A Model of Green Consumption Behavior Constructed by the Theory of Planned Behavior
Bibliographic record
Abstract
In recent years, global warming has become a widespread well-publicized issue. As the general public havebecome aware of the importance of protecting the environment, consumers have started to evidence a markedpreference for green products. This study constructed a model describing the relationships among perceivedbenefit of green consumption behavior, perceived risk, moral responsibility, normative belief, control strength,control belief, attitude, subjective norms, behavior control, behavior intention, and actual behavior.An analysis of 560 valid questionnaires resulted in six main findings: (1) Perceived benefit of green consumptionhas a significantly positive impact on consumer attitude. (2) Perceived risk of green consumption has asignificantly negative impact on consumer attitude. (3) Normative belief and moral responsibility both havesignificantly positive impacts on consumer subjective norms. (4) Control strength and control belief both havesignificantly positive impacts on consumer behavior control. (5) Attitude, subjective norms and behavior controlboth have significantly positive impacts on consumer behavior intention. (6) Behavioral intention and behaviorcontrol both have significantly positive impacts on actual consumer behavior. The results of this study providereference to industry managers in the formulation of green marketing strategy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".