Are plant-based milks unique products or dairy milk substitutes? A study of consumer perceptions, uses, and consumption motivations of plant-based and dairy milks
Bibliographic record
Abstract
Plant-based milks (PBMs) are positioned as dairy alternatives, yet consumer perception and sensory issues are barriers to acceptance. This study aimed to characterize milks based on sensory attributes, perceptions, and uses, and to determine dairy consumption motivations of consumers of both plant-based and dairy milks. Dairy, oat, pea, and blended milks ( n = 12) were evaluated by consumers ( n = 109) for overall liking (9-point hedonic scale), sensory attributes (Rate-All-That-Apply), perceptions and uses (Check-All-That-Apply), and dairy consumption motivation based on factors ‘Natural,’ ‘Need,’ ‘Normal,’ and ‘Nice.’ Dairy and oat milks were liked significantly more than pea milks and blends, generating two consumer clusters: dairy-oat-highlikers (high liking for dairy and oat milk) and dairy-plant-modlikers (moderate liking for dairy and specific plant-based milks). ‘Sweet,’ ‘smooth,’ and ‘creamy’ were identified as drivers of liking through Principal Component Analysis and Multifactor Analysis, respectively. Moreover, ‘simple,’ ‘traditional,’ ‘comforting,’ and varied product uses were correlated with most liked milks and associated with mean liking increase in Penalty Analysis. A key study finding was that plant-based milk consumers continued to consume dairy milk because of the perceived ‘Need’ for it ( p < 0.0001 ) . Subsequently, ‘Need’ was a prevalent consumption motivation across consumer clusters, regardless of varying degrees of liking for both plant-based and dairy milk. This study provides relevant consumer insights into the drivers of liking of PBMs and perceived ‘Need’ for dairy consumption. In developing PBMs, the aim may not be to replicate milk, but to shift focus on enhancing three key sensory attributes and creating a simpler product with more uses.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".