sonja radas, mario f. teisl, and brian roe an open...

SONJA RADAS, MARIO F. TEISL, AND BRIAN ROE

An Open Mind Wants More: Opinion Strength and theDesire for Genetically Modified Food Labeling Policy

Two opposing viewpoints exist in the literature; some suggest consum-ers are unconcerned and do not desire any genetically modified label-ing, while others indicate the opposite. The mixed results may bebecause consumers make finer distinctions than surveys have calledfor, and have evaluation schemes sensitive to information about the ben-efits and risks associated with genetically modified foods. We find con-sumers are quite nuanced in their preferences for genetically modifiedlabeling policy. Unexpectedly, consumers with less-defined views desiremandatory labeling of the most stringent type, while consumers withstronger viewpoints (either pro- or con-genetically modified) are morerelaxed in their labeling requirements.

There are two opposing viewpoints regarding consumers’ acceptance of

genetically modified (GM) foods and their desire for the labeling of these

foods. Industry leaders believe consumers accept these foods because the

public shows a willingness to consume them. For example, most milk in the

United States is produced with the use of bST hormone, even though bST-

free milk is available, clearly labeled, and advertised. In fact, except for

recent limited gains, initial sales for bST-free milk were so weak it almost

disappeared from the market (Webb 2006). In addition, some national sur-

veys indicate that consumer concerns toward GM foods are low and few

individuals desire any GM labeling (IFIC 2007). In contrast, as indicated by

Noussair, Robin, and Ruffieux (2004), most of the academic literature indi-

cates people are highly concerned about the GM technology (e.g., Huffman

et al. 2002; Loureiro and Bugbee 2005), are willing to pay to avoid GM

foods (e.g., McCluskey et al. 2003), and would like to see GM foods

labeled (e.g., Teisl et al. 2003a).

One problem is that many GM labeling studies (and potentially some

current labeling policies) approach the issue as one where the consumer’s

sole desire for information about GM foods is whether they are, in fact,

Sonja Radas is a senior research associate at the Institute of Economics, Zagreb, Croatia (sradas@

eizg.hr). Mario F. Teisl is a professor in the School of Economics, University of Maine, Orono, ME

([email protected]). Brian Roe is a professor in the Department of Agricultural, Environmental and

Development Economics, Ohio State University, Columbus ([email protected]).

The Journal of Consumer Affairs, Vol. 42, No. 3, 2008

ISSN 0022-0078

Copyright 2008 by The American Council on Consumer Interests

FALL 2008 VOLUME 42, NUMBER 3 335

genetically modified (Teisl and Caswell 2002). This approach may work

well for consumers who have lexicographic preferences where the pro-

cess of GM production must first be resolved before the consumer con-

siders any other quality attributes (Kaye-Blake, Bicknell, and Saunders

2005). However, because the use of biotechnology in food production

can have multidimensional effects on product quality (Caswell 2000),

consumers who want to know about some or all of the changes in product

attributes may find that such a labeling program provides information

that is inadequate, irrelevant, or that impedes their decision making

(Roe et al. 2001).

Another problem is that many studies often refer to the GM technology

in imprecise terms, whereas consumers appear to be capable of making

finer distinctions; hence, it is hard to interpret the attitude levels being

reported (Fischhoff and Fischhoff 2001). For example, early willingness-

to-pay studies commonly assumed that the genetic modification only pro-

vided benefits to consumers by lowering prices; only recently have studies

(e.g., O’Connor et al. 2005; Onyango and Govindasamy 2005; Hossain and

Onyango 2004) looked at situations where individuals may derive nonprice

benefits (e.g., improved nutritional characteristics). In turn, it is not surpris-

ing that survey respondents would respond negatively to GM content

because new technologies are often viewed as having long-term risks.

Indeed, when studies include a GM-related benefit, consumers are often

willing to buy these foods (e.g., Boccaletti and Moro 2000; Verdurme,

Gellynck, and Viaene 2001; Teisl et al. 2003b).

Because consumer acceptance of GM foods is linked to the perceived

risks and benefits of these foods (Boccaletti and Moro 2000; Chen and Li

2007; Curtis and Moeltner 2006; Lusk and Coble 2005; Moon and Bala-

subramanian 2004; Rosati and Saba 2000; Subrahmanyan and Cheng 2000)

and because consumers are heterogeneous in how they weigh these risks

and benefits (Kaye-Blake, Bicknell, and Saunders 2005), recent authors

have focused on segmenting consumers by how they evaluate GM foods

(e.g., Baker and Burnham 2001; Ganiere, Chern, and Hahn 2004; Jan, Fu,

and Huang 2007; Kontoleon 2003; O’Connor et al. 2005; Rigby and Burto

2005; Roosen, Thiele, and Hansen 2005; Verdurme, Gellynck, and Viaene

2001; Vermeulen 2004). Although Verdurme and Viaene (2003) segment

consumers to examine differences in demand for information about GM

foods, to our knowledge no one has examined whether consumers’ views

on GM labeling policy differs across segments.

Our objective here is to identify if consumers differ in their risk/benefit

evaluation of GM foods and how these differences may translate into dif-

ferent preferences for GM labeling policy.

336 THE JOURNAL OF CONSUMER AFFAIRS

Examining this issue is important both in terms of policy and research.

First, with respect to policy, consumers’ attitudes toward GM foods

appear to be quite sensitive to information about the potential benefits

and risks associated with these foods (Brown and Qin 2005; Huffman

et al. 2003c, 2007; Lusk et al. 2004), especially when they are uniformed

(Huffman et al. 2007); a condition illustrated across countries (e.g., United

States: Hallman and Hebden 2005; James 2004; Shanahan, Scheufele, and

Lee 2001; Italy: Boccaletti and Moro 2000; Hungary: Banati and Lakner

2006; China: Li et al. 2002; Belgium: Verdurme, Gellynck, and Viaene

2001; Greece: Arvanitoyannis and Krystallis 2005). Because consumer atti-

tudes toward GM foods and the demand for information about these foods

are related to how informed the consumer is (Costa-Font and Mossialos

2005), it is likely that preferences for GM labeling are linked to attitudes

toward GM foods. Documented consumer heterogeneity in these attitudes

suggests that a similar heterogeneity exists in preferences for GM labeling

policy. However, because it is difficult for labeling policies to differ across

consumers (i.e., labeling policy is practically restricted to ‘‘one size fits

all’’), differences in labeling preferences could lead to conflicts across

consumer groups.

In addition to policy concerns, consumer heterogeneity could provide

two reasons why the literature is mixed in terms of preferences for GM

foods and GM labeling policy. First, as mentioned, consumers appear to

be quite sensitive to information about GM foods and possibly to the

framing of survey questions (Cormick 2005). For example, many polls

and studies are similar by only asking simple yes/no questions about

labeling but differ in how they frame the question (Do you want GM foods

labeled?; Do you want mandatory labeling of GM foods?; Do you support

U.S. Food and Drug Administration’s [FDA]s policy of voluntary label-

ing of GM foods?). It is unclear from the literature whether respondents

who are heterogeneous in their preferences toward GM labeling view

these questions as asking different things. Second, mixed views on label-

ing policy could be explained if consumers who are heterogeneous in their

preferences for labeling are also likely to differ in how likely they are to

respond to a survey. For example, after reviewing twenty-five studies on

GM food preferences, Lusk et al. (2005) indicate that a major factor

explaining differences in results is the characteristics of the consumer

sample.

