Material and Immaterial Compensation as a Determinant of Employee Organizational Commitment
Bibliographic record
Abstract
The success and failure of any organization largely depend on talented and competent employees. Through human resource management (HRM) practices and policies, organizations strive to ensure committed employees. One of the fundamental practices they use is undoubtedly material and immaterial compensation. Adequate management of such compensation may contribute to greater employee engagement in achieving the set goals, realizing the mission, and fulfilling the vision of the organization. In such ways employees confirm their affiliation with the organization, which classifies them as committed employees. The paper assumes that the adequate application of material and immaterial compensation in organizations in Bosnia and Herzegovina (BiH) may improve employee organizational commitment. This ultimately has a positive impact on the effectiveness and efficiency of organizations. The research was conducted in 128 BiH organizations with more than 50 employees across four sectors. The hypotheses were tested applying the Principal Components Analysis (PCA) through the Kaiser-Meyer-Olkin (KMO) values and Bartlett's test of sphericity and the regression analysis. The results show a statistically significant positive impact of material and immaterial compensation on employee organizational commitment. Creating more agile policies and practices of human resource management, especially those related to material and immaterial compensation, can significantly improve employee commitment as well as the entire organizational effectiveness.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".