Strategic human asset management: evidence from North America
Bibliographic record
Abstract
Purpose Human resource management (HRM) theory has transitioned in recent decades towards “human capital” and “human assets” frameworks that emphasize strategic choice and “investment”, which are concepts borrowed from strategic management, accounting and economic theories. This paper aims to explore the perspectives of strategic human asset management theory, which involves strategic agility and knowledge management. Design/methodology/approach The research was based on semi‐structured interviews with 30 senior executives of multinational firms in Canada and the USA in 2009, following the global financial crisis. The qualitative findings were analyzed using the NVivo software (version 8) package. Findings The research findings suggest that many North American multinational firms recognize the value of this new interpretation of HRM and are attempting to implement it through “strategic human asset management” in their own firms. The paper concludes with some practical recommendations for line managers and HR professionals in their human assets management imperatives. Research limitations/implications The generalizability of the findings is limited by the relatively small sample size and qualitative nature of the study. However, they provide some interesting implications for HR professionals who wish to transform their role into that of a strategic business partner through innovative human asset management strategies. Originality/value The paper builds on previous research by exploring the applications of the concepts of strategic human asset management, strategic agility, and knowledge management within the context of US and Canadian multinational firms.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".