The Relative Effectiveness of Simultaneous versus Sequential Negotiation Strategies in Auditor‐Client Negotiations
Bibliographic record
Abstract
Abstract Since most audit engagements identify multiple proposed audit adjustments, auditors must decide how best to present and negotiate these adjustments with their clients. Prior research indicates that auditors can negotiate adjustments individually (a sequential strategy) or can negotiate multiple adjustments within the same negotiation setting (a simultaneous strategy). This paper examines the relative effectiveness of a simultaneous versus sequential negotiation strategy in eliciting concessions from clients and engendering greater client satisfaction. Participants negotiated audit adjustments with a simulated auditor who employed one of the two negotiation strategies. We find evidence that a simultaneous strategy elicits significantly greater total concessions from client managers and also generates more positive attitudes toward the auditor. We also manipulate the magnitude of the issues negotiated and find that significantly greater concessions are offered when larger issues are presented first. These findings have important implications for auditors, as they suggest that negotiating issues simultaneously and presenting larger issues first can result in significantly improved negotiation outcomes.
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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 | 0.029 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".