Coupling AI with empirical research – A case of 3D printed food technology

Abstract

3D-printed foods remain narrowly understood by consumers, limiting their ability to make informed choices and potentially obscuring the broader reality of this market. This research aims to investigate the factors influencing consumers’ knowledge, motivation, and intention to consume 3D foods. Leveraging Artificial Intelligence logic, we foreground consumers’ opinions and sentiments about 3D-printed food preferences, drawing on empirical data from two surveys.

The findings of sentiment analysis show that the level of knowledge is critical in forming consumer sentiments both, positive and negative. Survey 1 examines the variations in positive affect, negative affect, and behavioral intention toward 3D-printed foods, considering extensive and limited levels of knowledge adequacy. This investigation uses the Knowledge Attitude-Behaviour theoretical model. Survey 2 identifies relationships through Topic recognition and applies the approach-avoidance motivation theory to discern the connection between health choices. Motivation (approach)/(avoidance) for 3D-printed foods both, had a positive and significant effect on healthy food choices. While Motivation (avoidance) had a positive and significant effect on resistance to new foods, Motivation (approach) of 3-D foods was not supported. The findings represented the importance of motivational tendencies, such as approach and avoidance, in shaping decisions related to healthy eating and receptiveness to innovative food concepts.

This research presents an opportunity for researchers and practitioners to deepen their understanding of food development and consumer trends in the 3D-printed food market. Relying solely on User-Generated Content for conclusions may be insufficient. Additional research methodologies and data sources are necessary for a comprehensive understanding of behavior and food preferences.