Complacency and Giving Up Across Repeated Tournaments: Evidence from the Field
Bibliographic record
Abstract
ABSTRACT Tournament incentive schemes involve individuals competing against each other for a single or limited number of rewards (e.g., promotion, bonus, pay raise). Although research shows tournament schemes can have positive effects on performance, there is also evidence of dysfunctional intra-tournament behavior by top performers (complacency) and weak performers (giving up). However, few studies have examined behavior in organizational settings, not uncommon in practice, where tournaments are conducted on a repeated basis. We predict that complacency and giving up will generalize to settings where individuals repeatedly compete in successive short-duration tournaments. We test our predictions using archival data from a reservation center of a major hotel chain that employs repeated four-week tournaments where performance does not carryover from one competition to the next. Results show top performers quickly become complacent in response to success in early tournaments. The lowest-performing losers in early tournaments eventually appear to give up, but additional analysis indicates they only do so after unsuccessfully changing task strategy. Our results contribute to a better understanding of individual behavior in settings where individuals repeatedly compete against largely the same group of employees. Our evidence also suggests that tournaments are less effective at sustaining the motivation of the most capable performers and other approaches may be necessary.
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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".