Leveraging genetic data for predicting consumer choices of alcoholic products

Chen Zhu, Timothy Beatty, Qiran Zhao, Wei Si and Qihui Chen

China Agricultural Economic Review, 2023, vol. 15, issue 4, 685-707

Abstract: Purpose - Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data. Design/methodology/approach - A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices. Findings - The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation testp-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income). Originality/value - This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

Keywords: Consumer choice; Food choice prediction; Choice experiment; Genetic data; Machine learning (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:caerpp:caer-09-2022-0214

DOI: 10.1108/CAER-09-2022-0214

Access Statistics for this article

China Agricultural Economic Review is currently edited by Dr Fu Qin, Dr Jikun Huang, Dr Kevin Z Chen, Dr Weiming Tian, Prof Daniel Sumner, Prof Xian Xin and Prof Holly Wang

More articles in China Agricultural Economic Review from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().