Bibliographic record
Abstract
Abstract Local consumption improves the economic health of local communities and reduces the environmental impact of marketing activities, and hence it is important to understand factors that increase the likelihood of local consumption. Across eight studies in the laboratory and field, we show that the likelihood of local consumption increases as consumers' perceived control decreases with this effect being mediated by feelings of anticipated warm glow. We also identify two boundary conditions of this effect, namely perceived quality of the product and public self‐consciousness of the consumer. This research contributes to the literature in the following ways. First, it identifies perceived control as a novel self‐discrepancy‐based antecedent of the likelihood of local consumption. Second, it identifies anticipated warm glow as a novel affective mechanism underlying the effect of perceived control on the likelihood of local consumption. Third, it identifies perceived quality of the local product as a novel moderator of the effect of perceived control on the likelihood of local consumption. Fourth, it identifies public self‐consciousness of the consumer as another novel moderator of the effect of perceived control on the likelihood of local consumption. This research also contributes methodologically by demonstrating robustness of effects across a range of manipulations and measures including incentive‐compatible behavior and online behavior.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".