For all the above reasons, paraphrasing Rigby and Burton (2005), know-

ing the ‘‘average’’ preference for labeling is less important than understand-

ing how preferences differ across consumer segments and the relative sizes

of these segments.


METHOD

During the summer 2002, we administered a mail survey to a nationally

representative sample of 5,462 U.S. residents and an additional oversample

of Maine (710 individuals) residents. The samples were purchased from

a frame maintained by InfoUSA. The InfoUSA database contains informa-

tion about 250 million U.S. residents (more than 95 percent of U.S. house-

holds). The address information is continually updated using the U.S.

Postal Service’s National Change of Address system, allowing InfoUSA’saddress list to maintain a 93 percent accuracy rate.

The survey was administered with multiple mailings and with an incen-

tive paid for returned completed surveys (incentives were experimentally

manipulated and consisted of either a $1 bill, a $2 bill, a $2 value phone

calling card, or a $5 phone calling card; for further information about the

incentive scheme, see Teisl, Roe, and Vayda 2006). The oversample of

Maine residents was added to provide representative results for Maine state

policy makers (the Maine Agriculture and Forest Experiment Station pro-

vided some of the funding for this research).

In total, 375 Maine residents and 2,012 U.S. (non-Maine) residents

responded to the survey for a response rate of 53 and 37 percent, respect-

ively. The overall response rate of 39 percent is marginal, suggesting that

individual survey results may not be a valid representation of the knowl-

edge, practices, and attitudes of the U.S. adult population. However, our

purpose here is not to extrapolate our survey results to the aggregate popu-

lation but to examine differences in labeling preferences across different

types of consumers.

Our survey respondents are slightly older, more educated, and have

higher incomes compared to the characteristics of the U.S. adult population

(Table 1). Although our sample is more likely to be white, the stated differ-

ences in race may be reflective of a true underlying difference and/or may

reflect differences in the way the race questions are asked across the two

surveys. Specifically, our survey only allows respondents to choose one

race from a list of five racial categories, while the U.S. Census allows

respondents to choose multiple races from a list of fourteen racial catego-

ries. These differences in response categories and instructions could lead to

differences in reported racial composition.

The survey instrument consists of questions used to elicit respondents’

perceptions of various food technologies, knowledge of the prevalence of

GM foods, perceptions of potential benefits and risks of GM foods, reac-

tions to alternative GM labeling programs, and willingness to pay for or

avoid GM foods. The content and wording of questions is based upon


an analysis of issues raised in the labeling or consumer perception literature

(e.g., Boccaletti and Moro 2000; Hallman andMetcalfe 1994; Hoban 1999;

Huffman 2003a; Roe et al. 2001; Rousu et al. 2003; Teisl 2003), state and

federal policy needs, and previous focus group research (Teisl et al. 2002).

Further, they are based upon conceptualizations of consumer reactions to

labeling information as presented in Teisl, Bockstael, and Levy (2001) and

Teisl and Roe (1998).

As highlighted in the introduction, a reasonable condition for acquiring

insight into consumers’ attitudes toward the labeling of GM foods is to first

gain an understanding of how consumers evaluate the risks and benefits asso-

ciated with these foods. We then explore how heterogeneous consumers are

in their risk/benefits evaluations and segment them into more homogeneous

segments. After segmenting consumers and profiling the segments, we inves-

tigate their preferences for alternative GM labeling policies.

To gain an understanding of how consumers view the potential risks and

benefits of GM foods, we provided respondents with a list of sixteen poten-

tial benefits and sixteen potential risks of GM foods and asked them to rate

each one on importance. Ratings were coded on a Likert scale where 1 ¼not at all important, 3¼ somewhat important, and 5¼ very important. The

potential benefits generally accrue to the consumer (e.g., lower food prices),

while others accrue to the food producer (e.g., increased disease resistance

in crops). The scope of the risks is broader; some primarily concern con-

sumer health risks (e.g., unknown toxins produced), others concern pro-

ducer risks (e.g., spread of disease resistance to weeds), while others

are focused on environmental (e.g., risks to species diversity), social

(e.g., control of agriculture by biotechnology companies), or ethical

(e.g., ethical issues with genetic modification) problems.

We use factor analysis on the above benefits and risks to find the set of

underlying factors influencing consumers’ perceptions. Factor analysis is

TABLE 1

Characteristics of Respondents and of the U.S. Population

Survey U.S. Censusa

Percent male 50 48

Average age (years) 50 46

Average education (in years) 14.6 13

Racial distribution (percent)

White 89 75

Black 5 12

Other 6 13

Average household income $64,000 $55,000

aBased on 2000 Census data.


a data reduction technique used to investigate whether a group of variables

have common underlying dimensions and can be considered to measure

a common factor. Although the analysis can be used to summarize a larger

number of variables into a smaller set of constructs, ultimately the analy-

sis is not a hypothesis testing technique so it does not tell us what those

constructs are (Hanley et al. 2005). In turn, the validity of naming the con-

structs is contingent upon researcher judgment and should be interpreted

with some caution (Thompson and Daniel 1996).

For the factor analysis, we used principal components analysis followed

by varimax rotation. As is typical, factors with eigenvalues less than one are

dropped from further analysis as are variables with factor loadings of less

than .6 as these are not considered statistically significant for interpretation

purposes. To further verify the reliability of the factor analysis, we compute

Cronbach’s alpha on the original responses, aiming to have alphas greater

than the minimum value of .70 suggested by Nunnally and Bernstein

(1994).

The output from the factor analysis provides a reduced set of variables

that helps explain heterogeneity in consumer evaluations of the risks and

benefits of GM foods (i.e., we use the factor analysis to simplify across

variables). We use cluster analysis on the above output to group respond-

ents into homogeneous risk/benefit segments (i.e., we use the clustering

analysis to simplify across observations). These two techniques are com-

plementary (Gorman and Primavera 1983) and their combined use is famil-

iar across a variety of disciplines (e.g., Groves, Zavala, and Correa 1987;

Panda et al. 2006; Peterson 2002). In fact, this general procedure has

recently been used to segment consumers by their attitudes of GM foods

(e.g., Arvanitoyannis and Krystallis 2005; Christoph and Roosen 2006;

Onyango et al. 2004; Verdurme and Viaene 2003).

