The moderating role of human capital management practices on employee capabilities
Bibliographic record
Abstract
Purpose The purpose of this paper is to suggest and empirically test a model that explains employee capabilities from the knowledge‐based perspective. In this model, human capital management practices are employed as a moderator variable. Design/methodology/approach A valid research instrument was utilized to conduct a survey of 14,769 current employees of a major North American financial services institution. The model was tested by using the partial least squares (PLS) structural equation modeling technique. A thorough analysis of the role of moderator was carried out. Findings Findings provide support for the proposed model and show that employee capabilities depend on his or her training and development as well as job satisfaction levels. Job satisfaction in turn is affected by training and development, pay satisfaction, supervisor satisfaction, and job insecurity. These relationships are moderated by employee perceptions of human capital management practices. The model exhibits the highest predictive power when the employee perceptions of human capital management practices are also high. Research limitations/implications With respect to a moderator analysis, no interaction effects of human capital management policies and other constructs were discovered, and the moderator was referred to as a homologizer that modifies the strength of the relationships among constructs through an error term. It was discovered that PLS and moderated multiple regression (MMR) produced very similar structural relationships when a moderator was employed. Practical implications The findings may be utilized by knowledge management, organizational behavior, and human resources practitioners interested in the development of strong employee capabilities. Originality/value This paper represents one of the first documented attempts to utilize human capital management practices as a moderator in organizational models.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".