Investigating multiple components of attitude, subjective norm, and perceived control: An examination of the theory of planned behaviour in the exercise domain
Bibliographic record
Abstract
The presence of two subcomponents within each theory of planned behaviour (TPB) concept of attitude (affective and instrumental), subjective norm (injunctive and descriptive), and PBC (self-efficacy and controllability) has been widely supported. However, research has not examined whether the commonality of variance between these components (i.e. a general factor) or the specificity of variance within the subcomponents influences intention and behaviour. Therefore, the purpose of this study was to examine the optimal conceptualization of either two subcomponents or a general common factor for each TPB concept within an omnibus model. Further, to test whether conceptualizations may differ by population even within the same behavioural domain, we examined these research questions with 300 undergraduates (M age = 20) and 272 cancer survivors (M age = 61) for exercise behaviour. Results identified that a general subjective norm factor was an optimal predictive conceptualization over two separate injunctive and descriptive norm components. In contrast, a specific self-efficacy component, and not controllability or a general factor of PBC, predicted intention optimally for both samples. Finally, optimal models of attitude differed between the populations, with a general factor best predicting intention for undergraduates but only affective attitude influencing intention for cancer survivors. The findings of these studies underscore the possibility for optimal tailored interventions based on population and behaviour. Finally, a discussion of the theoretical ambiguity of the PBC concept led to suggestions for future research and possible re-conceptualization.
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".