1. Introduction
Recent developments in biotechnology and accelerating advancements in automation, artificial intelligence, and computing are creating a new wave of innovations. These advances in science can play an important role in transforming economies and societies by tackling global challenges ranging from hunger to climate change. In particular, recent tools and techniques in biological science (i.e., gene editing (GE) and genetically modified organisms (GM); Cummings and Peters (Reference Cummings and Peters2022)) enable us to develop new food and agricultural products (such as crops with climate resilience, high yield, and nutrient-dense features, etc.) that may impact our lives, from health to agriculture, in positive ways. GM is a process of inserting foreign genes – from the same species or distant species – into the crops' genomes to produce particular traits (National Academies of Science, Engineering, and Medicine, 2016; Jones et al., Reference Jones, Fosu-Nyarko, Iqbal, Adeel, Romero-Aldemita, Arujanan and Kasai2022; Shukla-Jones et al., Reference Shukla-Jones, Friedrichs and Winickoff2018). In contrast, GE, a relatively new innovation, is a process of modifying the genes that already exist within the crops (National Academies of Science, Engineering, and Medicine, 2016; Jones et al., Reference Jones, Fosu-Nyarko, Iqbal, Adeel, Romero-Aldemita, Arujanan and Kasai2022; Shukla-Jones et al., Reference Shukla-Jones, Friedrichs and Winickoff2018). Unlike GM, GE does not introduce foreign genes into the crops – closely mimicking random mutation in nature (National Academies of Science, Engineering, and Medicine, 2016; Shukla-Jones et al., Reference Shukla-Jones, Friedrichs and Winickoff2018).
The application of GM technology to food has faced significant backlash since its inception, even though GM food products on the market have passed food safety tests and are considered safe for human consumption (Mielby et al., Reference Mielby, Sandøe and Lassen2013). This backlash comes from the perception that GM is an unnatural way to modify (inserting the foreign genes); hence, it creates food safety concerns (Mielby et al., Reference Mielby, Sandøe and Lassen2013). Moreover, concerns about the uncertain ethical and environmental effects of GM technology are also affecting consumers' negative attitudes toward GM food (Chern et al., Reference Chern, Rickertsen, Tsuboi and Fu2002; Hess et al., Reference Hess, Lagerkvist, Redekop and Pakseresht2016). For instance, it almost took 20 years to approve GM-based salmon – modified to grow fast – due to resistance from both the public and the food industry (Mielby et al., Reference Mielby, Sandøe and Lassen2013). Although GE has the potential to alleviate the perceptions of unnaturalness as it mimics nature's random mutation (Kilders & Caputo, Reference Kilders and Caputo2021), the current discussion among scientists, producers, and policymakers is whether GE spurs similar alarms like GM among consumers.
The introduction of new technology often involves uncertainty, given that the long-term effects of adopting the technology are unknown (Ding et al., Reference Ding, Yu, Sun, Nayga and Liu2023). Consequently, individuals' decision-making under uncertainty is heavily influenced by their risk attitudes (Barham et al., Reference Barham, Chavas, Fitz, Salas and Schechter2014; Cocosila et al., Reference Cocosila, Archer and Yuan2009; Curran and Meuter, Reference Curran and Meuter2005; Stuck and Walker, Reference Stuck and Walker2019). Several studies (e.g., Amin et al., Reference Amin, Ahmad, Jahi, Nor, Osman and Mahadi2011; Connor and Siegrist, Reference Connor and Siegrist2010; Siegrist & Hartmann Reference Siegrist and Hartmann2020; Costa-Font and Gill, Reference Costa-Font and Gil2009; Sodano et al., Reference Sodano, Gorgitano, Verneau and Vitale2016; Hakim et al., Reference Hakim, Zanetta, de Oliveira and da Cunha2020; Rosati and Saba, Reference Rosati and Saba2000; Siegrist, Reference Siegrist1999; Zhu et al., Reference Zhu, Yao, Ma and Wang2018) documented the link between consumers' risk perceptions and their attitudes toward GM food. In particular, the relationship between the acceptance of GM food and risk perceptions of the application of GM technology in agriculture is negative (Bearth and Siegrist, Reference Bearth and Siegrist2016). Based on a meta-analysis of 15 studies, Bearth and Siegrist (Reference Bearth and Siegrist2016) identified that the correlation coefficients for the risk perception–acceptance relationship vary between −0.08 and −0.81.
