An open mind wants more: opinion strength and the desire for genetically modified food labeling policy.
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 surveys 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 indicates 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, genetically modified (Teisl and Caswell 2002). This approach may work well for consumers who have lexicographic preferences where the process of GM production must first be resolved before the consumer considers 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 provided 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 surprising 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 Balasubramanian 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 different preferences for GM labeling policy.
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 attitudes 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 labeling 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 labeling 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), knowing the "average" preference for labeling is less important than understanding how preferences differ across consumer segments and the relative sizes of these segments.
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 information about 250 million U.S. residents (more than 95 percent of U.S. households). The address information is continually updated using the U.S. Postal Service's National Change of Address system, allowing InfoUSA's address list to maintain a 93 percent accuracy rate.
The survey was administered with multiple mailings and with an incentive 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 provided 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, respectively. The overall response rate of 39 percent is marginal, suggesting that individual survey results may not be a valid representation Of the knowledge, practices, and attitudes of the U.S. adult population. However, our purpose here is not to extrapolate our survey results to the aggregate population 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 differences 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 categories. 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, reactions 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 and Metcalfe 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 associated 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 investigate 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 potential 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 = not at all important, 3 = somewhat important, and 5 = 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 consumer health risks (e.g., unknown toxins produced), others concern producer 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 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 analysis 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 constructs 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 respondents into homogeneous risk/benefit segments (i.e., we use the clustering analysis to simplify across observations). These two techniques are complementary (Gorman and Primavera 1983) and their combined use is familiar 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 followed 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 estimate 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-[t.sup.2], 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 segments. 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-[t.sup.2] statistic, one examines how the value of the pseudo-[t.sup.2] statistic changes as you decrease the number of clusters; the appropriate number of segments are identified when the pseudo-[t.sup.2] statistic is markedly smaller than the [t.sup.2] 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 variables. 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 respondent characteristics (gender, age, years of education, presence of children under eighteen present in household, respondent's food allergies, household 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 concerned 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 technologies (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 segment'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 engineered or genetically modified?" (responses are "yes" or "no") and "Have you ever seen a label indicating that a product is 'GMO-free' or 'does not contain genetically modified ingredients'?" (possible responses are "yes," "no," or "don't know"). We measure segments' attitudes toward the following components of GM labeling policy: (1) whether or not GM foods should be labeled, (2) whether the labeling policy should be mandatory 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 program, 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 modified foods.
A mandatory approach would require all food producers to test whether their product contains genetically modified ingredients. Once tested, the program could require either:
* all foods to display whether or not they contain genetically modified ingredients
* only foods containing genetically modified ingredients to display a label
* only foods not containing genetically modified ingredients to display a label
A voluntary approach would allow food producers to voluntarily test whether their product contains genetically modified ingredients. Once tested, the program would allow:
* 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 ingredients 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 prefer 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 Preservation 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 consumer 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 Concerned 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 Liken 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 differences across segments. A problem with performing such a large number of tests at a specific significance level is that the overall likelihood of inappropriately rejecting a null hypothesis is greater than the specified significance 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 segments or perform an n-way cross tabulation test for significant differences in proportions across segments). The significance for these "n-way" tests is 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 differences. The significance for these latter pair-wise tests ([[alpha].sup.p]) is set by using the formula: 1 - [(1 - [[alpha].sup.p]).sup.n] =. 10. This procedure maintains the overall probability of committing a type I error to 10 percent (Hand and Taylor 1987).
The factor analysis on the benefit variables indicates that two factors (Table 2) explain respondent reactions. (1) Kaiser's overall measure of sampling adequacy is high (.95) indicating the factor model is appropriate; values 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 generation" (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 explains 50 percent of variance, while factor PB explains 9 percent of variance.
Factor analysis of risks similarly yields two factors; again, Kaiser's overall measure of sampling adequacy is high (.90). We call factor 1 health/ environmental risk (HER) as it relates to risks directly impacting the consumer 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 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 limitations 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. Hence, higher (lower) factor scores indicate a higher (lower) level of importance for that risk/benefit factor.
[FIGURE 1 OMITTED]
To determine the number of segments, we begin by inspecting the hierarchical tree from the cluster analysis of the risk/benefit factors (Figure 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-[t.sup.2] 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 = 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:
* Risk avoiders: this segment is intermediate in size (n = 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.
* Risk dismissers are the smallest segment (n = 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 respondents believe the technology can bring benefits at a low personal risk.
* Balanced but interested is the largest group (n = 896) and finds both benefits and risks important; unlike the other segments, these respondents 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," "green opponents," "balancers," and "enthusiasts." Their enthusiasts are similar to our risk dismissers in that they both discount health and environmental risks and have some interest in GM-related benefits. Their balancers are similar to out balanced but interested segment in that they both place importance on the benefits and risks of GM foods. Finally, the halfhearted and green opponent segments are similar to our risk avoiders in that they are both concerned about health and environmental risks and discount any GM-related benefits.
[FIGURE 2 OMITTED]
The factor analysis (Table 4), investigating whether respondent concerns 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 sampling 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 variables. 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 three segments, the significance for all pair-wise tests is set at the 3.4 percent 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; Subrahmanyan 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 education 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 balanced 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 respondents are the most concerned, while risk dismissers are the least. Judging from how concerned they are about GM-related health and environmental risks, it is surprising that risk avoiders are not the most worried of the three respondent segments with respect to food technologies. One possible explanation 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 balanced 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 production 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 contained 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 positively; 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, similar 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 voluntary testing and labeling (6.3 percent of risk avoiders, 8.9 percent of risk dismissers, 5.2 percent of balanced but interested respondents), risk dismissers 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 interested respondents).
