U.S. consumer support for genetically modified foods: Time trends and assessments of four GM attributes

ABSTRACT

There is a large literature about consumer acceptance of GM foods dating back almost three decades, but there are fewer studies that investigate how support for specific GM attributes contribute to general support for novel plant technologies. In addition, there is little information on how support has changed over time. Using survey data from 2018 to 2023 in a U.S. State (Vermont) (n = 3101), we analyze changes in support for a variety of GM attributes over time. There are three major findings. First, there is movement toward neutrality in support for various GM attributes, but opposition continues. Second, there is variability in support for different GM attributes. People are most supportive (least opposed) to GM attributes that improve flora (plant health or drought tolerance), and most opposed (least supportive) of attributes that impact fauna (specifically fish). Third, multivariate regression reveals that assessments of individual GM attributes contribute to levels of overall support of the use of GM technologies in agricultural production.

KEYWORDS:

• Attitude
• consumer behavior
• food labels
• genetic modification
• GM
• GMO
• novel technologies
• support

Acknowledgments

This research was supported USDA NIFA award numbers VT-H01404, VT-H01811, VT-H02113, and VT-H02706.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Novel food production techniques go by a variety of names and the vocabulary is becoming more diverse. In the recent literature, authors are distinguishing newer techniques and distancing the research away from GM, by using terms such as gene editing (GE), bioengineering (BE), disruptive technologies (DT), and gene technology (GT). Because this paper includes not only genetic modification but also gene editing, because the average consumer continues to refer to the general process of genetic engineering as genetically modification, we choose GM to describe the range of plant engineering techniques.^52,110

2. The n varies by univariate, ANOVA, and MANCOVA analyses due to the number of respondents to specific questions and the number of variables used in each analysis.

Additional information

Funding

The work was supported by the National Institute of Food and Agriculture [VT-H01404, VT-H01811, VT-H02113, and VT-H02706].