There are three different techniques to measure an individual's risk attitude. Prospect theory argues that an individual's risk-taking behavior is asymmetric about a reference point (Kahneman and Tversky, Reference Kahneman and Tversky1979). For example, individuals' risk behavior may change based on whether they perceive themselves in the domains of gain or loss (Kahneman and Tversky, Reference Kahneman and Tversky1979). Posed in a different way, individuals' risk attitudes depend on scenarios. In contrast, studies such as Hollenbeck et al. (Reference Hollenbeck, Ilgen, Phillips and Hedlund1994) and Zuckerman and Kuhlman (Reference Zuckerman and Kuhlman2000) showed that the risk attitude of individuals depends on their personality traits rather than their situations. Later, Weber et al. (Reference Weber, Blais and Betz2002) demonstrated – an important development in the field of risk analysis – that both personality traits and scenarios are important factors in explaining individuals' risk behaviors. The above-mentioned studies utilized either individual traits or situations-based scale to measure individuals' risk attitude. However, in our study, following the findings of Weber et al. (Reference Weber, Blais and Betz2002), we employed the Nicholson et al. (Reference Nicholson, Soane, Fenton-O'Creevy and William2005) scale, which captures both scenarios and personality traits while measuring individuals' overall risk propensities.
In addition, people are more sympathetic and closer to animals than plants, given that humans are also animals, and both humans and animals are emotional beings (Yunes et al., Reference Yunes, Teixeira, von Keyserlingk and Hötzel2019). Consequently, people have a tendency to be more concerned about the effects of new biotechnology and farming techniques on animal health and welfare than plants (Mench, Reference Mench and Mench1999; Yunes et al., Reference Yunes, Teixeira, von Keyserlingk and Hötzel2019; Nian et al., Reference Nian, Gao and Zhao2021). Moreover, risk-averse individuals prefer outcomes with low uncertainty to outcomes with high uncertainty (Werner, Reference Werner2008). Combining the findings of Werner (Reference Werner2008) with key results of Mench (Reference Mench and Mench1999) and Yunes et al. (Reference Yunes, Teixeira, von Keyserlingk and Hötzel2019), we can deduce that consumers' attitudes toward GM and GE food may differ depending on whether the food is a plant- or animal-based and that their risk attitudes may explain this attitudinal difference. Surprisingly, none of the existing studies considered consumers' attitudinal differences toward plants and animal products while investigating consumer behavior toward GM and GE food. Failing to capture this attitudinal difference while studying public attitudes toward GM and GE food may provide either overestimated or underestimated consumer response.
To address this research gap, our study focuses on the following two objectives: (1) to determine the effects of US consumers' day-to-day risk propensity on their level of acceptance of both GM and GE foodFootnote 1 and (2) to investigate the differences in US consumers' level of acceptance of GM and GE plants and animal products and identify the link between consumers' risk propensity and this attitudinal difference.
Our research contributes to the existing literature in two ways. First, our study identifies differences in US consumers' attitudes toward plants and animal products both in the context of GM and GE and detects a factor (i.e., individuals' risk attitudes) that can explain these attitudinal differences toward GM and GE plants and animal products. To the best of our knowledge, our research is the first to examine the link between consumers' risk behavior and the differences in their attitudes toward plants and animal products in the context of both GM and GE. Second, our study compares US consumers' acceptance of GM and GE both in the context of plants and animal products. Although recent studies have investigated consumer attitudes toward GM versus GE food products, most have been focused on non-US consumers (Muringai et al., Reference Muringai, Fan and Goddard2020; Son and Lim, Reference Son and Lim2021; Bearth et al., Reference Bearth, Kaptan and Kessler2022). The exceptions include Ding et al. (Reference Ding, Yu, Sun, Nayga and Liu2023) and Shew et al. (Reference Shew, Nalley, Snell, Nayga and Dixon2018), which compare US consumers' attitudes toward GM and GE but only focus on plant-based products.
The remainder of the paper is organized as follows. Section 2 includes a literature review followed by a discussion about the characteristics of the sample and the methodology of the study in Section 3. The descriptive analyses of the data and regression models are provided in Section 4. The last section includes policy implications, limitations, and conclusions.
