Food Quality and Preference 20 (2009) 586–596

Contents lists available at ScienceDirect

Food Quality and Preference

journal homepage: www.elsevier .com/locate / foodqual

Information effects on consumer attitudes toward three food technologies: Organicproduction, biotechnology, and irradiation

Mario F. Teisl a,*, Sara B. Fein b, Alan S. Levy b

a School of Economics, 5782 Winslow Hall, University of Maine, Orono, ME 04469, USAb Center for Food Safety and Applied Nutrition, US Food and Drug Administration, 5100 Paint Branch Road, College Park, MD 20740, USA

a r t i c l e i n f o

Article history:Received 15 January 2008Received in revised form 8 July 2009Accepted 8 July 2009Available online 5 August 2009

Keywords:Food safetyEnvironmental safetyNutritionKnowledge

0950-3293/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.foodqual.2009.07.001

* Corresponding author. Tel.: +1 207 581 3162; faxE-mail address: teisl@maine.edu (M.F. Teisl).

a b s t r a c t

It is important to understand how information supplied to consumers affects their attitudes about foodtechnologies because these attitudes can impact market behavior. As technologies are actively promotedand cross-promoted, the relation between one’s knowledge of, and attitude toward, a technology maywell depend on the source of one’s information. We examine the relation between knowledge and atti-tudes toward food technologies and find that greater self-rated knowledge of each technology is associ-ated with positive attitudes about that technology. We also find strong negative cross-informationaleffects; increased knowledge of one technology leads to more negative attitudes of other technologies.This effect may be due to negative information being provided by opponents of specific technologies.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Economic theory states that individuals’ buying behaviors arepartially driven by their tastes and preferences (attitudes) towardproduct attributes. Economists and social psychologists recognizethe importance of information in attitude formation, especiallywhen the attribute is not easily verifiable (i.e., a credence attri-bute). Some degree of prior information is necessary for consumersto be aware of and care about these attributes. Such was the casewith nutritional attributes of food in the 1980’s (e.g., Teisl, Levy,& Derby, 1999) and such is the case today with certain methodsof producing and processing foods, such as organic production, bio-technology and irradiation. The interplay between information andattitude formation is one of particular interest to food industrymembers and to food regulators because the newness of some ofthese technologies to the marketplace, coupled with the inabilityfor most consumers to verify which technology is being used in afood’s production, is likely to induce many consumers to searchfor information about these technologies.

Information is only one part of the consumer acceptance puzzle.For example, many technology proponents assume that individualswho hold negative attitudes toward a new technology simply needmore information to correct their ‘knowledge deficit’. However,several studies (e.g., Eden, Bear, & Walker, 2008; Hansen, Holm,Frewer, Robinson, & Sandøe, 2003; Scholderer & Frewer, 2003)challenge this assumption. Thus, it is important to understand

ll rights reserved.

: +1 207 581 4278.

how information supplied to consumers affects their attitudesabout food technologies because these attitudes may significantlyimpact the food market. It has been suggested that advocacygroups have the potential power to slowdown or halt the develop-ment and implementation of food technologies through their pre-sentation of information (Huffman, Rousu, Shogren, & Tengene,2002). For example, many groups (e.g., the Organic ConsumersAssociation) who promote organic technologies may also providenegative information about irradiation and biotechnology (e.g.,see v-Fluence, 2009). Likewise, there have been charges that irradi-ation supporters have been claiming that organic foods may haveincreased levels of food pathogens. In addition, differences in infor-mation dissemination have been used to explain differences be-tween the European Union and the United States in terms ofattitudes toward food technologies (Huffman et al., 2002; Marks,Kalaitzandonakes, & Zakharova, 2002), which may impact traderelations. To the extent that various technologies are actively pro-moted and cross-promoted, the relation between knowledge andattitude may well depend on the source of one’s information. Forexample, one’s views about irradiation may differ depending onwhat one hears about organic production. If attitudes toward dif-ferent food technologies are interrelated, there is value in deter-mining how knowledge about one is associated with views aboutanother.

The complexity of lay attitudes toward various food technolo-gies suggests that attitudinal studies need to acknowledge the po-tential for cross-information effects from different informationsources. Our objective is to examine the relation between con-sumer attitudes toward three food technologies (organic produc-

M.F. Teisl et al. / Food Quality and Preference 20 (2009) 586–596 587

tion, biotechnology and irradiation) and the socio-economic andinformation-related antecedents of these attitudes. We extendthe literature by examining four different attitudinal dimensions(i.e., perceived effects of the technology on long-term humanhealth, environmental safety, nutrition, and pathogen levels) whileallowing for cross-informational effects.

2. Literature review

Several experimental studies (e.g., Cardello, 2003; Fox, Hayes, &Shogren, 2002; Fox & Olsen, 1998; Frewer, Scholderer, & Bredahl,2003; Hayes, Fox, & Shorgen, 2002; Schogren, Fox, Hayes, & Roo-sen, 1999; Tengene, Huffman, Rousu, & Shogren, 2003) have exam-ined consumer attitudes toward organic production, biotechnologyand irradiation as a function of specific messages about the costsand benefits of these technologies. Although these studies providevaluable information on how consumers react to specific pieces ofinformation, their experimental settings may not accurately reflecthow information is likely to be acquired and processed in themarketplace.

Other studies (e.g., Boccaletti & Moro, 2000; Cox, Evans, & Lease,2007; Cox, Evans, & Lease, 2008; Frezen, Majchrowicz, Buzby, &Imhoff, 2000; Hallman & Aquino, 2003; Levy, 2001; McClusky, Ou-chi, Grimsrud, & Wahl, 2001; Nayga, Aiew, & Nichols, 2005; Rodri-guez, 2007) have used survey approaches to study the relationbetween attitudes toward, and self-rated knowledge of, food tech-nologies. In general, these studies find a positive relation betweenknowledge and attitudes toward a given food technology; althoughsome find a negative relation (e.g., Boccaletti & Moro, 2000) or norelation (Cox et al., 2007, 2008) between knowledge, informationand acceptance of foods derived from new technologies. However,many of these studies refer to the technologies in imprecise andoverlapping terms (notable exceptions are Cox et al., 2007, 2008,which provide relatively detailed descriptions of irradiation andgenetic modification, respectively, and how they affect the productof study) so it is hard to know how consumers understand thetechnology being rated and therefore hard to interpret the atti-tudes and knowledge levels being reported (Fischhoff & Fischhoff,2001). Consumers are capable of making finer distinctions thanmost survey measures have called for, and they appear to havecomplex evaluation schemes (Hansen et al., 2003) for food technol-ogies that are quite sensitive to new evidence.

