A multi-situational perspective on the study of strategic HRM: Contributions from convention theory
Bibliographic record
Abstract
Multilevel research in the area of human resource management (HRM) has put inadequate attention to the relevance of situational complexity. This paper is an attempt to address this inadequacy. It brings situational complexity – the notion of coordination in situ – into the debate by introducing a conceptual framework that (1) emphasizes the strategic role of HR managers as competent coordinators, (2) outlines possible ways to engage in coordination situations and (3) relates the adjustment of HRM practices to contextual demands, for instance due to competing conventions occurring in particular coordination situations. Strategic HRM is thus understood as compromising work. Especially, multinational companies need their HRM practices to be responsive to various contexts, being able to switch between multiple situations or levels of coordination (e.g., compliance with global standards versus the local set-up of adequate training). Hence, we suggest to put the focus of analysis on concrete HRM situations and to view the strategic work of HR managers as daily coordination activity at multiple engagement levels. Drawing on convention theory – a fairly new approach to HRM research – and based on our model we portray the strategic HRM practice of ‘investing in talent’ across contexts and conclude with theoretical and practical implications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".