Exploring the Role of Advertising Types on Improving the Water Consumption Behavior: An Application of Integrated Fuzzy AHP and Fuzzy VIKOR Method
Bibliographic record
Abstract
In recent years, many cities have suffered from a shortage of drinking water, mainly due to population growth. Hence, the desire to curb undue water consumption through the identification of the main factors affecting consumer behavior has become very important in managing drinking water supplies. Modifying the consumption pattern means institutionalizing of a sustainable culture in water consumption among consumers and the identification of the main criteria affecting their behavior. In 2018, a survey was applied to examine the role of mass media advertising in modifying the water consumption pattern in Iran. An integration of fuzzy AHP and fuzzy VIKOR was proposed based on group decision making, and fuzzy trapezoidal sets used to model linguistic variables and to deal with uncertainty in opinions. We devised and conducted a questionnaire with 24 main criteria and 8 sub-criteria to measure the impact of advertising on water consumption. The case study population in this study included all urban households over 15 cities of Iran. A total of 5630 questionnaires were distributed among the various populations with cluster method. Then, by analyzing the results, advertisements using animation had the highest impact on consumer behavior, among the available alternatives, and could play a significant role in modifying the water consumption pattern. Additionally, a fuzzy evaluation technique is performed to validate the result of the applied method. Subsequently, a sensitivity analysis was conducted to validate the stability of the final ranking. Finally, the prioritization results of the types of advertising by the proposed method were compared with the results of the fuzzy AHP method.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".