Open AccessPerceived Norms Influence Perceptions of Risk and Attitudes for Food Technologies
Author(s) -
M. LaCour,
E. S. Beyer,
J. L. Finck,
M. F. Miller,
T. Davis
Publication year - 2019
Publication title -
meat and muscle biology
Language(s) - English
Resource type - Journals
ISSN - 2575-985X
DOI - 10.22175/mmb.10837
Subject(s) - risk perception , theory of planned behavior , psychology , neophobia , perception , social psychology , variance (accounting) , applied psychology , control (management) , developmental psychology , business , management , accounting , neuroscience , economics
ObjectivesUnderstanding the factors that influence consumer attitudes and risk perception is critical for effective marketing of new food technologies. Many variables impact attitudes and risk perception. However, food technology research has largely focused on demographic variables, and often only single technologies (e.g., GMOs). Our goal was to determine how psychological variables differentially influence attitudes and risk perception for a range of food technologies: antibiotics, hormones, vaccines, GMOs, sustainability, and animal welfare technologies. We examined how attitudes and risk perception for these technologies related to four social psychological variables from the Theory of Planned Behavior (TPB): perceived norms, past behavior, familiarity, and perceived control. In addition, we measured general Food Technology Neophobia (FTN), Trust in Science (TIS), chemical reasoning (CR).Materials and MethodsParticipants (n = 394) provided demographics followed by TPB, attitude, and risk perception surveys for each of the six technologies. Then they completed FTN, TIS, and a CR survey measuring dose–response beliefs (DR), beliefs in unknown risks (UR), the role of risk in society (RS), and naturalness/knowledge of chemicals (NKC). Multiple regression analyses were used to test for associations among the survey measures.ResultsThe multiple regression models were all significant (p 0.10 in bold) from selected coefficients of regression models predicting risk perceptions and attitudes (rows) for each technology (columns)