2. Literature review
Ongoing scientific research focusing on consumer acceptance of and willingness to pay (WTP) for GM food indicates that individuals' demographic characteristics, psychological traits along with information provision may play important roles in explaining their attitudes toward GM food (Delmond et al., Reference Delmond, MnCluskey, Yormirzoev and Rogova2018; Hu et al., Reference Hu, Zhong and Ding2006; Huang and Peng, Reference Huang and Peng2015; Şanlıer & Sezgin, Reference Şanlıer and Ceyhun Sezgin2020). For example, Şanlıer and Sezgin (Reference Şanlıer and Ceyhun Sezgin2020) identified that female participants were more suspicious of GM food products than male participants. Moreover, Hu et al. (Reference Hu, Zhong and Ding2006) and Huang and Peng (Reference Huang and Peng2015) found that Chinese consumers' education level negatively affects their trust in and WTP for GM food products, respectively. On the other hand, Huang and Peng (Reference Huang and Peng2015) showed that the negative media coverage of biotechnology affects Chinese consumers' perceptions of GM food safety.
Studies that examine consumer acceptance of GE food and its comparison with that of GM food show that consumers are more accepting of GE food as compared to GM food (Kilders and Caputo, Reference Kilders and Caputo2021; Muringai et al., Reference Muringai, Fan and Goddard2020; Shew et al., Reference Shew, Nalley, Snell, Nayga and Dixon2018; Yang and Hobbs, Reference Yang and Hobbs2020; Marette et al., Reference Marette, Disdier and Beghin2021; Ding et al., Reference Ding, Yu, Sun, Nayga and Liu2023). However, the preferences depend on consumers' perceptions and scientific knowledge (Shew et al., Reference Shew, Nalley, Snell, Nayga and Dixon2018; Son and Lim, Reference Son and Lim2021), the nature of information provision (Yang and Hobbs, Reference Yang and Hobbs2020), information sources (Muringai et al., Reference Muringai, Fan and Goddard2020), and tangible benefits of technology (Kilders and Caputo, Reference Kilders and Caputo2021).
Given that GE is a relatively new technology, studies focusing on the role of risk perception on consumers' attitudes toward GE food are very limited. The exception includes Kato-Nitta et al. (Reference Kato-Nitta, Tachikawa, Inagaki and Maeda2022), Ding et al. (Reference Ding, Yu, Sun, Nayga and Liu2023), and Paudel et al. (Reference Paudel, Kolady, Just and Ishaq2023). Kato-Nitta et al. (Reference Kato-Nitta, Tachikawa, Inagaki and Maeda2022) studied the public risk perceptions of the agricultural application of GM and GE in three different countries (i.e., Germany, Japan, and the United States). They argued that government's regulations for emerging food technologies affect public risk perceptions of GM and GE food. In other words, consumer's risk perception of GE food is higher in a country with strict regulations for food technology than a country with less strict regulations for food technology. For example, given the strict regulations for GM food in German, consumers from German showed least positive attitudes toward GE food as compared to Japanese and US consumers. They also found that although German participants had greatest interest in the application of biotechnology in food and highest trust in policymakers, they had a very little knowledge of GE. Moreover, they revealed that risk perceptions of applying GM and GE technologies in food are significantly lower in the United States – a country with least strict regulations for GM food. In other words, US consumers showed the most positive attitude with perceptions of the lowest risk of applications of GM and GE technologies in food (Kato-Nitta et al., Reference Kato-Nitta, Tachikawa, Inagaki and Maeda2022). Komoto et al. (Reference Komoto, Okamoto, Hamada, Obana, Samori and Imamura2016) also identified similar findings while comparing US consumers with Japanese and French consumers. Specifically, authors showed that, among the United States, Japan, and France, US participants showed the lowest concern of GM food.
Ding et al. (Reference Ding, Yu, Sun, Nayga and Liu2023) and Paudel et al. (Reference Paudel, Kolady, Just and Ishaq2023) estimated consumers' valuations of GE food using risk and ambiguity concepts and information provision, respectively. Specifically, Ding et al. (Reference Ding, Yu, Sun, Nayga and Liu2023) investigate the effects of risk and ambiguity on Chinese consumers' WTP for newly introduced GE rice. Using an online survey in China with a multiple price list method, the authors found that Chinese consumers' risk and ambiguity aversions negatively affect their valuations for GE rice (Ding et al., Reference Ding, Yu, Sun, Nayga and Liu2023). The authors also showed that Chinese consumers were willing to pay a premium for conventional rice to avoid both GM and GE rice. Moreover, Chinese consumers were willing to pay less premium for conventional rice when the alternative option is GE rice than when it is GM rice, indicating the tendency to accept GE rice more than GM rice (Ding et al., Reference Ding, Yu, Sun, Nayga and Liu2023). In contrast, Paudel et al. (Reference Paudel, Kolady, Just and Ishaq2023) investigated the effect of information and innovator's reputation on US consumers' WTP for GE soybean oil and GE apples. They found that US consumers were willing to pay a premium for GE soybean oil when they received information about technology and health and environmental benefits. However, this information provision did not affect consumers' valuations for GE apples.
