Green Marketing Activities to Support Corporate Reputation on a Sample from Turkey
Bibliographic record
Abstract
In today’s world of competition, companies are under pressure to produce environment-friendly products and services. As consumers get more sensitive on environment protection, their purchasing behavior develop on that direction as well as. Under these circumstances, a concept called “green marketing” is widely discussed in both academic and industrial circles. Green marketing is basically the promotion of environmentally safe and beneficial products; it also includes the development of ecologically safer products, recyclable packaging, green promotion activities and green labels. Moreover, it’s claimed that there is a correlation between green marketing and good corporate recognition. The purpose of the study is to investigate relationship between green marketing activities and corporate reputation on the sample of Aygaz, one of the most important energy and LPG Companies in Turkey. For this purpose, a well-prepared questionnaire complying with the literature was carried out on 526 participants who are automobile drivers in different regions of Turkey. Research data is evaluated in the program of SPSS with t-test, factor, frequency, correlation and variance analyses. According to the results of the field study, 87.3% of the consumers have stated that there is a relationship between the green marketing activities and the corporate recognition. In the research results we do not reveal any direct relation between the consumers’ purchase preferences and green marketing practices in the sample of Aygaz brand.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".