Several studies have examined the relation between attitudestoward food technologies and individuals’ characteristics; exceptfor the impact of gender, the results are mixed. Females are gener-ally found to have more positive attitudes toward organic foods(Govindasamy & Italia, 1999; Hwang, Roe, & Teisl, 2005; Weir &Andersen, 2001) and negative views toward foods produced bybiotechnology (Grobe, Douthitt, & Zepda, 1997; Hossain, Onyango,Adelaja, Schilling, & Hallman, 2002; McClusky et al., 2001) and irra-diated foods (Fox, 2002). However, Tengene et al. (2003) and Kane-ko and Chern (2003) find no gender differences. Most studiesindicate that older individuals are less likely to purchase organicfoods (Buzby, Ready, & Skees, 1995; Govindasamy & Italia, 1999;Underhill & Figueroa, 1996; Weir & Andersen, 2001; Weir, Ander-sen, & Millock, 2005); however, Zellner and Degner (1989) find theopposite effect. Similar inconsistency characterizes the relation be-tween age and attitudes toward biotechnology; different research-ers find positive (Bennett, D’Souza, Rosenberger & Smith, 2003;Boccaletti & Moro, 2000), negative (Grobe et al., 1997; Hossainet al., 2002; Hwang et al., 2005) and no (Kaneko & Chern, 2003)relation. Fox (2002) indicates an unclear relation between ageand attitudes toward irradiation.

Education has been found to have positive (Weir & Andersen,2001) or negative (Govindasamy & Italia, 1999; Zellner & Degner,

1989) effects on organic food purchases. Similarly, some research-ers have found that more educated individuals are more acceptingof biotechnology (Boccaletti & Moro, 2000; Hossain et al., 2002)and irradiation (Fox, 2002; Rimal, Fletcher, & McWatters, 1999),while other have found no or weak education effects (Bennett,D’Souza, Rosenberger, & Smith, 2003; Kaneko & Chern, 2003). Someof the mixed or weak effects noted above may be because people’sreactions to food technologies are not just a function of the educa-tion level but are also a function of the type of education attained.For example, there is evidence that people’s attitudes toward foodtechnologies increases with level of science education (Peters,2000; Sturgis & Allum, 2000, 2001).

3. Theoretical framework

Psychologists define attitude as a learned predisposition to aspecific object, in this case a food technology, measured in termsof specific positive/negative beliefs about the technology. Thesebeliefs are themselves a function of ones’ experiences with the ob-ject (Xj), and externally provided information (Ij) as well as individ-ual differences such as cognitive ability (C), motivation (M) andaffect (E) (Jonassen, 1991).

Bjk ¼ f ðXj; IjC;M; EÞ ð1Þ

where Bjk denotes the belief about technology j (here j = organic,biotechnology or irradiation) along the kth attitudinal dimension(here k = long-term health risks, environmental safety nutritionalquality and bacterial safety).

While psychologists acknowledge information sources andexperience as important determinants of attitude, it is often hardto identify relevant information exposures and experience with ab-stract objects like food technologies. Here we take advantage of theunevenness in the marketplace of likely experience and informa-tion availability with respect to emerging food technologies toshed light on the effects of experience and information source onan individual’s attitude toward alternative food technologies.Information about alternative food technologies is often providedby commercial (e.g., groups like the Council for BiotechnologyInformation: http://www.whybiotech.com/resources/factsheets_food.asp and Delicious Organics: http://www.deliciousorgan-ics.com/controversies/whyorganic.htm) and non-commercial orga-nizations (e.g., the Organic Consumers Association: http://www.organicconsumers.org/articles/article_16444.cfm) who championthe value and benefits of a technology. Exposure to such informa-tion is likely confounded with relevant product experience. Be-cause of the asymmetry in the availability of products andinformation in the marketplace, it is reasonable to assume thatconsumers who consider themselves well-informed about a cer-tain technology are likely to have gotten a substantial amount ofinformation from the advocates of that technology. We proposethat the pattern of self-reported knowledge levels (SRKj) with re-spect to specific alternate food technologies can serve as a proxyfor where consumers are getting their information and productexperience.

Xj þ Ij ¼ f ðSRK1; SRK2; . . . SRKjÞ ð2Þ

Substituting (2) into (1) leads to

Bjk ¼ f ðSRK1; SRK2; . . . SRKj;C;M; EÞ ð3Þ

The above expression relates specific technology attitudes to expe-riences with and information about the technology itself and alsorelated technologies.

We hypothesize a set of observable variables that may serve asreasonable proxies for cognitive, motivational and affective atti-tude antecedents. Following usual practice, we use demographic

588 M.F. Teisl et al. / Food Quality and Preference 20 (2009) 586–596

characteristics and self-reported measures of knowledge, attitudesand practices to proxy for such antecedents (i.e., most of the liter-ature we cite either implicitly or explicitly use similar variables asproxies for these theoretical constructs). For example, a person’sage, education and gender may reflect his or her cognitive abilities,experiences and health-related motivations. Although proxies, bydefinition, are not measures of the actual construct, they are oftenused/necessary when analyzing non-experimental data. In addi-tion, medical and psychological studies provide some support forsome of these proxies (e.g., education as a proxy for cognitive abil-ities; see Berry, Gruys, & Sackett, 2006; Roe et al., 2008). Althoughpossible, we do not think that the study quality is unreasonablydeteriorated by their use, especially since these proxies are onlyused as control variables and are not the main variables of interest.

4. Data

The US Food and Drug Administration’s Food Safety Survey wasconducted by telephone during the summer of 2001; 4482 respon-dents completed the telephone interview. Telephone numberswere selected using the list-assisted GENESYS Sampling System.The respondent in the household was designated using the next-birthday method. Sampling weights were constructed to accountfor the number of adults living in the household; the number oftelephone lines reaching the household; and differential coopera-tion, response and contact rates by gender, educational attainment,and race/ ethnicity. The GENESYS Sampling System excludes areacodes and telephone exchanges dedicated to cell phone numbers,thus, cell phone numbers are not included in the sample weights.

The survey contained questions meant to elicit respondents’perceptions of food risks, their knowledge/awareness of foodpathogens, food safety labeling and food safety advisories, andtheir food handling and consumption practices. One section ofthe survey, administered to only half of the respondents(n = 2186), focused on individuals’ awareness and attitudes aboutthree food production technologies. In this section, respondentswere asked to self report their level of knowledge about organicproduction, biotechnology, and irradiation.

For each question, respondents were provided a short definitionof the technology, shown in Fig. 1, and were asked to use a 10-

Fig. 1. Knowledg

point Likert type response scale to evaluate their level of knowl-edge. Individuals who reported a knowledge score of three orabove for a technology were then asked a series of questions de-signed to elicit their health and environmental attitudes about thattechnology (Fig. 2). Given the short descriptions of the technolo-gies, respondents’ stated knowledge and attitudes reflect their gen-eral conceptions of these technologies.

Individuals reporting a knowledge score of 1 or 2 were notasked these follow-up questions because survey pre-testing indi-cated that respondents reacted negatively to the questions whenthey had just told the interviewer that they did not know muchabout the subject. Note that this self-screening procedure may leadto sample-selection biases; however, initial analyses using appro-priate sample-selection modeling techniques (see Greene, 2002for an excellent summary of the motivations, statistical propertiesand procedures for this technique) indicated no sample-selectionbias. Qualitatively, the results with and without the sample-selec-tion correction were similar. As a result, we report the simplerregression output.

5. Empirical model

Here we are interested in identifying the factors important inexplaining respondents’ health and environmental attitudes to-ward food technologies; we are specifically interested in howexposure to different informational messages (as proxied by self-reports of knowledge) can impact attitudes. Given the availabledata, we model the relation for any individual as:

Ajk ¼ a1MALEþ a2AGEþ a3AGE2 þ a4EDUCþ a5EDUC2

þ b1PROCANXþ b2FSKNOWþ b3NOBUY

þ a6FOODSICKþ a7KNOWORGþ a8KNOWBIO

þ a9KNOWIRR ð4Þ

where Ajk is a variable denoting the individual’s rating of the jthtechnology (j = organic, biotechnology, irradiation) along the kthattitude measure (k = long-term healthfulness, environmentalsafety, nutritional quality, and bacterial safety – see Fig. 2). Thedependent variables were recoded so the directions of effects areconsistent across the four attitude questions. Specifically, long-term

e questions.

