Effect of Concession‐Timing Strategies in Auditor–Client Negotiations: It Matters Who Is Using Them
Bibliographic record
Abstract
Abstract In this study, we examine how norms about the use of negotiation strategies by different parties in an auditor–client negotiation influence the relative efficacies of these negotiation strategies. We conduct an experiment with experienced auditors/financial managers as participants, who enter into a negotiation on an income‐decreasing audit adjustment with a hypothetical client/auditor who uses a strategy where the same concessions are given either at the start, gradually, or the end of the negotiation. We find that the concession‐end strategy is more effective than the concession‐start strategy when used by auditors; however, the reverse is true when these same strategies are used by financial managers. The concession‐gradual strategy leads to superior outcomes when used by either auditors or clients. We also provide evidence that auditors’ and financial managers’ perceptions of the norms relating to the use of these strategies correspond to what we propose in our theory.
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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 | 0.028 | 0.197 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".