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Analysis of consumer requests for reduced-salt meals on a Chinese meal delivery app.
- Source :
- Transactions in Urban Data, Science & Technology; Sep-Dec2024, Vol. 3 Issue 3/4, p109-120, 12p
- Publication Year :
- 2024
-
Abstract
- The average salt intake of Chinese residents far exceeds the recommended standard. As food delivery becomes increasingly popular among the Chinese public, salt reduction for takeaways is important to reduce salt intake of Chinese residents. However, studies related to salt reduction of takeaways are still very few; especially, no study has explored consumers' attitudes towards salt level in takeaway meals. The purpose of the study was to objectively measure consumers' request for reduced-salt options when ordering meals online, from real takeaway orders. Consumer messages from 718 restaurants on a meal delivery app called ELEME in China were collected between July and December 2020. Reduced-salt messages from all consumer messages placed by consumers when ordering meals were extracted to determine the extent of customized salt reduction requests and to analyze the content of those requests. Feature words from messages identified through AI machine learning (Term Frequency and Term Frequency-Inverse Document Frequency method) were extracted and analyzed. Out of 25,982 consumer messages, 10,549 (40.6%) were reduced-salt messages. Consumers, in general, had the demand to customize dishes with less salt – "less salt" was the most frequently mentioned word for taste preference. Populations with special health and nutritional needs may have a higher demand for reduced-salt meals according to these messages. The study showed definite patterns of demand in a sizable minority of orders and identified the feature words and concepts that could feed into future efforts to create an effective choice architecture in online meal delivery platforms. [ABSTRACT FROM AUTHOR]
- Subjects :
- RESIDENTS
SALT
FOOD
RESTAURANTS
MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 27541231
- Volume :
- 3
- Issue :
- 3/4
- Database :
- Complementary Index
- Journal :
- Transactions in Urban Data, Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 181565993
- Full Text :
- https://doi.org/10.1177/27541231241298191