How Employees Choose their Commuting Transport Mode: Analysis Using the Stimulus-Organism-Response Model
Bibliographic record
Abstract
Although transport mode choice in commuting from home to work has been studied extensively, no prior research has investigated mode choice as an emotional response to external stimuli using the stimulus-organism-response (SOR) model. Therefore, this study applies the SOR model to explore commuters’ transport mode choice behaviour. The stimulus variables include trip characteristics, transport infrastructure and services, environment, and work characteristics; the organism variable includes the travel experience and attitude of the individual; and the response variables include use of public transport, private transport, and e-hailing. Data were collected using a questionnaire survey of 500 formal-sector workers in Jakarta; 430 respondents provided valid responses for analysis. The survey data were analysed using partial least squares-structural equation modelling. The results showed that the stimulus variables, namely, trip characteristics, transport infrastructure, environment, and work characteristics, had indirect effects on the choice of e-hailing through organism factors (travel experience and attitude). Also, the environment and work characteristic variables had an indirect effect on the choice of private transport through organism factors. Stimulus variables had no indirect effects on public transport usage. When travel experience was the stimulus variable, the indirect effect on public transport usage through attitude as the organism variable was significant. The response to the use of transport modes showed dynamic behaviour, depending on the provided stimulus and organism. These findings can be beneficial for establishing a more comprehensive strategy that includes the provision of infrastructure, improvement of transit service, the built environment, and employers’ policies to realise a sustainable commuting trip.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".