The Gap between Attitudes and Behavior in Ethical Consumption: A Critical Discourse
Bibliographic record
Abstract
Background: Although consumers are increasingly concerned with ethical factors when forming product opinions and making purchase decisions, recent studies have highlighted significant differences between consumers’ ethical consumption intentions and their actual buying behavior.Various dimensions concerning how consumers make purchase and consumption decisions and the driving forces behind them have been identified through this study. Objectives: This paper aims to explore the factors leading the gap between attitudes and behavior of consumers in relation to ethical consumption. Methods: The desk review carried out on various related studies reflects that the factors that obstruct the process of ethical consumption and thereby being responsible in forming attitude-behavior gap, which can be helpful in the course of management decision implications worth encouragement of ethical consumption behavior. Moreover, conceptual framework that has been developed for ethical consumption also indicates the factors responsible for creating ethical intention-behavior gap. Findings: This study derives concepts on ethical consumption from literature survey and identifies consumers’ understanding level on ethical consumption. Likewise, the study also provides a comprehensive picture of factors impeding ethical consumption among consumers while providing some theoretical and analytical applications. Conclusions: Price, quality, taste, brand image of products and convenience are some of the considerable issues while buying, due to which consumers’ ethical concerns towards society and environment are not transformed into their consumption behavior.
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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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| 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".