We use a two-stage clustering approach; a hierarchical algorithm fol-

lowed by k-means clustering (Punj and Stewart 1983; Singh 1990). For

the hierarchical analysis, we use Ward’s method with squared Euclidean

distances as prescribed in the marketing research literature (Punj and

Stewart 1983; Singh 1990). The resulting hierarchical tree allows us to esti-

mate the number of segments (k); the tree graphically represents increased

similarity of observations within a segment by larger linkage distances. In

addition, we examine three goodness-of-fit statistics (pseudo-F, pseudo-t2,and the cubic clustering criterion) commonly used to determine the number

of clusters (SAS 2004). The procedure is as follows: we perform several

cluster analyses where the number of segments (k) is set. We then examine

the relative size of the three statistics to help identify the number of seg-

ments. Larger values of the pseudo-F are considered better, and cubic


clustering criteria larger than two or three are preferred, those between zero

and two used with caution and those less than zero indicate outliers (SAS

2004). To use the pseudo-t2 statistic, one examines how the value of the

pseudo-t2 statistic changes as you decrease the number of clusters; the

appropriate number of segments are identified when the pseudo-t2 statisticis markedly smaller than the t2 statistic associated with the next smaller

number of segments (SAS 2004).

To aid in the interpretation of how the segments differ in terms of their

evaluations of the risks and benefits of GM foods, we examine each of the

segments in relation to their mean scores on the risk/benefit factor vari-

ables. Next, in order to better understand what types of individuals fall

into the different risk/benefit segments, we use analysis of variance

(ANOVA) and cross-tabulation analysis to examine differences across

segments in their socioeconomic profiles, their other (non-GM) food-

related concerns, and their food-related behaviors. In addition to respon-

dent characteristics (gender, age, years of education, presence of children

under eighteen present in household, respondent’s food allergies, house-

hold income), we also use two variables to measure respondents’ concerns

with food production. The first variable is based on a question, designed to

measure a respondent’s general concern with food production, ‘‘How con-

cerned are you about the way foods are produced and processed in the

US?’’ Responses were coded on a Likert scale where 1 denotes ‘‘not

at all concerned,’’ 3 denotes ‘‘somewhat concerned,’’ and 5 denotes ‘‘very

concerned.’’

The second variable is based on a factor analysis of seven questions

designed to measure concerns related to the use of specific food technol-

ogies (use of antibiotics, pesticides, artificial growth hormones, irradiation,

artificial colors/flavors, pasteurization, and preservatives). Specifically, in

the questionnaire we provided a list of eight technologies (the seven listed

above with the addition of ‘‘use of genetically modified ingredients’’), we

then asked respondents to ‘‘Review the list and rate how concerned you are

with each item.’’ Again, responses were coded on a Likert scale where

1 denotes ‘‘not at all concerned,’’ 3 denotes ‘‘somewhat concerned,’’

and 5 denotes ‘‘very concerned.’’ To investigate whether responses to these

latter concerns are correlated, we use factor analysis as a data reduction tool

using the same general procedure as presented earlier. If concerns about

specific food production technologies are highly correlated, then we can

consider them as measuring a smaller set of constructs, which expresses

their concerns with food technologies.

We finish our discussion of the segment profiles by examining each seg-

ment’s food-related behaviors. Here we asked two frequency (‘‘How often


do you buy organic foods?,’’ ‘‘How often do you read the nutrition labels on

the foods you purchase?’’) and three ‘‘activity’’ (‘‘Do you grow your own

vegetables?,’’ ‘‘Do you regularly shop at a farmers’ market or health food

store?,’’ ‘‘Do you adhere to a vegetarian diet?’’) questions. Responses for

both frequency questions are coded on a Likert scale where 1 denotes

‘‘never’’ and 5 denotes ‘‘always.’’ Responses to the activity questions

are binary (‘‘yes,’’ ‘‘no’’).

After describing the consumer segments and their profiles, we use

ANOVA and cross-tabulation analysis to explore consumer segments’

awareness, experiences, and attitudes toward GM foods and the structure

of GM food labeling policies. To measure their awareness and experiences,

we asked respondents ‘‘Have you ever heard of food being genetically engi-

neered or genetically modified?’’ (responses are ‘‘yes’’ or ‘‘no’’) and ‘‘Have

you ever seen a label indicating that a product is �GMO-free’ or �does notcontain genetically modified ingredients’?’’ (possible responses are ‘‘yes,’’

‘‘no,’’ or ‘‘don’t know’’). Wemeasure segments’ attitudes toward the follow-

ing components of GM labeling policy: (1) whether or not GM foods should

be labeled, (2) whether the labeling policy should bemandatory or voluntary,

(3) whether the GM foods or the non-GM foods should carry the label, (4)

which organization should be in charge of overseeing a GM labeling pro-

gram, and (5) what pieces of information should be included on a GM label.

We had a series of questions to determine respondent preferences for

GM labeling policy. First, to determine whether or not a respondent desired

a GM labeling policy we asked them the following binary (‘‘yes’’ or ‘‘no’’)

question: ‘‘Would you like to see labels on food indicating whether or not

the product contains genetically modified ingredients?’’ Response to the

above question gives us some baseline information of whether or not

a respondent desires GM labeling; however, we are also interested in

how the GM labeling policy should be structured (especially since different

structures can lead to different market impacts). In turn, if an individual

answered yes to the above question, we then presented him or her with

the following information and question:

There are several ways to implement a food labeling program for genetically mod-

ified foods.

Amandatory approach would require all food producers to test whether their productcontains genetically modified ingredients. Once tested, the program could require

either:

d all foods to display whether or not they contain genetically modified ingredients

d only foods containing genetically modified ingredients to display a label

d only foods not containing genetically modified ingredients to display a label


A voluntary approachwould allow foodproducers to voluntarily testwhether their prod-

uct contains genetically modified ingredients. Once tested, the program would allow:

d only foods not containing genetically modified ingredients to display a label

How do you think a testing and labeling program should be implemented in the

United States?

1. Testing is mandatory and all foods must display a label

2. Testing is mandatory and only foods containing genetically modified ingredients

display a label

3. Testing is mandatory and only foods not containing genetically modified ingre-

dients display a label

4. Testing is voluntary and only foods not containing genetically modified ingredients

display a label

5. Testing and labeling are unnecessary.

The above question allows respondents to tell us how the program

should be structured and also allows some respondents to change their mind

about labeling (i.e., individuals who at first stated that they desired labeling

could now decide it is unnecessary). To aid in the analysis, responses to the

above question were coded as five separate binary variables.

Next we consider the choice of certifying agency. For those respondents

who still consider GM labeling as necessary, we provided a list of fourteen

organizations and asked respondents ‘‘Which organization would you pre-

fer to oversee a labeling program for genetically modified foods?’’ The list

of organizations included U.S. Department of Agriculture (USDA), FDA,

U.S. Environmental Protection Agency (EPA), Greenpeace, Natural

Resources Defense Council, Organic Consumer Association, Identity Pres-

ervation Program, Cert ID–Genetic ID Inc., Union of Concerned Scientists,

Consumer’s Union, National Institute of Health (NIH), American Medical

Association (AMA), American Heart Association (AHA), and American

Cancer Society (ACS). To simplify the comparison among the three con-

sumer segments, we divided the above fourteen organizations into four

broad categories: governmental food agencies (containing USDA, FDA,

and EPA), environmental organizations (Greenpeace, Natural Resources

Defense Council, Organic Consumer Association), scientific organizations

(Identity Preservation Program, Cert ID–Genetic ID Inc., Union of Con-

cerned Scientists, Consumer’s Union), and health associations (NIH,

AMA, AHA, and ACS), and test for differences in preferences for each

broad group across segments. We also test for differences across segments

for each governmental organization (small cell sizes preclude these more

detailed tests for each of the other organizations in the list).