When we investigate this issue more deeply, we find that segments exhibit differences in preferences for the structure of mandatory GM labeling and testing programs. Balanced but interested want mandatory testing and labeling of all foods (28.9 percent of risk avoiders, 21.8 percent of risk dismissers, 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 comparatively 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 governmental agencies are the preferred choice to a majority of respondents (similar to Huffman et al. 2003b), there are some differences among respondent segments (Table 7). Specifically, risk dismissers are the most likely to prefer 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 agencies and their positive evaluation of their traditional handling of food labeling 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 individuals. 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 program for GM foods seem directly at odds with the current reality. Most respondents desire a federal agency to administer this program, apparently 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 organization in charge of the program (Blaine and Powell 2001; Slovic 1993), the government's reticence to administer such a program may be inadvertently 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), 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 information. 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.
We uncover three segments of respondents with different attitudes to the risks and benefits of GM foods. One segment is very worried about potential 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 technologies are likely to be nuanced (Fein, Levy, and Teisl 2002; Fischhoff and Fischhoff 2001) and we find these nuanced attitudes translate into differences 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 samples inappropriately, or cause respondents from the different segments to differ in their response rates, could lead to incorrect interpretation of aggregate 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 labeling policy than the other two groups. Yet, according to standard research theory (Brehm 1993; Groves, Singer, and Coming 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 desirous 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 question, could have a big influence on survey responses, especially with these open-minded respondents. Analogously, this would imply that the dynamics in a GM food market are still relatively fluid as a large proportion of consumers may be still open to new information.
Arvanitoyannis, Ioannis S. and Athanasios Krystallis. 2005. Consumers' Beliefs, Attitudes and Intentions towards Genetically Modified Foods, Based on the 'Perceived Safety vs. Benefits' Perspective. International Journal of Food Science & Technology, 40 (4): 343-360.
Baker, Gregory A. and Thomas A. Burnham. 2001. Consumer Response to Genetically Modified Foods: Market Segment Analysis and Implications for Producers and Policy Makers. Journal of Agricultural and Resource Economics, 26: 387-403.
Banati, Diana and Zoltan Lakner. 2006. Knowledge and Acceptance of Genetically Modified Foodstuffs in Hungary Journal of Food and Nutrition Research, 45 (2): 62-68.
Bennett, Brian and Gerald D'Souza. 2003. Genetically Modified Fish and Seafood: Consumer Attitudes, Marketing Strategies and Policy Implications. Presented at the 7th Annual International Consortium on Agricultural Biotechnology Research Conference, Ravello, Italy, June 29 to July 3.
Blaine, Katija and Douglas Powell. 2001. Communication of Food-Related Risks. AgBioForum, 4 (3-4): 179-185.
Boccaletti, Stefano and Daniele Moro. 2000. Consumer Willingness-to-Pay for GM Food Products in Italy. AgBioForum, 3 (4): 259-267.
Brehm, John. 1993. The Phantom Respondents. Opinions Surveys and Political Representation. Ann Arbor: University of Michigan Press.
Brown, J. Lynne and Wei Qin. 2005. Testing Public Policy Concepts to Inform Consumers about Genetically Engineered Foods. Choices, 20 (4): 233-237.
Caswell, Julie A. 2000. Labeling Policy for GMOs: To Each His Own? AgBioForum, 3 (1): 53-57.
Chen, Mei-Fang and Hslao-Lan Li. 2007. The Consumer's Attitude toward Genetically Modified Foods in Taiwan. Food Quality and Preference, 18 (4): 662-674.
Christoph, Inken B. and Jutta Roosen. 2006. Acceptance and Attitude towards Genetically Modified Products in Germany. Proceedings of the 10th ICABR International Conference on Public Goods and Public Policy for Agricultural Biotechnology, Ravello, Italy.
Cormick, Craig. 2005. Lies, Deep Fries, and Statistics! The Search for the Truth between Public Attitudes and Public Behaviour towards Genetically Modified Foods. Choices, 20 (4): 227-231.
Costa-Font, Joan and Elias Mossialos. 2005. Is Dread of Genetically Modified Food Associated with the Consumers' Demand for Information? Applied Economies Letters, 12 (14): 859-863.
Curtis, Kynda R. and Klaus Moeltner. 2006. Genetically Modified Food Market Participation and Consumer Risk Perceptions: A Cross-Country Comparison. Canadian Journal of Agricultural Economics-Revue Canadienne D Agroeconomie, 54 (2): 289-310.
Fein, Sara, Alan S. Levy, and Mario F. Teisl. 2002. American Attitudes toward Three Alternative Food Technologies: Genetic Modification, Irradiation and Organic. American Public Health Association, Philadelphia, November 10-14.
Fischhoff, Baruch and Ilya Fischhoff. 2001. Public Opinions about Biotechnology. AgBioForum, 4 (3-4): 155-162.
Ganiere, Pierre, Wen S. Chem, and David Hahn. 2004. Who are Proponents and Opponents of Genetically Modified Foods in the United States? Working Paper: AEDE-WP-0037-04. Department of Agricultural, Environmental and Development Economics, The Ohio State University.
