Human Resource Implementation: An Attribution Lens
Bibliographic record
Abstract
HR process research was established to understand the effects of how HR practices are implemented to achieve desired organizational performance (Sanders et al., 2021). In addition to research on HRM implementation, the HR process research also includes research on HRM system strength (Bowen & Ostroff, 2004), and HR attributions (Nishi et al., 2008), both based on psychological attribution theories. While much progress has been made in establishing these three research streams, they are mainly studied in isolation (Sanders et al, 2023). In this symposium, we reveal how further progress in the HR process research can be made when connecting elements of these three pillars within the HR process research. The four papers presented in this symposium address the aim of connecting elements by focusing on HRM implementation from an HRM system strength and/or HR attributions perspective, advancing HR process research in general, and HRM implementation research more specifically. The symposium will also discuss practical implications of HR implementation drawing on an attributional lens, led by Dr. Kristin Saboe, Head of Employee Voice, Google, USA. Line manager’s interpretation of intended HR practices: A psychological model Author: Andrew Ng; University of New South Wales Author: Hugh Bainbridge; University of New South Wales Author: Karin Sanders; University of New South Wales Linking HCHRM with Organizational Commitment: Is Implementation the Key? Author: Frances Jorgensen; Royal Roads University Author: Adelle Bish; North Carolina Agricultural and Technical State University Pay-for-Performance Implementation, HR Attributions and Well-being: A Cross-Cultural Study Author: Huadong Yang; University of Liverpool Author: Peng Wang; Author: Chunyu Xiu; Author: Shumin Li; Beijing Foreign Studies University Advancing the HRM Process and Implementation Research Agenda Author: David E. Guest; King's College London Author: Ricardo Rodrigues; King's College London
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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".