HRM Policies and Firm Performance: The Role of the Synergy of Policies
Bibliographic record
Abstract
For both academics and practitioners, an insight into the relationship between Human Resource Management (HRM) and performance is essential. In exploring this link, HRM scholars have arrived at a point where the universalistic approach of the performance effects of best HRM practices are criticized. In an effort to move beyond a best-practice mode of theorizing, scholars have proposed different bundles of HRM practices that relate to better performance (Huselid, 1995). An emerging stream of literature proposes that systems of HRM practices have synergic performance effects (e.g. Delery & Doty, 1996). Scholars from the latter research stream argue that systems of HRM practices in so called ‘High Performance Work Systems’ (HRM Systems) lead to significant effects on firm performance, and hence propose that ‘ideal’ systems of HRM practices (i.e. best-systems) lead to superior firm performance (Becker & Huselid, 1998). Against this backdrop, Delery and Doty (1996) called upon scholars to adopt a ‘configurational mode of theorizing’ and indeed sparked a plethora of research in search of ideal-type HRM systems (Becker & Huselid, 1998; Lepak et al., 2006). Taking stock of this field today, its theoretical and empirical advancement is still hindered by ‘deficient empirical support’, in part because researchers have focused on bundles of large numbers of practices. For instance, Guest et al. (2003) identified 48 HRM practices and grouped them into nine HRM domains, but concluded that these formed no coherent factors. Also, measuring and examining the interactions between large numbers of practices is empirically very complex (Martín-Alcázar, Romero-Fernández & Sánches-Gardey, 2005: 645). Individual practices’ interactions with many variables are not as easily empirically testable. Some 20 years after the emergence of the perspective of HRM configurations, this perspective has yet to deliver on its promise. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".