Ganiere, Pierre, Wen S. Chern, and David Hahn. 2006. A Continuum of Consumer Attitudes toward Genetically Modified Foods in the United States. Journal of Agricultural and Resource Economics, 31 (1): 129-149.
Gorman, Bernard S. and Louis H. Primavera. 1983. The Complementary Use of Cluster and Factor Analysis Methods. Journal of Experimental Education, 51 (4): 165-68.
Grimsrud, Kristine M., Jill J. McCluskey, Maria L. Loureiro, and Thomas I. Wahl. 2004. Consumer Attitudes to Genetically Modified Food in Norway. Journal of Agricultural Economics, 55 (1): 75-90.
Grobe, Deana, Robin Douthitt, and Lydia Zepeda. 1997. Consumer Risk Perception Profiles for the Food-Related Biotechnology, rBGH. In Strategy and Policy in the Food System, edited by Caswell and Cotterill (157-170). Storr, CT: Food Marketing Policy Center.
Groves, Frank D., Diego E. Zavala, and Pelayo Correa. 1987. Variation in International Cancer Mortality: Factor and Cluster Analysis. International Journal of Epidemiology, 16 (4): 501-508.
Groves, Robert M., Eleanor Singer, and Amy Coming. 2000. Leverage-Salience Theory of Survey Participation: Description and an Illustration. Public Opinion Quarterly, 64: 288-308.
Hallman, William K. and W. Carl Hebden. 2005. American Opinions of GM Food: Awareness, Knowledge, and Implications for Education. Choices, 20 (4): 239-242.
Hallman, William K. and Jennifer Metcalfe. 1994. Public Perceptions of Agricultural Biotechnology: A Survey of New Jersey Residents. Food Policy Institute, Cook College, Rutgers, The State University of New Jersey, Rutgers, New Jersey. Available at: www.foodpolicyinstitute.org (accessed July 8, 2008).
Hand, David J. and C.C. Taylor. 1987. Multivariate Analysis of Variance and Repeated Measures: A Practical Approach .for Behavioral Scientists. London: Chapman and Hall.
Hanley, Anthony J.G., James B. Meigs, Ken Williams, Steven M. Haffner, and Ralph B. D'Agostino, Sr. 2005. Re: "(Mis)use of Factor Analysis in the Study of Insulin Resistance Syndrome." American Journal of Epidemiology, 161 (12): 1182-1184.
Hoban, Thomas J. 1999. Public Perceptions and Understanding of Agricultural Biotechnology. Economic Perspectives 4 (4). Available at http://usinfo.state.gov/joumals/ites/1099/ijee/bio-hoban.htm, accessed July 8, 2008.
Hoban, Thomas J. 1997. Consumer Acceptance of Biotechnology: An International Perspective. Nature Biotechnology, 15 (March): 232-234.
Hossain, Ferdaus and Benjamin Onyango. 2004. Product Attributes and Consumer Acceptance of Nutritionally Enhanced Genetically Modified Foods. International Journal of Consumer Studies, 28 (3): 255-267.
Hossain, Ferdaus, Benjamin Onyango, Adesoji Adelaja, Brian Schilling, and William Hallman. 2004. Consumer Acceptance of Food Biotechnology: Willingness to Buy Genetically Modified Food Products, Journal of International Food & Agribusiness Marketing, 15 (1/2): 53-76.
Huffman, Wallace E., Matthew Rousu, Jason F. Shogren, and Abebayehu Tengene. 2003a. Consumers' Resistance to Genetically Modified Foods in High Income Countries: The Role of Information in an Uncertain Environment. D. Gale Johnson Lecture--Topic II. University of Chicago, April 25.
Huffman, Wallace E., Matthew Rousu, Jason F. Shogren, and Abebayehu Tengene. 2003b. Who Do Consumers Trust for Information: The Case of Genetically Modified Foods? Iowa State University working paper.
Huffman, Wallace E., Jason F. Shogren, Matthew Rousu, and Abebayehu Tengene. 2003c. Consumer Willingness to Pay for Genetically Modified Food Labels in a Market with Diverse Information: Evidence from Experimental Auctions. Journal of Agricultural and Resource Economics, 28 (3): 481-502.
Huffman, Wallace E., Matthew Rousu, Jason F. Shogren, and Abebayehu Tengene. 2002. Consumer Resistance to GM-Foods: The Role of Information in an Uncertain Environment. Iowa Agricultural and Home Economics Experiment Station No. 6590.
Huffman, Wallace E., Matthew Rousu, Jason F. Shogren, and Abebayehu Tegene. 2007. The Effects of Prior Beliefs and Learning on Consumers' Acceptance of Genetically Modified Foods. Journal of Economic Behavior and Organization, 63 (1): 193-206.
IFIC (International Food Information Council). 2007. Food Biotechnology: A Study of U.S. Consumer Attitudinal Trends, 2007 Report, International Food Information Council Foundation, Washington, DC. Available at http://www.ific.org/research/biotechres.cfm, accessed July 8, 2008.
James, Jennifer S. 2004. Consumer Knowledge and Acceptance of Agricultural Biotechnology Vary. California Agriculture, 58 (2): 99-105.
Jan, Man-set, Tsu-tan Fu, and Chung L. Huang. 2007. A Conjoint/Logit Analysis of Consumers' Responses to Genetically Modified Tofu in Taiwan. Journal of Agricultural Economics, 58 (2): 330-347.
