Strategic human asset management: evidence from North America
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
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.023 |
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