Effects of Nudging and Privacy Control on Online Physician Reviews: Evidence from a Field Experiment
Bibliographic record
Abstract
Patients using healthcare services have substantial privacy concerns when writing online reviews of their physicians that may deter them from sharing information potentially helpful for others. This gives rise to a dilemma in which patients have to trade off between privacy and social welfare, leading to fewer and less informative reviews. To examine this trade off, we study how nudging strategies together with privacy control affect reviewers’ decisions related to the provision of reviews, disclosure of sensitive information, and identity revelation. We conducted a large-scale two-stage field experiment, complemented by lab experiments, to establish causality. Results reveal that nudging with an open appeal, compared to nudging with targeted benefits, increases the likelihood of patients submitting reviews. However, nudges that highlight benefits to self increase the proportion of patients revealing sensitive medical information in their reviews and the likelihood of them revealing their identity. Additionally, preemptive privacy control increases the likelihood of identity revelation without adversely impacting the sharing of sensitive information in reviews. Our findings highlight how nudging and privacy control influence patients’ review provision behavior. This study offers strategic implications for online platforms navigating the complex interplay of consumer motivations and privacy concerns in healthcare.
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 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 | 0.044 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".