Waiting to Take the First Step? Procrastination Behaviors in the Role Transition from Worker to Manager
Bibliographic record
Abstract
The purpose of this research was to explore predictors of employees’ procrastination behaviors when approaching the transition from worker to manager. I drew on the Extended Model of Action Phases and Temporal Motivation Theory to argue that both personal and situational factors are related to career procrastination behaviors. Specifically, I hypothesized that trait passive procrastination, trait active procrastination, and work constraints are positively related to transition procrastination behaviors, whereas trait proactivity and work resources are negatively related to transition procrastination behaviors. I further hypothesized that career self-efficacy and subjective temporal proximity are two mechanisms through which these effects occur. These hypotheses were tested in a correlational study with two on-line surveys separated by one month. Data were gathered from a sample of employees working in various organizations and industries in Canada (N = 282). Consistent with the hypotheses, I found that trait passive procrastination, trait active procrastination, and constraints were positively related to transition procrastination behaviors, whereas resources were negatively related to transition procrastination behaviors. In contrast to my prediction, trait proactivity was not related to transition procrastination behaviors. As expected, career self-efficacy mediated relations between traits (passive procrastination, proactivity), environmental factors (resources and constraints) and transition procrastination behaviors. Subjective temporal proximity mediated relations between traits (passive procrastination, proactivity) and transition procrastination behaviors, but only in cross-sectional and not in time-lagged tests. Based on these findings, companies can reduce employees’ career procrastination behaviors by providing more career development resources and mitigating potential organizational constraints, which can build employees’ self-efficacy and encourage developmental behaviors.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".