The role of job involvement and career commitment between person–job fit and organizational commitment: a study of higher education sector
Bibliographic record
Abstract
Purpose Organizations try to appoint individuals who fit in with their values and goals. Employees who do not fit in with the organization's core values and goals prefer not to stay on for a longer period of time. The present study is an attempt to explore the impact of person–job (P–J) fit on organizational commitment through job involvement and career commitment with an application of a serial multiple mediation model. Design/methodology/approach Data have been collected through a structured questionnaire. The population comprised the teachers, working in the higher education sector. The different constructs used have been duly validated with the help of exploratory and confirmatory factor analyses. Further data reliability and scale validity have been checked too. In order to test the serial multiple mediation model, the authors adopted a regression-based approach and bootstrapping method suggested by Hayes (2012; 2013). Accordingly, PROCESS developed by Hayes (2012) has been used. Findings The results revealed that job involvement and career commitment mediate the relationship between P–J fit and organizational commitment individually as well as together. Research limitations/implications The present study is confined to government degree colleges only. The element of subjectivity might not have been checked completely as teachers have responded on the basis of their own experience and perceptions regarding the items in the questionnaire. The study is cross sectional in nature. Practical implications The paper addresses the interest of wide spectrum of stakeholders including the management, organizations and employees. So, the authors have extended general implications, which are for all those organizations that want to improve person–organization (P–O) fit and commitment of their employees. These implications will help organizations to take specific initiatives to improve the P–J fit of their employees, which will subsequently enhance their commitment level. Originality/value The findings of the present study will help the stakeholders in the higher education sector to identify best employees and undertake the initiatives to generate better job involvement and commitment, which will be a win–win strategy for both (employees as well as the organizations).
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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 | 0.004 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".