The Role of Informal Controls and a Bargaining Opponent's Emotions on Transfer Pricing Judgments
Bibliographic record
Abstract
Abstract While accounting research has demonstrated the role of a decision maker's own emotions during judgments, psychology research proposes that others’ emotions provide an informational signal to assess an opponent's limits, cooperativeness, and toughness during bargaining. We examine how a bargaining opponent's emotions provide information signals that can be used by a selling division manager during transfer pricing decisions and whether informal control system choices by corporate management to foster cooperation can create a context that influences how managers react to these signals. In an experiment, when informal controls to encourage cooperation were absent (less collaborative environment), managers’ selling price estimates were more conciliatory when the opponent was described as displaying negative emotions than when described as displaying positive emotions. However, when informal controls to cooperate were present (more collaborative environment), managers’ selling price estimates were more conciliatory when the opponent displayed positive rather than negative emotions. Path analyses suggest that managers’ perception of their opponents’ signals is the mechanism by which opponents’ emotions influence transfer‐price decisions. This study highlights the role of others’ emotions as information signals during accounting bargaining and provides insight into the context dependency of opponents’ emotions under various control system structures.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".