Doing the Difficult Stuff: Influence of Self-Determined Motivation Toward the Environment on Transportation Proenvironmental Behavior
Bibliographic record
Abstract
Past research has shown that as proenvironmental behaviors (PEBs) become more difficult, the level of self-determined motivation becomes a more powerful predictor of behavior participation. The purpose of this study was to (1) examine the perceived difficulty of transportation PEB in two contexts using self-determination theory and (2) examine the mediation effect of autonomous and controlled motivation toward the environment. Results indicate that when the transportation behavior was perceived as difficult to perform, autonomous motivation was associated with more frequent participation. When the transportation behavior was perceived to be easy, motivation type had no influence on behavior frequency. Mediation analysis indicated a significant total indirect effect of perceived environmental competence on frequency of easy PEB via both types of motivation, but for difficult behaviors only autonomous motivation was a significant mediator. This study suggests that increasing individuals' autonomous motivation toward the environment and sense of environmental competence supports participation in difficult PEBs, potentially leading to a larger environmental benefit. Key Words: Self-determination theory—Autonomous and controlled motivation—Proenvironmental behaviors—Public transportation—Perceived difficulty.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".