3. Data and method
3.1. Participants
We conducted an online survey of US consumers between December 14, 2021, and January 18, 2022, yielding 1,318 survey responses. However, we used 1,196 responses after dropping observations that failed to pass our data quality filters. Our data quality filters focus on the age of respondents and the attention questions. Our attention check questions were directed choice questions where the questions explicitly tell participants how to respond to the question.Footnote 2 There were three attention choice questions in the survey. Respondents who reported their age below 18 or above 100 years and/or answered at least one attention question incorrectly were omitted from analyses. We designed a web-based survey using Qualtrics software. The survey was distributed to respondents using Mechanical Turk (MTurk) – an online platform that allows individuals to participate in research studies and provides monetary compensation for their valuable time. Participants who were primary shoppers and 18 years of age or older were eligible for the online survey. On average, participants spent around 12 minutes taking the online survey and received a $3.33 of participation fee. It should be noted that the Institution Review Board (IRB) of the authors' university approved this study, and respondents provided informed consent.
3.2. Data
Respondents were asked a number of questions designed to capture their level of acceptance of GM and GE products, day-to-day risk attitudes, and demographic features as described below:
Participants were asked to report their level of acceptance of GM and GE plants and animal products on a five-point Likert scale ranging from “unacceptable (=1)” to “totally acceptable (=5).” To estimate participants' subjective knowledge, we asked participants to self-report their knowledge about GM and GE on four-point scales anchored by “nothing at all (=1)” and “a great deal (=4).” In addition, we asked eight true or false questions regarding GM and GE technologies (four questions for each topic) to determine participants' objective knowledge of GM and GE. Based on the participants' responses, we developed an objective knowledge index for each topic (i.e., objective knowledge of GM and GE) by dividing the total number of correct answers by four. Consequently, each objective knowledge index ranges from 0 to 1. The higher the value of the index, the higher the knowledge of the topic.
As noted earlier, our study utilized a modified version of the Nicholson et al. (Reference Nicholson, Soane, Fenton-O'Creevy and William2005) scale to estimate participants' risk-taking attitudes. Specifically, participants were asked to self-report their risk-taking attitudes in the following five domains: (1) recreational risks (e.g., bungee jumping, scuba diving, and rock climbing), (2) health risks (e.g., poor diet, smoking, and high alcohol consumption), (3) financial risks (e.g., declined job offer with better position and salary, and quitting a job without another to go to), (4) safety risks (e.g., cycling and motorbike riding without a helmet, and speeding while driving a car), and (5) social risks (e.g., challenging a decision or rule publicly and standing for election). Respondents answered each item using a five-point Likert scale ranging from “never (=1)” to “very often (=5).” We calculated respondents' risk propensity by averaging their responses to these five domains. However, in our dataset, the first category (never = 1) and the last category (very often = 5) accounts only <1% and <5% of observations, respectively. Therefore, to avoid having a few observations at the tails of the five-point scale, the first and last categories were condensed, providing a three-point scale. The merged first two categories (i.e., never (=1) and rarely (=2)) were labeled as low-risk propensity, while we labeled the merged last two categories (i.e., often (=4) and very often (=5)) as a high-risk propensity. The middle category (i.e., quite often (=3)) was labeled as a medium-risk propensity.
We also collected respondents' demographic information such as age, gender, race, education level, and household size. The demographic variables (except household size) were processed to utilize in our analyses. In particular, data collected on respondents' age was in ranges. Therefore, the midpoint technique was utilized to convert age ranges into numerical values. We used a dummy variable for gender, denoting females as 1 and males 0. Likewise, a dummy variable for college education was created. We assigned a value of 1 if the respondent has a college degree and a value of 0 otherwise. To include participants' race, we created another binary variable, “White” which took a value of 1 if the participant was white and a value of 0 otherwise.
3.3. Methods
We used ordered probit models to measure the effects of participants' risk attitudes on their level of acceptance of both GM and GE plants and animal products. Specifically, we ran four ordered probit regressions. The dependent variables used in our analyses are the level of acceptance of GM plants, GM animal products, GE plants, and GE animal products. As noted earlier, this study utilized five-point Likert scales to measure participants' level of acceptance of GM and GE plants and animal products (i.e., unacceptable (=1), somewhat unacceptable (=2), neither unacceptable nor acceptable (=3), somewhat acceptable (=4), and totally acceptable (=5)).