Fig. 2. Attitude questions.

M.F. Teisl et al. / Food Quality and Preference 20 (2009) 586–596 589

healthfulness and bacterial safety are coded as follows: ‘more likelyto cause long-term health problems/have harmful germs’ is codedas 0; ‘same as conventional foods’ is coded as 1; and ‘less likely’ iscoded as 2. Environmental safety and nutritional quality are codedas follows: ‘more safe/nutritious’ is coded as 2; ‘same as conven-tional foods’ is coded as 1; and ‘less safe/nutritious’ is coded as 0(Figs. 1 and 2). Because these dependent variables are ordered-cat-egorical, the models were estimated using ordered-logistic regres-sion (see Judge, Hill, Griffiths, Lutkepohl, & Lee, 1988 for a morecomplete description of the technique). We use Cragg and Uhler’s(1970) R2 measure as our goodness-of-fit measure because it per-forms well against other standard R2 measures (Lacy, 2006).

MALE indicates the gender of the respondent and is coded 1 ifmale and 0 if female. AGE is the age of the respondent. Given thatother health awareness and attitude research indicates the potentialfor a non-linear relation between a person’s age and their attitudes,we also include the quadratic term for AGE. EDUC denotes the indi-vidual’s education level (in years); education is treated similarly tothe AGE variable (quadratic) to allow for non-linearity.

We used factor analysis with the response data from five ques-tions to develop two attitude and knowledge variables (space con-straints preclude a detailed presentation of this procedure;interested readers can contact the first author). Factor analysis isa data reduction technique used to investigate whether a groupof variables have common underlying dimensions and can be con-sidered to measure a common factor. Although the analysis can beused to summarize a larger number of variables into a smaller setof constructs, ultimately the analysis is not a hypothesis testingtechnique so it does not tell us what those constructs are (Hanley,Meigs, Williams, Haffner, & D’Agostino, 2005). In turn, the validityof naming the constructs is contingent upon researcher judgmentand should be interpreted with some caution (Thompson & Daniel,1996).

For the factor analysis we used principal components analysisfollowed by Varimax rotation. As is typical, factors with Eigen val-ues less than one are dropped from further analysis as are variableswith factor loadings of less than 0.6 as these are not consideredstatistically significant for interpretation purposes.

The factor analysis indicates that two factors explain respon-dent responses (Table 1). The first factor reflects a general levelof anxiety towards commonly used food production and process-ing methods (called PROCANX in the empirical model); this resultis consistent with a factor analysis on another national data set(Hwang et al., 2005). In their study, these authors indicate theuse of antibiotics and pesticides as being the top two food produc-tion concerns for US consumers, and find these concerns are notcorrelated to concerns about the use of biotechnology andirradiation.

The second factor measures the respondent’s level of knowl-edge or awareness of less commonly known food safety informa-tion (called FSKNOW). Note that all factor loadings are positiveindicating that each of the factor scores are positively correlatedto the variables originally used in their construction. In turn,although each of the factor scores are normalized to a mean ofzero, the direction of each score is positively correlated to thedirection of the original variables. Hence, higher (lower) factorscores indicate a higher (lower) level of importance of that factor.

NOBUY in the model indicates the individual’s prior experiencein avoiding foods because of information. Specifically, NOBUY isthe response to the question ‘‘In the past year, have you stoppedbuying specific kinds of food that you are especially worried aboutbecause of their safety?” NOBUY is coded 1 if the individual an-swered yes and 0 otherwise. FOODSICK denotes whether respon-dents reported that they, or someone in their household,experienced an illness they thought was caused by eating spoiledor unsafe food.

Table 1Results of factor analysis, rotated factor loadings (n = 2162).

Anxiety about farming methods Food safety awareness

Views pesticide residues as a serious food safety problem 0.78 <0.04Views antibiotic residues as a serious food safety problem 0.75 0.17Has seen safe-handling labels on meat/poultry products �0.24 0.73Knows of specific food pathogens 0.25 0.68Knows of specific food advisories 0.39 0.63

Table 3Joint distribution of whether respondents know or do not know about foodproduction methods.

Biotechnology Irradiation n Percent

Knows about organicKnows Does not know 316 18.8Does not know Knows 148 8.8Knows Knows 702 41.8Does not know Does not know 513 30.6

Total 1679 100.0

Does not know about organicKnows Does not know 34 6.9Does not know Knows 34 6.9Knows Knows 20 4.0Does not know Does not know 412 82.2

Total 500 100.0

Table 4Attitudes toward three food production technologies on four dimensions.a

Organic Biotechnology Irradiation

Mean n Mean n Mean n

Long-term healthfulness 0.479 1633 �0.236 991 �0.153 825Environmental safety 0.575 1644 �0.127 1006 �0.165 845

590 M.F. Teisl et al. / Food Quality and Preference 20 (2009) 586–596

KNOWORG, KNOWBIO, and KNOWIRR are variables denotingthe respondent’s self-reported knowledge of organic production,biotechnology, and food irradiation. These variables are similar tothose used in other studies (i.e., they are based on self-reports)and thus are not objective measures of knowledge. However, sub-jective knowledge evaluations seem to be better predictors of atti-tudes and market behavior because objective measures ofknowledge are only weakly associated with attitudes (Hallman &Aquino, 2003).

We hypothesize that a1 will be positive in the biotechnologyand irradiation equations (indicating males have more positiveattitudes toward these technologies) because males have relativelymore scientific knowledge (Bak, 2001) and females appear to bemore cautious/conservative in their handling of risks (Cardello,2003). Because production/processing anxiety may measure a gen-eral attitude against conventional food production and processingtechnologies, we expect the parameters to be negative in the bio-technology and irradiated equations. Consistent with the literaturewe hypothesize that the own-knowledge parameters (e.g., theparameter on organic knowledge in the organic equation) will bepositive; however, we anticipate that the cross-knowledge effect(e.g., the parameter on organic knowledge in the irradiation equa-tion) will be negative.

Nutritional quality 0.343 1655 �0.052 1030 �0.297 872Bacterial safety 0.313 1636 0.067 1030 0.432 867

a Attitudes were recoded from the codes used in the regression analysis so that�1 denotes a negative attitude; 0 denotes neutral, and +1 denotes a positive atti-tude. Therefore, a positive mean indicates the attitude was positive and a negativemean indicates the attitude was negative.

6. Results

We begin the results by presenting descriptive statistics onrespondents’ socio-economic and knowledge characteristics(Table 2). We then examine the distribution of self-reportedknowledge about each food technology in relation to knowledgeabout the other technologies (Table 3). We follow with a descrip-tion of the attitudes toward each technology (Table 4). The regres-sion results are then presented by technology: organic production(Table 5), biotechnology (Table 6), and irradiation (Table 7).

Table 2Socio-economic and knowledge characteristics of respondents.

