The Construction of the Green Distribution Model and its Application on Consumers Perception
Bibliographic record
Abstract
The green logistics item as a part of distribution processes represents an innovative perspective in many views. This perspective is current from an offer and demand point of view. Many authors examine only the businesses aspect, while labour market acceptance is important. The aim of this article is to create and verify a green distribution model and this examines the green distribution perception from the consumer’s point of view in a context of chosen demographic characteristics. The creation of a green distribution model is supported by secondary research at which consists of four parts – input, transport, production and sale. Model verification was taken with primary research which base was created of 409 respondents. In the study, we use many statistical and mathematical, as well as scientific and philosophical methods. Among the most significant belong Cronbach’s alpha and McDonald’s omega. We used to verify and estimate model reliability, correlation analysis for relation research, one-way ANOVA test for research hypotheses verification and cluster analysis for identification of possible hidden clusters. The model can be considered a reliable one. Results indicate a low influence of distribution ecological factor in a consumer’s perspective, as well, it can be stated, the age, contrary to sex, represents a significant factor in a green distribution perception. Results can be used in both the academic and commercial spheres in various fields and disciplines. The primary survey was conducted in Slovakia, but it would be appropriate to examine the model in other countries, as well as to identify factors that may affect the model of green distribution in the future.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".