Inferring Commitment from Rates of Organizational Transition
Bibliographic record
Abstract
Organizations often implement changes that can signal their values. However, the most objectively efficient changes do not necessarily serve as the best signals. Across seven experiments, we investigate how different rates of transition influence people’s perceptions of how committed organizations are to the values underlying changes or improvements. We find that slower, less efficient transitions signal greater commitment compared with faster, more efficient transitions that reach otherwise identical endpoints (Experiment 1). Using mediation and moderation strategies, we demonstrate that this discontinuity occurs because people assume slower transitions require relatively more effort to enact (Experiments 2 and 3). Moreover, these commitment inferences persist beyond the point at which changes end (Experiment 4), when further improvement along the same dimension is no longer possible (Experiment 5), and regardless of whether the organization decided to transition either quickly or slowly (Experiment 6). This effect reverses, however, when people can directly compare slower and faster transitions that ultimately reach identical endpoints (Experiment 7). Taken together, these findings suggest that people often infer greater commitment from slower transitions that unfold over time, even when those transitions are objectively inferior to faster alternatives. Data are available at https://doi.org/10.1287/mnsc.2017.2980 . This paper was accepted by Yuval Rottenstreich, judgment and decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".