Survey (2001) US Census (2000)

Male (%) 48 48Age (mean) 45 47Years of education (mean) 14 13White (%) 79 75Household income (mean) $50,400 $57,000

Knowledge level (mean)a

Organic production 4.9 naBiotechnology 3.3 naIrradiation 3.0 na

Percent knowing aboutb

Organic production 77 naBiotechnology 50 naIrradiation 42 na

‘na’ denotes information not available.a Mean of the 10-point scale knowledge scale.b Respondents were defined as knowing about a technology if they rated them-

selves as three or higher on a 10-point scale where 1 means ‘not at all informed’ and10 means ‘very well informed’.

6.1. Descriptive results

Our survey respondents are relatively representative of thecharacteristics of the US adult population (Table 2). In general,respondents are more knowledgeable about organic methods andequally less knowledgeable about the other two technologies.

Table 3 shows that the likelihood of knowing about biotechnol-ogy or irradiation is conditional on claiming to know about organicmethods. Among those who know about organic methods, 41.8%also know about both other technologies, and an additional27.6% know about one of the other technologies. Thus, of thosewho know about organic, 69.4% also claimed to know about eitherbiotechnology or irradiation or both. Among those who do notknow about organic technology, only 4% know about both othertechnologies, and a total of 17.8% know about one or both of theother technologies. The relative ratio of 4 (69.4/17.8) suggests thatknowing about organic and knowing about the other food produc-tion technologies share common external causes that are not sim-ply due to individual internal factors such as interest or curiosity;otherwise we would find a more equal relative ratio. We believethat these common external causes are likely to be associated withorganic technology experience and information. There is no sign ofa similar effect due to knowing about either biotechnology orirradiation. The comparable relative ratios for biotechnology and

Table 5Organic food production: results of ordered-logistic regression – respondent attitudes on four dimensions.a

Long-term healthfulness(n = 1571)

Environmental safety(n = 1583)

Nutritional quality(n = 1593)

Bacterial safety(n = 1574)

Estimate Estimate Estimate Estimate

Intercept �1 �0.7892 �6.804** �3.2264* 0.4571Intercept 0 1.7656 �4.8667** �0.4269 2.8380Male 0.0832 �0.2455* �0.2233* 0.1511Age 0.0975** 0.059** 0.0200 0.0559**

Age quadratic �0.0010** �0.00063** �0.0003 �0.0005**

Education �0.2093 0.7326** 0.2882 �0.1768Education quadratic 0.0074 �0.0215** �0.0100 0.0035Anxiety about farming methods 0.3851** 0.241** 0.1447** 0.0925Food safety awareness 0.1210* 0.1494* �0.0980 �0.0131Stopped buying a specific food because of safety

concerns0.0896 �0.1056 0.2802* 0.1014

Household member had foodborne illness in pastyear

0.2394* 0.2826* 0.2195* �0.2977**

Organic knowledge level 0.0977** 0.0826** 0.1224** 0.0570*

Biotechnology knowledge level 0.0169 0.0255 �0.4269 �0.0223Irradiation knowledge level �0.1286** �0.0935** �3.2264 �0.1368**

Cragg/Uhler R2 0.11 0.11 0.04 0.07

a *Denotes significant at the 5% level; **denotes significant at the 1% level.

Table 6Biotechnology: results of ordered-logistic regression – respondent attitudes on four dimensions.a

Long-term healthfulness(n = 952)

Environmental safety(n = 961)

Nutritional quality(n = 985)

Bacterial safety(n = 985)

Estimate Estimate Estimate Estimate

Intercept �1 �0.9144 �2.1073 �6.7106** �1.3333Intercept 0 1.6146 0.3718 �4.1411* 1.1358Male 0.0876 �0.1315 0.0097 0.2308Age 0.0225 0.0037 0.0201 0.0159Age quadratic �0.0001 0.0001 �0.0002 �0.0002Education �0.1938 0.1064 0.4968 �0.0119Education quadratic 0.0061 �0.0034 �0.0132 0.0011Anxiety about farming methods �0.2037** �0.4639** �0.2215** 0.0590Food safety awareness �0.0606 �0.1417 �0.1112 0.0250Stopped buying a specific food because of safety

concerns�0.4811** �0.2518 �0.6519** �0.2705

Household member had foodborne illness in pastyear

�0.3539* �0.1021 0.1487 0.0915

Organic knowledge level �0.0794* �0.0405 �0.0023 �0.0364Biotechnology knowledge level 0.0785* �0.0677 0.0947* 0.0296Irradiation knowledge level �0.0251 0.0505 0.0215 �0.0347Cragg/Uhler R2 0.08 0.10 0.08 0.02

a *Denotes significant at the 5% level; **denotes significant at the 1% level.

Table 7Irradiation: results of ordered-logistic regression – respondent attitudes on four dimensions.a

Long-term healthfulness(n = 786)

Environmental safety(n = 801)

Nutritional quality(n = 828)

Bacterial safety(n = 825)

Estimate Estimate Estimate Estimate

Intercept �1 �2.9776 �3.3341 �5.6433* �6.7295**

Intercept 0 �0.6400 �0.5586 �1.9880 �5.3040*

Male 0.7442** 0.6451** 0.0359 0.7445**

Age 0.0535* �0.0123 �0.0648* 0.0473*

Age quadratic �0.0003 0.0003 0.0008** �0.0003Education �0.0894 0.0872 0.3391 0.5544Education quadratic 0.0038 �0.0022 �0.0082 �0.0146Anxiety about farming methods �0.4197** �0.1049 �0.4590** 0.1900*

Food safety awareness 0.1242 0.1888* �0.1086 0.2389**

Stopped buying a specific food because of safetyconcerns

�0.5487** �0.1511 �0.2138 �0.6047**

Household member had foodborne illness in pastyear

�0.2471 0.1103 �0.1956 0.3463*

Organic knowledge level �0.1225** �0.0934** �0.0657 �0.0196Biotechnology knowledge level 0.0107 0.0328 �0.0256 �0.0173Irradiation knowledge level 0.1175** 0.0905* 0.2146** 0.0586Cragg/Uhler R2 0.20 0.09 0.17 0.16

a *Denotes significant at the 5% level; **denotes significant at the 1% level.

M.F. Teisl et al. / Food Quality and Preference 20 (2009) 586–596 591

592 M.F. Teisl et al. / Food Quality and Preference 20 (2009) 586–596

irradiation, for example, are 1.5 (96.8/62.8) and 1.4 (92.2/67.7),respectively.

Note our hypothesis is not simply based on the examination ofone ratio but of a comparison across ratios; a few examples exam-ining the marginal probabilities of knowledge across individualpairs of technologies may be helpful as these ‘marginals’ hold theknowledge of the unexamined technology constant. This helps usexamine the relationships between knowledge of technology pair-ings without confounding these relationships with changes inknowledge of the unexamined technology.

When we examine the population that is ignorant of the organictechnology, we find that the effect of a change in knowledge aboutbiotechnology increases the percent of people knowing about irra-diation by 4.9 times {20/[20 + 34]}/{34/[34 + 412]}. Similarly, achange in knowledge about irradiation increases the percent ofpeople knowing about biotechnology by an equivalent 4.9 times{20/[20 + 34]}/{34/[34 + 412]}. This symmetry continues whenyou examine the effect of knowledge of irradiation {148/[148 + 34]}/{513/[513 + 412]} or biotechnology {316/[316 + 34]}/{513/[513 + 412]} on the percent knowing about organics (holdingthe other technology in ignorance); in both cases the percentknowing about organics is about 1.5–1.6 times higher.

