Validation of the Theory of planned behaviour in different cultural settings – international comparison of drunk-driving across 5 countries
Bibliographic record
Abstract
Key-words. driving under the influence; alcohol; theory of planned behaviour; international survey; road safety culture Background. Theory of Planned Behaviour (TPB, Ajzen, 1991) is a psychological model which is widely used in traffic research to predict e.g. drunk-driving (Moan & Rise, 2011; Rivis et al., 2011, Chan et al., 2010). Some studies question its validity in cross-cultural application especially in developing countries (Nordfjærn et al., 2011). Objectives. The objective of the present study is to validate the TPB model in different cultural settings. The study is based on the results of a spin-off of ESRA (E-Survey of Road users’ Attitudes; Meesmann et al., 2018) in 2017. It investigates cross-cultural differences in self-declared drunk-driving and related behaviour determinants (TPB), such as social norms, attitudes, self-efficacy and habits in Austria, Brazil, Canada, India, Nigeria and the United Kingdom. Methods. In each county a representative sample of the national adult population (N=500) was requested to complete an online survey. Linear regression models were used to investigate the association between the behaviour determinants and self-declared drunk-driving. The models were fitted on the whole sample and the national samples separately. Results. The results show that unsafe road safety attitudes, personal and perceived acceptability of drunk-driving, a low self-efficacy to control drunk-driving, unsafe intention and habits were significantly associated with self-declared drunk-driving. The linear regression models of drunk-driving explained a satisfactory amount of variance in all countries but was lower in Nigeria (R2 Nigeria .48 all other countries >.61). Conclusions. The results of this study support the validity of the TPB model in these cross-cultural settings. The intention is to elaborate this investigation to the 32 countries of the ESRA2 survey, which will be conducted in November 2018.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".