A Self-Regulatory Model of Resource Scarcity
Bibliographic record
Abstract
Academics have shown a growing interest in understanding how resource scarcity impacts consumer psychology. However, to date, no overarching theory exists to help explain the breadth of findings surrounding resource scarcity. To address this theoretical gap, we propose a self-regulatory model of resource scarcity based on the notion that humans are motivated to reduce the discrepancy between one’s current level of resources and a more desirable level of resources. In this model, we propose that consumers cope with resource scarcity through two distinct psychological pathways: a discrepancy-reduction route aimed at directly resolving the discrepancy and a discrepancy-compensation route that compensates for the discrepancy by enhancing the self in other domains. We also offer a variety of theoretically driven moderators that determine which of the two routes is more likely to be adopted in response to resource scarcity. Finally, we conclude with a research agenda for consumer behavior researchers interested in contributing to a more nuanced understanding of the psychology of resource scarcity.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".