Agriculture and Human Values

, Volume 34, Issue 4, pp 819–831 | Cite as

Which livestock production claims matter most to consumers?

Article

Abstract

Consumers are becoming increasingly interested in how their food is produced. Many studies have focused on consumers’ preferences and willingness-to-pay for specific production-related claims (labels) on food products. However, few studies have asked consumers to rank the importance of different production claims. In this study, we use a best-worst scaling approach to have consumers rank the importance of seven common production claims used on food products. Rankings are obtained across four product types: beef, milk, chicken, and eggs. Results of the study show that consumers often prefer specific components of more encompassing claims (e.g., animals were not treated with growth hormones, no GMOs used in production) as opposed to the broader, more encompassing claim itself (such as product is certified organic). The majority of preference shares were captured by the top three claims, though the order of these preferences appears to vary for meat and non-meat animals.

Keywords

Livestock production claims Best-worst scaling Consumer preference Labeling 

Introduction

Consumers are becoming increasingly interested in their food choices. While food choices have often been viewed as low-involvement decisions requiring little (if any) information search or product evaluation (Hawkins and Mothersbaugh 2013), the rise of food label claims has caused food choices to become higher-involvement decisions. Consumers are not only faced with sorting through and prioritizing the standard attributes such as brand, price, and shelf life, but now consumers are also considering a new set of attributes: food production practices. For many consumers, buying a gallon of milk is much more complex than finding the preferred fat content and expiration date. Now, consumers are asking about how the cows were treated, what they were fed, whether they received growth hormones and/or antibiotics, whether the milk is organic, and so on. In the current marketplace, labels exist for nearly every question.

Research has shown that consumers place a premium on label claims related to food production methods. There has been extensive research on consumer preferences and willingness to pay for products that carry organic labels (see Yiridoe et al. 2005; Hughner et al. 2007 for reviews) as well as specific labels related to animal welfare (see Norwood and Lusk 2011; Lagerkvist and Hess 2011 for reviews). However, much of the existing literature asks consumers to consider one production claim on a food product at a time. This is much cleaner for experimental purposes, but it does not necessarily mimic the shopping environment that consumers face. Rather, consumers are presented with a wide variety of labels and label claims, sometimes providing redundant information. For instance, many products display the USDA Organic label as well as the Non-GMO Project Verified label. While utilizing both labels is acceptable, a requirement for obtaining organic certification is that genetically-modified organisms may not be used in production (USDAa 2011); thus, the Non-GMO Project label is at least somewhat repetitive. Gao and Schroeder (2009) and Barreiro-Hurle, Gracia, and de-Magistris (2010) have provided evidence that consumers may experience a decreasing marginal utility for additional label claims, yet it is not clear which specific pieces of information generate more or less utility for consumers.

The purpose of this study is to examine consumer preferences for livestock production claims. We utilize a best-worst scaling approach to determine which production claims are most and least important to consumers when making purchase decisions. We compare results across four livestock-derived products (beef, milk, chicken, and eggs) to determine whether preferences differ by species or across meat and non-meat products. Our results provide insight on how consumers sort and prioritize labeling information on food products and can serve as a signal of which production practices are valued in the marketplace.

The role of labels in food choice

Labels can be an important tool for producers and consumers. For producers, labels serve as a differentiation tool which will ideally lead to increases in (1) consumer awareness of a particular product feature (say, humanely raised) and (2) willingness to pay for the food product, assuming the labeled attribute is of value to consumers. An increased willingness to pay, in turn, should allow producers to generate increased sales revenue. Golan and colleagues (2001) note that producers will continue to add information (labels) to product packaging as long as the benefits of adding each new piece of information outweigh the costs.

For consumers, labels are most commonly recognized as tools to aid in the decision making process (Caswell and Padberg 1992). Caswell and Mojduszka (1996) note that consumers can readily search for certain product quality attributes (e.g., if an apple is bruised) before purchase and can experience other attributes (e.g., flavor) upon consumption, yet there may be quality attributes that consumers cannot verify even after purchasing and consuming the product (e.g., if the apple is organic). When consumers can easily search for or experience product attributes, information labeling programs are less likely to be necessary. When product attributes cannot be verified (termed credence attributes), however, labels are likely to be an important tool to resolve any issues of imperfect information between producer and consumer (Golan et al. 2001). In effect, labels help convert credence attributes into search attributes for the consumer (Caswell and Mojduszka 1996).

Caswell and Padberg (1992) argue, however, that labels should not be narrowly viewed as a point-of-purchase information source. For example, labels can also influence product design, serve as a public surveillance assurance (allowing consumers to feel confident in the food system), and define public values (Caswell and Padberg 1992). The ability for labels to signal public values has received much attention in the literature. While Caswell and Padberg (1992) discuss how labeling regulations can signal to the public which health and nutrition attributes should be valued, several other scholars have discussed how labels allow consumers to express their values or “vote with their dollars” (DuPuis 2000; Raynolds 2000; Golan et al. 2001; Barham 2002; Conner 2004; Guthman 2007).

