An Extended Model of Theory of Planned Behaviour in Predicting Exercise Intention
Bibliographic record
Abstract
The main purpose of the present study was to propose and test an extended model with the addition of perceived need in predicting exercise participation, drawing upon the theory of planned behaviour. Cross-sectional data was collected via self-administered surveys from general adults sample (n = 217). The instrument was first validated using exploratory and confirmatory factor analysis to test for unidimensionality, convergent and discriminant validity. Model and hypotheses testing were performed using structural equation modelling (SEM). The extended model accounted for a substantial portion of the variance in exercise intention (R2 = 0.798). Specific findings revealed that: (1) all predictors were significantly correlated with exercise intention; (2) attitude components, perceived control, and perceived need predicted exercise intention; (3) instrumental attitude emerged as the strongest predictor of intention. This study has important implications for marketing practitioners, consumer researchers, and public policy makers interested in the determinants of exercise participation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".