Effects of Person-HRM fit on Implementation behavior and High-Performance Work Systems
Bibliographic record
Abstract
Recently, the high performance work systems (HPWS) literature has witnessed a burgeoning interest in exploring its influence measured at the within-organization level. Since extant studies in this line of research often treat human resource management (HRM) systems as independent variables, a fundamental question of why variability develops within organizations could not be addressed properly. To fill the void, this article attempts to integrate studies on HR devolution to the line with the HPWS-performance relationship, thereby placing the role of first-line managers (FLMs) at the center of inquiry. By doing so, the current research directly determines factors that contribute to the emergence of work-group HPWS, and articulate further the process through which strategically adopted HR practices are manifested in actual HRM systems, and subsequent outcomes. Analyses of 117 work group data confirmed that FLM’s person-HRM fit (i.e., value and ability dimensions) significantly influences his/her implementation behavior of espoused HR practices, which is, in turn, associated with HPWS at the group level. In addition, there was a positive interaction between value fit and climate for HR implementation in explaining variance in both implementation behavior and realized HPWS. Most conspicuously, it was found that climate strength appears to substitute for effects of FLM’s ability fit perception in predicting HR implementation. This research discusses how findings uniquely extends the current discourse of the SHRM and HR devolution literature.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".