The main independent variable in our models is the participants' risk attitudes. As noted earlier, participants' risk attitudes were estimated using five domains developed by Nicholson et al. (Reference Nicholson, Soane, Fenton-O'Creevy and William2005), and each participant's responses were summed and divided by 5 to get an average score with a higher score suggesting higher-risk propensity (Cronbach's alpha = 0.86). Moreover, due to data sparsity, we converted five categories into three categories and labeled them as high-risk propensity, medium-risk propensity, and low-risk propensity. Participants' subjective and objective knowledge of GM and GE, along with their demographic characteristics (i.e., age, gender, race, level of education, and household size), were also included in ordered probit models as control variables.
We also ran two ordinary least square (OLS) regressions to capture the differences in participants' level of acceptance of GM and GE plants and animal products and determine the key factors contributing to these differences. Therefore, our dependent variables are the differences in participants' acceptance of GM and GE plants and animal products, while the independent variables are participants' subjective and objective knowledge of GM and GE, risk attitudes, and demographic characteristics.
4. Results
4.1. Descriptive statistics
Table 1 reports summary statistics of respondents' demographic characteristics. As noted in Table 1, the average age of participants is around 39 years old. Respondents of our online survey are slightly younger than the age distribution of the US adult population (i.e., 18 years or older). Around 43 percent of respondents were female, which is eight percent lower than the United States average of 51 percent (United States Census Bureau, 2021). Our sample has more college degrees than the US national average (United States Census Bureau, 2021). Around 76 percent of respondents have at least a Bachelor's degree. Regarding race, around 79 percent of respondents reported their race as white, which is very close to the US national average of 76 percent (United States Census Bureau, 2021). The average family size in our sample is 3.23, which is higher than the US national average family size of 2.54 (United States Census Bureau, 2021).
Summary statistics of participants' demographic features
Note: Reported values are average and, in parentheses, standard deviation.
Figure 1 reports respondents' attitudinal differences depending on whether GM/GE food is plant- or animal-based with 95% confidence intervals. We used error bars to show the 95% confidence interval. As mentioned earlier, our online survey asked participants to self-report their level of acceptance of GM and GE plants and animal products on a five-point Likert scale. According to Figure 1, respondents' level of acceptance of GM and GE plants were, on average, 3.63 and 3.5, respectively. However, at 95% confidence interval, their acceptance of GM and GE plants are not statistically different. The same is true for participants' acceptance of GM and GE animal products (GM animal products = 3.32 vs. GE animal products = 3.26; not statistically different at 95% confidence interval). Overall, comparison of GM and GE food acceptability in Figure 1 indicates that participants show similar acceptance of GM and GE food. However, as noted in Figure 1, comparing plants versus animal products of GM or GE shows that participants' level of acceptance, on average, differs depending on whether the food is plant- or animal-based. In particular, respondents' levels of acceptance of GM (GE) plants are significantly higher than those of GM (GE) animal products (GM plants = 3.63 vs. GM animal products = 3.32; and GE plants = 3.5 vs. GE animal products = 3.26).
Differences in participants' level of acceptance of GM and GE plants and animal products (with 95% confidence intervals).
As mentioned earlier, our online survey assessed respondents' risk attitudes in five major domains, and based on their responses, participants' risk propensities were grouped into low, medium, and high risk. Our survey results found that around 46% of participants are low-risk propensity individuals, while 27% of respondents have a medium-risk propensity. The remaining 27% of respondents are categorized as high-risk propensity individuals. Using a simple univariate analysis, Table 2 shows participants' acceptance differences between plants and animal products (both in the context of GM and GE) based on their risk propensities. As noted in Table 2, low-risk propensity participants' acceptance of GM and GE plants is higher than that of animal products. Specifically, on average, low-risk propensity participants' level of acceptance of GM (GE) plants was 0.504 (0.408) points higher than their mean acceptance level of GM (GE) animal products. These results are statistically significant at the 1 percent significance level (see Table 2). Like low-risk propensity individuals, on average, medium-risk propensity respondents were more accepting of GM and GE plants than animal products (Difference: GM = 0.306, p = 0.001, and GE = 0.189, p = 0.010); see Table 2. However, on average, medium-risk propensity individuals' acceptance differences between plants and animal products (both in the context of GM and GE) are less than that of low-risk propensity respondents (Difference in acceptance for GM: Low-risk propensity = 0.504 vs. medium-risk propensity = 0.306; difference in acceptance for GE: Low-risk propensity = 0.408 vs. medium-risk propensity = 0.189; mean differences are different at 95% confidence level); see Table 2.
