Green Stimuli Characteristics and Green Self-Identity Towards Ethically Minded Consumption Behavior with Special Reference to Mediating Effect of Positive and Negative Emotions
Bibliographic record
Abstract
Research studies related to ethical consumerism has been gaining increasing attention in the last decade due to growing importance with environmental pollution. Research studies pointed out a gap between ethical consumers’ behavior and intention which is common in Sri Lanka as well. Hence the study used emotions and self-identity as two key drivers which assist in exploring the intention-behavior gap that has not been researched so far. Therefore the research problem addressed is “whether the positive and negative emotions aroused as a result of consumer subjective evaluation to stimuli, impact on the ethically minded consumption behavior?”. The study focused only on environmental friendly electrical household appliances and the population is the academics and professionals who reside Gampaha and Colombo suburbs and who bought environmental friendly electrical household appliances within the last one year of duration. The unit of analysis is individual consumers and the convenience sampling method used. 200 individual respondents contributed to the study and the data collection was done through a self-administered questionnaire. The study has used Smart PLS 3.2 software and the results showed that the green stimuli characteristics and green self-identity significantly influence ethically minded consumer behavior and only positive emotions act as a significant mediator. Most importantly if the consumer’s perceived effectiveness is high, despite the presence of emotions ethically minded consumer behavior will be triggered more. In conclusion, marketers have to use positive emotions when creating the stimuli and should give more priority for assuring the individuals small step for protecting the environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".