Corporate governance, human capital resources, and firm performance: Exploring the missing links
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it