Health Consciousness and Its Effect on Perceived Knowledge, and Belief in the Purchase Intent of Liquid Milk: Consumer Insights from an Emerging Market
Bibliographic record
Abstract
This study is based on the influence of consumers' health consciousness (HC), perceived knowledge (PK) and beliefs affecting the attitude and purchase intent (PI) of the consumers. The outcome of this study is obtained through an exclusive survey conducted on a randomly selected sample of 712 households who purchase liquid milk (LM) in the cities of Dhaka and Chittagong in Bangladesh. A structured questionnaire is used to interview these participants to obtain data which are analysed employing descriptive statistics, Confirmatory Factor Analysis, and Structural Equation Modelling. The results of the analyses corroborate that consumers' health consciousness has a positive impact on perceived knowledge, belief, and attitude, but not on purchase intent. In addition, belief affects both the attitude and PI positively. Although consumers' perceived knowledge is too low to constitute their attitude towards LM, it has a positive, significant impact on the PI. The results also reveal that more than a third of the respondents consume LM several times per month, followed by more than a quarter of the sampled respondents who consume LM several times per week, and these consumption patterns have a positive and significant influence on the PI. Moreover, the monthly income of the family, age, and labelling preference are significantly correlated with PI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".