Promoting social sustainability: Psychological and contextual factors influencing mobility scooter adoption among older adults in a developing country
Bibliographic record
Abstract
Equitable transportation for minority groups, such as older generations, is fundamental to social sustainability, as active participation in society improves their quality of life. Mobility scooters have the potential to promote independence among older adults so that they can participate in society alongside reducing dependency on motor vehicles, contributing to more sustainable urban mobility solutions. However, in developing countries, existing literature reveals a gap in quantitively assessing and comparing different factors influencing their adoption, especially psychological factors, such as external shame. This study examines mobility scooter acceptance among the elderly (aged 60+), in Tehran, a large metropolitan area, and Gorgan, a small city in Iran. An extended Technology Acceptance Model (TAM) with seven constructs, including the novel introduction of "Shame," was assessed using data from 805 respondents through face-to-face interviews. The second part of the study measured observed heterogeneity using multi-group analysis (MGA) to examine the effects of two moderators: city size and daily physical challenges. The results highlight “Shame” as a predictor of mobility scooter acceptance, with different impacts between the two cities. In the small city, “Shame” negatively moderates other constructs, while in the large city, perceived usefulness is a more decisive predictor; suggesting that practicality holds greater sway in such contexts. These results highlight the importance of psychological factors, especially "Shame," in predicting mobility scooter acceptance based on city size. In light of these findings, tailored policies and strategies are proposed to address the unique psychological and contextual barriers faced by older adults in small and large cities, advancing social equity in sustainable transportation for the elderly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".