Barham (2002) argues that values-based labeling is a social movement, allowing consumers to push back against neoliberal capitalism. Proponents of values-based labeling contend there is a need to challenge the conventional (also described as industrial, corporate, global) agrifood system because it is environmentally and socially unsustainable (McMichael 2000; Raynolds 2000; Barham 2002; Conner 2004; Guthman 2007). These labels typically emphasize process (over product) and quality (Barham 2002); such an emphasis on process makes production labeling claims a seemingly natural subset of values-based labels.

Interestingly, Guthman (2007) acknowledges the role of labels in counter-cultural movements (specifically, organic and fair trade); however, she also notes “these labels are in some respects analogs to the very things they are purported to resist, namely property rights that allow these ascribed commodities to be traded in a global market” (p. 456). For these labels to gain traction in the market, standards must be established and verified, which establishes barriers to entry for producers. This allows the producers who are able to participate in the market to generate profits because consumers are willing to pay a premium for the products carrying the label (Guthman 2007). The realization of profits, in turn, will attract more producers to enter the market. Jaffee and Howard (2010) point out that the success of the organic and fair trade labels has attracted more corporate participants in these markets, which has ultimately resulted in a weakening of the standards. Thus, some question whether these labeling movements truly resist the principles of neoliberalism or if they have succumbed to (and possibly even embraced) market forces. In the present study, we do not attempt to contribute to the debate on whether values-based labeling movements function as intended; rather, our purpose is to identify which production claims, which we argue are one subset of values-based labels, are more or less important to consumers. Research has rarely presented consumers with the full set of labeling claims they face in the marketplace, so our results will offer insight into the prioritization of those labeling claims along with the values they represent.

Selection of production claims and livestock products

To determine which production-related claims should be utilized in this study, we conducted background research in several Midwest grocery stores to compile a comprehensive list of labels and labeling claims currently in use. For the purposes of this study, a labeling claim is defined as being “production-related” when the claim is referring to an on-farm practice. We visited all ‘tiers’ of grocery stores in the background research phase, including discount grocers (Aldi), local supermarkets, supercenters (Walmart and Meijer), and specialty stores (Whole Foods, Trader Joe’s). In each store, we analyzed a common set of products: beef, pork, poultry, milk, and eggs.

This search yielded a number of common claims; however, some were more production-related than others. For example, one group of labels tended to deal more with the processing of meat products (e.g., ‘no preservatives’, ‘no fillers’, ‘no nitrates’). We excluded these types of claims from the analysis as they generally occur after the production stage. A second group of claims were excluded on the basis of being too vague. These were claims such as ‘agriculturally sustainable’ and ‘environmentally friendly’. While these most likely relate to actual production, how they were defined was less clear (and likely less clear to consumers). A third vague term which warrants a separate discussion is the term ‘natural’. While the USDA Food Safety and Inspection Service has defined ‘natural’ for meat and poultry products (containing no artificial ingredient or added color and is minimally processed; USDAb 2011), consumers appear to have a great deal of confusion with the word (Abrams et al. 2010). Thus, we did not include ‘natural’ in the final set of production claims. Further, based on the USDA definition, ‘natural’ is more similar to a processing-phase claim than a production-phase claim. A final common claim that we excluded from this study was ‘local’. Using the claim ‘local’ is not expressing any specific production practice, per se; rather, it promotes the location of production, which is beyond the scope of interest in this study.

The goal of this study was to focus on specific on-farm practices. The final list consisted of seven production claims:

  • Product is certified organic.

  • Animals were humanely raised.

  • Animals were grass-fed (or raised on a vegetarian diet).

  • Animals were not administered growth hormones.

  • Animals were not administered antibiotics.

  • Animals were raised in a free-range (or cage-free) environment.

  • Genetically modified organisms were not used in the production of this product (Non-GMO).

These seven claims were the most common across a wide range of livestock products. Further, previous research has examined consumers’ preferences for each of these claims, but typically in a more isolated fashion.1 Only the organic, humane, and non-GMO claims had actual certification labels (USDA Organic; humane labels varied, but the more common ones were Certified Humane and Animal Welfare Approved; Non-GMO Project). The remaining claims were stated on product packaging. The exact phrasing for two of the claims is dependent on species. For instance, in terms of the feed composition, we found beef products were generally labeled as ‘grass-fed’ whereas poultry products contained the phrase ‘raised on a vegetarian diet’. Similarly, we found that beef products tended to be labeled as ‘free-range’ whereas poultry was labeled as ‘cage-free’.

It is important to note overlap exists in this list of production claims. For instance, both the organic and humanely raised labels (regardless of humane certifying organization) prohibit the use of growth hormones in animals (HFAC 2013; USDA 2013). Further, the USDA website notes “federal regulations have never permitted hormones or steroids in poultry, pork, or goat” (USDAb 2011). In reality, the organic and humane claims encompass almost all of the other claims, with the exception of the grass-fed (vegetarian diet) claim (HFAC 2013; USDA 2013). However, it is less clear whether consumers are aware this labeling is repetitive. Numerous studies have shown perceptions of organic are broad and inaccurate at times (see Yiridoe et al. 2005 and Hughner et al. 2007 for reviews), so it is possible consumers view each claim as a new piece of information. Producers’ decisions to provide multiple claims on the same package suggest some skepticism about consumers’ knowledge.

