The road taken and the path forward for HR devolution research: An evolutionary review
Bibliographic record
Abstract
Abstract Devolving HR responsibilities to the line is a distinguishable feature in modern human resource management (HRM). Given its considerable influence on the modus operandi of adopted HRM systems, scholars have delved into barriers to and facilitating conditions of line managers' fulfilling their HR duties and its subsequent outcomes over the past several decades. To deepen our understanding of the current state of the HR devolution literature, we adopt an evolutionary approach that moves beyond a conventional theme‐centric review. Exploring the extant literature from a chronological stance offers a unique perspective since such an approach allows us to track and evaluate the development trajectory from its inception. Indeed, our evolutionary review reveals that critical observations on HR devolution practices documented by early studies (e.g., conflicts between line managers and HR professionals) have not been as articulated as they should be in recent endeavors (e.g., focus on line managers' competency and motivation). Through this review, we shed new light on the HR devolution literature and call for revisiting important yet unresolved issues that still resonate today. In addition, we contemplate the future of HR devolution practices amid a changing business landscape and suggest promising research avenues accordingly. By looking back at the road taken and paving the path forward, the current review will offer a guidepost benefiting both aspiring researchers and seasoned scholars in the field.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".