We finish our examination of labeling policies by analyzing differences

in preferences, across segments, for various pieces of information that


could be part of a GM label. Here we provided respondents a list of seven

items of information (see Table 8 later in this article) that could be included

on a GM label and then asked them to rate on a Likert scale how important

each piece of information is to them (responses coded as 1 denoting ‘‘not

important at all,’’ 3 denotes ‘‘somewhat important,’’ and 5 denotes ‘‘very

important’’).

For all the ANOVA and cross-tabulation analyses, there are a potentially

large number of follow-up tests required to determine the full set of differ-

ences across segments. A problem with performing such a large number of

tests at a specific significance level is that the overall likelihood of inap-

propriately rejecting a null hypothesis is greater than the specified signifi-

cance level (called alpha inflation). To reduce the likelihood of committing

such a type I error, we used the following procedure. First we test whether

a variable is significantly different across all n segments (e.g., we perform

an n-way ANOVA to test for significant differences in means across seg-

ments or perform an n-way cross tabulation test for significant differences

in proportions across segments). The significance for these ‘‘n-way’’ testsis set at the 10 percent level. If we find a significant difference across the

n segments, we then test individual pairings of segments for significant dif-

ferences. The significance for these latter pair-wise tests (ap) is set by using

the formula: 1 – (1 – ap)n ¼ .10. This procedure maintains the overall prob-

ability of committing a type I error to 10 percent (Hand and Taylor 1987).

RESULTS

The factor analysis on the benefit variables indicates that two factors

(Table 2) explain respondent reactions.1 Kaiser’s overall measure of sam-

pling adequacy is high (.95) indicating the factor model is appropriate; val-

ues greater than .80 are considered sufficiently high for analysis (SAS

2004). We call factor 1 own benefits (OB) because the variables loading

highly on this factor mostly accrue directly to consumers. We call factor

2 producer benefits (PB) because these generally impact the producer. Note

that at the time of the survey, most approved GM foods were ‘‘first gen-

eration’’ (primarily providing producer-valued attributes—Schneider and

Schneider 2006); second-generation foods (those providing consumer-

valued attributes) are only now beginning to be developed. Factor OB

1. Initial factor analysis led to the dropping of two benefit (‘‘increased vitamins and minerals,’’

‘‘increased antioxidant levels’’) and five risk (‘‘control of agriculture by biotechnology companies,’’

‘‘ethical issues with genetic modification,’’ ‘‘risks to species diversity,’’ ‘‘damage to topsoil,’’ ‘‘risks

to wildlife and insects’’) variables. For brevity, only the final factor analysis results are provided.


explains 50 percent of variance, while factor PB explains 9 percent of

variance.

Factor analysis of risks similarly yields two factors; again, Kaiser’s over-

all measure of sampling adequacy is high (.90). We call factor 1 health/environmental risk (HER) as it relates to risks directly impacting the con-

sumer through perceived deteriorations in food safety or are related to

potential negative environmental impacts. We call factor 2 producer risk(PR) as it describes risk born by the producer. HER explains 63 percent of

variance, while factor PR explains 11 percent.

An important component of many of the variables loading highly on

HER seems to be the uncertainty of long-term impacts. The high level

of concern surrounding unknown long-term impacts is a consistent theme

explaining consumers’ negative reactions to new food technologies. This

TABLE 2

Respondents’ Importance Ratingsa of Various Benefits and Risks Potentially Associatedwith GM Foods, with Rotated Factor Loadings

Importance Factor 1 Factor 2

Benefits

Decreased need for pesticides on crops 4.12 .29 .67

Increased food production in poorer countries 3.92 .21 .64

Lower food prices 3.84 .69 .38

Decreased need for antibiotics in meat 3.82 .17 .70

Decreased total fat/saturated fat 3.76 .68 .37

Increased disease resistance in crops 3.69 .29 .73

Increased protein in foods 3.58 .72 .36

Longer shelf life for fresh fruits and vegetables 3.53 .70 .38

Removal of allergens from foods 3.46 .69 .34

Decreased need for irrigation of crops 3.45 .26 .71

Increased flavor of fruits and vegetables 3.43 .79 .26

Increased frost resistance in crops 3.25 .37 .68

Foods modified to contain vaccines against diseases 3.09 .74 .20

Increased size of fruits and vegetables 2.79 .79 .17

Risks

Unknown long-term health effects 4.42 .83 .26

Increased risk of antibiotic-resistant bacteria 4.38 .77 .30

Increased use of pesticides 4.21 .52 .61

Unknown or unanticipated toxins produced 4.19 .85 .25

Unknown long-term environmental effects 4.18 .75 .38

Genetic contamination of the environment 4.13 .70 .46

Increased use of herbicides 4.11 .50 .64

Unknown or unanticipated allergens introduced 3.92 .79 .26

Spread of disease resistance to weeds 3.87 .28 .89

Spread of pest resistance to undesirable weeds 3.86 .28 .88

Spread of herbicide tolerance to weeds 3.85 .28 .89

aWhere 1 ¼ not at all important, 3 ¼ somewhat important, and 5 ¼ very important. Values in bold

indicate relatively high factor loading (greater than .6).


hypothesis is consistent with Slovic (1987) who indicates a major factor

impacting a consumer’s evaluation of a new technology is the degree to

which risks are ‘‘unknown’’; that is, risks that are not observable or evident,

have effects that are delayed, or are not definitively known to science

(Marks 2001). For example, concerns about long-term health impacts seem

to explain consumers’ initial opposition to pasteurization (Huffman 2003a)

and seems to be a factor in consumer acceptance of GM foods (Hoban

1997). In general, consumers trust food scientists’ abilities to determine

the short-term safety of new food technologies but understand the limita-

tions scientists face in determining long-term impacts (Levy and Derby

2000). Interestingly, the level of technical knowledge about a new food

technology does not seem to impact consumers’ concerns; it is the lack

of experience with the technology (Levy 2001).

Computation yields Cronbach’s alphas of .91, .83, .92, and .92 for OB,

PB, HER, and PR, respectively, indicating our analyses have a high degree

of reliability. Further, all item-to-total correlations equal .66, .52, .74, and

.74 for OB, PB, HER, and PR, respectively (indicating a high degree of

internal consistency). Note that all factor loadings are positive (Table 2)

indicating that each of the four factor scores are positively correlated to

the variables originally used in their construction. In turn, although each

of the four factor scores are normalized to mean zero, the direction of each

score is positively correlated to the direction of the original variables.

FIGURE 1

Hierarchical Cluster Segmentation

0

500

1000

1500

2000

2500

Link

age

Dis

tanc

e

Tree DiagramWard`s method

Squared Euclidean distances


Hence, higher (lower) factor scores indicate a higher (lower) level of impor-

tance for that risk/benefit factor.

