Personality and contextual predictors of career advancement procrastination: An application of the social cognitive model of career <scp>self‐management</scp>
Bibliographic record
Abstract
Abstract This research explored procrastination in the context of career self‐management, a construct that we refer to as career advancement procrastination (CAP). Drawing on the career self‐management model extension of social cognitive career theory, we hypothesized that personality traits (i.e., trait passive procrastination and trait active procrastination) and contextual factors (i.e., career resources and career barriers) have effects on passive CAP and active CAP via career self‐efficacy. Hypotheses were tested on a sample of employed Canadians in a two‐wave study (N = 201). As predicted, we found that trait passive procrastination was positively related to passive CAP, trait active procrastination was positively related to active CAP, and career barriers were related to both passive CAP and active CAP. We also found positive indirect effects of trait passive procrastination and career barriers, and negative indirect effects of career resources, on both passive CAP and active CAP via career self‐efficacy. Taken together, these findings suggest that companies can decrease CAP by helping employees curb their dispositional procrastination tendencies, as well as by reducing career barriers and increasing career resources, all of which should also aid in increasing employees' career self‐efficacy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".