Participants' level of acceptance of GM and GE plants and animal products based on their risk propensities (univariate analysis)
Note: The reported p-values in columns 4 and 7 test equivalency between the average level of acceptance of plant- and animal-based GM and GE food products using a t-test, respectively. The values reported in parentheses are standard errors. The differences in level of acceptance based on risk propensities (in columns 3 and 4) are statistically different from each other at 95% confidence interval, irrespective of GM and GE food.
Regarding high-risk propensity individuals, our univariate results show that, on average, their attitudes toward GM and GE plants and animal products remain the same. In particular, the mean differences in acceptance of plants and animal products in the context of high-risk propensity individuals are not statistically significant (Difference: GM = −0.043; p = 0.433, and GE = 0.037; p = 0.515); see Table 2.
Respondents were also asked to self-report (i.e., subjective knowledge) their knowledge of GM and GE technologies. Respondents reported a moderate knowledge of GM, with a mean of 2.63. However, they reported little knowledge of GE (a mean of 2.33); see Table 3. The mean difference between participants' self-reported knowledge of GM and GE is statistically significant (with a 95% confidence level). We also measured participants' objective knowledge of GM and GE technologies. We developed two objective knowledge indexes to assess respondents' knowledge of GM and GE separately. Survey results show that, on average, participants correctly answered only 41% (40%) of questions related to GM (GE). We found no statistically significant mean differences between participants' objective knowledge of GM and GE. Our results indicate that participants believe that they know more about GM than GE technologies. However, their objective knowledge indexes indicate that their level of knowledge of GM and GE technologies is similar and low (see Table 3; whole sample).
Participants' knowledge of GM and GE
Note: Reported values are average and, in parentheses, standard deviation.
4.2. Regression results
4.2.1. Risk attitudes and acceptance of GM and GE
Tables 4 and 5 report the regression results from the four ordered probit regressions that determined the effects of participants' risk attitudes on their level of acceptance of GM and GE technologies to food in the context of both plants and animals, respectively. In Tables 4 and 5, the marginal effects were estimated (at the average of independent variables) for the last two response categories (i.e., somewhat acceptable and totally acceptable).Footnote 3
Marginal effects (y = 4, somewhat acceptable and y = 5, totally acceptable): factors affecting the level of acceptance of GM plants and animal products (Sample size = 1,196)
Note: *** p ≤ 0.001, ** p ≤ 0.01, * p ≤ 0.05. Values in parentheses are the standard errors.
Marginal effects (y = 4, somewhat acceptable and y = 5, totally acceptable): factors affecting the level of acceptance of GE plants and animal products (Sample size = 1,196)
Note: *** p ≤ 0.001, ** p ≤ 0.01, * p ≤ 0.05. Values in parentheses are the standard errors.
There was no statistically significant difference between low- and medium-risk propensity participants in terms of their level of acceptance of GM and GE plants and animal products (see Tables 4 and 5). However, high-risk propensity individuals were around 2 (4) and 9 (12) percentage points more likely to somewhat or totally accept GM (GE) plants, respectively, as compared to low-risk propensity participants (at least p ≤ 0.01); see Tables 4 and 5. Likewise, they were 7 (10) and 19 (17) percentage points more likely to somewhat or totally accept GM (GE) animal products, respectively (p ≤ 0.001); see Tables 4 and 5. Our findings that both low- and medium-risk propensity individuals were less accepting of the application of technologies in food (i.e., GM and GE) suggest targeted awareness campaigns to help low- and medium-risk propensity consumers better understand foods created through GM and GE technologies.
We found a statistically significant relationship between participants' subjective knowledge and their level of acceptance of GM and GE food. Specifically, respondents who reported higher subjective knowledge of GM were more likely to somewhat or totally accept GM plants and animal products than participants with low subjective knowledge of GM (at least p ≤ 0.01); see Table 4. These results were also true for GE plants and animal products (p ≤ 0.001); see Table 5.
Concerning demographic characteristics, female participants were less likely to accept GM and GE food products than male participants. In particular, female respondents were about two (two) and six (four) percentage points less likely to somewhat or totally accept GM plants (GM animal products), respectively, as compared to male respondents (at least p ≤ 0.05); see Table 4. Similarly, female respondents were around two (five) and three (four) percentage points less likely to somewhat (totally) accept GE plants and animal products, respectively (p ≤ 0.01); see Table 5. Respondents who identified themselves as white were three (four) and four (four) percentage points more likely to somewhat or totally accept GM (GE) animal products, respectively, than other ethnicities (at least p ≤ 0.05); see Tables 4 and 5.