The effect of knowing about organics does not have a symmetriceffect on knowledge about the other technologies. For example,with a population ignorant about irradiation we find that the effectof a change in knowledge about organics increases the percent ofpeople knowing about biotechnology by five times {316/[316 + 513]}/{34/[34 + 412]}. However, with a population ignorantabout biotechnology we find that the effect of a change in knowl-edge about organics increases the percent of people knowing aboutirradiation by only three times {148/[148 + 513]}/{34/[34 + 412]}.That the effect of organic knowledge has a relatively larger impacton knowing about biotechnology relative to irradiation is consis-tent with our examination of commercial and non-profit organic-supporting websites and products. These website are more likelyto mention and criticize biotechnology relative to irradiation. Thus,the above relationship; i.e., knowledge of organics increasesknowledge of both biotechnology and irradiation but raises knowl-edge of biotechnology relatively more, is consistent with ourhypothesis.

Attitudes toward organic technology are generally positivewhile attitudes toward biotechnology and irradiation are generallynegative (Table 4). Note the coding of the attitude measures inTable 4 are different than the coding used in the regressions; thecoding used here subtracts one from the dependent measures usedin the regressions; this is done to highlight whether mean attitudesare negative or positive .There is also substantial evidence ofnuanced attitudes towards the three technologies (we used analy-sis of variance to test differences in attitudes across measures andtechnologies; full results are available from the first author). Con-sumers see the main benefits of organic technology to be environ-mental safety and positive long-term health effects. Consumers aremost concerned about the potential long-term health implicationsof foods produced with biotechnology, followed by concerns aboutnegative environmental consequences. Consumers see a positivevalue of irradiation in that it reduces the danger of bacterial con-tamination in food, but they are concerned about its effects onnutritional quality. In general, each technology has a distinctiveprofile of positive and negative attributes.

6.2. Regression results

The antecedents of attitudes toward food production technolo-gies were examined in terms of demographic, experience and self-rated knowledge variables. We present results from 12 regressionequations, one for each of the four attitudinal dimensions of each

of the three technologies (Tables 5–7); however, we first providesome general fits statistics for the models.

Three of the four irradiation models had the strongest fit, asmeasured by the pseudo-R2. Three of the organic and three of thebiotechnology models also had relatively good fits. Note that thesegoodness-of-fit statistics are constructed from a relationship be-tween a restricted and unrestricted likelihood function, akin tothe F statistic provided by OLS regression. They measure the signif-icance of the regression as a whole, and attempt to provide a scor-ing similar to the standard R2 measure; however, even with aperfect fit many of these never approach 1 (e.g., McFadden,1979). In terms of empirical consistency, our models provide agoodness-of-fit similar or better to many published models (e.g.,Easterlin, 2006; Jekanowski, Williams, & Schick, 2000; Meullenetet al., 2003; Verbeke & Ward, 2006).

6.2.1. Demographic variablesGender and age are the two main demographic variables related

to attitudes toward food production technologies, each having asignificant effect in 5 and 6 of the 12 equations, respectively. Theireffects vary by technology; men are more negative than women to-ward organic food production, but, similar to results from Hwanget al. (2005), they are more positive than women toward irradia-tion. Gender is not a strong factor for attitudes toward biotechnol-ogy. Age has a significant quadratic effect on attitudes towardorganic food production, with middle-aged respondents more po-sitive about organic production than either older or youngerrespondents. Age is not a factor for attitudes toward biotechnology.Age has a significant positive linear effect on attitudes toward irra-diation related to long-term health and bacterial safety but there isa significant quadratic effect of age on attitudes toward possible ef-fects of irradiation on food nutrition, such that middle age respon-dents are more negative toward irradiation than either younger orolder respondents on this dimension.

Education is a significant factor on attitudes in only 1 of theequations. Interestingly, there is a non-linear effect between edu-cation and respondent attitudes toward organic production’s envi-ronmental safety; individuals become more positive toward thistechnology as they become more educated, but the increase in atti-tudes comes at a diminishing rate.

6.2.2. Experience variablesThe three experience variables, anxiety toward other agricul-

tural technologies, food safety related information seeking andawareness, and recent food borne illness, are important factorsfor attitudes toward food processing technologies; having signifi-cant effects in 9, 4 and 6 of the 12 equations, respectively. As withage and gender, the effects of experience on attitudes toward foodprocessing technologies differ by technology.

Anxiety toward other agricultural technologies is the strongestfactor for predicting attitudes toward food production technolo-gies, but its effects also differ by technology. Respondents whoare more concerned about conventional food production methodsare significantly more positive toward organic food productionbut are more negative toward biotechnology and irradiation onthree of the four attitudinal dimensions (long-term healthfulness,environmental safety, and nutritional quality). An importantexception is ‘bacterial safety’; on this attitude dimension, individ-uals with higher levels of anxiety are more positive towardirradiation.

The food safety knowledge variable, which reflects a generalalertness to food safety related stories and materials, is signifi-cantly related to positive attitudes towards organic food produc-tion on two dimensions (long-term healthfulness, environmentalsafety) and irradiation on two dimensions (environmental safetyand bacterial safety), but it is not significantly related to attitudes

M.F. Teisl et al. / Food Quality and Preference 20 (2009) 586–596 593

toward biotechnology. Individuals who had stopped buying a spe-cific food because of safety concerns think that organic foods aremore nutritious while foods produced by biotechnology are lessnutritious. These individuals also think that foods produced by bio-technology and irradiated foods are more likely to cause long-termhealth problems and that irradiated foods are more likely to con-tain harmful bacteria.

Recent foodborne illness experience, a likely determinant ofpersonal relevance and perceived importance of food safety con-cerns, is positively related to the view that irradiation increasesbacterial safety and negatively related to the same dimension fororganic production. Recent foodborne illness experience is posi-tively related to the other three attitudinal dimensions for organicfood production, and it has a single negative association with per-ceived effectiveness of foods produced by biotechnology to lead tobetter long-term health. Here again, there are nuanced effects onattitudes toward these technologies.

6.2.3. Self-rated knowledge variablesThe three self-rated knowledge variables (organic knowledge,

biotechnology knowledge, and irradiation knowledge) have signif-icant effects on attitudes toward the food technologies in 7, 2 and 6of the 12 equations, respectively. In all cases of statistically signif-icant associations between knowledge of a technology and atti-tudes toward the same technology, the associations are positive.In contrast, the cross-technology effects of self-rated knowledge,when they exist, are always in the opposite direction. Greaterself-rated knowledge about organic food production has a signifi-cant negative effect on attitudes toward irradiation in two out offour equations, and greater self-rated knowledge about irradiationhas a significant negative effect on attitudes toward organic inthree out of four equations. Self-rated knowledge about biotech-nology has no significant effect on attitudes toward other technol-ogies, and most of the time, knowledge level of the othertechnologies is not related to attitudes toward biotechnology.The exception is that greater knowledge of organic foods is nega-tively associated with the long-term healthfulness dimension offoods produced with biotechnology.

