Revisiting and Advancing HR Process Research: Exploring New Horizons
Bibliographic record
Abstract
HR process research was established to explain the 'black box' in the relationship between HR practices and organizational performance. Bowen and Ostroff’s (2004) framework on HRM system strength, along with Nishii, Lepak, and Schneider's (2008) model of HR attributions, have served as foundational pillars that initiated a stream of HR process research. The five papers presented in this symposium conceptually build upon but challenge the core ideas of these two frameworks. They also methodologically advance HR process research by demonstrating its predictive validity, enhancing research designs and analyses, and enriching research contexts. By revisiting these foundational frameworks, the papers in the symposium encourage to apply of novel concepts and rigorous methods to unveil new horizons in HR process research. The symposium will conclude with Prof. Kaifeng Jiang providing insightful feedback on each paper and discussing how these papers contribute to the advancement of HR process research. HRM systems strength in a crisis Author: Frances Jorgensen; Royal Roads U. Author: Adelle Bish; North Carolina A&T State U. HRM process theory – An examination of its core elements and added value Author: Mats Ehrnrooth; Hanken School of Economics Author: Jennie Sumelius; Hanken School of Economics Author: Sven Hauff; Helmut Schmidt U. Are We Going Together? A Multi-Level Study of HRM system strength on Voluntary Employee Turnover Author: Karin Sanders; UNSW Business School, Australia Author: Andrew Dhaenens; UNSW Sydney Author: Milad Jannesari; UNSW Sydney Business School, Australia Line managers’ implementation of pay for performance on unit-level outcomes in Chinese MNCs Author: Chunyu Xiu; HR attribution research Author: Huadong Yang; U. of Liverpool Author: Rory Donnelly; U. of Liverpool The link between HRM, attributions about HR practices and customer satisfaction: A team-level lens Author: Ricardo Rodrigues; King's College London Author: David E. Guest; King's College London
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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 | 0.143 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.035 | 0.093 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.009 | 0.018 |
| 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".