The Efficacy of Third‐Party Consultation in Preventing Managerial Escalation of Commitment: The Role of Mental Representations*
Bibliographic record
Abstract
Abstract Avoiding continued investment in poorly performing projects is an important function of management control systems. However, prior research suggests that managers fail to use accounting information indicating that a project is performing poorly to discontinue it; that is, they escalate commitment to the project. We perform two experiments to investigate the efficacy of a potential control mechanism, third‐party consultation, in preventing managerial escalation of commitment. We hypothesize that the information‐processing objective (that is, purpose) assigned to consultants influences the mental representations they construct to process and store information, which ultimately influences their recommendations regarding the continuation of a poorly performing project. Results suggest that consultants will not construct mental representations amenable to making high‐quality project‐continuation recommendations unless they are assigned that specific purpose. Results further suggest that applying additional effort likely will not overcome the adverse effects of having inappropriate mental representations when making project‐continuation recommendations. An implication of our study is that third‐party consultants likely will not prevent managerial escalation of commitment unless consultants have a specific mandate of making a project‐continuation recommendation in mind when they encounter relevant accounting information.
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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 | 0.010 | 0.112 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".