To determine the number of segments, we begin by inspecting the

hierarchical tree from the cluster analysis of the risk/benefit factors (Fig-

ure 1); three relatively large linkage distances suggest three relatively

homogeneous segments. The pseudo-F statistic indicates that three or four

segments are appropriate (Table 3), while the pseudo-t2 indicates either

three or six possible segments. Finally, the cubic clustering criteria indicate

either a two- or three-segment solution. Hence, we conclude there are three

segments of respondents and consequently perform k-means segmentation,

where k ¼ 3. This number of segments is similar to those found in the

literature (two segments: Mora et al. 2007; three segments: Baker and

Burnham 2001; Jan, Fu, and Huang 2007; Onyango et al. 2004; Vermeulen

2004; four segments: Christoph and Roosen 2006; Ganiere, Chern, and

Hahn 2006; O’Connor et al. 2005, 2006; Verdurme and Viaene 2003).

By examining the segments in relation to their mean scores on the risk/

benefit factor scores (Figure 2), we describe the segments as:

d Risk avoiders: this segment is intermediate in size (n ¼ 677) and not

interested in the potential benefits of GM foods but are very worried

about health and environmental risks potentially associated with GM

food.

d Risk dismissers are the smallest segment (n¼ 482) and rate their OBs

and PBs higher than health and environmental risks; in fact, they are

the least worried about these potential risks. It seems these respond-

ents believe the technology can bring benefits at a low personal risk.

d Balanced but interested is the largest group (n ¼ 896) and finds both

benefits and risks important; unlike the other segments, these respond-

ents are not strongly committed to any of the above points of view.

Characterization of the above segments is similar to other studies. For

example, Verdurme and Viaene (2003) find four segments: ‘‘halfhearted,’’

TABLE 3

Goodness-of-Fit Statistics from the Cluster Analysis

Number of Clusters Pseudo-F Pseudo-t2 Cubic Clustering Criteria

Two 536.32 382 5.54

Three 555.15 239 4.2

Four 567.18 214 20.1

Five 534.98 325 212.1

Six 489.23 144 213.74


‘‘green opponents,’’ ‘‘balancers,’’ and ‘‘enthusiasts.’’ Their enthusiasts are

similar to our risk dismissers in that they both discount health and envi-

ronmental risks and have some interest in GM-related benefits. Their bal-

ancers are similar to out balanced but interested segment in that they both

place importance on the benefits and risks of GM foods. Finally, the half-

hearted and green opponent segments are similar to our risk avoiders in thatthey are both concerned about health and environmental risks and discount

any GM-related benefits.

The factor analysis (Table 4), investigating whether respondent con-

cerns for specific food technologies are correlated, reveals a one-factor

solution is appropriate (i.e., the number of eigenvalues larger than 1.0 is

one and all factor loadings exceed .71). Kaiser’s overall measure of sam-

pling adequacy is high (.87), and the factor explains 59 percent of variance.

The Cronbach’s alpha is .88 and all item-to-total correlations exceed .65.

Again, since all the factor loadings are positive (Table 4), the direction of

the factor score is positively correlated to the direction of the original vari-

ables. Hence, higher (lower) mean scores on the technology concern factor

indicate a higher (lower) level of concern.

In terms of their profiles (Table 5), we find that the three segments differ

markedly in terms of their socioeconomic characteristics (given we have

FIGURE 2

Mean Plot of OB, HER, PB, and PR, by Segment

-1.5

-1

-0.5

0

0.5

1

Risk AvoidersRisk DismissersBalanced But Interested

OB HER PB PR


three segments, the significance for all pair-wise tests is set at the 3.4 per-

cent level). Although risk avoiders and balanced but interested individuals

are similarly more likely to be female, they are both different from risk

dismissers, who are more likely to be male. This is consistent with other

studies that document males are more likely to discount GM risks (Fein,

Levy, and Teisl 2002; Grimsrud et al. 2004; Grobe et al. 1997; Hossain

et al. 2004; McCluskey et al. 2003; Mendenhall and Evenson 2002; Sub-

rahmanyan and Cheng 2000; Verdurme and Viaene 2003). Risk avoiders

are the youngest segment and balanced but interested respondents are the

oldest. Previous literature is somewhat mixed in the effect of age on GM

preferences, some find older individuals are more negative about these

foods (Grimsrud et al. 2004; Grobe et al. 1997; Hossain et al. 2004), while

others find the opposite (Bennett et al. 2003; Boccaletti and Moro 2000) or

no effect (Fein, Levy, and Teisl 2002; Kaneko and Chern 2003).

Balanced but interested individuals are less educated than risk dismissers

and risk avoiders. Although Boccaletti and Moro (2000) and Hossain et al.

(2004) indicate that more educated people have more positive views about

GM foods, our result that education is not a significant driver of GM-related

risk assessments (i.e., risk avoiders and risk dismissers have similar edu-

cation levels yet hold opposing views regarding GM-related risks) is also

seen by Verdurme and Viaene (2003). Risk avoiders are more likely than

the other two segments to have children, whereas risk dismissers and bal-

anced but interested individuals are similar. Risk avoiders and balanced but

interested individuals are both more likely than risk dismissers to have

a food allergy. Risk avoiders and risk dismissers have higher incomes than

balanced but interested individuals.

Both food production concern variables are significantly different across

segment membership. For both concern measures, balanced but interested

TABLE 4

Respondents’ Concern Levelsa for Various Food Production Technologies, with FactorLoadings

Concern Factor 1

Use of antibiotics 3.79 .81

Use of pesticides 4.15 .79

Use of artificial growth hormones 4.03 .78

Use of irradiation 3.63 .77

Use of artificial colors or flavors 3.24 .75

Use of pasteurization 2.93 .75

Use of preservatives 3.32 .72

aWhere 1 ¼ not at all concerned, 3 ¼ somewhat concerned, and 5 ¼ very concerned. Values in bold

indicate relatively high factor loading (greater than .6).


TABLE 5

Respondents’ Socioeconomic Characteristics, Concern Levels, and Food-RelatedBehaviors, by Segment

Variables

Risk

Avoiders

Risk

Dismissers

Balanced But

Interested

Test Results across

All Segments

Socioeconomic

Gender (percent male) 42.9 61.5 43.8 v2(2) ¼ 47.5; p , .00

Age (in years) 48.8 52.3 54.4 F(2, 1984) ¼ 25.0; p , .00

Education (in years) 15.1 14.9 13.9 F(2, 2013) ¼ 44.6; p , .00

Percent with children under

18 present in household

37.4 30.5 28.6 v2(2) ¼ 14.2; p , .00

Percent with food allergies 6.2 2.5 5.6 v2(2) ¼ 8.4; p ¼ .02

Income (in US$) 72,300 66,000 54,300 F(2, 1828) ¼ 21.7; p , .00

Food concerns

Overall concerna 3.9 3.2 4.1 F(2, 2050) ¼ 126.7; p , .00

Concern with food technologiesb 20.03 20.77 0.40 F(2, 1869) ¼ 247.8; p , .00

Food-related behaviors

Frequency of buying organic

foodsc2.5 2.0 2.4 F(2, 2048) ¼ 34.8; p , .00

Percent grow own vegetables 40.0 33.9 32.8 v2(2) ¼ 9.4; p , .00

Percent shop at farmers’ markets 35.7 22.1 29.9 v2(2) ¼ 24.4; p , .00

Percent vegetarian 4.8 1.7 2.4 v2(2) ¼ 11.2; p , .00

Frequency of reading nutrition

labelsd3.7 3.3 3.8 F(2, 2050) ¼ 34.3; p , .00

Results of Pair-wise Tests

Risk Avoiders vs.

