Negative Relationships Between Self-Efficacy and Performance Can Be Adaptive: The Mediating Role of Resource Allocation
Bibliographic record
Abstract
This research speaks to the ongoing debate regarding the role of self-efficacy in self-regulation. Specifically, we argue that both positive and negative relationships between self-efficacy and resource allocation are part of an adaptive process. We present the results of two empirical studies demonstrating that a negative relationship between self-efficacy and resource allocation is not always maladaptive and, in fact, can lead to positive indirect effects on performance. In Study 1, we observed natural fluctuations in self-efficacy as individuals completed a mathematics test, finding that the tendency to reduce resource allocation with high self-efficacy is most clearly observed when time is scarce. In turn, an inverted-U relationship between resource allocation and overall performance under high time scarcity emerged such that moderate levels of resource allocation resulted in the highest levels of performance. Study 2 used an experimental design in which self-efficacy was manipulated. Replicating core findings from Study 1, individuals drew upon self-efficacy to balance resource allocation across competing demands. We conclude with a discussion of the theoretical and practical implications of our results.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".