Key Findings

This report covers a lot of results, but it is not exhaustive. Over the next few months, Faunalytics will be conducting a few additional analyses. These will include a more detailed look at how taste ratings differ by label, as well as an examination of the animal product alternatives eaten by people with different diets.

Together, these three phases of research give us a strong idea of people’s preferences for the different labels.

In the third and final phase of this project, a large, nationally representative sample of participants made direct, head-to-head comparisons between the eight best performers from the second phase of the study.

In this three-phase project, we started off by crowd-sourcing a list of potential terms for meat alternatives. We then narrowed the list of suggestions down to 20 and tested them for appeal with meat consumers on Mechanical Turk. The terms included options like direct protein, harmless, and eco. We also included vegan and plant-based. Participants rated how good each product label sounded to them and indicated how likely they were to buy it.

However, to our knowledge, Faunalytics’ current project is the first to rigorously compare these terms and others using validated scientific methodology and a nationally representative sample of consumers.

A poll of 1,163 social media users suggested that plant-based might be perceived more positively and as more of a dietary choice than a lifestyle choice ( Food Navigator, 2018 ), which provided encouragement for the adoption of the term.

The term “plant-based” has been widely adopted as an alternative to “vegan” and “vegetarian” ( Christian Science Monitor, 2018 ). There are good theoretical reasons for this move: The term focuses on what a product contains rather than what it lacks, and it doesn’t have the baggage associated with veganism. However, not much research has examined the relative merits of the terms “plant-based” and “vegan.”

Over the past few years, describing products as vegan has increasingly been considered a bad idea. For example, industry leaders have recommended that companies avoid using “v-words” on their meat-free products ( Food Navigator, 2018 ).

Full Report

Phase 1: Sourcing Terms

In the first stage of this project, we crowd-sourced a list of potential terms for meat alternatives using a Facebook convenience sample largely made up of meat-eaters. We received 107 responses from 37 individuals.

The research team refined this list by removing duplicate and very similar responses, as well as responses that did not provide specific suggestions, leaving 27 options.

We considered all responses from the brainstorming session in light of their possible positive and negative associations, as well as their ability to convey the source or benefits of the product. Using these criteria, and striving to include a wide range of options, we narrowed the list of terms to 20 to include in Phase 2: clean, direct protein, earth-based, earth powered, eco, enlightened, feel-good, future, harmless, kojo (a word with no inherent meaning in English), longevity, mindful, planet friendly, plant forward, plant powered, plant strong, plantiful, sun powered, sustainable, and zero cholesterol.

Phase 2: Sound And Likelihood Of Purchase

Phase 2 Sample And Method

In the second stage of the project, the list of 20 terms was subjected to more rigorous testing. After excluding duplicates and people who failed an attention check, the sample included 565 participants from Amazon’s Mechanical Turk.

Methodological note: Each participant rated half of the new terms, which means the study had better than 80% power to detect even small differences (dz = .20) between the labels in the full sample. The power to detect differences within subgroups is lower, but this study provides a general sense of the patterns, which we used to make predictions for Phase 3.

First, participants were randomly assigned to either see or not see a short description of the characteristics and benefits of plant-based foods. However, reading this preamble did not significantly affect the average responses for any of the labels, so this is not discussed further (the preamble can be seen in the survey instrument).

Then, all participants were presented with mock-ups of 12 plant-based products (examples are shown in Figure 1). Ten of the products had a randomly determined label from the full list of 20. The other two products, which everyone rated for benchmarking purposes, were labeled vegan plant-based. Each product was randomly assigned a type for each participant: cheese slices, chicken nuggets, and burgers. Thus, each participant saw a mix of these three product types and 12 different labels.



Figure 1. Product Mock-Ups.

Products were presented one at a time, in random order. Participants were asked two questions about each product:

“Imagine this product is widely available at grocery stores, restaurants, and markets. How likely are you to try it?”

(rated on a 5-point scale from not at all likely to extremely likely) ; and

; and “How good or bad does this product sound to you?”

(rated on a 7-point scale from extremely bad to extremely good)

At the end of the survey, participants were given a basic Food Frequency Questionnaire (FFQ) to determine whether they consume animal products and/or animal product replacements, and several demographic questions.

Phase 2 Results

Our target audience was people who currently eat meat, so vegans and vegetarians (n = 35) were excluded from the analyses.

Figures 2 and 3 below show the average sound and likelihood of trying the product for each label.

Notes. This scale ranged from 1, extremely bad, to 7, extremely good, with a neutral midpoint of 4. The error bars show the 95% confidence interval for each estimate.