Risk Dismissers

Risk Avoiders vs.

Balanced

But Interested

Risk Dismissers vs.

Balanced

But Interested

Socioeconomic

Gender (percent male) v2 ¼ 38.2; p , .00 v2 ¼ 0.14; p ¼ .17 v2(1) ¼ 38.3; p , .00

Age (in years) t ¼ 3.8; p , .00 t ¼ 7.2; p , .00 t ¼ 2.3; p ¼ .02

Education (in years) t ¼ 1.3; p ¼ .19 t ¼ 9.0; p , .00 t ¼ 6.5; p , .00

Percent with children under

18 present in household

v2(1) ¼ 5.9; p ¼ .02 v2(1) ¼ 13.5; p , .00 v2(1) ¼ 0.51; p ¼ .48

Percent with food allergies v2(1) ¼ 8.1; p , .00 v2(1) ¼ 0.21; p ¼ .65 v2(1) ¼ 6.6; p ¼ .01

Income (measured in US$) t ¼ 1.9; p ¼ .06 t ¼ 6.5; p , .00 t ¼ 4.0; p , .00

Food concerns

Overall concern t ¼ 11.3; p , .00 t ¼ 4.2; p , .00 t ¼ 15.6; p , .00

Concern with food technologies t ¼ 12.8; p , .00 t ¼ 9.6; p , .00 t ¼ 22.1; p , .00

Food-related behaviors

Frequency of buying organic

foods

t ¼ 8.0; p , .00 t ¼ 1.9; p ¼ .05 t ¼ 6.8; p , .00

Percent grow own vegetables v2(1) ¼ 4.4; p ¼ .04 v2(1) ¼ 8.7; p , .00 v2(1) ¼ 0.17; p ¼ .67

Percent shop at farmers’ markets v2(1) ¼ 24.3; p , .00 v2(1) ¼ 5.8; p ¼ .02 v2(1) ¼ 9.5; p , .00

Percent vegetarian v2(1) ¼ 7.8; p , .00 v2(1) ¼ 6.6; p ¼ .01 v2(1) ¼ 0.7; p ¼ .40

Frequency of reading nutrition

labels

t ¼ 6.6; p , .00 t ¼ 0.9; p ¼ .32 t ¼ 8.0; p , .00

aResponse to How concerned are you about the way foods are produced and processed in the US?

Response is 1 for ‘‘not at all concerned’’ to 5 ‘‘very concerned.’’bOutput of factor analysis of respondent concerns with seven food technologies: higher concern is

reflected by a higher factor score.cResponse to How often do you purchase organic foods? Response is 1 for ‘‘never’’ to 5 ‘‘always.’’dResponse to How often do you read the nutrition labels on the foods you purchase? Response is 1 for

‘‘never’’ to 5 ‘‘always.’’


respondents are the most concerned, while risk dismissers are the least. Judg-

ing from how concerned they are about GM-related health and environmen-

tal risks, it is surprising that risk avoiders are not themost worried of the three

respondent segments with respect to food technologies. One possible expla-

nation is that risk avoiders are less concerned about these other food-related

risks because they have already altered their food shopping behaviors to

reduce their exposure to these risks. For example, risk avoiders are more

likely than risk dismissers to shop at farmer’s markets and health food stores,

and to purchase organic foods. Risk avoiders are also more likely than bal-

anced but interested individuals to grow their own vegetables, and shop at

farmer’s markets and health food stores, although they are similar in their

purchases of organic foods. While risk dismissers are similar to balanced

but interested individuals in growing their own vegetables, they are less

likely to shop at farmer’s markets and health food stores, and buy organic

foods. Thus, risk avoiders, although having relatively high interest in the

risks of GM foods, may have lower levels of concern regarding other pro-

duction risks (e.g., pesticide residues, irradiation) because they feel they

already take actions to avoid foods produced with these technologies.

We now turn our attention to respondent segments’ experiences with,

and attitudes toward, the structure of labeling polices for GM foods. Most

respondents in each segment have heard about GM foods; however,

a majority of each segment has not seen a GM-free food label (Table 6).

Risk avoiders are the most familiar with GM foods possibly because they

are more sensitized to the issue and are more familiar with GM-free labels,

possibly because of where they shop. Similar to Verdurme and Viaene’s

(2003) balanced segment, our balanced but interested segment is relatively

unaware of GM foods.

The survey allowed two points where respondents could state they

thought GM labeling was unnecessary. We first asked respondents if they

would like to see labels on food indicating whether or not the product con-

tained GM ingredients. We then asked respondents a follow-up question

where we asked for a more detailed response over how food products

should be labeled. Individuals who said no to the first question skipped

the second question. In turn, there are two possible sets of results for

the second question: one conditioned on those who said yes to the first

question (i.e., frequencies reflect responses from only those individuals

who answered the second question) and the other based upon the entire

sample of respondents. Results in Table 6 correspond to the first type

of result, whereas the latter estimates are provided in the text.

When asked whether they would like to see GM labels on foods (in the

United States, GM labels are voluntary and appear as GM-free labels only


on foods not containing GM ingredients), most respondents answered posi-

tively; although risk dismissers are significantly less likely to want GM

labeling relative to the other two segments (91.3 percent of risk avoiders,

65.1 percent of risk dismissers, 92.7 percent of balanced but interested

respondents), which is in accordance with their general lack of concern

about food technologies (Nayga, Fisher, and Onyango 2006). Again, sim-

ilar to Verdurme and Viaene’s (2003) balanced segment, our balanced but

interested segment has the highest demand for GM-related information.

Even after being provided additional information about the potential

structure of GM labeling, few of the respondents who initially stated they

were in favor of labeling switch to thinking that labeling is unnecessary

(8.7 percent of risk avoiders, 34.9 percent of risk dismissers, 7.2 percent

of balanced but interested respondents). Risk dismissers are significantly

more likely to think labeling is unnecessary relative to risk avoiders.