Once the labeling claims were selected, the next decision was which products to use in the study. We sought to select products which many consumers purchase regularly and that represent a variety of livestock-derived products. Additionally, we wanted an array of products that would include both livestock meat products and livestock non-meat products. It could be the case, for example, that an individual feels more strongly about a free-range/cage-free environment for animals which are continuously productive (as opposed to being fed for immediate slaughter). Conversely, raising cattle as grass-fed may only be important to beef consumers; it may be less important for dairy consumers. Given these considerations, the four product categories we selected were beef meat products, milk, chicken meat products, and eggs.2

Data and methods

To determine the importance of the selected production claims, two common approaches have been suggested in the literature. First, respondents could be asked to rate the importance of each of the seven claims on a scale from 1 to 5 (where 1 = not important and 5 = very important). While these are relatively simple and straightforward questions for respondents to answer (Lee et al. 2007), the rating approach has some weaknesses. Namely, respondents could rate all seven production claims as very important (or not important); thus, no trade-offs have to be made between claims. Additionally, there is no guarantee all respondents will uniformly interpret the scale (Finn and Louviere 1992; Lusk and Briggeman 2009).

An alternative to a rating system is the best-worst scaling approach. Introduced by Finn and Louviere (1992), this approach forces respondents to discriminate (make trade-offs) between production claims. In a given choice set of production claims, respondents would be asked to select one claim as most important and one claim as least important; then, this procedure would be repeated multiple times with different sets of production claims. Ultimately, this exercise provides an estimate of where each production claim would fall on a scale of importance for respondents (Finn and Louviere 1992; Lusk and Briggeman 2009). The best-worst scaling method has been used increasingly in the agricultural economics literature to determine consumers’ food values (Lusk and Briggeman 2009), preferences for sustainable farming practices (Sackett et al. 2013), and preferences for USDA market reports (Pruitt et al. 2014).

To design the production claim choice sets, we used a 27 main-effects orthogonal experimental design, following Finn and Louviere (1992). From the full factorial, we generated a fractional factorial design in an effort to minimize respondent fatigue; Finn and Louviere (1992) note that any fractional factorial design must be a balanced and orthogonal set of the full factorial for consistent model estimation (see explanation of model and analysis in the “Best-worst data analysis” section). The optimal design that was generated contained eight choice sets in which all claims were seen by respondents a total of four times. The resulting design consisted of four choice sets with four production claims, three choice sets with two production claims, and one choice set with six production claims. The choice sets were held constant across the four product blocks for comparison purposes. Figure 1 provides a sample best-worst question from the milk product block.

Fig. 1

Sample best-worst question (milk product block)

Survey

We distributed an online survey through Clear Voice Research in the spring of 2014. Clear Voice Research recruits participants from a large panel that is designed to be representative of the U.S. population.3 In total, 1176 responses were collected; however, we removed 137 observations for incompleteness or incorrectly answering trigger (attention-check) questions (Mason and Suri 2012). The final number of usable responses was 1039–approximately 260 responses per product block.

Upon formally agreeing to participate in the study (using standard IRB protocol), we screened participants to determine (1) whether they were a practicing vegan and (2) whether they regularly purchased beef, milk, chicken, or eggs. Practicing vegans were excluded from participating in this study since they would not consume livestock products; participants who also did not regularly purchase at least one of the four product categories were ineligible for participation. Respondents who met this criteria were randomly assigned to one of the product blocks which they regularly purchased. Within each block, we provided an example best-worst question to demonstrate that two answers would need to be provided for each question. In total, participants answered eight best-worst scaling questions (as described previously).

Table 1 provides the demographic profile of our sample respondents. As can be seen in the table, our sample had an even distribution of males and females (50.8% female). Though the panel was meant to be representative of the U.S. population, there was an over-sampling of older (ages 55 and up) and an under-sampling adults in the highest income category (annual household income of $100,000 or more). Thirty-eight percent of respondents had obtained a bachelor’s or graduate degree, and the vast majority of respondents (91.4%) were the primary shopper in their household. We conducted tests to detect demographic differences across product blocks. Our testing revealed the chicken product block of respondents had a slightly higher proportion of males and respondents with no children under 12 living in the household relative to the other product blocks.

