Who Really Matters? Revenue Implications of Stakeholder Satisfaction in a Health Insurance Company
Bibliographic record
Abstract
ABSTRACT: This study examines the revenue implications of satisfaction measures in a setting with multiple stakeholders. I obtain a proprietary database from a leading health insurance company that measures satisfaction levels of multiple stakeholders, including: (1) clients that purchase insurance plans for their employees, (2) patients who use the insurance plans, and (3) doctors who provide medical services. Using multi-stakeholder satisfaction data over a 20-quarter period, I find that satisfaction measures are multi-dimensional, and that future revenues are positively associated with certain, but not all, dimensions of client, patient, and doctor satisfaction. Drawing on stakeholder theory, I predict that the revenue implications of stakeholder satisfaction vary with the power of different stakeholder groups. Specifically, I examine the moderating effects of the percentage of voluntary patients, business unit age, and market penetration on the relation between stakeholder satisfaction and future revenues, finding evidence consistent with my prediction.
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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".