Some people care what you think: Normative beliefs and physical activity intentions in the presence of high and low intrinsic regulation
Bibliographic record
Abstract
The Theory of Planned Behaviour proposes that attitudes, subjective norms, and perceived behavioural control predict physical activity (PA) intentions and subsequently behaviour (Ajzen, 1991). Recently, the complexity of the subjective norm-intentions relationship was highlighted by Kim et al. (2019), who found the relationship between normative beliefs (i.e., perceptions of normative pressure) and PA intentions to be stronger for individuals with higher motivation to comply with those normative beliefs. However, individuals with lower motivation to comply still had moderate levels of PA intentions regardless of their level of normative beliefs. Interpreting this finding, a possible buffer against normative beliefs is the degree to which individuals' PA behaviours are already intrinsically regulated. The current study examined differences in the relationship between normative beliefs and PA intentions, as moderated by motivation to comply, for individuals with low vs. high intrinsic regulation. Analysis of data gathered from 204 undergraduate students (173 females, 30 males, 1 non-binary) at two time points revealed that for individuals with high intrinsic regulation, normative beliefs positively predicted PA intentions (b = .29, p = .006). However, for individuals with low intrinsic regulation, there was a significant interaction between normative beliefs and motivation to comply with respect to predicting intentions (b = .16, p = .026). Specifically, normative beliefs negatively predicted intentions for PA (b = -.49, p = .008) when individuals had lower motivation to comply. These findings indicate that individuals' existing levels of intrinsic regulation should be considered when attempting to exert social influence on PA behaviours.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".