Experimental evidence for the intention–behavior relationship in the physical activity domain: A meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: Most contemporary theories of physical activity include an intention construct as the proximal determinant of behavior. Support of this premise has been found through correlational research. The purpose of this paper was to appraise the experimental evidence for the intention-behavior relationship through meta-analysis. METHODS: Studies were eligible if they included: (1) random assignment of participants to intervention/no intervention groups; (2) an intervention that produced a significant difference in intention between groups; and (3) a measure of behavior was taken after the intention measure. Literature searches were concluded in December 2010 among five key search engines. RESULTS: This search yielded a total of 1,033 potentially relevant records; of these, 11 passed the eligibility criteria. Random effects meta-analysis procedures with correction for sampling bias were employed in the analysis. The sample-weighted average effect size derived from these studies was d+ = .45 (95% CI .30 to .60) for intention, yet d+ = .15 (95% CI .06 to .23) for behavior. CONCLUSIONS: These results demonstrate a weak relationship between intention and behavior that may be below meaningful/practical value. We suggest that prior evidence was probably biased by the limits of correlation coefficients in passive designs. It is recommended that contemporary research apply models featuring intention-behavior mediators or action control variables in order to account for this intention-behavior gap.
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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 | 0.035 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".