Does Personality Moderate the Theory of Planned Behavior in the Exercise Domain?
Bibliographic record
Abstract
This study investigated the moderating influence of the five-factor model of personality (FFM) on the theory of planned behavior (TPB) in the exercise domain. Although an analysis of all possible moderation effects was conducted, it was hypothesized that high extraversion (E) and conscientiousness (C) individuals would demonstrate significantly stronger relationships between intentions and exercise behavior than those low in E and C. Conversely, it was expected that high neuroticism (N) individuals would show a significantly weaker relationship between intention and exercise behavior than those low in N. A total of 300 undergraduate students completed measures of the FFM, TPB, and a 2-week follow-up of exercise behavior. Two-group structural equation models of the TPB were created using a median split for each personality trait. Overall, 5 significant (p < .05) moderating effects were found. Specifically, N was found to moderate the effect of subjective norm on intention. E also moderated the effects of subjective norm on intention as well as intention on behavior. C moderated the effects of affective attitude on intention and intention on behavior. Theorized influences for the presence or absence of personality moderators are discussed. The results generally support the possibility of personality being a moderator of the TPB but highlight the need for future research and replication.
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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 | 0.015 |
| 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 | 0.001 | 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".