Career commitment, proactive personality, and work outcomes: a cross-lagged study
Bibliographic record
Abstract
Purpose – The aim of this paper is to examine the relationships of career commitment to turnover intention, internal networking, job embeddedness, and turnover, and whether proactive personality moderates these relationships. Design/methodology/approach – Data were collected at two points in time, spaced by a six-month interval, from a sample of employees working in diverse organizations (n=312 at Time 1 and n=186 at Time 2). Hypotheses were tested using moderated multiple (linear and logistic) regression analyses. Findings – Career commitment was positively related to Time 1 turnover intention, with this relationship being stronger at high levels of proactivity. Proactive personality also interacted with career commitment in predicting Time 2 internal networking and job embeddedness, such that these relationships were significantly positive only at low levels of proactivity. Finally, career commitment was positively related to Time 2 turnover, but this relationship was not moderated by proactive personality. Practical implications – Findings suggest organizations should enhance the within-organization opportunities of people with high career commitment and proactivity. In contrast, they should work at maintaining the employability of people with high career commitment and low proactivity, as these individuals may become stuck in their organization. Originality/value – This study contributes to the understanding of the relationships of career commitment and proactive personality to organization-relevant outcomes. It also breaks new ground by showing that career commitment may influence attitudes and behavior distinctively as a function of individuals' levels of dispositional proactivity.
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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 | 0.003 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".