Hold Me Accountable: Anonymity and Prosocial Behavior in Services
Bibliographic record
Abstract
Problem definition: Many services rely on the prosocial behavior of consumers that benefits the wider community for effective delivery, such as bike-sharing schemes, self-service checkouts in grocery stores, and pathology screening services, yet there are frequent challenges in motivating honest, cooperative user behavior in these services. We build on existing research to argue that the degree of anonymity of users toward the service provider can be used to facilitate their prosocial behavior. Anonymity has two effects: It removes the ability of individuals to build a public reputation as someone who is prosocial, but it also removes accountability to others. Although studies have examined the effect of reputational motivation, studies of the effect of accountability on prosocial behavior have been limited to laboratory and online settings, where accountability, mostly toward their peers or to a “higher power,” only benefited other participants. Thus, little is known about how accountability toward the service provider can affect prosocial behavior that benefits the wider public. Methodology/results: We investigate this context with a unique proprietary data set from a pooled asymptomatic pathological screening program. We find that increasing anonymity by removing names from test kits distributed by the service provider decreased voluntary participation by 22%. Social pressures in larger groups partially substituted for the reduced accountability from removing names, providing an insight that can help mitigate reduced accountability in settings where anonymity is preferred. Managerial implications: For managers, we emphasize the value of accountability to the service provider as a motive for prosocial behaviors such as honesty and cooperation. For policymakers, we provide insight into designing healthcare screening and public interaction services. Funding: This work was supported, in part, by the Tulane Supporting Impactful Publications Program. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0667 .
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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 | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".