Engaging employees through compensation fairness, job Involvement, organizational commitment: The roles of employee spirituality
Bibliographic record
Abstract
This paper aims to investigate the role of Employee Spirituality to moderate between Compensation fairness and Employee Engagement, Job involvement and Employee Engagement, organizational commitment, and employee engagement. In this survey, 279 respondents were collected with a 75 percent response rate (139 respondents) from May to July 2020 and a 93.3 percent rate (140 questionnaires) from August until September 2020. Validity used Confirmatory Factor Analysis used KMO and Bartlett’s test, and the reliability test was based on Cronbach-Alpha. Moreover, Kolmogorov-Smirnov test is used for normality test, Park test is implemented for Heteroscedasticity and Multicollinearity test. Moderator Regression Analysis is used to identify the moderator types. The results indicate that Employee Spirituality fully moderated (Pure moderator) between Compensation fairness and Employee Engagement and between Organizational Commitment and Employee Engagement. Moreover, Employee Spirituality partially moderated between Job Involvement and Employee Engagement. The research suggests to implement the model in a narrow scope and considers many variables outside the Compensation fairness, Job Involvement, and Organizational Commitment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".