Using Social Marketing Principles to Describe Local Isfahan Managers' Attitude about Using New Energy Resources
Bibliographic record
Abstract
Objective: Though rich in oil and gas, Iran, like many other countries in Middle East, increasingly recognizes the need to diversify energy sources, to ensure security of supplies and provide for more consistent energy costs. Renewable energies (e.g. wind, solar and geothermal) are realistic options without the environmental impacts of conventional fuels. Developing and using of this renewable resources and changing energy policy, was under the influence of managers' Attitudes and consequently their support of this strategy. The purpose of this paper at first is to understand managers' values about the new energy resources, and then to analyze the impact of social marketing mix on Managers' feelings.Methods: The paper employed structural equation modeling (SEM) to investigate the relationship between the social marketing and Attitude. Required data were collected from local managers in Isfahan by questionnaire method. For Data gathering and analyzing mixed (qualitative and quantitative) Methods are used.Results: findings show that managers' Attitudes towards new energy resources were positive. Also, the current social marketing mix (e.g. product, price, place, promotion, people and policy) of new energy centers are suitable. As another result, managers' Attitudes cannot influence on the social marketing mix.Conclusion: The paper recommends the integration of principles of social marketing in community programs aimed at dealing with environmental issue. In particular, it suggests identification of competing groups in the community, construction of specific programs for different segments, addressing the no-monetary prices that the change may incur on the different groups, location of appropriate places for distribution of messages, using supportive laws, indentifying people views and using TV media and Internet services as well as public means of communication and promotion.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".