The Impact of Superior-Subordinate Identity and <i>ex post</i> Discretionary Goal Adjustment on Subordinate Expectancy of Reward and Performance
Bibliographic record
Abstract
ABSTRACT Firms often evaluate subordinate performance relative to a difficult but attainable goal set at the beginning of the evaluation period. For many, a mechanism exists by which these goals may be adjusted downward at the end of the period to account for an uncontrollable negative event. We examine, experimentally, how the knowledge that a downward ex post discretionary goal adjustment is possible affects subordinates’ expectancy of reward and performance in periods where a negative uncontrollable event occurs, and whether high identity, defined as high perceived social connectedness between the superior and subordinate, moderates this effect. We find that high superior-subordinate identity can offset the otherwise negative impact of the potential for downward ex post discretionary goal adjustment on subordinates’ expectancy of reward and performance. Thus, creating an organizational culture that promotes identity between superiors and subordinates can complement incentive-based controls in motivating subordinate performance. JEL Classifications: C91; J33; M41; M52. Data Availability: Please contact the authors.
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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".