Influences on Consumers' Recycling Intentions of Compact Fluorescent Lamps—Mercury as a Factor
Bibliographic record
Abstract
The purpose of the current study is to understand consumers’ behavioral intentions in situations involving both positive and negative potential impacts on the environment. The case of energy efficient Compact Fluorescent Lamps (CFLs) with their potential for mercury pollution is an example of this type of trade-off. Past studies have confirmed the usefulness of the Theory of Reasoned Action for identifying the antecedents influencing recycling rates, however, none have looked at situations where conflicting environmental trade-offs were involved. Stepwise regression analysis was used to develop a core model which explains R2=.561 of the intention to recycle. Significant antecedents include the peer group subjective norm of recycling CFLs (Beta=.661), the attitude towards recycling of CFLs (Beta=.417), the attitude towards the overall environmental friendliness of CFLs (Beta=-.344), and the attitude towards the number of sites available for recycling of CFLs (Beta=.212). Adding the impact of past recycling behavior increases the model’s explanatory power to .726. Important policy implications result from the finding that the number of people who would ‘always or usually’ recycle CFLs increased to 90% by enhancing the convenience of recycling. A significant managerial implication results from the contradictory findings that the attitude towards mercury is not significantly correlated with intentions to recycle, however the attitude towards the environmental friendliness of CFLs was negatively related to recycling intentions. This potentially indicates that there is a lack of understanding of the net positive impact of CFLs and there is potential confusion about the related environmental trade-offs. Recommendations for policy and marketing responses are suggested.
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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 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".