.912 0.071 0.900

17 Pasta Sauces 0.057 0.968 0.078 0.882 0.036 0.897

18 Seasonings 0.083 0.842 0.057 0.633 0.064 0.727

19 Rice 0.060 0.808 0.049 0.815 0.049 0.788

20 Pasta 0.076 0.818 0.090 0.883 0.060 0.756

21 Potato Products 0.064 0.854 0.053 0.901 0.000 0.000

22 Side Dishes 0.064 0.894 0.060 0.890 0.054 0.898

23 Snacks 0.072 0.830 0.066 0.946 0.072 0.797

24 Snack/Cereal/Energy Bars 0.076 0.847 0.067 0.920 0.065 0.889

25 Fruit Snacks 0.062 0.937 0.075 0.913 0.000 0.000

26 Soup 0.056 0.878 0.072 0.870 0.035 0.842

27 Savory Spreads 0.065 0.952 0.065 0.919 0.059 0.931

28 Sweet Spreads 0.074 0.938 0.085 0.954 0.061 0.902

29 Sweeteners/Sugar 0.076 0.932 0.072 0.854 0.052 0.758

30 Cereal 0.068 0.925 0.072 0.907 0.059 0.879

M 0.066 – 0.076 – 0.049 –

Max 0.096 – 0.123 – 0.075 –

Mdn 0.064 – 0.072 – 0.051 –

Min 0.038 – 0.040 – 0.000 –

SD 0.012 – 0.020 – 0.019 –



external proxy variables (E, MP, I, CL, and T) were regressed to estimate marginalimpacts and combined explanatory power. Model 1 represents the followingproposition: The magnitude of total organic adoption diffusion across foodcategories is a linear combination of external factors. Likewise, Models 2 and 3test the validity of the following proposition: The magnitude of seal qualified andnon-seal qualified organic adoption diffusion across food categories is linearcombinations 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.) Fstatistics for the three models suggest that none of the models are statisticallysignificant at a 99% level. Model 1 is statistically significant at the 95% levelconfidence, Model 3 is statistically significant at a 90% level, and Model 2 is notstatistically significant. As expected, the insignificance of Model 2 suggests thatexternal factors representing competitive rivalry and thus adoption diffusionconstraints appear to have little impact on intracategory variance in diffusion ratesof seal qualified adoptions. The combination of entry threats, intercategory marketpower, product complexity, promotional effort, and adoption timing has littleimpact on the adoption decision among those firms that are able to have theirrespective product lines approved for the NOP seal. There is not strong evidence ofpossible multicollinearity among the independent variables. Exploration of theresidual tables revealed that some heteroskedasticity may be present; this iscontrolled 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 toproxy potential competitive rivalry and market-entry threat, is found to be astatistically significant, positive determinant in all three models. However, the impactof E on organic adoptions appears to vary by whether the product is approved to usethe organic seal. The magnitude of E’s estimated parameter is greater and has agreater level of statistical significance in Model 3 relative to Model 2. Consequently,entry threats appear to contribute to certain firms facing the decision to innovatewithout qualifying for the NOP seal to stay competitive. MP, as proxied by the

TABLE 4. Empirical Results of Second-Stage Diffusion Model, White Heteroskedasticity-

Consistent SEs

Model 1 Model 2 Model 3

Parameter fn p fa p fb P

Constant 0.0525 0.000 0.0645 0.000 0.0318 0.008

E 0.0008 0.001 0.0008 0.056 0.0013 0.001

MP �0.0038 0.001 �0.0033 0.008 �0.0040 0.021

I �0.0007 0.082 �0.0004 0.237 �0.0005 0.205

CL 0.0225 0.002 0.0093 0.134 0.0204 0.048

T 0.0001 0.421 0.0012 0.056 �0.0001 0.438

Adj.R2 0.242 – 0.058 – 0.171 –

F 2.856 – 1.359 – 2.193 –

P 0.037 – 0.274 – 0.088 –



average number product lines per company in each of the defined food categories, isstatistically negative in all three models as expected. As with E, the magnitude ofMP’s estimated parameter varies by whether the product is approved to use theorganic seal. MP’s estimated parameter is greater in magnitude and has a greaterlevel of statistical significance in Model 3 relative to Model 2. This implies that firmsin food categories where each firm has on average a high number of product lines areunder more pressure to innovate without NOP seal qualification to stay competitiveor that firms with greater market power have little incentive to acquire sealcertification 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 foundto negatively impact diffusion rates in all three models. The estimated coefficient forthe ingredient variable is statistically significant at the 90% level in Model 1 and isnot significant in either Models 2 or 3. The results of this test imply that the initialdecision to adopt organic practices is constrained by the complexity of input supplychains, but has little impact on whether a given product line is approved to usethe NOP seal. A better measure for product complexity which accounts for thetype of ingredients used in a given product line ought to be explored infuture empirical research.The average number of promotional claims used in the marketing of each food-

