Goal Progress Velocity as a Determinant of Shortcut Behaviors
Bibliographic record
Abstract
Employees often have a great deal of work to accomplish within stringent deadlines. Therefore, employees may engage in shortcut behaviors, which involve eschewing standard procedures during goal pursuit to save time. However, shortcuts can lead to negative consequences such as poor-quality work, accidents, and even large-scale disasters. Despite these implications, few studies have investigated the antecedents of shortcut behaviors. In this research, we propose that employees engage in shortcut behaviors to regulate their velocity (i.e., rate of progress). Specifically, we predict that when individuals experience slower-than-referent velocity, they will (a) believe that the goal is unlikely to be met via standard procedures and (b) experience feelings of frustration. In turn, we expect these psychological states to be related to the perceived utility of shortcuts, especially when shortcuts are perceived as viable means to achieve the goal. Finally, we predict that the perceived utility of shortcuts will be positively related to actual shortcut behaviors. We tested these predictions using a laboratory experiment in which we manipulated velocity and unobtrusively observed shortcuts (Study 1, N = 147), as well as a daily diary study in which employees reported their velocity and shortcut behaviors over 5 consecutive workdays (Study 2, N = 395). Both studies provided support for our predictions. In sum, this research provides evidence to suggest that the experience of slow progress can lead to shortcuts not only by casting doubt on employees’ perceived likelihood of meeting the goal but also by producing feelings of frustration.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".