Figure 2. Sound of the Product.

Note. This scale ranged from 1, not at all likely, to 5, extremely likely, with a midpoint of 3 (moderately likely). The error bars show the 95% confidence interval for each estimate.

Figure 3. Likelihood of Trying the Product.

Methodological note: For the analyses comparing these labels, we standardized and averaged the sound and likelihood items to give them equal weight. Results presented this way are available in the Supplementary Materials.

As you can see in the graphs above, the labels were rated quite similarly overall. Feel-good scored significantly better than vegan and plant-based (p = .005 and p = .002, respectively), but no other differences attained statistical significance.

With scores of 4.1 and 4.0, respectively, vegan and plant-based were rated as sounding “neither bad nor good” on average. And scores of 2.5 and 2.6 on the other scale mean that the average person is between “slightly likely” and “moderately likely” to try them: not exceptionally positive, but not bad either, considering these are all meat consumers.

We also computed the averages for demographic subgroups. They can be found with our pre-registration for Phase 3 because we used those patterns to inform our hypotheses. However, we did not conduct significance tests because of the small sample sizes and number of comparisons—the results for subgroups from Phase 2 should be considered very exploratory.

Phase 3: Head-To-Head Comparisons

The third and final phase of this project pitted the most promising labels against each other in a head-to-head comparison, like seeing them next to each other in the freezer section at the grocery store. This time, for ease of interpretation, we focused on the most widely known and accepted meat-free product: the veggie burger. To ensure that packaging couldn’t overshadow the labels, we also stuck to text rather than product mock-ups for the comparisons.

This study included two standard labels—vegan and plant-based—as well as the six highest-scoring labels from the second phase: clean, direct protein, feel-good, kojo, planet friendly, and zero cholesterol. (It is worth noting, again, that only feel-good was rated significantly higher than vegan and plant-based.)

Phase 3 Sample And Method

Data were collected in October/November 2018. A census-balanced, representative sample of U.S. adults was recruited through the research firm Toluna. After excluding duplicates and people who failed an attention check, the sample included 1,431.

As in the second phase of research, analyses were conducted on people who consume meat only (n = 1,383; 48 veg*ns removed). This is more than the 1,340 that we planned for in our pre-registered power analysis, meaning that our regression analyses have better than 99% power to detect effects. In other words, we can be very confident that the results would be the same if the study were run again.

For this study, we were most interested in the differences in how terms are perceived. To assess these differences, we used a pairwise comparison technique created by Oishi, Schimmack, Diener, & Suh (1998) that has since been used in other research (e.g., Sheldon, Elliot, Kim, & Kasser, 2001).

In this method, participants were asked to make a series of quick pairwise comparisons between all possible pairs of labels (28 comparisons in total). The order of the pairs was randomized, as was the left-right presentation of each pair. Participants were given five response options per choice. For example:

Each pairwise response provides two scores between -2 and +2: one per label. In the example above, choosing “second sounds a bit better” would score +1 for vegan and -1 for direct protein. Each label appears seven times (once with every other label). Therefore, each participant will have an overall preference score for each of the eight labels that ranges from -14 to +14.

Methodological note: This method eliminates the influence of response bias because the method focuses on relative preference, and each participant’s average score will be 0 by design. In addition, because every participant compares every pair of labels, there is no missing data to contend with, which prevents the design itself from introducing bias.

Next, participants rated each label using product mock-ups. They rated each product label for impact on animals, environmental friendliness, healthfulness, and taste using 5-point scales (e.g., 1, very bad for animals, to 5, very good for animals). Finally, they completed demographic questions and indicated their current consumption of animal products and alternatives. All of these questions can be seen in the survey instrument.

Phase 3 Results

Methodological note: We pre-registered a set of hypotheses and analyses for this study, based on the results of the Phase 2 study. These can be found on Faunalytics’ Open Science Framework page. We note in the text where the results supported or rejected our pre-registered hypotheses.

A Note On The Graphs

The way the labels are scored, as described above, produces a range of possible scores from -14 to +14 (most tend to fall in the middle, close to 0). Therefore, the graphs below show a range that includes positive and negative numbers, reflecting above- and below-average scores.

The scores do not have much meaning on their own, but the purpose of this study was to look at differences between labels and groups. To read the graphs, focus on the differences between labels or groups rather than the numbers themselves.

The error bars show the 95% confidence interval for each estimate. The error bars give a good sense of which ratings are significantly different. If they overlap by more than about 25% of their length, the difference is not statistically significant.

How Do The Labels Compare?

