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Article
Publication date: 6 September 2023

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

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…

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 test p-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.

Details

China Agricultural Economic Review, vol. 15 no. 4
Type: Research Article
ISSN: 1756-137X

Keywords

Article
Publication date: 8 August 2023

Hyeyoung Lim, Brian Lawton and John J. Sloan

This article aims to synthesize published research on the policing of Asian communities in the United States.

Abstract

Purpose

This article aims to synthesize published research on the policing of Asian communities in the United States.

Design/methodology/approach

This is a systematic literature review using PRISMA 2020 guidelines.

Findings

Sixteen studies were reviewed. Five examined violence by police against Asian community members and reported rates for Asians closer to those against Whites than against members of other groups. One study found no relationship between violence against police and increased minority representation on the force. Four studies reported conflicting results regarding traffic stops of Asian motorists and in general perceptions of police anti-Asian bias. One study illustrated how racialization processes reproduce inequality both between racial-ethnic categories and within them. Five studies examined Asian community members’ general attitudes toward/satisfaction with police and reported—with qualifications—generally favorable attitudes and satisfaction with them.

Originality/value

This is the first systematic literature review of policing Asian communities in the United States.

Details

Policing: An International Journal, vol. 47 no. 2
Type: Research Article
ISSN: 1363-951X

Keywords

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