Table 1

Demographic composition of survey respondents (N = 1039 respondents)

Variable

Definition

Sample proportion

Female

1 if female; 0 if male

0.508

Age 18–34

1 if age is 18–34 years; 0 otherwise

0.096

Age 35–54

1 if age is 35–54 years; 0 otherwise

0.302

Age 55+

1 if age is 55 years or more; 0 otherwise

0.602

LowInc

1 if annual household income is $0–$49,999; 0 otherwise

0.501

MedInc

1 if annual household income is $50,000–$99,999; 0 otherwise

0.356

HighInc

1 if annual household income is $100,000 or more; 0 otherwise

0.143

Bach Deg

1 if obtained a bachelor’s degree; 0 otherwise

0.247

Grad Deg

1 if obtained a graduate or professional degree; 0 otherwise

0.139

Northeast

1 if resides in Northeast U.S. census region; 0 otherwise

0.232

Midwest

1 if resides in Midwest U.S. census region; 0 otherwise

0.263

South

1 if resides in South U.S. census region; 0 otherwise

0.307

West

1 if resides in West U.S. census region; 0 otherwise

0.198

Metro

1 if resides in metropolitan area; 0 otherwise

0.348

Farm Fam

1 if has a farming background; 0 otherwise

0.114

Kids12

1 if children under the age of 12 reside in the household; 0 otherwise

0.179

PrimShop

1 if primary shopper in household; 0 otherwise

0.914

PMImport

1 if ranks production claims as important in food purchase decisions; 0 otherwise

0.469

Best-worst data analysis

In the best-worst framework, a choice set with J options (production claims, in this case) yields J (J − 1) best-worst combinations that an individual could choose. The individual’s choice will be the pair of production claims that maximizes the difference in importance—as perceived by the individual.

As discussed previously, the best-worst framework allows us to examine where each production claim falls on a continuum (scale) of importance to consumers. In this framework, let \({{\gamma }_{j}}\) represent the location of production claim j on the underlying scale of importance, and let the true level of importance for individual i be given by \({{I}_{ij}}={{\gamma }_{j}}+{{\varepsilon }_{ij}},\) where \({{\varepsilon }_{ij}}\) is a random error term. The probability that an individual selects production claim j as most important and production claim k as least important in a choice set with J total claims is the probability that \({{I}_{ij}}-{{I}_{ik}}\) is greater than all other J (J – 1) – 1 differences in the choice set. Assuming the \({{\varepsilon }_{ij}}\) are independently and identically distributed type I extreme value, then this probability takes on the multinomial logit (MNL) form as shown in Eq. 1:
$$\text{Prob}\left( j\text{ is chosen best and }k\text{ is chosen worst} \right)=\frac{{{e}^{{{\gamma }_{j}}-{{\gamma }_{k}}}}}{\mathop{\sum }_{l=1}^{J}~\mathop{\sum }_{m=1}^{J}{{e}^{{{\gamma }_{l}}-{{\gamma }_{m}}}}-J}$$
(1)

We estimated the \({{\gamma }_{j}}\) parameters via maximizing the log-likelihood function based on the probability statement in Eq. 1. The estimated \({{\gamma }_{j}}\) represents the level of importance for production claim j relative to another production claim which was dropped to avoid the dummy variable trap. In our model specification, the importance of each production claim is relative to the importance of the “Animals were grass-fed (raised on a vegetarian diet)” production claim.

Using the estimates, we calculated preference shares for each of the production claims. These shares offer the probability that a given production claim would be selected as most important (Lusk and Briggeman 2009) and are much easier to interpret than the estimated MNL coefficients. For instance, the levels of importance are reported on a ratio scale such that if Production Claim X has a preference share three times as large as Production Claim Y, it can be concluded that Production Claim X is three times as important as Production Claim Y. The share of preference calculation is shown in Eq. 2:
$$\text{Share of preference for production claim }j=\frac{{{e}^{{{{\hat{\gamma }}}_{j}}}}}{\mathop{\sum }_{k=1}^{J}{{e}^{{{{\hat{\gamma }}}_{k}}}}}$$
(2)

One weakness with the MNL model is that the estimated \({{\gamma }_{j}}\) represent only the average importance of production claim j across respondents. This specification does not allow for differences in claim importance across individuals, which are likely to exist in reality. Thus, in addition to the MNL model, we estimated a random parameters logit (RPL) model for each of the four treatment blocks. RPL models are more general than the standard MNL because they allow each coefficient to vary randomly across respondents. Further, it allows for efficient estimation when there are repeated choices by the same respondents, which is the case in this study (Revelt and Train 1998). In the RPL specification, let the importance parameter for individual i and production claim j be specified as \({{\tilde{\gamma }}_{ij}}={{\bar{\gamma }}_{j}}+{{\sigma }_{j}}{{\mu }_{ij}},\) where \({{\bar{\gamma }}_{j}}\) and \({{\sigma }_{j}}\) are the mean and standard deviation of \({{\gamma }_{j}}\) in the population, and \({{\mu }_{i}}\) is a random term normally distributed with mean zero and unit standard deviation.

