Sustainable Development of Shared Mobility in China in Relation to the Privacy Paradox of Users
Bibliographic record
Abstract
Shared mobility is an important part of a smart city transportation system. However, during the short period it has been in effect, privacy leakages have frequently occurred, and as travellers are increasingly paying attention to their privacy, leakages hinder the rapid development of shared mobility. Therefore, it is important to explore the origin of the privacy paradox in the context of shared mobility and propose some targeted measures for improvement. The privacy paradox has been attested in numerous studies, where, despite their obvious concern that their privacy will be compromised, users continued to adopt services that may compromise it. This study constructs a model for the privacy paradox based on the theory of planned behaviour, privacy calculus theory, and construal level theory. A questionnaire survey was conducted with 301 Chinese college students to quantitatively analyse the relationship between the main factors of users’ privacy paradox in the context of shared mobility. The study results showed that (a) the privacy paradox does exist in shared mobility among college students; (b) both perceived benefit and trust have a significant positive effect on near future disclosure intention, with trust being the prime motivator; (c) both privacy concern and perceived risk have significant negative effects on distant future disclosure intention, with privacy concern being the key ingredient; and (d) both near and distant future disclosure intentions have positive effects on privacy disclosure behaviour, with near future disclosure intention having a more significant influence. Further, to promote the healthy and sustainable development of China’s shared mobility industry, countermeasures and suggestions have been proposed for users, ride-sharing enterprises, and the government according to the research results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".