Evidence map›Paper›PMID 36839390›Full record

ArticleNutrients2023

Consumer Attitudes and Preferences for Healthy Boxed Meal Attributes in Taiwan: Evidence from a Choice Experiment.

Min-Yen Chang, Jui-Chi Lin, Han-Shen Chen

Abstract read
In one paragraph

Article in Nutrients, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Min-Yen ChangDepartment of Accounting, Jiaxing University, Jiaxing 314001, China.ORCID 0000-0003-0745-8404
Jui-Chi LinDepartment of Health Industry Technology Management, Chung Shan Medical University, Taichung City 40201, Taiwan.
Han-Shen ChenDepartment of Health Industry Technology Management, Chung Shan Medical University, Taichung City 40201, Taiwan.ORCID 0000-0002-5201-2615

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Consumers have realized the importance of a healthy diet, hoping to reduce the occurrence of obesity and chronic diseases. Therefore, healthy boxed meals with low calories and high protein are gaining attention. This study divided the attributes of healthy boxed meals into five categories, namely, taste, nutrition facts, traceability certification, carbon footprint label, and price, and investigated the relationship between consumer preferences and willingness to pay (WTP) using a choice experiment. A purposive sampling procedure was used to collect 495 valid questionnaires. The results indicate the following: (1) when purchasing healthy boxed meals, the respondents were most concerned with traceability certification and nutrition facts; (2) the respondents were willing to pay a premium for meals with traceability certification (NTD 4.6) and nutrition facts (NTD 4.4); (3) respondents > 40 years with an average monthly salary of > NTD 30,000 who worked out regularly and were in the process of muscle building, fat loss, or weight control had higher WTP for meals with nutrition labels; and (4) female respondents who were 30-39 years old with a college or university education or above had higher WTP for meals with traceable ingredients. The results may help healthy boxed meal companies understand and pay attention to consumer needs, which will, in turn, provide a reference for future product development and marketing strategies.

Indexed as

Food LabelingMealsAdultAttitudeConsumer BehaviorFemaleHumansSurveys and QuestionnairesTaiwancarbon footprintconsumer behaviorsustainable development goalstraceable agricultural productswillingness to pay

Identifiers

PMID36839390
PMCPMC9961088

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.