Analytics and Algorithms in Human Resource Management
Bibliographic record
Abstract
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
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".