Most studies exploring the public acceptance of genetically modified food (GMF) are based on social trust and the establishment of a causal model. The underlying premise is that social trust indirectly affects public acceptance of GMF through perceived risks and perceived benefits. The object of social trust is trust in people, organizations, and institutions. Different from the social trust, epistemic trust refers to people’s trust in scientific knowledge behind the technology of concern. It has been shown that epistemic trust, like social trust, is also an important factor that affects the public perception of applicable risks and benefits. Therefore, it is necessary to incorporate epistemic trust into the causal model to derive a more complete explanation of public acceptance. However, such work has not been conducted to date. The causal model proposed in this paper integrated epistemic trust and social trust and divided social trust into trust in public organizations and trust in industrial organizations. A representative questionnaire survey (N = 1091) was conducted with Chinese adults. The model was analyzed by the partial least squares structural equation modeling (PLS-SEM) method. Three major findings were obtained: First, epistemic trust is an important antecedent of perceived risks and perceived benefits and exerts a significant indirect effect on the acceptance of GMF. Secondly, trust in industrial organizations negatively impacts perceived risks, while trust in public organizations positively impacts perceived benefits. Thirdly, contrary to the common opinion, trust in industrial organizations did not exert a significant direct effect on perceived benefits, and trust in public organizations did not demonstrate a significant direct effect on perceived risks. Therefore, trust in industrial organizations and trust in public organizations utilize different influence paths on GMF acceptance. This study enriches the understanding of the influence path of trust with regard to the acceptance of emerging technologies and is of great significance to relevant risk-management practices.

Measurement invariance refers to the equivalence of measurement instrument in different groups. Research on social science often involves comparing different groups, such as whether the relationship between two variables is the same in male and female groups. Measurement invariance is a prerequisite of these studies because if the measurement tools are not equivalent, we cannot distinguish the difference between the degree of measurement tools and the empirical results. The causal model proposed by Michael Siegrist is one of the baseline models for studying public acceptance of genetically modified food, but only a few studies have tested the invariance of the causal model. Thus, it is difficult for researchers to judge the reliability of some conclusions about group comparison, such as whether the risk perception of men is lower than that of women. In this study, we use sample data about China (N = 1091) to test the invariance of the causal model among groups with different genders and knowledge levels. The test results show that the model has full invariance across gender, and only factor loading invariance has no measurement error invariance across knowledge levels. The results of this study show that the conclusion about group comparison on gender in previous studies is credible, but the reliability of the measurement of the differences between knowledge level groups needs to improve before meaningful comparison can be made.