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Record W3048787163 · doi:10.1108/shr-06-2020-0055

Transforming human resources management in the age of Industry 4.0: a matter of survival for HR professionals

2020· article· en· W3048787163 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

VenueStrategic HR Review · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAI and HR Technologies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsOriginalityAnalyticsBusinessHuman resourcesValue (mathematics)Human resource managementResource (disambiguation)Knowledge managementPublic relationsMarketingManagementPolitical scienceEconomicsComputer science

Abstract

fetched live from OpenAlex

Purpose This study aims to contribute to the old debate about the need for transformation of human resource (HR) professionals and HR services. It proposes the advent of people analytics as an unprecedented opportunity to support this transformation toward a more strategic positioning. Design/methodology/approach This paper carried out a review of the use or willingness to use analytics by HR professionals. Findings Although HR professionals have been able to transform themselves over the years from a posture largely dominated by the administrative role, to one that includes compliance, the transformation remains insufficient considering the challenges faced by organizations. The advent of the fourth industrial revolution has put people back at the center of organizations’ concerns, but HR seems to be neither equipped nor ready to seize this unprecedented opportunity to play a more strategic role. Originality/value Transforming human resource management to fit Industry 4.0 is not a necessity, but a matter of survival for HR professionals.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.123
GPT teacher head0.334
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