Accounting Performance and Capacity Investment Decisions: Evidence from California Hospitals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Capacity decisions involve trade‐offs between the cost of capacity and the opportunity costs of lost sales. Accounting researchers posit that accounting performance provides sufficient information about these trade‐offs and thus can be used to formulate simple rules to assist capacity decisions. Empirical research has not examined the role of accounting information in capacity investment decisions at the department level in a multiproduct firm in the presence of social costs. Empirical analyses using department‐level data from California hospitals for the period 1998–2005 show that hospitals are more likely to make capacity investments in departments with high accounting performance. However, in the presence of demand variability, the association between accounting performance and capacity investment is attenuated because of the resulting increase in noise in accounting performance measures. Thus, the weight on accounting performance as a decision tool for capital investments reduces when there is demand variability. Another factor that reduces the weight on accounting performance is capacity utilization. Higher capacity utilization can lead to turning away or rerouting of patients to other hospitals and negatively impacts reputation and quality of care, which increases the hospital's social costs. Hence, hospitals do not require high accounting performance before investing in a department with high capacity utilization. This empirical evidence of the role of accounting performance in capacity investment decisions fills a gap in the capacity investment literature and furthers our understanding of the interactions between accounting performance and the operational determinants of firms’ capacity investment behavior.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it