Respondents who received a college degree were more likely to accept GM plants and animal products than respondents with no college degree (at least p ≤ 0.05); see Table 4. They were also more likely to accept GE animal products (p ≤ 0.01); see Table 5. We also found very minimal (but statistically significant) effects of participants' age on their level of acceptance of GM and GE animal products (see Tables 4 and 5).
4.2.2. Plants versus animal products
Using the OLS regression method, we also compared the differences in participants' level of acceptance of GM and GE plants and animal products based on their risk propensity while controlling their knowledge and demographic features (see Table 6). As noted earlier, we ran two different OLS regression models. The dependent variable in our model 1(2) of Table 6 is the difference in respondents' level of acceptance of GM (GE) plants and animal products. Our independent variables include respondents' risk attitudes, knowledge, and demographic features (see Table 6).
Plants versus animal products acceptance
Note: *** p ≤ 0.001, ** p ≤ 0.01, * p ≤ 0.05. Values in parentheses are the robust standard errors.
As mentioned earlier, our simple univariate analysis (in Table 1) found that both low- and medium-risk individuals were more accepting of plants than animal products in the context of GM and GE food. Moreover, medium-risk individuals' acceptance differences between GM and GE plants and animal products were lesser extent than those of low-risk participants (see Table 1). However, OLS regression results show that both low- and medium-risk individuals differentiate between GM and GE plants and animal products the same way when controlling their knowledge and demographic characteristics (see Table 6). In other words, on average, both low- and medium-risk participants were more accepting of plants than animal products and differentiating plants and animal products in similar ways (see Table 6). On the other hand, comparing low- and high-risk participants, regression results reveal that high-risk participants' acceptance difference between GM (GE) plants and animal products was, on average, 43 points (27 points) less than low-risk participants. These differences are statistically significant, at least at a 1 percent level (see Table 6). Our regression results reveal that while high-risk propensity individuals do not differentiate between plants and animal products, both low- and medium-risk propensities participants' acceptance depends on whether the food product is plant- or animal-based.
There is no statistically significant relationship between participants' knowledge (i.e., subjective and objective) and their differences in acceptance of plants and animal products (see Table 6). All the demographic variables except college education are not statistically significant.Footnote 4 Moreover, participants' education level was statistically significant only for GE food. We found a negative relationship between participants' college degree and their acceptance differences between GE plants and animal products. On average, participants with college degrees differentiate 19 points less between GE plants and animal products compared to respondents with no college degree (at least p ≤ 0.05); see Table 6.
5. Discussion and conclusion
This study examined the extent to which consumers' level of acceptance of GM and GE food products is driven by their risk attitudes in the United States. Moreover, we also investigated the difference in consumers' attitudes toward plants and animal products in the context of both GM and GE. We utilized Nicholson et al. (Reference Nicholson, Soane, Fenton-O'Creevy and William2005)'s risk attitude scale to measure participants' day-to-day risk tendencies. From theoretical and empirical perspectives, we expected that a consumer's risk attitude is an important factor in predicting his/her acceptance of GM and GE food. The findings of our study support this anticipation. In particular, individuals with high-risk propensity were more likely than low-risk propensity individuals to accept GM and GE food. We didn't find any statistically significant differences between low- and medium-risk propensity individuals in terms of their level of acceptance of GM and GE food. In addition, our results also showed that US consumers, on average, show the same level of acceptance of GM and GE food. Although recent studies, such as Ding et al. (Reference Ding, Yu, Sun, Nayga and Liu2023) and Son and Lim (Reference Son and Lim2021) showed that consumers in South Korea and China prefer GE food to GM food, our findings reveal that US consumers show the same level of preference for GM and GE food. Future studies may focus on multi-country elicitation of consumer valuations and acceptance of GE food compared to GM food to better understand the heterogeneous preferences across countries.
Our results found differences in consumers' attitudes toward plants and animal products in the context of both GM and GE. Moreover, we identified that this attitudinal difference can be explained by consumers' risk propensity, the key result of this study. Specifically, our findings reveal that both low- and medium-risk propensity consumers differentiate between plants and animal products; the latter is less acceptable than the former, indicating a tendency to have more concerns about the application of biotechnology to animals than plants. However, individuals with high-risk propensity do not differentiate between plants and animal products in the context of both GM and GE. Overall, we found attitudinal differences regarding plants and animal products among low- and medium-risk consumers but not among high-risk consumers. As mentioned earlier, risk-averse individuals prefer outcomes with low uncertainty to outcomes with high uncertainty (Werner, Reference Werner2008). Therefore, the findings of our study suggest that low- and medium-risk consumers might be more uncertain (compared to high-risk consumers) about the effects of GM and GE on animal health and welfare. Consequently, their level of acceptance of GM and GE plants and animal products differ, accepting the former more than the latter.