Results

Likelihood ratio tests revealed that individual models for each product category block were preferred over one pooled model across products in both the MNL and RPL specifications. Further, a final likelihood ratio test showed that the RPL specification was preferred to the MNL specification. Thus, we focus our discussion on the RPL estimates in Table 2 and preference shares presented in Fig. 2.4

Table 2

RPL estimates for production claims by product block

Production method

Block 1: beef

Block 2: milk

Block 3: chicken

Block 4: eggs

Estimate (SE)

Std. dev. (SE)

Estimate (SE)

Std. dev. (SE)

Estimate (SE)

Std. dev. (SE)

Estimate (SE)

Std. dev. (SE)

Animals were not administered growth hormones

1.352**

(0.140)

1.606**

(0.118)

1.668**

(0.169)

2.155**

(0.176)

2.626**

(0.140)

1.483**

(0.145)

3.134**

(0.152)

1.267**

(0.152)

No genetically-modified organisms used in production (non-GMO)

1.356**

(0.172)

2.471**

(0.173)

1.139**

(0.185)

2.535**

(0.179)

2.601**

(0.180)

2.517**

(0.179)

2.741**

(0.180)

2.502**

(0.177)

Animals were humanely raised

1.255**

(0.140)

1.671**

(0.130)

1.608**

(0.152)

1.832**

(0.152)

2.318**

(0.150)

1.780**

(0.145)

3.131**

(0.178)

2.109**

(0.162)

Animals were not administered antibiotics

0.633**

(0.124)

1.495**

(0.137)

0.770**

(0.124)

1.459**

(0.147)

2.112**

(0.128)

1.324**

(0.129)

2.554**

(0.137)

1.041**

(0.143)

Animals were raised in a free-range (cage-free) environmenta

−0.039

(0.117)

1.344**

(0.135)

0.120

(0.106)

1.137**

(0.132)

0.949**

(0.131)

1.566**

(0.142)

1.632**

(0.160)

1.919**

(0.156)

Product is certified organic

−1.197**

(0.175)

2.291**

(0.171)

−1.792**

(0.194)

2.344**

(0.194)

−0.104

(0.165)

2.209**

(0.155)

0.095

(0.171)

2.224**

(0.181)

Animals were grass-fed (raised on a vegetarian diet)b

0.000

 

0.000

 

0.000

 

0.000

 

Number of individuals

256

 

264

 

272

 

247

 

Log likelihood

−3113

 

−3026

 

−3069

 

−2643

 

**denotes p < 0.01

aFor beef and milk, the term ‘free-range’ was used; for chicken and eggs, the term ‘cage-free’ was used

bFor beef and milk, the term ‘grass-fed’ was used; for chicken and eggs, the term ‘raised on a vegetarian diet’ was used

Fig. 2

Production claim preference shares by product block

As seen in Table 2, all production claims for the beef and milk product blocks were significantly different from the ‘Animals were grass-fed’ claim at a 1% significance level with the exception of the ‘Animals were raised in a free-range environment’ claim. In the chicken and eggs product blocks, we found that all production claims were significantly different (p < 0.01) from the ‘Animals were raised on a vegetarian diet’ claim with the exception of the ‘Product is certified organic’ claim. It is also important to note that the standard deviation estimates are significant for all of the production claims, suggesting there is heterogeneity in consumers’ preferences for these claims—an issue we explore in detail later in this section.

For ease of interpretation, we used the coefficients in Table 2 to calculate preference shares for each of the production claims by product block. The production claim preference shares by product block are presented in Fig. 2. The most important production claims were ‘Animals were not administered growth hormones,’ ‘No genetically-modified organisms used in production,’ and ‘Animals were humanely raised.’ Together, these three claims captured 73.1, 75.1, 74.5 and 75.3% of beef, milk, chicken, and egg preference shares, respectively. The production claim ‘Animals were not administered antibiotics’ ranked in the middle importance for all products while the ‘Free-range/cage-free environment,’ ‘Grass-fed/vegetarian diet,’ and ‘Product is certified organic’ claims were ranked as the least important production claims.

Comparing the beef and milk product blocks, note that the no growth hormones and humanely raised claims were rated as more important under the milk product block, whereas beef consumers placed slightly more importance on the non-GMO, organic, and grass-fed production claims. With chicken and eggs, we see that the no growth hormones, humanely raised, and cage-free claims are slightly more important in the egg purchase decision; however, chicken consumers placed more importance on the non-GMO, no antibiotics, organic, and vegetarian fed production claims. There appeared to be some differences between product categories based on animal species. The ‘Animals were not administered antibiotics’ claim proved to be more important for the chicken and eggs blocks relative to the beef and milk blocks, whereas the dietary claim (vegetarian fed for chicken and eggs; grass-fed for beef and milk) was far less important for respondents in the chicken and eggs product blocks.

