Identifying Iranian Consumers’ Preferences towards Functional Dairy Products
Bibliographic record
Abstract
INTRODUCTION: During the past few decades, production and marketing of functional foods has increased in many countries including Iran. Considering the fact that consumers’ preferences play an important role in the success of marketing a product to increase consumption, this study was conducted in Iran to fill the knowledge gap in this regard. METHODS: The theory of social marketing served as the framework of this study. Qualitative data were collected via eight semi-structured focus group discussions, between May and September 2014. Participants were 65 women (44 housewives and 21 employed women), aged 23–68 years, selected by purposeful sampling technique, considering maximum diversity. All focus group discussions were audio recorded and transcribed verbatim. Analysis of the qualitative content of the data was conducted using MAXQDA® software. RESULTS: The findings showed that there were quite diverse preferences among studied women in regards to different aspects of a product and its social marketing strategies. The preferences towards functional dairy were categorized in 4 main groups: (i) characteristics of products including sensory and non-sensory characteristics; (ii) price; (iii) place of the product supply; and (iv) promotion strategies of products categorized in three subgroups of informing and educating, advertising, and recommending. CONCLUSION: This diversity should be considered both in production of dairy foods and their promotion plans. This understanding can contribute to success of interventions to increase consumption of these products among consumers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".