Notes. Scale range: -14 to +14 (A participant’s score for a label would be +14 if they said it sounds much better than every other label, or -14 if they said every other label sounds much better than it). Error bars represent confidence intervals of the estimates.

Figure 4. Average Label Scores.

As shown in the graph above, feel-good was rated the highest on average, followed by vegan. Plant-based scored the lowest. In other words, when these labels were directly pitted against each other, people tended to choose feel-good and vegan a lot, and plant-based the least.

The findings for plant-based supported our hypothesis, but the findings for vegan did not. We had guessed that all of the new labels would do better than both standard labels. We were also surprised to see that vegan outperformed plant-based by so much.

Not all of the differences you see in the graph are significant. For example, you may notice that there is a lot of overlap in the error bars for kojo, direct protein, planet friendly, and zero cholesterol. The average ratings of those labels are not significantly different. That is, you should consider them to be the same.

Methodological note: The details of the pairwise analyses are included in the Supplementary Materials.

Explaining The Ratings: Taste, Health, Animals, Or Environment?

To understand why we see this pattern, we conducted a regression analysis. That is, we looked at the associations between how good people thought each label sounded overall on the one hand, and several product features on the other: their ratings of its healthiness, taste, impact on animals, and environmental friendliness. (Graphs of the average ratings for each label are provided in the Supplementary Materials).

We found that, of these, the only significant predictor was taste: When people thought a label sounded tasty, they liked the sound of it more. Their beliefs about how healthy, good for animals, or environmentally friendly the labels were varied quite a bit, but didn’t have any effect on overall preferences.

The importance of taste perceptions can partially explain why feel-good did so well and plant-based so poorly, but the regression analysis showed that taste perceptions account for only a small amount of the variance in people’s overall label ratings. There is a lot of room for other factors to come into play.

Methodological note: The details of this regression analysis are included in the Supplementary Materials.

Consumer Characteristics

Taste—as well as impact on health, animals, and the environment—are features of the product that may influence how much people like the sound of it. As we saw above, taste was the only product feature of the four we tested that had a significant influence.

However, we conducted this study in part because we believed that characteristics of the consumer are important as well. In everyday life, it is often apparent that things appeal differently to different people. In the domain of meat alternatives, quantifying differences in label appeal may provide valuable insight into marketing strategies.

We examined the impact of six consumer characteristics on preferences. Those were: age, gender, ethnicity, region of the U.S., income level, and status as a current consumer or non-consumer of meat alternatives.

Methodological note: The details of this regression analysis are included in the Supplementary Materials.

Gender and age had a significant impact on participants’ preferences, as did whether they consume meat alternatives or not. On the other hand, ethnicity/race, region, and income did not significantly affect preferences. This doesn’t mean that there were no significant differences by ethnicity/race, region, or income for any label, but they don’t have much explanatory power and may not be reliable, so we haven’t described them in this report.

Graphs of the label ratings for all demographic groups are provided at the end of the Supplementary Materials.

The next sections describe the specific results for gender, age, and consumption status.

Gender And Age

The graphs below show how gender and age influenced responses to each of the eight labels.

Feel-good: This label was liked significantly more by younger people than older people (p = .001).

Vegan: This label was significantly less appealing to men than women (p < .001), especially younger men. As we had hypothesized, younger men rated vegan lower on average than women and older men (p = .04).

Direct protein: This label was significantly more appealing to men than women (p < .001), especially younger men. As we had hypothesized, younger men rated direct protein significantly higher on average than women and older men (p < .05).

Plant-based: Men liked this label significantly less than women did (p = .001). We had hypothesized that, like vegan, younger men would particularly dislike plant-based, but this was not supported (p = .33).

Kojo: This label was significantly more appealing to men than women (p < .001), and to younger than older people (p < .001).

Zero cholesterol: The appeal of this label was strongly influenced by age, with older people liking it significantly more than younger people (p < .001).

Clean and planet friendly: Age and gender had no significant effect on ratings of these labels (all ps > .11).





Note. Gender = “Other” suppressed because of the very small sample size.

Figure 5. Label Preferences By Gender And Age.



Consumers And Non-Consumers Of Animal Product Alternatives

Participants were categorized as consumers of animal product alternatives using a broad definition: having eaten a meat, dairy, or egg alternative at least once in the past three months. Among the omnivores who were included in these analyses, 58% were categorized as alternative consumers. (Please note that a previous version of this report erroneously stated 43%.) The graph below shows a breakdown of the labels by alternative consumption status.

Figure 6. Label Preferences By Alternative Consumption Status.

The regression analyses for individual labels showed that consumption status had a significant influence on ratings of several labels.