Looking at the results more broadly, we can see that the no growth hormones production claim is most important to respondents for all product categories except beef which has it ranked slightly lower (only 0.1% lower) than the non-GMO production claim. However, there appears to be some variation between meat and non-meat products for the second most important claim. For milk and eggs, the second most important claim is the humanely raised claim with non-GMO claim as the third most important. Conversely, the non-GMO claim ranked second in importance for the beef and chicken product blocks with the humanely raised following in third. While humanely raised was in the top three claims across all products, perhaps this claim is of higher importance for non-meat products where the animals are continual producers of a product rather than the case when animals are intended for slaughter. Secondly, information on the animals’ diet seems to be much more important for beef and milk purchasers than for chicken and eggs purchasers. One reason for this may be that the ‘vegetarian fed’ claim is relatively new in the labeling world (Price 2008). Further, since chickens are naturally omnivores (versus cattle who are naturally herbivores), having a vegetarian diet may be less important relative to cattle having a grass-fed diet. Finally, it is interesting to note the lack of importance respondents placed on the ‘Product is certified organic’ production claim—a finding which held across all four product blocks. This result is especially surprising given that the USDA Organic standards encompass many of the claims which were ranked as more important than organic (USDA 2013). One explanation for this result may be that consumers are less aware of how organic is defined for livestock-derived products compared to produce crops (fruits and vegetables). The most common definitions for organic are ‘no chemicals, no pesticides, no fertilizers’ (Yiridoe et al. 2005; Hughner et al. 2007), so consumers may find it difficult to translate these definitions to livestock-derived products.

Heterogeneity in preferences for production claims

As mentioned previously, Table 2 reveals that the standard deviation estimates are significant for all production claims in all product blocks, indicating there is heterogeneity in consumers’ preferences for production claims. To explore possible sources of heterogeneity, we estimated preference shares for each individual and then regressed the preference share for each claim against a host of sociodemographic factors, including gender, age, income, education, geographic region, rural/urban status, farming background, children in the household, primary shopper, and the overall importance placed on production claims in one’s food purchasing decisions. For brevity, we ran the regressions for each production claim using the pooled data across all product blocks, including indicator variables for each product block to capture product-specific differences rather than running separate regressions for each production claim in each product block.

Table 3 reports the results of the estimated regressions. Results reveal that females were more likely to prefer the no hormones claim compared to males, while males were more likely to prefer the free-range/cage-free and grass-fed/vegetarian diet production claims. Interestingly, low and medium income families tended to rank the humanely raised claim as 8.2 and 5.3% more important, respectively, compared to high income families. This finding is in contrast to a review of animal welfare studies that suggests income is positively related to preferences and willingness to pay for animal welfare (Lagerkvist and Hess 2011). High income families, on the other hand, tended to prefer the no antibiotics and organic production claims relative to low income families. There were little differences by age group or education status. We observed that 18–34 year olds ranked organic as 4.0% more important than consumers ages 55 and older and individuals with a graduate degree ranked the non-GMO claim as 6.0% less important than those consumers who do not have a college education.

Table 3

Regression estimates for preference shares (N = 1039)

Variable

Dependent variable: preference share for

No hormones

Non-GMO

Humanely raised

No antibiotics

Free-range/cage-free

Grass-fed/vegetarian diet

Organic

Intercept

0.215***

(0.031)

0.266***

(0.044)

0.209***

(0.039)

0.113***

(0.020)

0.064***

(0.016)

0.052***

(0.005)

0.082***

(0.019)

Femalea

0.034**

(0.015)

−0.019

(0.021)

0.0002

(0.018)

0.005

(0.010)

−0.014*

(0.007)

−0.005**

(0.002)

−0.0003

(0.009)

Age1834b

−0.020

(0.026)

0.001

(0.037)

−0.035

(0.033)

0.0004

(0.017)

0.010

(0.013)

0.004

(0.004)

0.040**

(0.016)

Age3554b

−0.001

(0.017)

0.013

(0.024)

0.006

(0.021)

−0.016

(0.011)

−0.007

(0.008)

−0.002

(0.003)

0.008

(0.010)

LowIncc

−0.026

(0.022)

−0.015

(0.031)

0.082***

(0.027)

−0.031**

(0.014)

0.016

(0.011)

−0.001

(0.003)

−0.027**

(0.013)

MedIncc

−0.0003

(0.021)

−0.016

(0.030)

0.053**

(0.027)

−0.013

(0.014)

−0.006

(0.011)

−0.004

(0.003)

−0.015

(0.013)

Bach Degd

0.024

(0.017)

−0.032

(0.024)

0.001

(0.022)

0.015

(0.011)

−0.004

(0.009)

−0.003

(0.003)

−0.001

(0.011)

Grad Degd

−0.0001

(0.022)

−0.060*

(0.031)

0.024

(0.028)

0.020

(0.015)

0.001

(0.011)

−0.005

(0.003)

0.020

(0.013)

Northeaste

−0.020

(0.021)

0.022

(0.030)

0.007

(0.026)

0.004

(0.014)

0.013

(0.011)

0.005

(0.003)

−0.032**

(0.013)

Midweste

0.007

(0.021)

0.032

(0.029)

−0.014

(0.026)

0.004

(0.014)

0.006

(0.010)

0.00

(0.003)

−0.040***

(0.013)

Southe

0.0001

(0.020)

0.012

(0.028)

−0.016

(0.025)

0.015

(0.013)

0.008

(0.010)

0.006**

(0.003)

−0.026**

(0.012)

Metrof

−0.030**

(0.015)

0.042**

(0.021)

−0.024

(0.018)

0.007

(0.010)

0.001

(0.007)

0.002

(0.002)

0.002

(0.009)

Farm Famg

0.004

(0.022)

0.026

(0.032)

−0.025

(0.028)

