Do Micro-Mobility Services Take Away Our Privacy? Focusing on the Privacy Paradox in E-Scooter Sharing Platforms
Bibliographic record
Abstract
E-scooter sharing is gaining popularity while riders’ privacy concerns still remain, due to their destructive threat to individuals. Based on the APCO macro model, this study examines the relationships among antecedents (i.e., privacy experiences, privacy awareness, usage regularity, and geographical regularity), privacy concerns, and the outcome (i.e., continuance intention to use e-scooter sharing platforms). An interesting phenomenon is that quite a few users have continuance intention to use even when they have privacy concerns, which has rarely been explored with the concept of psychological distance. This research therefore further investigates the relationship between privacy concerns and users’ continuance intention by adding four different types of psychological distance (i.e., temporal, spatial, interpersonal, and platform-self distances) as moderating variables in our research model, drawing on construal level theory. Research findings are expected to contribute to literature on privacy paradox, the APCO macro model, and construal level theory, along with some practical implications.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".