Kaneko, Naoya and Wen S. Chem. 2003. Consumer Acceptance of Genetically Modified Foods: A Telephone Survey. Consumer Interests Annual, 49: 1-13.
Kaye-Blake, William, Kathryn Bicknell, and Caroline Saunders. 2005. Process versus Product: Which Determines Consumer Demand for Genetically Modified Apples? Australian Journal of Agricultural and Resource Economics, 49 (4): 413-427.
Kontoleon, Andreas. 2003. Accounting for Consumer Heterogeneity in Preferences over GM Foods: An Application of the Latent Market Segmentation Mode. Presented at the BIOECON Workshop on Economics and Biodiversity Conservation, Venice, Italy, August 28-29.
Levy, Alan S. 2001. Report on Focus Groups on Food Irradiation Labeling. U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Washington, DC.
Levy, Alan S. and Brenda Derby. 2000 Report on Consumer Focus Groups on Biotechnology. U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Washington, DC. Available at http://www.cfsan.fda.gov/-comm/biorpt.html, accessed July 8, 2008.
Li, Quan, Kynda R. Curtis, Jill J. McCluskey, and Thomas I. Wahl. 2002. Consumer Attitudes toward Genetically Modified Foods in Beijing, China. AgBioForum, 5 (4): 145-152.
Loureiro, Maria L. and Marcia Bugbee. 2005. Enhanced GM Foods: Are Consumers Ready to Pay for the Potential Benefits of Biotechnology? The Journal of Consumer Affairs, 39 (1): 52-70.
Lusk, Jayson L. and Keith H. Coble. 2005. Risk Perceptions, Risk Preference, and Acceptance of Risky Food. American Journal of Agricultural Economics, 87 (2): 393-405.
Lusk, Jayson L., Lisa O. House, Carlotta Valli, Sara R. Jaeger, Melissa Moore, Bert Morrow, and W. Bruce Traill. 2004. Effect of Information about Benefits of Biotechnology on Consumer Acceptance of Genetically Modified Food: Evidence from Experimental Auctions in the United States, England, and France. European Review of Agricultural Economics, 31 (2): 179-204.
Lusk, Jayson L., Mustafa Jamal, Lauren Kurlander, Maud Roucan, and Lesley Taulman. 2005. A Meta-Analysis of Genetically Modified Food Valuation Studies. Journal of Agricultural and Resource Economics, 30 (1): 28-44.
Marks, Leonie. 2001. Communicating about Agrobiotechnology. AgBioForum, 4 (3-4): 152 154.
Mario, F. Teisl and Julie A. Caswell. 2002. Information Policy and Genetically Modified Food: Weighing the Benefits and Costs. Proceedings of the 2nd World Congress of Environmental and Resource Economists, Monterey, CA, June.
McCluskey, Jill J., Hiromi Ouchi, Kristine M. Grimsrud, and Thomas I. Wahl. 2003. Consumer Response to Genetically Modified Food Products in Japan. Agricultural and Resource Economics Review, 32 (2): 321-333.
Mendenhall, Catherine and Robert E. Evenson. 2002. Estimates of Willingness to Pay a Premium for Non-genetically Modified Foods: A Survey. In Market Development for Genetically Modified Foods, edited by Santaniello, V., Evenson, R., Zilberman, D. (52-62). Wallingford: CABI Publishing.
Moon, Wanki and Siva K. Balasubramanian. 2004. Public Attitudes toward Agrobiotechnology: The Mediating Role of Risk Perceptions on the Impact of Trust, Awareness, and Outrage. Review of Agricultural Economics, 26 (2): 186-208.
Mora, Cristina, Davide Menozzi, Corrado Giacomini, Cecilia Cantoni, Matteo Massari, and Gianluca Morelli. 2007. The Attitude of Italian Consumers towards GM Foods. Paper presented at Food Culture: Tradition, Innovation and Trust--A Positive Force for Modern Agribusiness Parma, Italy, June.
Nayga, Rodolfo M., Mary Gillett Fisher, and Benjamin Onyango. 2006. Acceptance of Genetically Modified Food: Comparing Consumer Perspectives in the United States and South Korea. Agricultural Economics, 34 (3): 331-341.
Noussair, Charles, Stephane Robin, and Bernard Ruffieux. 2004 Do Consumers Really Refuse to Buy Genetically Modified Food? Economic Journal, 114 (492): 102-120.
Nunnally, Jura C. and Ira H. Bernstein. 1994. Psychometric Theory (3rd ed.). New York: McGraw-Hill.
O'Connor, Elaine, Cathal Cowan, Gwilym Williams, John O'Connell, and Maurice Boland. 2005. Acceptance by Irish Consumers of a Hypothetical GM Dairy Spread that Reduces Cholesterol. British Food Journal, 107 (6): 361-380.
O'Connor, Elaine, Cathal Cowan, Gwilym Williams, John O'Connell, and Maurice P. Boland. 2006. Irish Consumer Acceptance of a Hypothetical Second-Generation GM Yogurt Product. Food Quality and Preference, 17 (5): 400-411.
Onyango, Benjamin and Ramu Govindasamy. 2005. Consumer Willingness to Pay for GM Food Benefits: Pay-off or Empty Promise? Implications for the Food Industry. Choices, 20 (4): 223-226.
Onyango, Benjamin, Ramu Govindasamy, William Hallman, Ho-Min Jang, and Venkata S Puduri. 2004. Consumer Acceptance of Genetically Modified Foods in Korea: Factor and Cluster Analysis. Selected paper presented at the Northeast Agricultural and Resource Economics Association Meeting, Halifax, Nova Scotia, Canada, June 20-23.