0.013

(0.015)

−0.012

(0.011)

0.0003

(0.003)

−0.007

(0.014)

Kids12h

0.014

(0.020)

0.020

(0.029)

−0.043*

(0.026)

−0.005

(0.013)

−0.001

(0.010)

0.004

(0.003)

0.011

(0.012)

PrimShopi

−0.026

(0.024)

0.017

(0.034)

−0.0004

(0.030)

−0.003

(0.016)

0.003

(0.012)

−0.006*

(0.004)

0.016

(0.015)

PMImportj

0.006

(0.014)

0.025

(0.020)

−0.015

(0.017)

0.011

(0.009)

−0.009

(0.007)

−0.001

(0.002)

−0.018**

(0.008)

Chicken prod typek

0.025

(0.020)

0.022

(0.028)

−0.020

(0.024)

0.021

(0.013)

−0.013

(0.010)

−0.033***

(0.003)

−0.003

(0.012)

Eggs prod typek

0.009

(0.020)

−0.008

(0.028)

0.048*

(0.025)

−0.002

(0.013)

0.001

(0.010)

−0.038***

(0.003)

−0.010

(0.012)

Milk prod typek

0.051**

(0.020)

−0.023

(0.028)

0.044*

(0.025)

−0.019

(0.010)

−0.019*

(0.010)

−0.007**

(0.003)

−0.027**

(0.012)

R2

0.026

0.019

0.032

0.031

0.028

0.198

0.044

*, **, ***denotes p < 0.10, p < 0.05, p < 0.01, respectively

aEffect relative to males

bEffect relative to individuals ages 55 and older

cEffect relative to individuals with annual household income of $100,000 or more

dEffect relative to individuals who have not earned a 4-year college degree

eEffect relative to individuals who live in the Western region of the U.S

fEffect relative to individuals who live in a non-metropolitan area

gEffect relative to individuals who do not have a farming background

hEffect relative to individuals who do not have children under 12 in the household

iEffect relative to individuals who are not the primary shopper in the household

jEffect relative to individuals who do not consider production methods to be important in food buying decisions

kEffect relative to individuals in the beef product block

There tended to be some differences in preferences based on the geographic location of consumers. Consumers in the Southern U.S. were more interested in the grass-fed/vegetarian diet claim compared to consumers in the West; however, Western consumers ranked the organic production claim as significantly more important than consumers in the Northeast, Midwest, and Southern regions (3.2, 4.0, and 2.6% more important than these regions, respectively). This may likely be explained by the abundance of organic livestock production in the Western region of the U.S., particularly in California (ERS 2013). Consumers who lived in metropolitan areas placed more importance on non-GMO production claims relative to those living in non-metropolitan areas, yet the reverse was true for the no hormones claim.

While we expected those consumers with a farming background to have different preferences for production claims relative to individuals with no agricultural background, no differences were observed between the two groups. Individuals with young children in the home exhibited lower preference shares (4.3% less) for the humanely raised production claim relative to those without young children, while primary shoppers placed less emphasis on the grass-fed/vegetarian diet claim compared to non-primary shoppers.

The final three rows in Table 3 reveal the differences in preferences for production claims based on the type of product. Consistent with our Fig. 2 results, we found that consumers in the milk product block ranked the no hormones claim as more important than consumers in the beef product block. Additionally, individuals in the milk and eggs blocks exhibited higher preference shares for the humanely raised production claim relative to those in the beef product block. Consumers in the beef product block, conversely, placed significantly more weight on the grass-fed/vegetarian diet relative to consumers in all other product blocks. Further, the free-range/cage-free and organic production claims were more important in the beef product block compared to the milk product block.

From Table 3, it should be noted that the R-squared values are relatively low across all of the preference share regressions, explaining less than 5% of the variability in preferences for six of the seven production claims. The grass-fed/vegetarian diet regression is the one exception, with an R-squared of 0.198; however, this higher value is likely driven by the highly significant product block results. Thus, while Table 2 suggests heterogeneity in preferences exists, our analysis shows the heterogeneity is not likely explained by differences in sociodemographic factors.

Conclusion

Production claims are becoming increasingly popular on food products. While many production claims have been studied in isolation (e.g., what you would be willing to pay for cage-free eggs?), little research has forced consumers to prioritize different production claims. We use a best-worst scaling framework to examine the importance of seven common production claims. We compare the importance of these claims across four product types—beef meat products, milk, chicken meat products, and eggs—to determine if the importance of claims varies by species or between meat and non-meat products.

Results of our study show that the ‘Animals were not administered growth hormones’ claim was one of the most important across all product types. This was a particularly interesting finding in the case of chicken as the USDA prohibits the use of hormones in poultry (USDAb 2011). Whether consumers know this, however, is unclear. Poultry products that utilize this label claim must accompany the claim with the following statement: “Federal regulations prohibit the use of hormones.” (USDAb 2011), but this is often in very fine print. The ‘No genetically-modified organisms used in production’ and ‘Animals were humanely raised’ claims were also rated as very important to respondents, though there are clear order differences for these two claims between meat and non-meat animal products. The attention to non-GMO claims was not surprising given the recent ballot initiatives in many states (Center for Food Safety 2014). Claims viewed as less important were ‘Animals were grass-fed (or raised on a vegetarian diet)’, ‘Animals were raised in a free-range (cage-free) environment’, and ‘Product is certified organic’.