Although few individuals are in favor of the current U.S. policy of volun-

tary testing and labeling (6.3 percent of risk avoiders, 8.9 percent of risk

TABLE 6

Respondent’s Experience with, and Desires for, GM Food Labeling, by Segment

Risk

Avoiders

Risk

Dismissers

Balanced But

Interested

Test Results across

All Segments

Percent hearing about GM foods 84.4 69.4 62.7 v2(2) ¼ 85.0; p , .00

Percent seeing a GM-free label 17.5 12.1 9.4 v2(4) ¼ 27.3; p , .00

Percent wanting GM labels 91.8 66.6 93.6 v2(2) ¼ 219.0; p , .00

Percent thinking testing

and labeling are unnecessary

0.5 2.2 0.9 v2(2) ¼ 5.4; p ¼ .07

Percent wanting voluntary testing

with only non-GM foods labeled

6.9 13.3 5.4 v2(2) ¼ 18.6; p , .00

Percent wanting mandatory

testing and labeling

92.5 84.5 93.7 v2(2) ¼ 19.3; p , .00

with all foods labeled 31.5 32.8 48.8 v2(2) ¼ 47.3; p , .00

with only GM foods labeled 55.3 48.0 42.0 v2(2) ¼ 23.0; p , .00

with only non-GM foods labeled 5.7 3.7 2.9 v2(2) ¼ 6.9; p ¼ .03


Risk Avoiders

vs. Risk

Dismissers

Risk Avoiders

vs. Balanced

But Interested

Risk Dismissers

vs. Balanced

But Interested

Percent hearing about GM foods v2(1) ¼ 34.9; p , .00 v2(1) ¼ 84.9; p , .00 v2(1) ¼ 5.6; p ¼ .02

Percent seeing a GM-free label v2(1) ¼ 7.7; p ¼ .02 v2(1) ¼ 28.9; p , .00 v2(1) ¼ 5.6; p ¼ .06

Percent wanting GM labels v2(1) ¼ 116.3; p , .00 v2(1) ¼ 2.0; p ¼ .15 v2(1) ¼ 170.5; p , .00

Percent thinking testing and

labeling are unnecessary

v2(1) ¼ 5.0; p ¼ .02 v2(1) ¼ 0.71; p ¼ .40 v2(1) ¼ 2.7; p ¼ .10

Percent wanting voluntary testing

with only non-GM foods labeled

v2(1) ¼ 9.1; p , .00 v2(1) ¼ 1.4; p ¼ .24 v2(1) ¼ 18.0; p , .00

Percent wanting mandatory testing

and labeling

v2(1) ¼ 9.6; p , .00 v2(1) ¼ 1.3; p ¼ .25 v2(1) ¼ 18.6; p , .00

with all foods labeled v2(1) ¼ 0.1; p ¼ .70 v2(1) ¼ 40.1; p , .00 v2(1) ¼ 20.5; p , .00

with only GM foods labeled v2(1) ¼ 4.0; p ¼ .05 v2(1) ¼ 23.0; p , .00 v2(1) ¼ 2.8; p ¼ .09

with only non-GM foods labeled v2(1) ¼ 1.6; p ¼ .21 v2(1) ¼ 6.7; p ¼ .01 v2(1) ¼ 0.4; p ¼ .51


dismissers, 5.2 percent of balanced but interested respondents), risk dis-

missers are more likely to prefer voluntary labeling relative to the other

segments. Balanced but interested individuals and risk avoiders have

the strongest overall preference for mandatory labeling (85.0 percent of risk

avoiders, 56.3 percent of risk dismissers, 87.7 percent of balanced but inter-

ested respondents).

When we investigate this issue more deeply, we find that segments

exhibit differences in preferences for the structure of mandatory GM label-

ing and testing programs. Balanced but interested want mandatory testing

and labeling of all foods (28.9 percent of risk avoiders, 21.8 percent of riskdismissers, 45.7 percent of balanced but interested respondents), while the

other two segments prefer that only GM foods carry a label (50.8 percent of

risk avoiders, 32.0 percent of risk dismissers, 39.3 percent of balanced but

interested respondents). That there is a difference in opinion here between

the risk avoiders and the balanced but interested individuals may be

expected by the difference between these segments in their level of

food-related concern. However, balanced but interested individuals are also

significantly less aware of GM foods than risk avoiders and Huffman et al.

(2007) indicate that uninformed individuals are more receptive to new

information about GM foods. Hence, the difference in awareness of

GM foods may explain the difference in preference for labeling policy.

It is interesting that despite pronounced concerns risk avoiders are com-

paratively more inclined toward the option that only foods containing GM

ingredients display a label. This may be because they are already more

familiar with GM labels and more educated. Another possibility is that

the risk avoiders understand that if only GM foods need to carry a label,

then the GM labels may act as a warning label. Thus, their policy preference

may be a way to punish producers of GM products.

The bottom line, though, is that most respondents (about 80 percent of

risk avoiders, 54 percent of risk dismissers, and 85 percent of balanced but

interested respondents) want mandatory labeling and testing with either all

foods, or only GM foods labeled. This is in stark contrast to the prevailing

policy in the United States where GM labels are voluntary and appear only

on foods not containing GM ingredients.

Next we consider preference for certifying agency. Although govern-

mental agencies are the preferred choice to a majority of respondents (simi-

lar to Huffman et al. 2003b), there are some differences among respondent

segments (Table 7). Specifically, risk dismissers are the most likely to pre-

fer government agencies, and risk avoiders are the least likely. Of those

choosing a government agency, most individuals want either the USDA

or the FDA; however, across segments, balanced but interested individuals


are least likely to prefer the USDA and the most likely to prefer the FDA to

run a GM labeling program. Respondent familiarity with these two agen-

cies and their positive evaluation of their traditional handling of food label-

ing may explain respondents’ strong desire for these agencies. Risk

avoiders are more likely to prefer environmental organizations or scientific

organizations relative to risk dismissers and balanced but interested indi-

viduals. Although two of the scientific organizations listed (the Identity

Preservation Program and Genetic ID) are currently the largest independent

certifiers of GM-free foods in the United States, it is not surprising few

people chose scientific organizations as few have been exposed to GM-free

labels. It may be initially surprising that relatively few respondents want

environmental groups to administer GM labeling because at least two of

these groups (the Organic Consumers Association and Greenpeace) have

strongly supported this type of labeling. However, respondents may make

a clear distinction between an organization’s ability to promote advocacy

and their ability to administer a labeling program.

Again, respondent preferences for who should administer a labeling pro-

gram for GM foods seem directly at odds with the current reality. Most

respondents desire a federal agency to administer this program, apparently

TABLE 7

Respondent Preferences for Labeling Organizations,a by Segment

Risk

Avoiders

Risk

Dismissers

Balanced But

Interested

Test Results across

All Segments

U.S. government agencies 68.2 83.0 77.4 v2(2) ¼ 25.3; p , .00

Department of Agriculture 47.5 52.4 41.3 v2(2) ¼ 8.8; p ¼ .01

FDA 48.8 44.4 53.9 v2(2) ¼ 6.3; p ¼ .04

EPA 3.7 3.1 4.7 v2(2) ¼ 1.3; p ¼ .53

Environmental organizations 11.0 4.8 3.9 v2(2) ¼ 26.8; p , .00

Scientific organizations 13.1 5.2 8.4 v2(2) ¼ 15.3; p , .00

Health organizations 7.7 7.0 10.2 v2(2) ¼ 3.7; p ¼ .15


Risk Avoiders

vs. Risk

Dismissers

Risk Avoiders

vs. Balanced

But Interested

Risk Dismissers

vs. Balanced

But Interested

U.S. Government agenciesb v2(1) ¼ 20.4; p , .00 v2(1) ¼ 13.6; p , .00 v2(1) ¼ 3.7; p ¼ .05

