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Analytics and Algorithms in Human Resource Management

2023· article· en· W4385222014 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAI and HR Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAnalyticsComputer scienceData scienceHuman resource managementKnowledge management

Abstract

fetched live from OpenAlex

The accelerating development and application of digital technologies have been one of the major forces shaping today’s world of work and management. The growing use of human resource analytics (HRA) and algorithmic management has been a major trend and fashion in human resource management (HRM) in the past ten years. HRA uses enhanced information technology to collect, analyze and report employee and work data to support people-related decision-making (Margherita, 2021; Marler & Boudreau, 2017a). It enables not only advanced and nuanced description and visualization of people data but also predictive capabilities of future trends that enable HR professionals to prescribe best practices to support organizational success (Margherita, 2021; Yuan et al., 2021). Relatedly, algorithmic management (AM), which uses computer-programmed procedures to collect and analyze people data to automate HRM practices, has spread from the online gig economy, where it originated, to various sectors and industries. Despite their growing implementation and the growing knowledge of their impacts, much is still unknown about the antecedents and outcomes of analytics and algorithms in HRM. This symposium consists of four presentations that delve into different aspects of HRA and algorithmic management. With our diverse focuses, theoretical perspectives, and methodological approaches, we hope to bring together a community interested in this topic and catalyze new thoughts and dialogues to further the knowledge of the future of HRM. Exploring the Adoption, Implementation, and Evaluation of HR Analytics Author: Steven McCartney; Maynooth U., Ireland Author: Na Fu; Trinity Business School, Trinity College Dublin The Technology Capture: The Role of Human Resource Analytics in HRM’s Professional Project Author: Yao Yao; Telfer School of Management, U. of Ottawa HR Analytics and HRM's Strategic Positioning: Navigating the Uncertainties of an Emerging Technology Author: Felix Diefenhardt; WirtschaftsU. Wien Author: Marco Rapp; WU Vienna Author: Verena Bader; WU Vienna Author: Wolfgang Mayrhofer; WU Vienna Algorithmic Management and Job Engagement: The Mediating Role of Exchange Relationships Author: Na Liu; IMT School for Advanced Studies Lucca Author: Sophie Anna De Winne; KU Leuven Author: Rein De Cooman; KU Leuven Author: Nicola Lattanzi; IMT School for Advanced Studies Lucca

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 score0.742
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.033
GPT teacher head0.267
Teacher spread0.234 · 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