Corporate governance, human capital resources, and firm performance: Exploring the missing links
Bibliographic record
Abstract
This study explores the associations between human capital resources, firm performance, and corporate governance mechanisms. Based on the survey results of the “50 most attractive employers” conducted by Universum Global 2010, human resource, performance, and governance data was collected for the period from 2007 to 2011. Drawing on the strategic human capital and resource management, international governance, and organizational literature, this study examines the extent to which corporate governance mechanisms moderate the relationships between firm performance and human capital resources and posits that human resource performance is positively associated with corporate governance mechanisms that support and enhance strategic human resource management policies. Panel regression analyses are conducted to test the study’s hypotheses. The results show that human capital resources are positively related to firm performance, and that some corporate governance mechanisms may negatively affect performance when interacted with human capital variables. Furthermore, human resource performance is significantly related to some governance mechanisms, with interaction effects between human capital and other organizational attributes showing differential impacts. Overall, the results support a contingency-based view of strategic human resource management in the context of large and attractive global employers and highlight the importance of governance design in supporting investments and deploying human resources and capabilities at the firm and industry levels and across national boundaries.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".