Exploring Commuter’s Preferences and Future Intentions to Use Ride‐Sharing: A Case Study From a Developing Country
Bibliographic record
Abstract
This study investigates transportation choices with a specific focus on ride‐sharing practices. The main aim of the study was to understand the current modes of transport, the primary reasons for choosing them, ride‐sharing experiences, and future ride‐sharing intentions within the context of Islamabad, Pakistan. The final analyses were based on 294 respondents, including 88 respondents with prior ride‐sharing experience. The sample was skewed toward male participants (80.6%), reflecting national mobility patterns. Logistic regression was employed to investigate the relationship between different factors toward individuals’ intentions to use ride‐sharing as a future commuting option. The results indicate that gender, previous ride‐sharing experience, preferences for companionship during ride‐sharing, and the primary mode of transportation for shopping emerged as significant factors influencing future ride‐sharing intentions. Males are nearly three times more likely to adopt ride‐sharing (Exp ( β ) = 2.9) than females ( β = 1.07, p < 0.01). Similarly, individuals with previous ride‐sharing experience ( β = 0.94, p < 0.01) have a 2.6 times higher likelihood of choosing ride‐sharing in the future. Moreover, respondents preferring larger groups while ride‐sharing exhibit higher adoption intentions ( β = 0.26, p = 0.02, Exp ( β ) = 1.3). In contrast, individuals primarily using motorcycles ( β = −1.53, p = 0.02, Exp ( β ) = 0.2) or personal cars ( β = −1.72, p = 0.01, Exp ( β ) = 0.2) for shopping are less inclined to shift toward ride‐sharing. The model achieves a Nagelkerke pseudo R 2 of 0.23, explaining 23% of the variance in future ride‐sharing intentions. This research yields valuable insights that could guide initiatives aimed at fostering ride‐sharing adoption and encouraging individuals to utilize this mode of transportation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".