7. Discussion

The reason consumers are more likely to know about (and havemore positive attitudes toward) organic foods is probably that or-ganic foods are widely available and clearly labeled, consistentwith recent work on food attitudes and familiarity (Tenbult, deVries, Dreezens, & Martijn, 2008). In contrast, irradiated foods arelabeled but generally unavailable, and foods derived from biotech-nology are available but generally not labeled (Brandt, Spease,June, & Brown, 2003). Because they are labeled, consumers cangain direct knowledge about organic foods through experiencewith the products. In addition, since organic products are readilyidentifiable, consumers have an increased motivation to gatherinformation about these products, either passively (e.g., they maypay more attention to media articles about organic foods) or ac-tively (e.g., they may be more inclined to actively search the webfor more information about organic foods). Producers may alsohave more incentive to advertise these products, both on- andoff- the internet. For example, a recent study by v-Fluence (2009)indicates that most on-line content about beef production comesfrom organic and grass-fed beef producers as opposed to conven-tional producers (and that this content is generally critical of con-ventional production). The above result suggests that productavailability and the presence of labeling increases a person’s self-rated knowledge of a technology.

The fact that the joint distribution of self-rated knowledgeabout the three food production technologies is skewed such thatfeeling informed about the other technologies is associated withfeeling informed about organic foods suggests that occasions forobtaining information about either biotechnology or irradiationexist primarily in conjunction with information about organicfoods. A possible hypothesis is that currently, consumers arebecoming educated about all three technologies through theirinteraction with information about organic food. Our results showthat people who know more about organic technology respondnegatively toward the other technologies; this could be becausethey were exposed to negative information about the other tech-nologies while receiving information about organic production.For example, commercial (e.g., Delicious Organics, 2009; Planet Or-ganic, 2009) and non-commercial organic organizations (e.g., theOrganic Consumer Association., 2009) present negative informa-tion about both biotechnology and irradiation (with stories linkingthese foods to increasing butterfly mortality; negatively affectingbee behavior; causing infertility, cancer and damage to the im-mune system). Further, many producers of organic foods marketand label their products as specifically not using another technol-ogy (e.g., Garden of Eatin’ Tortilla Chips state they do not containingredients produced by biotechnology).

That consumers are learning about the three technologiesthrough their interaction with information about organic foodmay be driven by the availability of organic products in the pres-ence of explicit organic labeling, which allows information to belinked to actual product experience, which in turn may have great-er intrinsic interest to consumers than hypothetical discussions ofthe pros and cons of an abstract technology. Although the abovehypothesis needs to be tested, exposure to information about or-ganic production apparently leads to exposure to informationabout biotechnology and irradiation technologies. To the extentthat people with more alertness to food safety related stories andmaterials represent ‘‘early adopters” of current information mes-sages in the market place, these effects portend future trends inattitudes toward the food technologies.

The analysis, which uses four different attitudinal measures,provides information to policy makers, consumers and industrygroups on how consumer attitudes about three food technologiesare being shaped. The varied responses across technologies andattitudinal measures support Fischhoff and Fischhoff’s 2001 con-tention that attitudes toward technologies are likely to be nuanced.

Similar to previous literature (IFIC, 1998, Frezen et al., 2000;Hallman & Aquino, 2003; Levy, 2001; McClusky et al., 2001), wefind that greater self-rated knowledge of each technology is associ-ated with positive attitudes about that technology. This is consis-tent with a ‘‘reinforcement effect” where repeated exposure toinformation increases positive attitudes (Lau & Coiera, 2007; Za-jonc, 1968). An important new finding is the presence of negativecross-information effects: knowledge of organic or irradiation(but not biotechnology) is associated with negative views towardthe other two technologies. Additionally, it appears that individualdifference factors, either demographic characteristics such as ageor gender, or experiential characteristics such as level of concernabout food safety or information seeking behavior, have differentdirectional effects on certain attitude dimensions depending onthe specific technology. The overarching pattern is that each tech-nology has a distinctive attitude profile and that individual atti-tudes towards the different technologies are stronglyinterconnected.

It is not surprising to find that individual attitudes toward var-ied issues are related. Sometimes this is because the issues them-selves are related to larger moral or political values that a personmay hold and which determine their attitudes toward the individ-ual issues. Correlation may also occur when available information

594 M.F. Teisl et al. / Food Quality and Preference 20 (2009) 586–596

is skewed in favor of one technology and against another. In thepresent case, it is not immediately clear what mechanisms mightbe responsible for the observed interconnectedness between atti-tudes toward the three food technologies. What is troubling is thatthe observed interconnectedness between food technology atti-tudes arises in a situation where the public admits it knows rela-tively little about two of the technologies.

Policy makers, educators and marketing professionals need tounderstand the current determinants of consumer attitudes to-ward the food technologies, in particular the role played by infor-mation level and information sources, so they can better evaluatecurrent information policies and programs. Consumers’ attitudestoward food technologies may have important consequences onthe public acceptance and dissemination of these technologies inthe real world (Entine, 2006). To the extent that these attitudes re-flect inappropriate risk/benefit tradeoffs, they could presumablylead to losses in consumer welfare.

An important finding in this respect is that knowledge aboutorganic production seems to be the gateway to knowledge aboutthe other technologies. Very few people knew about biotechnol-ogy or irradiation if they did not also know about organicmethods, whereas many people knew about organic productionwho did not know about one or both of the other technologies.As noted above, knowledge of organic methods is generallynegatively related to attitudes towards the other twotechnologies.

Decisions by producers to disclose information on product la-bels about organic or biotechnology production methods are forthe most part voluntary in the USA. Disclosure that a product hasbeen irradiated, however, is required. If our hypothesis about therelation between exposure to food labels and information searchis correct, we see how labeling decisions by producers and regula-tors may be shaping consumer attitudes about all of these technol-ogies in unintended ways.

Further, this hypothesis may present an interesting twist to thebiotechnology labeling debate. Many supporters of the technologyhave fought to avoid any labeling requirement for foods producedthrough biotechnology, while opponents of the technology havefought equally hard to impose labeling requirements. If our

Table A1Questions used in the factor analysis – For reviewers.

Pesticide residue seriousness Would you say that pesticide residues in food areof a problem, or not a food safety problem at all?

1. VERY SERIOUS FOOD SAFETY PROBLEM2. SERIOUS FOOD SAFETY PROBLEM3. SOMEWHAT OF A FOOD SAFETY PROBLEM4. NOT A FOOD SAFETY PROBLEM AT ALL

Antibiotic residue seriousness Would you say that antibiotic residues in food areof a problem, or not a food safety problem at all?

1. VERY SERIOUS FOOD SAFETY PROBLEM2. SERIOUS FOOD SAFETY PROBLEM3. SOMEWHAT OF A FOOD SAFETY PROBLEM4. NOT A FOOD SAFETY PROBLEM AT ALL

Seen safe-handling labels Have you seen safe-handling labels on some raw m

1. YES2. NO

Knows of specific advisories Have you heard or read about any possible healthor read about any possible health problems relateHave you heard anything about mercury as a prob1. YES2. NO3. NOT SURE

Knows of specific food pathogens Have you ever heard of Salmonella as a problem iHave you ever heard of Campylobacter as a probleHave you ever heard of Listeria as a problem in foHave you ever heard of E coli as a problem in food1. YES2. NO

hypothesis above is correct, then the lack of biotechnology labelingmay have hindered the ability of biotechnology proponents to pos-itively influence attitudes toward foods produced with biotechnol-ogy, either directly (by allowing for informed consumer experiencewith the foods and through product advertising) or indirectly (byaltering consumers’ information search behavior). However, it isalso possible that the presence of biotechnology labeling could in-duce opponents of the technology to increase their level of infor-mation dissemination (which could lead to negative attitudeformation), or labeling may allow for increased information speci-ficity (e.g., opponents could begin to target products or companies,not just the technology).