Our findings have some policy implications. Although biotechnology has the potential to solve world hunger problems, the future innovation and regulation of biotechnologies will be influenced by consumers' acceptance of biotechnologies in agriculture. Our findings that US consumers' level of acceptance differ depending on whether the GM/GE food is a plant or animal product and that their risk attitudes explain these attitudinal differences suggest that policymakers, the food industry, and researchers need to consider these attitudinal differences while studying public attitudes toward GM and GE food. Any research to understand public attitudes toward GM and GE food without considering these attitudinal differences will result in either overestimating or underestimating consumer response. In addition, targeted public awareness campaigns, depending on individuals' risk attitudes and their preferences for plants and animal products, may increase the acceptance of GM and GE foodFootnote 5.
There are some limitations of our study which are worth noting. Our sample does not represent the US population in terms of gender and education. As noted earlier, the female participants in our sample are eight percent less than the US average. Moreover, our data sample has more college degrees than the US national average. Dialog about the potential risks involved in new technologies and the provision of information to improve participants' understanding of GM and GE technologies are beyond the scope of our study. Moreover, the Nicholson et al. (Reference Nicholson, Soane, Fenton-O'Creevy and William2005)'s overall risk propensity scale put equal weight on each dimension while measuring individuals' risk propensities. However, individuals may have different views on the relative importance of the dimensions. There are different techniques to measure an individual's risk attitude. Our study utilized the Nicholson et al. (Reference Nicholson, Soane, Fenton-O'Creevy and William2005) scale, combining individual and scenario-based approaches. Future studies can compare our findings using other individual- and situation-based risk attitude scales.
Our findings provide more research implications by identifying consumers' attitudinal differences between plants and animal products based on their day-to-day risk attitudes. Our research shows that US consumers with low- and medium-risk propensities have more concerns about the application of biotechnology to food animals than plants. However, individuals with high-risk propensity didn't differentiate between plants and animals in the context of GM and GE. Although this study identifies risk propensity as a factor in explaining consumers' attitudinal differences between biotechnology applications in animals and plants, future studies need to identify other factors affecting this attitudinal difference. For example, in general, people believe that food companies focus more on profit maximization than animal welfare. This belief may trigger different reactions when biotechnology is applied to animals compared to plants.
Data availability statement
Data will be available upon request.
Author contribution
Syed Imran Ali Meerza: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing-original draft. Alwin Dsouza: Conceptualization, Methodology, Validation, Writing – review & editing. Afsana Ahamed: Conceptualization, Data Curation, Supervision, Funding Acquisition, Project Administration, Writing – review & editing. Khondoker Mottaleb: Conceptualization, Validation, Writing – review & editing.
Financial support
Financial support for the research came from Dr Ahamed's Start-Up Research Fund provided by Arkansas Tech University.
Competing interests
The authors declare no conflict of interest.
Appendix A
Ordered probit coefficients: factors affecting the level of acceptance of GM plants and animal products
Note: *** p ≤ 0.001, ** p ≤ 0.01, * p ≤ 0.05. Values in parentheses are the standard errors.
Ordered probit coefficients: factors affecting the level of acceptance of GE plants and animal products
Note: *** p ≤ 0.001, ** p ≤ 0.01, * p ≤ 0.05. Values in parentheses are the standard errors.
Marginal effects (y = 1, unacceptable, y = 2, somewhat unacceptable, and y = 3, neither acceptable nor unacceptable): factors affecting the level of acceptance of GM plants and animal products (Sample size = 1,196)
Note: *** p ≤ 0.001, ** p ≤ 0.01, * p ≤ 0.05. Values in parentheses are the standard errors.
Marginal effects (y = 1, unacceptable, y = 2, somewhat unacceptable, and y = 3, neither acceptable nor unacceptable): factors affecting the level of acceptance of GE plants and animal products (Sample size = 1,196)
Note: ***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05. Values in parentheses are the standard errors.
Robustness check with 51% female participants (plants vs. animal products acceptance)
Note: We converted our sample to US representative in terms of gender using SMOTE. Therefore, the converted sample contains 51% female participants which is equal to US average. The qualitative nature of our results remains the same, showing the robustness of our findings. *** p ≤ 0.001, ** p ≤ 0.01, * p ≤ 0.05. Values in parentheses are the robust standard errors.
Objective knowledge questions
Open access
