Role of Green Marketing in the Adoption of Intelligent Food Containers
Bibliographic record
Abstract
The growing human population is causing food security concerns in many countries.As food security is the access by all people to enough food to live a healthy and productive life, the world must reduce food waste.At the consumer level, households are responsible for half of the total avoidable losses due to food becoming out-of-date, developing bad smell or taste, or having been forgotten in the fridge.This study investigates the factors leading to consumer adoption of intelligent food containers (IFC) -an emerging technology that can store food, monitor food quality and minimize food waste.In particular, the study focuses on the role of companies' green marketing in affecting consumers' willingness to adopt IFC.So doing, the study develops and tests a research model and related hypotheses by using the partial least squares structural equation modeling approach.The model was constructed based on the integration of Technology Adoption Model (TAM) with green marketing.An online survey was used to collect data from 153 households in Canada.The results suggest that improved TAM with the addition of green marketing mix is useful in explaining consumers' purchase intention toward IFC.The findings provide valuable information to marketers in the packaging technology sector to help them convince eco-friendly segment to purchase IFCs and enhance the pro-environmental purchasing behavior in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".