8. Conclusion

The ‘gateway’ results suggest strongly that source and tone ofinformation about biotechnology and irradiation have, until now,been dominated by organic foods contexts. This state of affairsmay not continue, particularly if the proponents of irradiationand biotechnology become more active in consumer education.The number of people who consider themselves informed aboutthese two technologies is still low, which means that, at least inthe USA, substantial numbers of people are available to learn moreabout the technologies. The relation between increased knowledgeand more positive attitudes suggests that as people become moreinformed about the different technologies their attitudes may be-come more positive. Food irradiation, in particular, becomes moreacceptable as consumers become more informed, principally be-cause their concerns about its effects on the environment andnutrition are eased.

Appendix A

See Table A1.

a very serious food safety problem, a serious food safety problem, somewhat

a very serious food safety problem, a serious food safety problem, somewhat

eat and poultry products?

problems related to eating sprouts, such as alfalfa or bean sprouts?Have you heardd to drinking juice that has not been pasteurized, that is unpasteurized juice?lem in some fish?

n food?m in food?od??

M.F. Teisl et al. / Food Quality and Preference 20 (2009) 586–596 595

References

Bak, H.-J. (2001). Education and public attitudes toward science: Implications forthe deficit model of education and support for science and technology. SocialScience Quarterly, 82, 779–795.

Bennett, B., D’Souza, G., Rosenberger, R., & Smith, D. (2003). Genetically modified fishand seafood: Consumer attitudes, marketing strategies and policy implications.Presented at the seventh annual international consortium on agriculturalbiotechnology research conference, June 29 to July 3, Ravello, Italy.

Berry, C. M., Gruys, M. L., & Sackett, P. R. (2006). Educational attainment as a proxyfor cognitive ability in selection: Effects on levels of cognitive ability andadverse impact. Journal of Applied Psychology, 91(3), 696–705.

Boccaletti, S., & Moro, D. (2000). Consumer willingness-to-pay for gm food productsin Italy. AgBioForum, 3(4), 259–267.

Brandt, M. B., Spease, C. J., June, G., & Brown, A. (2003). Prevalence of food safety,quality, and other consumer statements on labels of processed, packaged foods.Food Protection Trends, 23(11), 870–881.

Buzby, J. C., Ready, R. C., & Skees, J. R. (1995). Contingent valuation in food policyanalysis: A case study of a pesticide-residue risk reduction. Journal ofAgricultural and Applied Economics, 27(2), 613–625.

Cardello, A. V. (2003). Consumer concerns and expectations about novel foodprocessing technologies: Effects on product liking. Appetite, 40, 217–233.

Cox, D. N., Evans, G., & Lease, H. J. (2007). The influence of information and beliefsabout technology on the acceptance of novel food technologies: A conjointstudy of farmed prawn concepts. Food Quality and Preference, 18, 813–823.

Cox, D. N., Evans, G., & Lease, H. J. (2008). Australian consumers’ preferences forconventional and novel sources of long chain omega-3 fatty acids: A conjointstudy. Food Quality and Preference, 19, 306–314.

Cragg, J. G., & Uhler, R. (1970). The demand for automobiles. Canadian Journal ofEconomics, 3, 386–406.

Delicious Organics. (2009). Delicious Organics website – Links to geneticallymodified organisms <http://www.deliciousorganics.com/controversies/GMFOODS.htm>.

Easterlin, R. A. (2006). Life cycle happiness and its sources: Intersections ofpsychology, economics, and demography. Journal of Economic Psychology, 27,463–482.

Eden, S., Bear, C., & Walker, G. (2008). Understanding and (dis)trusting foodassurance schemes: Consumer confidence and the ‘knowledge fix’. Journal ofRural Studies, 24(1), 1–14.

Entine, J. (Ed.). (2006). Let them eat precaution: how politics is undermining the geneticrevolution in agriculture. Washington: American Enterprise Institute Press.

Fischhoff, B., & Fischhoff, I. (2001). Public opinions about biotechnology.AgBioForum, 4(3–4), 155–162.

Fox, J. A. (2002). Influences on purchase of irradiated foods. Food Technology, 56(11),34–37.

Fox, J. A., Hayes, D. J., & Shogren, J. F. (2002). Consumer preferences for foodirradiation: How favorable and unfavorable descriptions affect preferences forirradiated pork in experimental auctions. Journal of Risk and Uncertainty, 24,75–95.

Fox, J. A., & Olsen, D. G. (1998). Market trials of irradiated chicken. Radiation Physicsand Chemistry, 52, 63–66.

Frewer, L. J., Scholderer, J., & Bredahl, L. (2003). Communicating about the risks andbenefits of genetically modified foods: The mediating role of trust. Risk Analysis,23(6), 1117–1133.

Frezen, P. D., Majchrowicz, A., Buzby, J. C., & Imhoff, B. (2000). Consumer acceptanceof irradiated meat and poultry products. Issues in food safety economics (pp. 1–8). US Department of Agriculture, Agriculture Information Bulletin No. 757.

Govindasamy, R., & Italia, J. (1999). Predicting willingness-to-pay a premium fororganically grown fresh produce. Journal of Food Distribution Research, 30,44–53.

Greene, W. H. (2002). LIMDEP econometric modeling guide (Vol. 2). EconomtricSoftware, Castle Hill, Austrailia (Chapter E23).

Grobe, D., Douthitt, R., & Zepda, L. (1997). Consumer risk perception profiles for thefood-related biotechnology, rBGH. In Cotterill Caswell (Ed.), Strategy and policyin the food system (pp. 157–170). Storrs, CT: Food Marketing Policy Center.

Hayes, D. J., Fox, J. A., & Shorgen, J. F. (2002). Experts and advocates: How informationaffects the demand for food irradiation. Staff General Research Paper 10105. IowaState University, Department of Economics.

Hallman, W. K., & Aquino, H. (2003). Public perceptions of genetically modified food:An international comparison. Presented at the seventh annual internationalconsortium on agricultural biotechnology research conference, June 29 to July 3,Ravello, Italy.

Hanley, A. J., Meigs, J. B., Williams, K., Haffner, S. M., & D’Agostino, R. B., Sr. (2005).Re: (Mis)use of factor analysis in the study of insulin resistance syndrome.American Journal of Epidemiology, 161(12), 1182–1184.

Hansen, J., Holm, L., Frewer, L., Robinson, P., & Sandøe, P. (2003). Beyond theknowledge deficit: recent research into lay and expert attitudes to food risks.Appetite, 41, 111–121.

Hossain, F., Onyango, B., Adelaja, A., Schilling, B., & Hallman, W. (2002). Consumeracceptance of food biotechnology: Willingness to buy genetically modified foodproducts. Food Policy Institute, Rutgers University, NJ.

