Working Smarter and Working Harder: Combining Learning and Performance Goals to Improve Performance in a High-Complexity Task Environment
Bibliographic record
Abstract
In a high-complexity task environment individual productivity can be improved through exerting more effort (i.e., working harder) as well as by learning improved task strategies. I examine the productivity effects of both learning goals and performance goals in such an environment. I argue that in a high-complexity task environment learning can often be an important predictor of task performance. As such, focusing on learning may be at least as important as working harder. Using an experiment with graduate and undergraduate accounting student participants, I predict and find that learning goals alone lead to increased learning relative to performance goals alone and that directing effort away from conventional performance toward learning does not impair task performance. I further predict that productivity can be enhanced by combining learning and performance goals. I predict that when assigning both goal types simultaneously, the presence of a performance goal will impair learning. However, I find that combining the two goal types simultaneously does not harm learning and improves performance. I further predict and find that assigning both goal types sequentially such that performance goals are assigned only after learning goals have induced learning leads to better performance than using learning goals in isolation. My results provide an understanding of the relationships among goal type, learning, and performance. This understanding contributes to the extant academic literature on goal setting and will be relevant to managers when designing and implementing management control systems.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".