Expanded job scope model and turnover intentions: A moderated mediation model of Core-Self Evaluation and job involvement
Bibliographic record
Abstract
Existing study was conducted to make a combined examination of the mediating role of (a) Job involvement in linking expanded job scope model (EJSM) with turnover intentions and (b) investigate how the relationship among EJSM and turnover intention is conditional based on the level of Core Self-Evaluation (CSE) in employees.700 questionnaires were circulated among the employees of education and financial sector which yields 490 returns achieving a response rate of 70%. After initial data screening 420 complete responses were available for analyses. The results exhibit that Job involvement (JI) mediates the relationships between EJCM and turnover intentions. The results of the moderated mediation depict that JI mediates the relationships between job scope and high level of CSE in employees. The outcomes delivered valuable understandings for managers and consultants, especially to Human Resource professionals who are trying to facilitate the workforce in challenging working environment through improved job design. The businesses may encourage high level of employee involvement through redesigned job scope in presence of high order personality characteristics which helps to reduce turnover intentions. This paper contributed in the literature of job design in three different ways. First, existing research makes theoretical contribution by adding new dimension in existing JSM which is flexible work time. Second, it describes how dynamic work settings may refine employees’ abilities and behaviors. Third, the research deals with a unique view in research of job design by combining personality as a moderator (i.e., CSE).
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".