Audit Pricing for Strategic Alliances: An Incomplete Contract Perspective
Bibliographic record
Abstract
Abstract We study the pricing of audit services for strategic alliances, a governance structure involving an incomplete contract between separate firms. Since incomplete contracts do not specify all future contingencies, we expect that the nonverifiability of information and potential agency behavior in alliances increase audit complexity, resulting in higher audit fees. Our findings support this prediction. We then separate strategic alliances into joint ventures and contractual alliances, as the latter involve more complexity. We find that our audit fee results are largely driven by contractual alliances. We perform additional tests to rule out the concern that our audit fee results might be attributable to the impact of strategic alliances on distress risk, audit risk, or control risk. Contrary to the distress risk argument, we find that auditors arelesslikely to issue going‐concern modified opinions when there is an increase in strategic alliances. Contrary to the audit risk argument, we find that an increase in strategic alliances is unrelated to the likelihood of financial misstatements. Contrary to the control risk argument, we find that an increase in strategic alliances is unrelated to internal control weakness opinions.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".