Investigating the Key Success Factors of Social Marketing in Promoting Environmental Consciousness: A Dematel-Based Approach
Bibliographic record
Abstract
Due to the overuse of the environment and natural resources, our environment has suffered long-term damage and natural disasters have been exacerbated by climate change, resulting in a significant impact on people’s livelihood and security. People must consider saving the environment as everyone’s responsibility. Hence, Environmental consciousness should be promoted to inspire public participation. This study used the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to identify the key success factors of social marketing in promoting environmental consciousness. The DEMATEL method has been proven highly effective in gathering the views of experts and thereby providing information of greater reliability in many areas. The results of this research suggest that “Take advantage of existing successful campaign”, “Using appropriate media channel to increase the participation”, and “Enhancing campaign success by appropriate research” are the main strategies for promoting environmental consciousness. The findings of this study may be used in future success factor evaluations where social marketing is compared with other measures aiming to increase the efficiency of the campaign.
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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 | 0.035 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".