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. 2024 Sep 20;13(18):2983.
doi: 10.3390/foods13182983.

Is AI Food a Gimmick or the Future Direction of Food Production?-Predicting Consumers' Willingness to Buy AI Food Based on Cognitive Trust and Affective Trust

Tiansheng Xia  1 , Xiaoqi Shen  1 , Linli Li  1
Affiliations

Is AI Food a Gimmick or the Future Direction of Food Production?-Predicting Consumers' Willingness to Buy AI Food Based on Cognitive Trust and Affective Trust

Tiansheng Xia et al. Foods. .

Abstract

In recent years, artificial intelligence (AI) has been developing rapidly and has had a broad impact on the food industry, with food produced from AI-generated recipes already appearing to actually go on sale. However, people's trust and willingness to purchase AI food are still unclear. This study builds an integrated theoretical model based on cognitive trust and affective trust, taking into account consumers' quality value orientations, social norms, and perceived risks of AI food, with the aim of predicting and exploring consumers' trust and acceptance of AI food. This study utilized the questionnaire method and 315 questionnaires were collected. The results of structural equation modeling (PLS-SEM) indicated that food quality orientation, subjective norms, perceived trust, and affective trust all had a significant positive effect on consumers' purchase intentions. Perceived risk had a negative effect on affective trust and consequently on consumers' purchase intention, but the effect on cognitive trust was not significant. The results also suggest that cognitive trust is the basis of affective trust and that consumer trust and acceptance of AI food can be enhanced by augmenting two antecedents of cognitive trust (food quality orientation and subjective norms). Possible practical implications and insights from the current findings are discussed.

Keywords: AI food; PLS-SEM; affective trust; cognitive trust; consumer acceptance.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Research model. H means hypothesis.
Figure 2
Figure 2
Structural model, * p < 0.05, *** p < 0.001. Dash line means not significant.
See this image and copyright information in PMC

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