Institutional Approaches to Examining the Influence of Context on Human Resource Management
Bibliographic record
Abstract
Abstract This chapter reviews three related, but distinctive, institutional approaches to human resource management (HRM) policies within organizations. The approaches view institutions, organizations, and their HRM policies as conceptually separate, but ontologically connected. In other words, they view context and HRM as intertwined, meaning that institutions play a key role in constituting what firms are and what HRM is in different contexts. The chapter reviews work on HRM within (1) the “varieties” approaches of the varieties of capitalism and business systems frameworks, (2) historical institutionalism, and (3) the regulationist framework. The chapter highlights the similarities among, as well as the differences between, these frameworks. In contrast to some other research perspectives, these institutional approaches add value to HRM analyses by explaining key variation among the nature of firms and how that variation influences important outcomes, such as HRM policies and practices, employees’ skill development, job tenure patterns, and social inequality. They also provide frameworks to address (1) how and why HRM changes and (2) how national and international institutions influence the types of HRM that firms adopt and their ability to achieve different objectives in contrasting locations. Individually and collectively, they demonstrate the importance of context on the nature of organizations, what HRM is, and the links between HRM and organizational outcomes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".