Does perceived behavioural control mediate the relationship between power beliefs and intention?
Bibliographic record
Abstract
OBJECTIVES: In order to determine whether the relationship between power beliefs (Sigma(p)) and health-related behavioural intentions is mediated by perceived behavioural control (PBC) we used structural equation modelling of eight cross-sectional data sets. METHOD: Eight studies that examined health-related behaviours and employed representative samples totalling N = 4663 participants were analysed. All studies involved power belief items derived from pilot testing and employed standard multi-item measures of power beliefs, PBC and intention that were highly reliable. RESULTS: Confirmatory factor analysis confirmed the discriminant validity of power beliefs, PBC and intention. Structural equation modelling of relevant paths indicated that PBC only partially mediated the relationship between power beliefs and intention (Z(Sobel) = 5.15, p < .001; Z(Baron&Kenny) = 5.16, p < .001). Power beliefs had a significant direct relationship with intention even after PBC had been taken into account. CONCLUSION: The findings undermine Ajzen's contention that PBC mediates the power beliefs-intention relationship and suggests that it is important to employ measures of power beliefs in addition to measures of PBC in order to enhance the prediction of intentions to perform health-risking, or health-promoting, behaviours.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".