The Role of Personality and Fluid Intelligence in Precrastination: A Quantitative Study of Students in German Higher Education
Bibliographic record
Abstract
Precrastination, the tendency to complete tasks immediately even at the expense of efficiency, remains relatively unexplored. This study investigated the influence of the Big Five personality traits and fluid intelligence on the precrastination behavior of university students. Using a quantitative, cross-sectional design, N = 95 German students (M = 23.03 years, 45% female, 61% enrolled in full-time studies) completed self-report measures via an online questionnaire. A multiple linear regression analysis revealed that both conscientiousness (b = 1.16, SE = .16, t(88) = 7.18, p < .001) and fluid intelligence (b = -0.05, SE = .02, t(88) = -2.09, p = .040) had significant effects on precrastination behavior. The overall model demonstrated a high explanatory power (R² = .53, adjusted R² = .50). Mediation analyses further showed that fluid intelligence mediated both the relationship between openness to experience and precrastination behavior (ab = -.176, 95% CI [-.362, .037]) and partially the relationship between conscientiousness and precrastination behavior (ab = .080, 95% CI [.001, .222]). The results highlight that precrastination is not solely shaped by personality traits but is also influenced by cognitive abilities such as fluid intelligence. Limitations, including the modest sample size, restrict generalizability. These findings provide insights for interventions supporting task prioritization and cognitive self-regulation in students. Future research should employ larger samples and diverse designs to clarify underlying mechanisms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".