The most surprising result in this study was the lack of importance attributed to the ‘certified organic’ production claim. The USDA Organic requirements, as well as the different humane certification schemes, are quite comprehensive in nature. In fact, the organic and humane standards encompass virtually all of the other production claims we studied. For these reasons, we expected participants to sort the ‘certified organic’ and ‘humanely raised’ production claims to the top, yet this is not what we observed. Rather, respondents identified many of the individual components (such as no growth hormones, non-GMO, no antibiotics) of these broader certifications as more important. Possible explanations for this may be that consumers are unaware of the complete requirements for these certification systems or, in the case of organic, are less familiar with how the guidelines translate to livestock-derived products. Alternatively, consumers may only care about a few of the specific components (production practices) of these all-encompassing production claims and sort them accordingly. Based on findings from Jaffee and Howard (2010), another explanation may be that consumers believe the organic label has become too “mainstream” and question the stringency of its standards.

When considering how these importance rankings relate to consumers’ value systems, the interest in the ‘no growth hormones’, ‘non-GMO’, and ‘no antibiotics’ claims likely indicates a concern for (or value of) personal health and safety. DuPuis (2000) notes that the use of recombinant bovine growth hormone (rBGH) in dairy cows was a key factor (and the use of antibiotics to a lesser extent) in the growth of the organic milk market, particularly for parents who were concerned about the potential risks of rBGH for their children. Further, several studies have found consumers to be hesitant toward genetically modified food products for health and safety reasons, though some studies have also highlighted concern for the environment among GM-averse consumers (see Costa-Font et al. 2008 for a review). Preference for the ‘humanely raised’ and ‘cage-free/free-range’ production claims, on the other hand, most likely demonstrates a value for animal (as opposed to personal) wellbeing. Lagerkvist and Hess (2011) note, however, that the act of purchasing these products will likely still increase one’s personal utility because they are able to meaningfully express their value for animal welfare in the marketplace. Though the ‘organic’ production claim was less preferred in this study overall, a preference for this label could signal a value for the environment, animal welfare, and/or one’s personal health and safety. Research has shown consumers attribute a wide variety of benefits to products carrying this label, though organic production is less often associated with animal welfare benefits (Yiridoe et al. 2005; Hughner et al. 2007).

Limitations and areas for further research

Although we worked with a panel that was designed to be representative of the U.S. population, our sample characteristics do not fully mirror U.S. census data for all socio-demographic variables. For instance, we have an over-sampling of individuals ages 55 and older and an under-sampling of individual in the highest income category ($100,000 or more). We control for socio-demographics in our preference share regressions to account for differences in these subpopulations should they exist; however, it may limit the generalizability of our results. Another potential concern with online surveying is that panel participants may not be representative of the average consumer. Many survey panels, including the one used in this study, use opt-in panels rather than probability sampling. Lusk and Brooks (2011) compared two opt-in panels to a random, probability-based sample of the U.S. population and found the potential for sample selection and participation biases with opt-in panels; these sources of bias cannot be ruled out given the nature of the panel used in this study. Finally, while our model results suggest there is heterogeneity in preferences for production claims, these differences are not explained well by sociodemographic factors alone. Future research should look to explore the roles of attitudes and beliefs, for example, as predictors of preference shares.

Footnotes

  1. 1.

    While many studies exist for the production claims selected, we provide some examples of each here: preferences for organic (Kiesel and Villas-Boas 2007; Napolitano et al. 2010); humanely raised (Tonsor et al. 2009; Nocella et al. 2010); grass-fed (Sitz et al. 2005; Xue et al. 2010); no growth hormones (Lusk and Fox 2002; Alfnes and Rickertson 2003); no antibiotics (Lusk et al. 2006); free-range and cage-free (Michel et al. 2011; Heng et al. 2013); and non-GMO (Lusk and Fox 2002).

  2. 2.

    Originally, we designed the survey to look at ground beef and chicken breasts, specifically. However, upon consultation with our data collection partner, we opted to broaden the meat categories so as to not exclude consumers who may purchase beef steaks or chicken drumsticks, for example. For each of these categories, we did ask respondents to specify which types of meat or chicken products they purchase regularly.

  3. 3.

    For more information, visit http://www.clearvoiceresearch.com.

  4. 4.

    For those interested in the MNL results, please refer to Table 4 in the “Appendix” section.

Notes

Acknowledgements

This research was supported by USDA NIFA #ILLU-470-356 and funding from the American Jersey Cattle Association/National All-Jersey, Inc.

Compliance with ethical standards

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Agricultural and Consumer EconomicsUniversity of IllinoisUrbanaUSA
  2. 2.Department of Agricultural EconomicsUniversity of Nebraska-LincolnLincolnUSA

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