Panda, Unmesh Chandra, San jay Kumar Sundaray, Prasant Rath, Binod Bihari Nayak, and Dinabandhu Bhatta. 2006. Application of Factor and Cluster Analysis for Characterization of River and Estuarine Water Systems--A Case Study: Mahanadi River (India). Journal of Hydrology, 331 (3-4): 434-445.
Peterson, Leif E. 2002. Factor Analysis of Cluster-Specific Gene Expression Levels from cDNA Microarrays. Computer Methods and Programs in Biomedicine, 69 (3): 179-188.
Punj, Girish and David W. Stewart. 1983. Cluster Analysis in Marketing Research: Review and Suggestions. Journal of Marketing Research, 20:134-148.
Rigby, Dan and Michael Burto. 2005. Preference Heterogeneity and GM food in the UK. European Review of Agricultural Economics, 32(2): 269-288.
Roe, Brian and Mario F. Teisl. 2007. Genetically Modified Food Labeling: The Impacts of Message and Messenger on Consumer Perceptions of Labels and Products. Food Policy, 32: 49-66.
Roe, Brian, Mario F. Teisl, Huaping Rong, and Alan S. Levy. 2001. Characteristics of Successful Labeling Policies: Experimental Evidence from Price and Environmental Disclosure for Deregulated Electricity Services. Journal of Consumer Affairs, 35 (1): 1-26.
Roosen, Jutta, Silke Thiele, and Kristin Hansen. 2005. Food Risk Perceptions by Different Consumer Groups in Germany. Acta Agriculturae Scandinavica, Section C--Economy, 2 (1): 13-26.
Rosati, Simona and Anna Saba. 2000. Factors Influencing the Acceptance of Food Biotechnology. Italian Journal of Food Science, 12 (4): 425-434.
Rousu, Matthew, Daniel C. Monchuk, Jason F. Shogren, and Katherine M. Kosa. 2003. Consumer Perceptions of Labels and the Willingness to Pay for "Second Generation" Genetically Modified Products (with art evaluation of the FDA's proposed rules on labeling). RTI International Working paper.
SAS. 2004. SAS OnlineDoc[R] 9.1.3. Cary, NC: SAS Institute Inc.
Schneider, Keith R. and Renee G. Schneider. 2006. Genetically Modified Food. Document FSHN02-2, Food Science and Human Nutrition Department, University of Florida. http://edis.ifas.ufl.edu.
Shanahan, James, Dietram Scheufele, and Eunjung Lee. 2001. Attitudes about Agricultural Biotechnology and Genetically Modified Organisms. Public Opinion Quarterly, 65: 267-81.
Singh, Jagdip. 1990. A Typology of Consumer Dissatisfaction Response Styles. Journal of Retailing, 66 (1): 57-99.
Slovic, Paul. 1993. Perceived Risk, Trust, and Democracy. Risk Analysis, 13 (6): 675-582.
Slovic, Paul. 1987. Perceptions of Risk. Science, 236: 280-285.
Subrahmanyan, Saroja and Peng Sim Cheng. 2000. Perceptions and Attitudes of Singaporeans toward Genetically Modified Food. Journal of Consumer Affairs, 34 (2): 269-290.
Teisl, Mario F. 2003. What We May Have Is a Failure to Communicate: Labeling Environmentally Certified Forest Products. Forest Science, 49 (5): 1-13.
Teisl, Mario F., Nancy E. Bockstael, and Alan S. Levy. 2001. Measuring the Welfare Effects of Nutrition Information. American Journal of Agricultural Economics, 83 (1): 133-149.
Teisl, Mario F., Luke Garner, Brian Roe, and Michael E. Vayda. 2003a. Labeling Genetically Modified Foods: How Do U.S. Consumers Want to See It Done? AgBioForum, 6 (1-2): 48-54.
Teisl, Mario F., Lynn Halverson, Kelly O'Brien, Brian Roe, Nancy Ross, and Michael Vayda. 2002. Focus Group Reactions to Genetically Modified Food Labels. AgBioForum, 5 (1): 6-9.
Teisl, Mario F., Brian Roe, and Michael Vayda. 2006. Incentive Effects on Response Rates, Data Quality, and Survey Administration Costs. International Journal of Public Opinion Research, 18 (3): 364-373.
Teisl, Mario F., Brian Roe, Michael Vayda, and Nancy Ross. 2003b. Willingness to Pay for Genetically Modified Foods with Bundled Health and Environmental Attributes. Proceedings of the 7th ICABR International Conference on Public Goods and Public Policy for Agricultural Biotechnology, Ravello, Italy.
Thompson, Bruce and Larry G. Daniel. 1996. Factor Analytic Evidence for the Construct Validity of Scores: A Historical Overview and Some Guidelines. Educational and Psychological Measurement, 56 (2): 197-208.
VanBeselaere, Carla. 2003. Survey Response Behavior: Shirking in Internet and Telephone Surveys. Working Paper. California Institute of Technology. November 13.
Verdurme, Anneties, Xavier Gellynck, and Jacques Viaene. 2001. Consumer Segments Based on Acceptance of GM Foods'. Proceedings of the 5th ICABR International Conference on Public Goods and Public Policy for Agricultural Biotechnology, Ravello, Italy.
