The motivating power of streaks: Increasing persistence is as easy as 1, 2, 3
Bibliographic record
Abstract
• We examine streak incentives, or payments that increase for consecutive work tasks. • Streak incentives increase persistence more than larger, stable incentives. • This is driven by an increase in commitment to a goal of maximizing earnings. • Our work suggests that streak incentives can be a cost-effective motivational tool. Organizations often use financial incentives to boost employees’ commitment to work-relevant goals in an effort to increase persistence and goal achievement (e.g., to improve organizational efficiency or sales). We introduce and test a novel incentive scheme designed to enhance persistence by increasing commitment to the goal of maximizing earnings. Specifically, we test “streak incentives,” or rewards that offer people increasing payouts for completing multiple consecutive work tasks. Across six pre-registered studies (total N = 4,493), we show that, contrary to standard economic models suggesting people will complete more piece-rate work for larger rewards, people actually complete more work when compensated with streak incentives than with larger, stable incentives. We theorize that this occurs because, by encouraging consecutive task completion, streak incentives increase commitment to a goal of maximizing earnings, which in turn increases persistence. We also show that this effect is not driven by providing increasing rewards; rather, people’s goal commitment and motivation are boosted by the requirement that they complete work tasks consecutively to earn escalating payments. Taken together, our results suggest that designing incentives to encourage streaks of work is a low-cost way to increase goal commitment and therefore persistence in organizations and other contexts.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".