Huffman, W. E., Rousu, M., Shogren, J. F., & Tengene, A. (2002). Consumer resistanceto GM-foods: The role of information in an uncertain environment. IowaAgricultural and Home Economics Experiment Station No. 6590.

Hwang, Y.-J., Roe, B., & Teisl, M. F. (2005). An empirical analysis of United Statesconsumers’ concerns about eight food production and processing technologies.AgBioForum, 8(1), 40–49.

IFIC (International Food Information Council) (1998). Consumer attitudes towardfood irradiation. Washington, DC: International Food Information CouncilFoundation.

Jekanowski, M. D., Williams, D. R., II, & Schick, W. A. (2000). Consumers’ willingnessto purchase locally produced agricultural products: An analysis of an Indianasurvey. Agricultural and Resource Economics Review, 29(8), 43–53.

Jonassen, D. H. (1991). Objectivism versus constructivism: Do we need a newphilosophical paradigm? Educational Technology Research and Development,39(3), 5–14.

Judge, G. C., Hill, R. C., Griffiths, W. E., Lutkepohl, H., & Lee, T.-C. (1988). Introductionto the theory and practice of econometrics. John Wiley and Sons.

Kaneko, N., & Chern, W. S. (2003). Consumer acceptance of genetically modifiedfoods: A telephone survey. Consumer Interests Annual, 49, 1–13.

Lacy, M. G. (2006). An explained variation measure for ordinal response modelswith comparisons to other ordinal R2 measures. Sociological Methods Research,34, 469.

Lau, A. Y. S., & Coiera, E. W. (2007). Do people experience cognitive biases whilesearching for information? Journal of the American Medical InformaticsAssociation, 14, 599–608.

Levy, A. S. (2001). Report on focus groups on food irradiation labeling (pp. 1–18).Report to the Office of Scientific Analysis and Support, US Food and DrugAdministration, August 21.

Marks, L. A., Kalaitzandonakes, N., & Zakharova, L. (2002). On the mediarollercoaster: Will biotech foods finish the ride? Choices, Spring, 6–10.

McClusky, J. J., Ouchi, H., Grimsrud, K. M., & Wahl, T. I. (2001). Consumer response togenetically modified food products in Japan. IMPACT Center – Washington StateUniversity, September.

McFadden, D. (1979). Quantitative methods for analyzing travel behavior ofindividuals: Some recent developments. In D. Hensher & P. Stopher (Eds.),Behavioral travel modeling (pp. 279–318). London: Croom Helm.

Meullenet, J. F., Xiong, R., Hankins, J. A., Dias, P., Zivanovic, S., Monsoor, M. A., et al.(2003). Modeling preference of commercial toasted white corn tortilla chipsusing proportional odds models. Food Quality and Preference, 14, 603–614.

Nayga, R. M., Aiew, W., & Nichols, J. P. (2005). Information effects on consumers’willingness to purchase irradiated food products. Review of AgriculturalEconomics, 27(1), 37–48.

Organic Consumer Association. (2009). Organic Consumers Association website –Links to genetic engineering and biotechnology <http://www.organicconsumers.org/gelink.cfm> and to Information of Food Irradiation <http://www.organicconsumers.org/irradlink.cfm>.

Peters, H. P. (2000). From information to attitudes? Thoughts on the relationshipbetween knowledge about science and technology and attitudes towardtechnology. In M. Dierkes & C. von Grote (Eds.), Between understanding andtrust: The public, science and technology. Amsterdam: Harwood.

Planet Organic. (2009). Planet Organic website – Links to Why go organic <http://www.planetorganic.ca/goodfood/whygoorganic/>.

Rimal, A. P., Fletcher, S. M., & McWatters, K. H. (1999). Do handling and cookingpractices determine the selection of irradiated beef? Journal of Food DistributionResearch, 30(3), 1–11.

Rodriguez, L. (2007). The impact of risk communication on the acceptance ofirradiated food. Science Communication, 28(4), 476–500.

Roe, C. M., Mintun, M. A., D’Angelo, G., Xiong, C., Grant, E. A., & Morris, J. C. (2008).Alzheimer disease and cognitive reserve: Variation of education effect withcarbon 11–labeled Pittsburgh compound B uptake. Archives of Neurology,65(11), 1467–1471.

Schogren, J. F., Fox, J. A., Hayes, D. J., & Roosen, J. (1999). Observed choices for foodsafety in retail, survey and auction markets. American Journal of AgriculturalEconomics, 81, 1192–1199.

Scholderer, J., & Frewer, L. J. (2003). The Biotechnology Communication Paradox:Experimental Evidence and the Need for a New Strategy. Journal of ConsumerPolicy, 26, 125–157.

Sturgis, P. J., & Allum, N. C. (2000). The impact of knowledge on attitudes towardbiotechnology: Using regression models to simulate a better-informed public.Nottingham: British Psychological Society (Social Psychology Section).

Sturgis, P. J., & Allum, N. C. (2001). Gender differences in scientific knowledge andattitudes toward science: Reply to Hayes and Tariq. Public Understanding ofScience, 10(4), 427–430.

Tenbult, P., de Vries, N. K., Dreezens, E., & Martijn, C. (2008). Intuitive and explicitreactions towards ‘‘new” food technologies: Attitude strength and familiarity.British Food Journal, 110(6), 622–635.

Tengene, A., Huffman, W. E., Rousu, M., & Shogren, J. F. (2003). The effects ofinformation on consumer demand for biotech foods: Evidence from experimentalauctions (pp. 1–28). US Department of Agriculture Technical Bulletin No. 1903.

Teisl, M. F., Levy, A. S., & Derby, B. M. (1999). The effects of education andinformation source on consumer awareness of diet–disease relationships.Journal of Public Policy and Marketing, 18(2), 197–207.

Thompson, B., & Daniel, L. G. (1996). Factor analytic evidence for the constructvalidity of scores: A historical overview and some guidelines. Educational andPsychological Measurement, 56(2), 197–208.

Underhill, S., & Figueroa, E. (1996). Consumer preferences for non-conventionallygrown produce. Journal of Food Distribution Research, 27(2), 56–66.

596 M.F. Teisl et al. / Food Quality and Preference 20 (2009) 586–596

Verbeke, W., & Ward, R. W. (2006). Consumer interest in information cues denotingquality, traceability and origin: An application of ordered probit models to beeflabels. Food Quality and Preference, 17, 453–467.

Weir, M., & Andersen, L. M. (2001). Studies on consumer demand for organic foods – Asurvey (15 pp.). Working paper #1, AKF Copenhagen Denmark.

Weir, M., Andersen, L. M., & Millock, K. (2005). Information provision, consumerperceptions and values – The case of organic foods. In Karup & Russell (Eds.),Environment, information and consumer behaviour (pp. 161–178). Edward ElgarNorthampton, MA.

v-Fluence. (2009). As Food Inc’s aggressive campaign reflects, Organic and animaladvocates often dominate food debate. <http://www.v-fluence.com/blog/455/as-food-incs-aggressive-campaign-reflectsorganic-and-animal-advocates-often-dominate-food-debate>.

Zajonc, RB. (1968). Attitudinal effects of mere exposure. Journal of Personality andSocial Psychology, 9, 1–27.

Zellner, J. A., & Degner, R. L. (1989). Consumer willingness-to-pay for food safety.Paper presented at the Southern Agricultural Economics Meeting, Nashville,TN.