Department of Agriculture v2(1) ¼ 1.4; p ¼ .24 v2(1) ¼ 3.4; p ¼ .06 v2(1) ¼ 8.0; p ¼ .01

FDA v2(1) ¼ 1.1; p ¼ .30 v2(1) ¼ 2.3; p ¼ .13 v2(1) ¼ 5.7; p ¼ .02

Environmental organizations v2(1) ¼ 8.5; p , .00 v2(1) ¼ 23.7; p , .00 v2(1) ¼ 0.4; p ¼ .54

Scientific organizations v2(1) ¼ 12.3; p , .00 v2(1) ¼ 7.3; p , .00 v2(1) ¼ 3.0; p ¼ .08

Health organizations v2(1) ¼ 0.13; p ¼ .71 v2(1) ¼ 2.4; p ¼ .12 v2(1) ¼ 2.4; p ¼ .11

aScientific organizations include Identity Preservation Program, Cert Id, Union of Concerned Scientists,

and Consumer’s Union; environmental organizations include Greenpeace, Natural Resources Defense

Council, and Organic Consumers Association; health organizations include NIH, AMA, AHA, and

ACS; tests across segments not possible due to small cell sizes.bPair-wise test not indicated for EPA.


due to the relatively high level of trust people place in these agencies (Roe

and Teisl 2007)—yet, these agencies have been reticent to take on this task.

At the same time, the groups most active in promoting GM labeling, or

those currently involved with GM labeling, garner little public support.

Given the ability of a labeling program to communicate to consumers

depends, at least in part, on the degree to which consumers trust the orga-

nization in charge of the program (Blaine and Powell 2001; Slovic 1993),

the government’s reticence to administer such a program may be inadver-

tently hindering consumer acceptance of GM foods.

When examining respondents’ preferences regarding the importance of

various pieces of information that could be displayed on a GM label

TABLE 8

Preferences for Information Pieces to be on a GM Label; Importance, by Segmenta

Risk

Avoiders

Risk

Dismissers

Balanced But

Interested

Test Results across

All Segments

Which ingredients in a

product are GM

4.2 3.6 4.4 F(2, 1639) ¼ 75.3; p , .00

Why the ingredients are

genetically modified

3.2 2.9 3.8 F(2, 1634) ¼ 68.5; p , .00

How the ingredients are

genetically modified

3.3 2.8 3.8 F(2, 1630) ¼ 72.2; p , .00

Who is certifying the

information

4.2 3.6 4.4 F(2, 1632) ¼ 74.2; p , .00

Warnings associated with

modification

4.7 4.1 4.8 F(2, 1638) ¼ 97.8; p , .00

Benefits associated with

modification

3.6 3.5 4.2 F(2, 1627) ¼ 69.5; p , .00

Web site or phone number

where one could obtain

more information

4.2 3.7 4.5 F(2, 1634) ¼ 78.9; p , .00


Risk Avoiders

vs.

Risk Dismissers

Risk Avoiders

vs.

Balanced

But Interested

Risk Dismissers

vs.

Balanced

But Interested

Which ingredients in a product are GM t ¼ 6.9; p , .00 t ¼ 5.5; p , .00 t ¼ 13.0; p , .00

Why the ingredients are genetically

modified

t ¼ 3.8; p , .00 t ¼ 8.1; p , .00 t ¼ 11.0; p , .00

How the ingredients are genetically

modified

t ¼ 4.8; p , .00 t ¼ 7.7; p , .00 t ¼ 11.6; p , .00

Who is certifying the information t ¼ 7.5; p , .00 t ¼ 4.6; p , .00 t ¼ 12.7; p , .00

Warnings associated with modification t ¼ 10.6; p , .00 t ¼ 2.2; p ¼ .03 t ¼ 13.2; p , .00

Benefits associated with modification t ¼ 2.0; p ¼ .06 t ¼ 9.5; p , .00 t ¼ 10.6; p , .00

Web site or phone number where

one could obtain more information

t ¼ 7.0; p , .00 t ¼ 5.8; p , .00 t ¼ 13.1; p , .00

aResponses to Rate how important each piece of information is to you?Response is 1 for ‘‘not important

at all,’’ 3 ‘‘somewhat important,’’ and 5 ‘‘very important.’’


(Table 8), risk dismissers generally assign the least importance to all these

pieces of information and balanced but interested consistently ascribe

higher importance to each piece of information. That the risk avoiders

are not the more desirous of information is initially surprising. However,

Costa-Font and Mossialos (2005) provide some evidence that the demand

for information about GM food is a self-protective action. Risk avoiders

already engage in several self-protection actions (e.g., buying organic food,

shopping at farmers’ markets); it may be that we are observing a substitution

effect between information demand and other self-protective behaviors.

Altogether we can conclude that out of the three segments, balanced but

interested is the strictest regarding labeling. These respondents require

mandatory labeling of all foods and would like the most amount of infor-

mation. That information is significantly more important for them can be

explained by the fact that these respondents have not made up their mind

regarding GM foods, unlike the other two respondent segments that hold

strong views. Balanced but interested individuals are strongly worried

about risks, but they also find possible benefits very important.

CONCLUSIONS

We uncover three segments of respondents with different attitudes to the

risks and benefits of GM foods. One segment is very worried about poten-

tial health risks and does not consider potential benefits as important, while

another is almost a mirror image, relatively positive about benefits while

rating risks as much less important. The last and largest segment finds both

benefits and risks as important and, unlike the other two segments, does not

seem to hold strong opinions for or against GM foods.

The analysis supports the contention that attitudes toward new technol-

ogies are likely to be nuanced (Fein, Levy, and Teisl 2002; Fischhoff and

Fischhoff 2001) and we find these nuanced attitudes translate into differ-

ences in the desired form of information policy. For example, we find the

respondent segments are quite different in their preferences for the scope

and strictness of GM testing and labeling policy, who should be in charge of

such programs and the types of information to be placed on a GM label.

These differences in preferences imply potential conflict across consumers

for any one specific GM labeling policy.

This heterogeneity also has implications for research. The presence of

different respondent segments indicates that survey efforts that draw sam-

ples inappropriately, or cause respondents from the different segments to

differ in their response rates, could lead to incorrect interpretation of aggre-

gate or average results. This could help explain the different viewpoints


noted in the literature. For example, the segment of the population least

concerned with GM foods also exhibits very different preferences for label-

ing policy than the other two groups. Yet, according to standard research

theory (Brehm 1993; Groves, Singer, and Corning 2000; VanBeselaere

2003), this group is the least likely to respond to a GM foods survey.2

As a result, aggregate survey results may indicate a strong desire for a strict

labeling policy, whereas reactions in the marketplace or in the political

arena may be more muted.

In addition, the fact that the largest respondent segment is the most desir-

ous of, and open-minded about, both positive and negative GM information

may help explain the mixed results found in the literature. That is, it is likely

that information provided in a survey instrument, or the framing of a ques-

tion, could have a big influence on survey responses, especially with these

open-minded respondents. Analogously, this would imply that the dynam-

ics in a GM food market are still relatively fluid as a large proportion of

consumers may be still open to new information.

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