Verdurme, Annelies and Jacques Viaene. 2003. Consumer Beliefs and Attitude towards Genetically Modified Food: Basis for Segmentation and Implications for Communication. Agribusiness, 19 (1): 91-113.
Vermeulen, Hester. 2004. Genetically Modified White Maize in South Africa: Consumer Perceptions and Market Segmentation. Master's dissertation, Agricultural Economics, Extension and Rural Development, University of Pretoria, South Africa.
Webb, Tom. 2006. New parents reach for BST-free milk: Sales on rise years after dairy hormone controversy. Pioneer Press, Sept. 23.
(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.
(2.) Our data do not support a test of nonresponse bias but we were able to test whether respondents across segments differed in how quickly they responded to the surveys. Even after controlling for demographic characteristics, food shopping frequency, and general food-related concerns, we find that risk avoiders are more likely to respond to earlier survey mailings.
Sonja Radas is a senior research associate at the Institute of Economics, Zagreb, Croatia (email@example.com). Mario F. Teisl is a professor in the School of Economics, University of Maine, Orono, ME (firstname.lastname@example.org). Brian Roe is a professor in the Department of Agricultural, Environmental and Development Economics, Ohio State University, Columbus (email@example.com).
TABLE 1 Characteristics of Respondents and of the U.S. Population Survey U.S. Census (a) 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 (a) Based on 2000 Census data. TABLE 2 Respondents' Importance Ratings (a) of Various Benefits and Risks Potentially Associated with 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# (a) Where 1 = not at all important, 3 = somewhat important, and 5 = very important. Values in bold indicate relatively high factor loading (greater than .6). Note: Values in bold indicate relatively high factor loading (greater than .6) indicated with #. TABLE 3 Goodness-of-Fit Statistics from the Cluster Analysis Number of Cubic Clustering Clusters Pseudo-F Pseudo-[t.sup.2] Criteria Two 536.32 382 5.54 Three 555.15 239 4.2 Four 567.18 214 -0.1 Five 534.98 325 -12.1 Six 489.23 144 -13.74 TABLE 4 Respondents' Concern Levels' for Various Food Production Technologies, with Factor Loadings 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# (a) Where 1 = not at all concerned, 3 = somewhat concerned, and 5 = very concerned. Values in bold indicate relatively high factor loading (greater than .6). Note: Values in bold indicate relatively high factor loading (greater than .6) indicated with #. TABLE 5 Respondents' Socioeconomic Characteristics, Concern Levels, and Food-Related Behaviors, by Segment Balanced Risk Risk But Variables Avoiders Dismissers Interested Socioeconomic Gender (percent male) 42.9 61.5 43.8 Age (in years) 48.8 52.3 54.4 Education (in years) 15.1 14.9 13.9 Percent with children under 37.4 30.5 28.6 18 present in household Percent with food allergies 6.2 2.5 5.6 Income (in US$) 72,300 66,000 54,300 Food concerns Overall concern (a) 3.9 3.2 4.1 Concern with food technologies (b) -0.03 -0.77 0.4 Food-related behaviors Frequency of buying organic 2.5 2.0 2.4 foods (c) Percent grow own vegetables 40.0 33.9 32.8 Percent shop at farmers' markets 35.7 22.1 29.9 Percent vegetarian 4.8 1.7 2.4 Frequency of reading nutrition 3.7 3.3 3.8 labels (d) Test Results across Variables All Segments Socioeconomic Gender (percent male) [chi square] (2) = 47.5; p < .00 Age (in years) F(2, 1984) = 25.0; p < .00 Education (in years) F(2, 2013) = 44.6; p < .00 Percent with children under [chi square] (2) = 14.2; p < .00 18 present in household Percent with food allergies [chi square] (2) = 8.4; p = .02 Income (in US$) F(2, 1828) = 21.7; p < .00 Food concerns Overall concern (a) F(2, 2050) = 126.7; p < .00 Concern with food technologies (b) F(2, 1869) = 247.8; p < .00 Food-related behaviors Frequency of buying organic F(2, 2048) = 34.8; p < .00 foods (c) Percent grow own vegetables [chi square] (2) = 9.4; p < .00 Percent shop at farmers' markets [chi square] (2) = 24.4; p < .00 Percent vegetarian [chi square] (2) = 11.2; p < .00 Frequency of reading nutrition F(2, 2050) = 34.3: p < .00 labels (d) Risk Avoiders vs. Results of Pair-wise Tests Risk Dismissers Socioeconomic Gender (percent male) [chi square] = 38.2; p < .00 Age (in years) t = 3.8; p < .00 Education (in years) t = 1.3; p = .19 Percent with children under [chi square] (1) = 5.9; p = .02 18 present in household Percent with food allergies [chi square] (1) = 8.1; p < .00 Income (measured in US$) t = 1.9; p = .06 Food concerns Overall concern t = 11.3; p < .00 Concern with food technologies t = 12.8; p < .00 Food-related behaviors Frequency of buying organic t = 8.0; p < .00 foods Percent grow own vegetables [chi square] (1) = 4.4; p = .04 Percent shop at farmers' markets [chi square] (1) = 24.3; p < .00 Percent vegetarian [chi square] (1) = 7.8; p < .00 