Consumers’ Perceptions, Attitudes and Willingness to Pay towards Food Products with “No Added Msg” Labeling
Bibliographic record
Abstract
This research was carried out in order to determine consumers’ perceptions, attitudes and willingness-to-pay(WTP) towards food products with “No Added MSG” labeling. A total of 200 respondents within the area ofKlang Valley were interviewed using a close-ended questionnaire. The Contingent Valuation Method (CVM)was used to determine the consumers’ WTP for the consumption of safer beef. A logit model was used toestimate the premium that consumers are willing to pay for food products with “No Added MSG” labeling. Theresults pointed out that majority of the consumers know and have read about or heard of Monosodium Glutamate(MSG) and illnesses caused by MSG, but only a small portion of them have experienced these illnesses before.Not all of the consumers have high awareness towards illnesses caused by MSG due to lack of information.However, their perceptions and attitudes towards food products with “No added MSG” labeling were found to begenerally positive. Besides, the CV method which was used to determine and estimate consumers’ WTP towardsthe food products mentioned by using logit model showed that the most important and significant actors thatinfluenced a consumers’ WTP are gender (female), household size, household income, family member withchildren below the age of 12, price levels and education level (university). Results also revealed that respondentswere willing to pay a premium of about RM 0.43 for food products with “No added MSG” labeling. Based onthe study, it was found that the demand and consumption of “No Added MSG” is still high and increasing. Thiscurrent trend will certainly have effects on the present market. Hence, to ensure a better development of theseproducts, there is a need to formulate proper standards, policies and promotion programmes for these productsmore efforts in research and development (R&D) are needed to improve the production technologies and foodsafety systems for these “No Added MSG” food products.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".