category’s product (Cl) positively impacts innovation diffusion rate variance in allthree models, and the estimated parameters are statistically significant in Models 1and 3 at a 95% level of confidence. Differentiating effort impacts the diffusion ofnon-seal qualified adoptions more than the diffusion of seal qualified adoptions, asexpected. This implies that the diffusion of non-seal qualified adoptions is occurringat a greater rate among food categories that on average use more differentiatingeffort to market their product lines relative to other categories. This also implies thatthere may be a trade-off between the organic quality of a given product and otherpossible differentiating qualities that brand managers must consider when they aredebating 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 willadopt organic practices and be approved to use the NOP seal. This is based on theprincipal of the time value of money and the decreasing degree of uncertainty aboutthe NOP as it diffuses through the organic market. Accordingly, it was expected thathigher rates of diffusion among food categories that wait to see if the processinnovation will be a commercial success would be observed. In Model 1, T is foundto have the expected relational sign, yet the estimated parameter is not statisticallysignificant. T is an even more important determinant for seal qualified adoptions,evident in the greater magnitude of the positive parameter relative to the othermodels and its greater level of statistical significance. In the third model, T is notstatistically significant, and the estimated relational parameter has a negative sign.This suggests that adopters are learning over time that having the organic seal is abeneficial and necessary component within the organic marketing strategy. It alsomay suggest that the addition of T is irrelevant in determining the relative magnitudeof non-seal qualified organic innovation diffusion across food categories in thisinstance due to the length of time observed, the observed potential adopters arerelatively short-sighted in their choice of marketing strategies, or that the perceived



threat of imitation is high, and waiting to see if the innovative marketing strategywill be a commercial success is not an option.

CONCLUSIONS

Agribusinesses coexist in a heterogeneous market where each firm varies in the typeand level of strategic value-added activities. New combinations of these activities cancreate innovative quality attributes that fulfill consumer needs. Consumers arewilling to pay for that newness. Firms that differentiate in accordance to consumerdemand can create a competitive advantage; however, sustaining the competitiveadvantage requires the development of a process of continuous organizationallearning and systematic adaptation to changes in the firm’s external environment andinternal capabilities. Constant scanning of the external environment, learning, andadaptation to changing consumer desires are the keys to creating a sustainablecompetitive advantage.The adoption of organic food-production practices is one type of

marketing strategy available to agribusinesses in the United States; however, theimplementation of the NOP in October 2002 impacted the adoption decision.Current market structure at a category level is influenced by the power of buyersand suppliers and the extent of product differentiation. This, in turn, affects firm’sdecisions to adopt a given quality standard (the NOP) and thus the diffusionof an innovative quality standard through the industry. By combining severalparadigms, a better understanding of the nature of the competitive environmentwithin the organic market and the factors that influence the adoption of a qualitystandard 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 ofadoption. Adoption also is influenced by certain external factors including expectedconsumer demand for the product innovation, the current and future actions ofpotential 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 networksdetermine the net effect of an innovation’s diffusion through a society. Roger’sperspective implies that the primary driver of innovation diffusion is the adopter ordemander of the innovation and that firm’s interaction with other adopters. Theprimary factor explaining why firms fail to adopt is ignorance of the innovation’sexistence, relative net benefits, and constraints tied to limited accessibility of neededinputs and marketing outlets. In the case of firms faced with the decision to adoptorganic practices, potential organic adopters need to first know that organic is anoption to consider, that the relative net benefits from adopting organic givenregulatory constraints are greater than other options in their marketing strategy choiceset, and that the firm has access to needed inputs and markets to sell their products.Thus, adoption propagators and other market facilitators must improve informationabout the innovation for firms to make effective adoption decisions. Brown showedthat social change caused by the diffusion of an innovation is due to interactionsbetween an innovation’s demander and the innovation’s supplier. Accordingly, theultimate level of innovation diffusion is a function of the supplier’s ability to improve



the adopter’s informational and physical access to the innovation. According toBrown’s perspective on innovation diffusion, the act of nonadoption is the fault of thesupplier not being able to provide adequate market access to the innovation. As statedearlier, the different perspectives on innovation diffusion of Rogers and Brown do nothave to be mutually exclusive. This research suggests that an integrated model canshow the relationship between the supplier’s direct actions toward changing theadopter’s informational and physical access to the innovation and the adopter’sdecision 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 effortimpact the diffusion rates of organic practices across food categories. The impact ofthese external factors is more pronounced among those firms not able or willing togain NOP seal qualification; however, other factors clearly play a role. It is expectedthat a measure of relative benefits and other categorical controls are necessary toexplain more variation in organic adoption diffusion. Further research in matchingfood categories to external industry data and sales/scanner data determining aproduct innovation’s commercial success is suggested to provide a more robustsecond-stage model. In addition, a measure of a given adopter’s inherent competitiveadvantage prior to the inception of the NOP also may shed light on how pathdependency may impact the speed at which certain firms within particular foodcategories 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. TheGNPD data can be extended to build other samples able to operationalizealternative theoretical concepts and the identification of additional sources ofsecondary data reporting the nature of the organic food market with respect to thecharacteristics 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 multi-ingredient products, an agreed-upon solution was an amendment to the NationalList of allowable nonorganic substances. The list of allowable substances increasedfrom 5 to 38 (USDA, 2007). This action may broaden opportunities for organicadoption, but impact on consumers’ perceived quality of, and demand for, theresultant organic output is still unknown and may even be detrimental. Futureresearch should explore this change in the regulation.

ACKNOWLEDGMENTS

This article was presented at the 1st International EAAE Forum, Innovationand System Dynamics in Food Networks, in Innsbruck-Igls, Austria, February14–17, 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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