Frequency of reading nutrition t = 6.6; p < .00 labels Risk Avoiders vs. Balanced Results of Pair-wise Tests But Interested Socioeconomic Gender (percent male) [chi square] = 0.14; p = .17 Age (in years) t = 7.2; p < .00 Education (in years) t = 9.0; p < .00 Percent with children under [chi square] (1) = 13.5; < .00 18 present in household Percent with food allergies [chi square] (1) = 0.21; p = .65 Income (measured in US$) t = 6.5; p < .00 Food concerns Overall concern t = 4.2; p < .00 Concern with food technologies t = 9.6; p < .00 Food-related behaviors Frequency of buying organic t = 1.9; p = .05 foods Percent grow own vegetables [chi square] (1) = 8.7; p < .00 Percent shop at farmers' markets [chi square] (1) = 5.8; p = .02 Percent vegetarian [chi square] (1) = 6.6; p = .01 Frequency of reading nutrition t = 0.9; p = .32 labels Risk Dismissers vs. Balanced Results of Pair-wise Tests But Interested Socioeconomic Gender (percent male) [chi square] (1) = 38.3; p < .00 Age (in years) t = 2.3; p = .02 Education (in years) t = 6.5; p < .00 Percent with children under [chi square] (1) = 0.51; p = .48 18 present in household Percent with food allergies [chi square] (1) = 6.6; p = .01 Income (measured in US$) t = 4.0; p < .00 Food concerns Overall concern t = 15.6; p < .00 Concern with food technologies t = 22.1; p < .00 Food-related behaviors Frequency of buying organic t = 6.8; p < .00 foods Percent grow own vegetables [chi square] (1) = 0.17; p = .67 Percent shop at farmers' markets [chi square] (1) = 9.5; p < .00 Percent vegetarian [chi square] (1) = 0.7; p = .40 Frequency of reading nutrition t = 8.0; p < .00 labels (a) Response 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." (b) Output of factor analysis of respondent concerns with seven food technologies: higher concern is reflected by a higher factor score. (c) Response to How often do you purchase organic foods? Response is 1 for "never" to 5 "always." (d) Response to How often do you read the nutrition labels on the foods you purchase? Response is 1 for "never" to 5 "always." TABLE 6 Respondent's Experience with, and Desires for, GM Food Labeling, by Segment Balanced Risk Risk But Avoiders Dismissers Interested Percent hearing about GM foods 84.4 69.4 62.7 Percent seeing a GM-free label 17.5 121 9.4 Percent wanting GM labels 91.8 66.6 93.6 Percent thinking testing 0.5 2.2 0.9 and labeling are unnecessary, Percent wanting voluntary testing 6.9 13.3 5.4 with only non-GM foods labeled Percent wanting mandatory 92.5 84.5 93.7 testing and labeling with all foods labeled 31.5 32.8 48.8 with only GM foods labeled 55.3 48.0 42.0 with only non-GM foods labeled 5.7 3.7 2.9 Test Results across All Segments Percent hearing about GM foods [chi square] (2) = 85.0; p < .00 Percent seeing a GM-free label [chi square] (4) = 27.3; p < .00 Percent wanting GM labels [chi square] (2) = 219.0; p < .00 Percent thinking testing [chi square] (2) = 5.4; p = .07 and labeling are unnecessary, Percent wanting voluntary testing [chi square] (2) = 18.6; p < .00 with only non-GM foods labeled Percent wanting mandatory [chi square] (2) = 19.3; p < .00 testing and labeling with all foods labeled [chi square] (2) = 47.3; p < .00 with only GM foods labeled [chi square] (2) = 23.0; p < .00 with only non-GM foods labeled [chi square] (2) = 6.9; p = .03 Risk Avoiders vs. Risk Results of Pair-wise Tests Dismissers Percent hearing about GM foods [chi square] (1) = 34.9; p < .00 Percent seeing a GM-free label [chi square] (1) = 7.7; p = .02 Percent wanting GM labels [chi square] (1) = 116.3; p < .00 Percent thinking testing and [chi square] (1) = 5.0; p = .02 labeling are unnecessary Percent wanting voluntary testing [chi square] (1) = 9.1; p < .00 with only non-GM foods labeled Percent wanting mandatory testing [chi square] (1) = 9.6; p < .00 and labeling with all foods labeled [chi square] (1) = 0.1; p =.70 with only GM foods labeled [chi square] (1) = 4.0; p = .05 with only non-GM foods labeled [chi square] (1) = 1.6; p = .21 Risk Avoiders vs. Balanced Results of Pair-wise Tests But Interested Percent hearing about GM foods [chi square] (1) = 84.9; p < .00 Percent seeing a GM-free label [chi square] (1) = 28.9; p < .00 Percent wanting GM labels [chi square] (1) = 2.0; p = .15 Percent thinking testing and [chi square] (1) = 0.71; p = .40 labeling are unnecessary Percent wanting voluntary testing [chi square] (1) = 1.4; p = .24 with only non-GM foods labeled Percent wanting mandatory testing [chi square] (1) = 1.3; p = .25 and labeling with all foods labeled [chi square] (1) = 40.1; p < .00 with only GM foods labeled [chi square] (1) = 23.0; p < .00 with only non-GM foods labeled [chi square] (1) = 6.7; p = .01 Risk Dismissers vs. Balanced Results of Pair-wise Tests But Interested Percent hearing about GM foods [chi square] (1) = 5.6; p = .02 Percent seeing a GM-free label [chi square] (1) = 5.6; p = .06 Percent wanting GM labels [chi square] (1) = 170.5; p < .00 