The Role of Goal Source in Escalation of Commitment
Bibliographic record
Abstract
Escalation of commitment is an important decision problem that occurs across different decision contexts. Recognizing that escalation involves one's effort to achieve some form of a goal, researchers have attempted to understand escalation of commitment as a goal-pursuing activity. Prior research works have suggested that escalation situations consist of (1) an initial goal setting phase and (2) an escalation decision-making phase and have investigated how goal difficulty and goal specificity influence escalation decisions. However, they have neglected the potential role of the goal source in escalation situations. In this study, we aim to advance our understanding of escalation of commitment by examining the relationship between goal source and escalation. Specifically, by drawing on distinct characteristics of escalation situations, we conceptualize a new form of goal source, namely inherited goals, and examine its effect on escalation of commitment compared with self-set and assigned goals that are well-known goal sources in goal-setting theory (GST). We conducted two laboratory experiments and found evidence suggesting that individuals who had inherited goals (i.e., those who did not take part in initial goal setting and did not invest effort in pursuing the previous course of action) are less likely to fall into the escalation trap.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".