Measure human capital because people really matter: development and validation of human capital scale (HuCapS)
Bibliographic record
Abstract
Purpose Notwithstanding the findings of several published articles on human capital, there is scarcity of a comprehensive instrument to measure it. In this direction, the objective of present research is to develop a valid and reliable scale to assess human capital. Design/methodology/approach This research was divided into two parts. Study 1 focused on literature review of human capital measures, development of items and exploring the factor structure of human capital construct on a sample of 184 employees. Study 2 was based on the survey of 212 employees, and reliability assessment and confirmatory factor analysis was performed to validate the factor structure of human capital construct. Findings The findings can be summarized in two ways. Study 1 present that human capital scale is multidimensional consisting of employee capability, leadership and motivation, employee satisfaction and creativity. The findings of study 2 confirms the validity and reliability of three factor structure of human capital construct consisting of 18 items in total. Practical implications The study provides a multidimensional psychometric instrument which can help in measuring the human capital of the organization from the perspective of capabilities, satisfaction and creativity and leadership and motivation. Moreover, it can serve as an aid to human resource (HR) and human resource development (HRD) professionals for human capital assessment in the organizations. Originality/value This study provides a measure to assess human capital in Indian manufacturing sector organizations that makes a novel contribution to the area.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".