Understanding and Enhancing Food Conservation Behaviors and Operations
Bibliographic record
Abstract
This study explores the dynamics of food conservation behaviors and operations, shifting the lens from the prevalent narrative of food waste reduction to a marketing perspective that emphasizes consumer engagement in sustainable operations. Amidst the rapid urban transformation and economic progress of many countries, this research examines factors influencing individual behaviors toward responsible food operations. It aims to delineate the motivational drivers and deterrents affecting residents’ engagement in food conservation and operations, utilizing an adapted framework based on the theory of planned behavior. We employ partial least squares structural equation modeling to analyze responses from 390 residents. We find that perceived behavioral control, subjective norms, and attitudes significantly enhance intentions to conserve food. Moreover, environmental concerns amplify both attitudes and perceived behavioral control, while green marketing communications and knowledge elevate attitudes, environmental mindfulness, and conservation actions. A connection to nature is substantiated as a reinforcing factor for pro-environmental attitudes and operations. Notably, attitudes are identified as a critical mediator among the examined constructs. This investigation enriches sustainability scholarship by introducing a positive behavior-focused approach, advancing the discourse on sustainable operations. It offers actionable insights for market-driven interventions, policy-making (such as China’s lastest national policies on food security and rural region revitalization in 2024), and educational endeavors to mitigate food wastage and reinforce food supply chain resilience globally.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".