Exploring Malaysian Household Consumers Acceptance towards Eco-friendly Laundry Detergent Powders
Bibliographic record
Abstract
Due to the current environmental pressures and ever-rising prices of petrochemical feedstock, the detergentindustry is gradually moving towards the development of green and eco-friendly products. However, besides theproduction cost, the challenge for today’s detergent formulators still lies in increasing the quantity of green andeco-friendly surfactants in laundry detergent formulations without compromising their performance. Realizingthis, research was undertaken to develop the Asian market preferred low-density laundry detergent powders byincorporating green palm oil based surfactant (known as MES) and also by eliminating the use ofenvironmentally damaging phosphate-builders in the detergent formulation. Prior to commercialization of thisnewly developed eco-friendly low-density laundry detergent powder, a pilot survey was attempted over 112respondents using mall-intercept approach in one of the popular shopping complex in Kuala Lumpur city withthe aim to study consumers' preferences (format, brand, origin) and their purchasing behaviour(awareness/knowledge and perception) towards both commercial and MES based laundry detergent powders.The pilot survey results have indicated that the majority of respondents has high affinity towards green andenvironmental benefits offered by MES based laundry detergent powders. These positive results imply hugemarket potential for MES based laundry detergent powders and through effective marketing strategies andproduct awareness activities, this product is likely to attain success in the marketplace.
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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 | 0.000 | 0.001 |
| 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) | 0.004 | 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".