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论文推荐 | 消费者在中国外卖程序上对低盐餐的请求分析

北京城市实验室BCL  · 公众号  ·  · 2024-12-16 11:15

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本期为大家推荐的内容为论文《 Analysis of consumer requests for reduced-salt meals on a Chinese meal delivery app 》( 消费者在中国外卖程序上对低盐餐的请求分析 ),发表在 Transactions in Urban Data, Science, and Technology 期刊,欢迎大家学习与交流。

中国居民平均盐摄入量远远超过推荐标准。随着外卖在中国公众中越来越受欢迎,减少外卖的盐量对于减少中国居民的盐摄入量非常重要。但是,与外卖减盐相关的研究还很少;特别是,没有研究探讨消费者对外卖食品中盐含量的态度。这项研究的目的是从真实的外卖订单中客观地衡量消费者在网上点餐时对低盐选择的要求。2020年7月至12月期间,在中国一个名为“饿了么”的送餐应用程序上,收集了来自718家餐馆的消费者信息。从消费者在点餐时放置的所有消费者消息中提取盐减少消息,以确定定制盐减少请求的程度,并分析这些请求的内容。从通过AI机器学习(术语频率和术语频率-逆文档频率方法)识别的消息中提取特征词并进行分析。在25,982条消费者信息中,10,549条(40.6%)是低盐信息。一般来说,消费者需要定制含盐量更少的菜肴——“含盐量更少”是最常被提及的口味偏好词。根据这些信息,有特殊健康和营养需求的人群可能对低盐餐有更高的需求。该研究在相当多的少数订单中显示了明确的需求模式,并确定了可以用于未来工作的特征词和概念,以在在线送餐平台中创建有效的选择架构




论文相关

题目: Analysis of consumer requests for reduced-salt meals on a Chinese meal delivery app

消费者在中国外卖程序上对低盐餐的请求分析

作者: Wenyue Li, Chao Song, Ying Cui, Beisi Li, Zhongdan Chen, Paige Snider, Yue Ma, Ailing Liu, Ying Long * , and Gauden Galea *

发表刊物:

Transactions in Urban Data, Science, and Technology

DOI:

https://doi.org/10.1177/27541231241298191


摘要 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 .



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