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Revisiting and Advancing HR Process Research: Exploring New Horizons

2024· article· en· W4400440640 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAI and HR Technologies
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsProcess (computing)Process managementComputer sciencePsychologyBusinessProgramming language

Abstract

fetched live from OpenAlex

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

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.129
GPT teacher head0.340
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it