Individual Factors Influencing Career Growth Prospects in Contexts of Radical Organizational Changes
Bibliographic record
Abstract
This study examined the influence of individual factors (demographic variables, self-efficacy beliefs and personal growth initiative) on career growth prospects within the context of radical organizational changes such as downsizing, mergers and acquisition. Data were collected using the questionnaire method from 199 employees in branches of a commercial bank located in a major city in South-Western Nigeria. Results of the simple multiple regression analysis showed that educational attainment (? = -.15, p < .05), tenure in the banking sector (? = -.41, p < .01), basic monthly income (? = .46, p < .001) and job status (? = .34, p < .01) are significant demographic factors in career growth prospects. The analysis of covariance which controlled for covariates revealed significant differences in the career growth prospects of employees with low levels of self-efficacy and those with high levels – in favor of the latter. In contrast, high or low levels of personal growth initiative resulted in comparable levels of career growth prospects. Self-efficacy beliefs and personal growth initiative interacted significantly to affect career growth prospect such that greater levels of career growth prospect was expressed irrespective of whether an employee is high or low on personal growth initiative when self-efficacy belief is high. Implications of findings are discussed.
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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 | 0.003 |
| 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 | 0.000 |
| 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".