Percent thinking testing and [chi square] (1) = 2.7; p = .10 labeling are unnecessary Percent wanting voluntary testing [chi square] (1) = 18.0; p < .00 with only non-GM foods labeled Percent wanting mandatory testing [chi square] (1) = 18.6; p < .00 and labeling with all foods labeled [chi square] (1) = 20.5; p < .00 with only GM foods labeled [chi square] (1) = 2.8; p = .09 with only non-GM foods labeled [chi square] (1) = 0.4; p = .51 TABLE 7 Respondent Preferences for Labeling Organizations, (a) by Segment Balanced Risk Risk But Avoiders Dismissers Interested U.S. government agencies 68.2 83.0 77.4 Department of Agriculture 47.5 52.4 41.3 FDA 48.8 44.4 53.9 EPA 3.7 3.1 4.7 Environmental organizations 11.0 4.8 3.9 Scientific organizations 13.1 5.2 8.4 Health organizations 7.7 7.0 10.2 Test Results across All Segments U.S. government agencies [chi square] (2) = 25.3; p < .00 Department of Agriculture [chi square] (2) = 8.8; p = .01 FDA [chi square] (2) = 6.3; p = .04 EPA [chi square] (2) = 1.3; p < .53 Environmental organizations [chi square] (2) = 26.8; p < .00 Scientific organizations [chi square] (2) = 15.3; p < .00 Health organizations [chi square] (2) = 3.7; p = .15 Risk Avoiders vs. Risk Results of Pair-wise Tests Dismissers U.S. government agencies (b) [chi square] (1) = 20.4; p < .00 Department of Agriculture [chi square] (1) = 1.4; p = .24 FDA [chi square] (1) = 1.1; p = .30 Environmental organizations [chi square] (1) = 8.5; p < .00 Scientific organizations [chi square] (1) = 12.3; p < .00 Health organizations [chi square] (1) = 0.13; p = .71 Risk Avoiders vs. Balanced Results of Pair-wise Tests But Interested U.S. government agencies (b) [chi square] (1) = 13.6; 9 < .00 Department of Agriculture [chi square] (1) = 3.4; p = .06 FDA [chi square] (1) = 2.3; p = .13 Environmental organizations [chi square] (1) = 23.7; p < .00 Scientific organizations [chi square] (1) = 7.3; p < .00 Health organizations [chi square] (1) = 2.4; p = .12 Risk Dismissers vs. Balanced Results of Pair-wise Tests But Interested U.S. government agencies (b) [chi square] (1) = 3.7; p = .05 Department of Agriculture [chi square] (1) = 8.0; p = .01 FDA [chi square] (1) = 5.7; p = .02 Environmental organizations [chi square] (1) = 0.4; p = .54 Scientific organizations [chi square] (1) = 3.0; p = .08 Health organizations [chi square] (1) = 2.4; p = .11 (a) Scientific 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. (b) pair-wise test not indicated for EPA. TABLE 8 Preferences, for Information Pieces to be on a GM Label; Importance, by Segment (a) Balanced Risk Risk But Avoiders Dismissers Interested Which ingredients in a 4.2 3.6 4.4 product are GM Why the ingredients are 3.2 2.9 3.8 genetically modified How the ingredients are 3.3 2.8 3.8 genetically modified Who is certifying the 4.2 3.6 4.4 information Warnings associated with 4.7 4.1 4.8 modification Benefits associated with 3.6 3.5 4.2 modification Web site or phone number 4.2 3.7 4.5 where one could obtain more information Test Results across All Segments Which ingredients in a F(2, 1639) = 75.3; p < .00 product are GM Why the ingredients are F(2, 1634) = 68.5; p < .00 genetically modified How the ingredients are F(2, 1630) = 72.2; p < .00 genetically modified Who is certifying the F(2, 1632) = 74.2; p < .00 information Warnings associated with F(2, 1638) = 97.8; p < .00 modification Benefits associated with F(2, 1627) = 69.5; p < .00 modification Web site or phone number F(2, 1634) = 78.9; p < .00 where one could obtain more information Risk Avoiders Risk Avoiders Results of vs. vs. Balanced Pair-wise Tests Risk Dismissers But Interested Which ingredients in a t = 6.9; p < .00 t = 5.5; p < .00 product are GM Why the ingredients are t = 3.8; p < .00 t = 8.1; p < .00 genetically modified How the ingredients are t = 4.8; p < .00 t = 7.7; p < .00 genetically modified Who is certifying the t = 7.5; p < .00 t = 4.6; p < .00 information Warnings associated t = 10.6; p < .00 t = 2.2; p = .03 with modification Benefits associated t = 2.0; p = .06 t = 9.5; p < .00 with modification Web site or phone number t = 7.0; p < .00 t = 5.8; p < .00 where one could obtain more information Risk Dismissers Results of vs. Balanced Pair-wise Tests But Interested Which ingredients in a t = 13.0; p < .00 product are GM Why the ingredients are t = 11.0: p < .00 genetically modified How the ingredients are t = 11.6; p < .00 genetically modified Who is certifying the t = 12.7; p < .00 information Warnings associated t = 13.2; p < .00 with modification Benefits associated t = 10.6; p < .00 with modification Web site or phone number t = 13.1; p < .00 where one could obtain more information (a) Responses 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."
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|Author:||Radas, Sonja; Teisl, Mario F.; Roe, Brian|
|Publication:||Journal of Consumer Affairs|
|Date:||Sep 22, 2008|
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