Bibliographic record
Abstract
Purpose/Significance: In recent years, consumer behavior studies have shown that weather and air quality have a significant impact on consumers' purchasing behavior. Therefore, it is necessary to understand the different ways and causes of consumers' responses to weather conditions. However, the existing relevant research results are scattered in different disciplines and lack of summary and sorting of the research topic. By systematically reviewing the impact of air pollution on consumer decision-making, we can fully understand the change of consumer behavior caused by air pollution factors at the macro level and help enterprise managers to develop targeted marketing management strategies to avoid or reduce the impact of air quality on consumer decision-making behavior. Design/Method: This paper systematically reviews the impact of air pollution on consumers' decision-making behavior based on key keywords retrieved from major academic literature databases and search engine websites. In detail, with the framework of the stimulus-organism-response (S-O-R) model in the field of environmental psychology and based on the process perspective, the paper divides the impacts of air pollution on consumer's decision-making behavior into the change of body and mind before consumer decision-making process, change of the decision behavior in the process of consumption decision-making and consumption experience and evaluation behavior after the process of consumption decision making. Conclusion/Findings: By establishing and analyzing the thematic structure of studies on the impact of air pollution, the results show that air pollution has a wide range of impacts on consumers, ranging from health risks to mood changes and from changes in daily habits to changes in consumption behaviors of individuals and groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".